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Most Popular Titles With Kevin Burns
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1. Poseidon (2006)
PG-13 | 98 min | Action, Adventure, Drama
On New Year's Eve, the luxury ocean liner Poseidon capsizes after being swamped by a rogue wave. The survivors are left to fight for their lives as they attempt to escape the sinking ship.
Writers: Mark Protosevich, Paul Gallico | Director: Wolfgang Petersen | Stars: Richard Dreyfuss, Kurt Russell, Emmy Rossum, Josh Lucas
Votes: 77,124
2. The Girls Next Door (2005– )
TV-PG | 22 min | Comedy, Reality-TV, Romance
They're often blond, very ambitious and always beautiful. They're the young women who travel from towns and cities across America in hopes of sharing one man's incredible dream: life at the... See full summary »
Creator: Kevin Burns | Stars: Hugh M. Hefner, Holly Madison, Bridget Marquardt, Kendra Wilkinson
Votes: 3,116
3. Ancient Aliens (2009– )
90 min | Documentary, History, Sci-Fi
Science and mythology - and how they are the same thing.
Stars: Robert Clotworthy, Giorgio Tsoukalos, David Childress, Philip Coppens
Votes: 5,396
4. The Curse of Oak Island (2014– )
TV-PG | 60 min | Reality-TV
Rick and Marty Lagina, two brothers from Michigan with a life-long interest in the mystery of Oak Island, renew efforts to discover the legendary treasure with sophisticated machinery.
Stars: Robert Clotworthy, Dan Henskee, Rick Lagina, Marty Lagina
Votes: 848
5. Biography (1987– )
60 min | Documentary, Biography, History
The life stories of various historical figures and celebrities are told.
Creator: Agnes Nixon | Stars: Jack Perkins, Peter Graves, Harry Smith, Roddy McDowall
Votes: 908
6. Kendra on Top (2012– )
TV-14 | 30 min | Reality-TV
Hugh Hefner's ex-girlfriend deals with marriage, motherhood, celebrity and the paparazzi.
Stars: Kendra Wilkinson, Hank Baskett, Jessica Hall, Hank Baskett IV
Votes: 214
7. America's Book of Secrets (2012– )
44 min
From the facts behind the NSA spying scandals ("Big Brother") and the Boston Bombings ("American Terrorists") to America's secret prisons, Scientology, the Gold conspiracy and America's doomsday plans, this series features in-depth interviews with top journalists, law enforcement officials and whistleblowers.
Stars: Jonathan Adams, Barack Obama, Bill Clinton, John F. Kennedy
Votes: 249
8. Kendra (2009–2011)
30 min | Reality-TV, Romance
Kendra Wilkinson finds herself at a crossroads in her life as she leaves behind the Playboy Mansion lifestyle and ventures out to live on her own for the very first time.
Creator: Kevin Burns | Stars: Kendra Wilkinson, Hank Baskett, Eddie, Jessica Hall
Votes: 1,146
9. The Telling (2009 Video)
R | 84 min | Horror, Mystery, Thriller
In this horror anthology film, three girls pledge the most popular - and cruelest - sorority on campus. For their final task, they must tell the scariest story they know. Containing three ... See full summary »
Writer: Joe Lessard | Directors: Nicholas Carpenter, Harry Grigsby | Stars: Holly Madison, Christina Rosenberg, Nicole Zeoli, Najarra Townsend
Votes: 1,357
10. Bridget's Sexiest Beaches (2009– )
60 min | Documentary
Bridget Marquardt travels to various countries to explore their beaches, meet the locals, participate in various adventurous activities, and enjoy the nightlife.
Stars: Bridget Marquardt, Pilar M. Lastra, Amber Campisi, Cara Zavaleta
Votes: 163
11. E! True Hollywood Story (1996– )
Documentary, Biography, Crime
A documentary about celebrities lives before and after being famous.
Stars: Chip Bolcik, Phil Crowley, Beng Spies, Ken Baker
Votes: 596
12. Holly's World (2009– )
30 min | Reality-TV
Stars: Holly Madison, Angel Porrino, Josh Strickland, Laura Croft
Votes: 297
13. In Search of Aliens (2014– )
IN SEARCH OF ALIENS follows Giorgio Tsoukalos, an Ancient Astronaut theorist and leading contributor to Ancient Aliens, as he explores the fascinating evidence behind some of the Earth's most famous mysteries.
Stars: Giorgio Tsoukalos, Jonathan Bright, Peter Daughtrey, Alexander MacGillivray
Votes: 76
14. Sex at 24 Frames Per Second (2003 Video)
R | 101 min | Documentary
Documentary about the way sex was and is deliberately used in cinema to influence and manipulate human behaviour and development. This documentary uses real examples including the decline ... See full summary »
Writers: Kevin Burns, James Petersen, Steven Smith | Directors: Kevin Burns, Steven Smith | Stars: Brandy Snow, Shannon Tweed, Richard Roeper, Paul Verhoeven
Votes: 353
15. The Robinsons: Lost in Space (2004 TV Movie)
43 min | Adventure, Sci-Fi
In the year 2082, the Earth was invaded by aliens after it was long determine that existence of extra terrestrials was long thought to be science fiction. Major cities crumbled from the ... See full summary »
Writers: Irwin Allen, Douglas Petrie | Director: John Woo | Stars: Jayne Brook, Mike Erwin, Brad Johnson, Ryan Malgarini
Votes: 153
16. The Godfather Legacy (2012 TV Movie)
TV-14 | 95 min | Documentary
THE GODFATHER LEGACY goes deep inside Francis Ford Coppola's epic saga about the Corleone crime family and reveals how the Academy Award-winning film and its sequels became one of the most ... See full summary »
Writers: Kevin Burns, Brian Coughlin | Director: Kevin Burns | Stars: Peter Bart, Todd Boyd, James Caan, Francis Ford Coppola
Votes: 60
17. Playboy: Inside the Playboy Mansion (2002 TV Movie)
R | 93 min | Documentary
Writers: Edith Becker, Kevin Burns | Directors: Edith Becker, Kevin Burns | Stars: Hugh M. Hefner, Janet Pilgrim, Joyce Nizzari, Joy Tarbell
Votes: 255
18. Behind the Planet of the Apes (1998 TV Movie)
Not Rated | 127 min | Documentary
The story behind the making of the 5 "Planet of the Apes" movies.
Writers: Brian Anthony, David Comtois, Kevin Burns | Directors: Kevin Burns, David Comtois | Stars: Roddy McDowall, Mort Abrahams, Charlton Heston, Richard D. Zanuck
Votes: 1,080
19. The Time Tunnel (2006 TV Movie)
54 min | Sci-Fi
In 2006, an experiment of the Department of Energy in hot fusion to produce energy releases a storm in time along 240 minutes, changing history. Only the personnel in the core of the ... See full summary »
Director: Todd Holland | Stars: David Conrad, Dion Luther, Andrea Roth, Tawny Cypress
Votes: 202
20. Bible Secrets Revealed (2013– )
It is said to be one of the oldest books in the world. Has it been altered? If yes why? A remarkable journey back in time to see what the Old Testament and the New Testament is hiding from us.
Stars: Robert R. Cargill, Reza Aslan, Bart D. Ehrman, Mark Goodacre
Votes: 51
21. The Omen Legacy (2001 TV Movie)
100 min | Documentary
The true stories that spawned the eerie tale of Damien, a small boy with an angelic face, whose very name still conjures up thoughts of Satan. This documentary shares spine-tingling ... See full summary »
Writers: Naomi Pfefferman, Brent Zacky | Director: Brent Zacky | Stars: Jack Palance, David Seltzer, Blanche Barton, Doug Posey
Votes: 161
22. Marilyn Monroe: The Final Days (2001 TV Movie)
TV-14 | 90 min | Documentary, Biography
Documentary about the moviestar's last months including her tumultuous love affairs, drug and alcohol dependency, depression and eventual firing from her final film, 20th Century Fox's "... See full summary »
Writer: Monica Bider | Director: Patty Ivins Specht | Stars: James Coburn, Gene Allen, Steve Allen, Lauren Bacall
Votes: 602
23. Empire of Dreams: The Story of the 'Star Wars' Trilogy (2004 Video)
151 min | Documentary, History
An in-depth making of documentary about the original Star Wars trilogy, covering the productions of the three films and their impact on popular culture.
Writer: Ed Singer | Directors: Edith Becker, Kevin Burns | Stars: Robert Clotworthy, Kenny Baker, Jim Bloom, Leo Braudy
Votes: 2,199
24. Alien Nation: Dark Horizon (1994 TV Movie)
90 min | Sci-Fi, Drama
Alien life form comes to Earth to rescue his enslaved race.
Writers: Rockne S. O'Bannon, Diane Frolov, Andrew Schneider | Director: Kenneth Johnson | Stars: Gary Graham, Eric Pierpoint, Michele Scarabelli, Terri Treas
Votes: 946
25. Just Add Bridget (2010 TV Movie)
Follows the new life of former "Girls Next Door" star Bridget Marquardt and her boyfriend Nick Carpenter in their daily adventures.
Stars: Bridget Marquardt, Nicholas Carpenter
26. The President's Book of Secrets (2010 TV Movie)
Journey inside White House history to unveil fascinating truths behind secrets known only to the President.
Stars: Jonathan Adams, Jonathan Alter, Michael Chertoff, Susan Ford
Votes: 82
27. Alien Nation: Millennium (1996 TV Movie)
90 min | Sci-Fi
It's 1999, and as the end of the millenium approaches, people are attempting to find spiritual enlightenment. But a few people want to skip all the work that entails, and a holy Tenktonese ... See full summary »
Creator: Kenneth Johnson | Director: Kenneth Johnson | Stars: Gary Graham, Eric Pierpoint, Michele Scarabelli, Terri Treas
Votes: 585
28. The 'Alien' Saga (2002 TV Movie)
108 min | Documentary
The special edition of the Alien saga.
Director: Brent Zacky | Stars: John Hurt, Jay Benedict, Michael Biehn, Martin Bower
Votes: 317
29. Look, Up in the Sky! The Amazing Story of Superman (2006 TV Movie)
115 min | Documentary, History
The history of the comic book superhero, Superman in his various media incarnations.
Writers: James Grant Goldin, Steven Smith | Director: Kevin Burns | Stars: Kevin Spacey, Gerard Jones, Forrest J Ackerman, Elliot S. Maggin
Votes: 912
30. Alien Nation: Body and Soul (1995 TV Movie)
87 min | Sci-Fi
Sikes and Francisco are called in to a case when a mysterious young girl, who looks part Newcomer, part human, appears. Her huge, brutish counterpart tries to free her from the precinct, ... See full summary »
Creator: Kenneth Johnson | Director: Kenneth Johnson | Stars: Gary Graham, Eric Pierpoint, Michele Scarabelli, Terri Treas
Votes: 648
31. History Specials (2010– )
History, War
Creator: John McLaughlin | Stars: William Atwater, William Bodette, Lisa Gardner, Noah Shachtman
Votes: 10
32. Playboy: 50 Years of Playmates (2004 Video)
Not Rated | 73 min | Documentary
Writers: Nancy Goodell, Frank Martin | Director: Scott Allen | Stars: Ron Snow, Hugh M. Hefner, Pennelope Jimenez, Marilyn Monroe
Votes: 49
33. Batman Unmasked (2008 TV Movie)
46 min | Documentary
Various analysts propose a psychological examination of the Dark Knight.
Writer: Steven Smith | Stars: Robert Clotworthy, Paul Levitz, Robin S. Rosenberg, Cary A. Friedman
Votes: 237
34. HypaSpace (2002–2008)
25 min | Documentary
An informative look at news and gossip from the Entertainment Industry as it relates to SF.
Stars: Kim Poirier, Jonathan Llyr, Natasha Eloi, Danielle McGimsie
Votes: 87
35. Backstory (2000– )
As seen on AMC, "Backstory" is a series that takes a provocative and in-depth look at the making of popular motion pictures. Stars, directors, producers and other Hollywood players provide ... See full summary »
Stars: Rino Romano, Rudy Behlmer, Richard D. Zanuck, Roddy McDowall
Votes: 63
36. Food Paradise (2007– )
Stars: Mason Pettit, Tonya Beaudet, Jeff Lassen, Jay Leno
Votes: 36
37. Rodgers & Hammerstein: The Sound of Movies (1996 TV Movie)
100 min | Documentary, Music
Writers: Steve Bell, Louise Gallop-Roholt, Debra Sanderson | Director: Kevin Burns | Stars: Shirley Jones, Richard Rodgers, Lorenz Hart, Jeanne Crain
Votes: 79
38. Star Wars: The Legacy Revealed (2007 TV Movie)
155 min | Documentary
Film-makers and academics discuss the use of mythology in Star Wars and the impact of the films on society.
Writers: Kevin Burns, David Comtois, Steven Smith | Director: Kevin Burns | Stars: George Lucas, Linda Ellerbee, Tom Brokaw, Newt Gingrich
Votes: 357
39. Lost in Space Forever (1998 TV Movie)
Writers: Brian Anthony, Kevin Burns, Bill Mumy | Director: Kevin Burns | Stars: John Larroquette, Angela Cartwright, Mark Goddard, Jonathan Harris
Votes: 114
40. Blood and Glory: The Civil War in Color (2015 TV Mini-Series)
41. The Bunny House (2010 TV Movie)
30 min | Reality-TV
Aspiring Playmate Claire Sinclair is introduced to life at the Bunny House, and Hope Dworaczyk is honored in Las Vegas as the 2010 Playmate of the Year.
Creator: Kevin Burns | Stars: Darya Astafeva, Anna Sophia Berglund, Jason Burns, Cristal Camden
42. Hef's Runaway Bride (2011 TV Movie)
60 min | Documentary, Reality-TV
Director: Kevin Burns | Stars: Anna Sophia Berglund, Cooper Hefner, Crystal Hefner, Hugh M. Hefner
Votes: 13
43. Twentieth Century Fox: The Blockbuster Years (2000 TV Movie)
An amazing insider's look at nearly four decades of movie making including screen tests, behind-the-scenes footage and interviews with Tom Hanks, Raquel Welch, George Lucas, Oliver Stone, Robert Altman and much more.
Writers: Kevin Burns, Jerry Decker, Ed Singer | Directors: Kevin Burns, Shelley Lyons | Stars: Irwin Allen, Robert Altman, Don Ameche, Kevin Anderson
Votes: 96
44. Spine Tingler! The William Castle Story (2007)
82 min | Documentary, Biography
Chronicles the last great American showman, filmmaker William Castle, a master of ballyhoo who became a brand name in movie horror with his outrageous audience participation gimmicks.
Director: Jeffrey Schwarz | Stars: Forrest J Ackerman, John Badham, Diane Baker, Sidney D. Balkin
Votes: 421
45. History vs. Hollywood (2001– )
22 min | Documentary, History
Stars: Burt Reynolds, Josh Binswanger, Richard D. Zanuck, Geoffrey Wawro
Votes: 23
46. Secret Societies of Hollywood (2014– )
Stars: Mark Ebner, Wendy Feldman, Mark Dice
Votes: 5
47. The Science of Superman (2006 TV Movie)
51 min | Documentary
"Science of Superman" interviews the experts and uses highlights from comic books, television and film - including scenes from the 2006 summer blockbuster, "Superman Returns" - to examine ... See full summary »
Writers: Steven Smith, Jerry Siegel, Joe Shuster | Stars: Rino Romano, Mark Waid, Denny O'Neil, Mark Wolverton
Votes: 80
48. 20th Century-Fox: The First 50 Years (1997 TV Movie)
150 min | Documentary
The first half century of Twentieth Century Fox Film Corporation from its beginnings under Hungarian immigrant William Fox to it emergence as a major studio.
Writer: Kevin Burns | Director: Kevin Burns | Stars: James Coburn, Julie Andrews, David Brown, Red Buttons
Votes: 81
49. Starz Inside: The Face Is Familiar (2009 TV Movie)
Writer: Kevin Burns | Director: Kevin Burns | Stars: Rob Beddall, Veronica Cartwright, Luis Guzmán, Justin Humphreys
Votes: 95
50. Hugh Hefner: American Playboy Revisited (1998 TV Movie)
R | 93 min | Documentary, Biography
The life of Playboy's legendary editor and founder, Hugh M. Hefner is profiled in this compelling and highly personal portrait. Originally aired as part of A&E's Emmy Award-winning ... See full summary »
Writer: Ken Warun | Director: Kevin Burns | Stars: Hugh M. Hefner, Christie Hefner, Kimberley Conrad, Barbi Benton
Votes: 13


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subset: commoncrawl


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Most of them live in or around Istanbul or on the Aegean coast in the western part of the country.
In 410 B.C., some seven decades after their defeat at Salamis, the Persians jumped back into the "great game" of Aegean rivalry.
In a secret conclave he called for naval sorties that would set fire to the dockyards of other port cities around the Aegean.
sea between Greece and Asia Minor, 1570s, traditionally named for Aegeus, father of Theseus, who threw himself to his death in it when he thought his son had perished; but perhaps from Greek aiges "waves," a word of unknown origin.


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The air was chilly at West Virginia's Charles Town Races, and Cindy Levine snuggled beside Kenneth Black, the man she had agreed to marry.
It would be the second wedding for both; each had two children from previous marriages. Both were entrepreneurs. Kenny was opening an environmentally friendly dry-cleaning business. Cindy, before her divorce, had followed in the footsteps of her late father, Murray Levine, an early franchisee of McDonald's. In the 14th year of her first marriage, Cindy had bought two McDonald's franchises of her own in Hagerstown and Williamsport, Maryland. Her father once had owned ten, from Hagerstown to Carlisle, Pennsylvania.
In addition to raising two children, Cindy had found herself working 12 hours a day at her business. Ultimately, the strain was too much for the marriage. She and her husband divorced. "We had just grown into different people," says Cindy.
An attractive brunette, Cindy, then 40, had no trouble getting dates, but nobody pulled at her heartstrings. Then her daughter and Kenny's daughter were paired in a b'not mitzvah at their synagogue. Cindy and Kenny had been friends in high school but had lost touch until they were reunited by the religious ceremony. They went out for coffee while their daughters were practicing. They started dating. They fell in love.
Cindy was delighted with the romance, but worries about money nagged at her. "I was in love, but I also knew the number-one thing I had to do was protect my kids," she says. "I just didn't know how to bring it up."
Throughout the courtship, Cindy thought about the need to take steps to ensure that if she died, her business assets would go to her two children. Under Maryland law, if she were to die without a prenuptial agreement, her spouse might get one-third of her assets, the kids two-thirds.
Cindy didn't know how to bring up the subject. Neither did Kenny, who had been in a family business and wanted to protect family assets. So both kept their concerns to themselves. Each worried that raising the idea of a prenuptial agreement might offend the other.
That's how it was until Cindy and Kenny looked down at the entries in one of the races at Charles Town that night. A horse was named Sign a Prenup. The colt's sire was named Prenup. Prenup's dam was named Homewrecker.
Cindy and Kenny looked at each other. "You know," Cindy said, "we should really get a prenup." Kenny agreed. She gave him a hug. Moments later, Sign a Prenup loped around the Charles Town oval in first place and paid a nice price.
The following week Cindy went to visit Cheryl New, the lawyer who had handled her divorce. Like many couples starting out on their first prenuptial contract, Cindy and Kenny figured it wouldn't be hard. They assumed that New, who Cindy said had been wonderful during her divorce, could handle it easily.
But New told Cindy as soon as she arrived in the lawyer's high-rise office at Tysons Corner, "He needs his own lawyer."
Rule number one: The two sides have to have separate attorneys, and picking the lawyers is like walking a tightrope. You need opposing counsel, but not so opposing that it throws the marriage into jeopardy.
New suggested that Kenny pick someone New had dealt with before so they could work harmoniously but with their own client's interests foremost. She gave Cindy several names her fiancé might try. One was John Dondero, a lawyer from Silver Spring. Kenny met with Dondero and hired him.
Kenny and Cindy probably had waited too long to talk about their prenuptial agreement. Their wedding date already was set. Some attorneys, like Carol Schrier-Polak of Northern Virginia, refuse to handle prenups less than 60 days prior to the wedding. "I should make it 90 days," she says. "The biggest mistake I see couples make is waiting too long to bring up the prenup."
Although divorce and family-law specialists handle the bulk of prenups, some of the expertise needed is in the area of estate law, especially for the part that deals with what happens if one of the parties dies. At New's firm, Sandground New & Lowinger, the third name on the letterhead, Jeff Lowinger, is the financial guru.
Divorce lawyers who don't have someone in house like Lowinger often hire an estate lawyer to help with the death aspects of a prenup as opposed to the divorce issues.
In creating a prenuptial agreement, the first step is to compile a complete accounting of the parties' assets and liabilities.
"When you buy a used car," says Rockville attorney James S. Maxwell, "there is the principle of caveat emptor--it's up to the buyer to discover flaws in the item being sold. The seller has no burden to reveal problems."
But in a premarriage contract, both parties have a legal obligation to report everything fully and fairly. Should they fail to do so, the prenup is likely to be tossed out during a divorce proceeding. Then decisions the couple thought they had made about their property are put into the hands of a judge.
Ordinarily, the party more interested in a prenup is the wealthier one. But the requirement that both parties disclose everything they own, from trust funds to Redskins tickets, often gives the wealthier party pause. Not only do some people not want their spouses to know what they have, some families don't want their children to know.
In a recent case, the mother of a woman about to get married insisted that her daughter get a prenup to protect assets held in family trusts. The mother did not want her prospective son-in-law to know what trusts she had--nor did she want her daughter to know how much she was worth. The mother feared that if the daughter knew the extent of her wealth, she might stop working.
The situation created a dilemma for the mother, whose lawyer told her that failure to reveal the amount in her daughter's trust funds would make a prenuptial agreement unenforceable. The mother had no choice but to disclose the information.
As it turned out, the marriage lasted only a year, and the son-in-law departed without any of his wife's wealth. But the young woman discovered she had a legal right to the money in her trust, which no one had ever revealed to her. She dismissed her mother as trustee and took the money for herself.
There was nothing the mother could do. Had she failed to disclose the family assets, the prenup would have been invalidated and the divorced husband might have had some claim on earnings from his wife's trusts.
"You want to make sure you are fairly and fully disclosing all of your assets," says New. "What differentiates a prenup negotiation from a divorce is that in a prenup the people aren't mad at each other yet."
Washington may be the nation's prenuptial capital. As Teresa Heinz Kerry said during the last presidential campaign, "You have to have a prenup. You've got three kids with somebody else, you've got to have a prenup. You could be as generous or as sensitive as you want. But you have to have a prenup."
Nationally, 5 to 10 percent of marrying couples execute prenuptial agreements: Because prenups are not filed in court, it's impossible to get an exact number. In Washington, attorneys say, the number may be twice the national average.
In many places prenups are associated with older couples entering second marriages who want to protect their assets for their children. But in Washington, lawyers say, more and more younger couples get prenuptial agreements. They want to make sure that in a breakup, both keep what was theirs going into the marriage.
Among lawyers, there is debate about whether prenups are good or bad. They can create an awkward environment leading up to one of the most joyous moments in a person's life. Many couples on the eve of marriage are distracted by the impulse to try to extract one last concession from the person one is about to vow to share a life with.
By the time Cindy and Kenny agreed they needed a prenup, a wedding date had been set. Now the race was on to get an agreement written and signed before the marriage.
"The trick is not to have the prenup ruin your life," says Cindy. "It's not the most fun thing to do, especially when you are in the middle of planning a wedding."
Many attorneys find working on a prenup more unpleasant than a divorce. One lawyer says the best that can be said of a prenup is that if you survive the negotiations, you have a better chance of surviving the marriage.
At best, a prenup serves as a divorce insurance policy. While it can cost $15,000 to $20,000, a well-thought-out prenup can save multiples of that at divorce time. As with an insurance policy, you hope you won't need it, but if you do, it can make a divorce far simpler and easier. As one attorney explains it, the parties are more rational before one of them has committed adultery.
Attorney Patrick Dragga recently litigated a divorce case that cost his client $250,000. A prenup probably would have saved him $200,000. "My guy should be a poster child for prenup," says Dragga. "He spent half the value of his estate on the divorce."
Proponents of prenups, such as Carol Schrier-Polak, who speaks on the topic to legal and civic groups, say that forcing the parties to discuss finances before they marry, as difficult as it might be, has a long-term benefit.
Says Linda Ravdin, considered one of the nation's leading specialists, "The prenup negotiation forces a couple to put their thoughts about marriage on the table." She has seen many instances in which couples sat down to discuss money and discovered that their preconceptions and family experiences were different. "Sometimes a person thinks that because their parents commingled all the money and bank accounts that this was how everyone does it. Then they learn that in their spouse's family, it was the opposite."
The prenup offers an opportunity to honestly discuss the marriage. Occasionally the conversation reveals attitudes that cause couples to decide not to get married. That's why attorneys wish couples would do the prenup before sending out the invitations.
The prenuptial discussions begin with a working draft. Some attorneys ask the financially dominant party to prepare one. Others have models that they start from, often developed from years of writing such contracts.
In Ravdin's Bethesda practice, the side pushing for the prenup starts the process with a draft. "I tell my clients there is nothing to talk about until we see the terms drafted by the stronger party," she says. "Then we have something to negotiate from."
Negotiations on the agreement between Cindy and Kenny began, after the disclosure of assets, with a telephone discussion between Cheryl New and John Dondero. "You start throwing ideas around," says New. "What's hers, what's his, what's going to be marital property, what's going to be separate, what happens if they buy a house and put in uneven sums of money."
What can the parties expect in spousal support or alimony? Do things change if the couple is married for two years as opposed to 25 years? Will the prenup expire after a certain number of years if the financially weaker spouse has "proven" himself or herself?
Sometimes one attempts to mandate personal behavior on the part of the other. Almost every lawyer in Washington has a story about a client who tried to put language into a prenup regulating sexual activity. One prominent Washingtonian offered his wife-to-be financial inducements, to be paid upon the apparently inevitable divorce, to have sex a certain number of times a week.
If it sounds a little like prostitution, that's why a judge might not uphold such a payment in a divorce settlement.
Some people want a prenuptial agreement with financial penalties for straying. Although the wedding never took place, movie star Jennifer Lopez had a prenup written before her almost-marriage to Ben Affleck. It stipulated that he'd pay her $5 million in a divorce settlement if he'd been caught in another woman's bed.
Washington lawyers say financially weaker females often insist on such provisions.
The financially weaker party to a prenup almost always caves at the end of the negotiation, says Rockville attorney Patrick Dragga. On the morning of a wedding, a lawyer for one party tells the lawyer for the other, "No agreement, no wedding." With 400 guests arriving in three hours, the strain of such a demand is excruciating. At that point, says Dragga, the client, usually the woman, can't back out. She signs the agreement even if her lawyer advises her not to.
"When a client first comes to me," says Ravdin, "I ask them right out: Are you willing to walk away from this marriage if the terms aren't right? It's not a question that is easy to answer, and most people who are in love know that in their heart of hearts, the true answer is no. You know when you get that answer that if the dominant spouse insists, your client is going to cave. So you have to get the best deal you can."
One of the key elements in whether a prenuptial agreement will be upheld by a court is whether it was voluntary. In Maryland, DC, and Virginia, a prenup voluntarily signed even under the pressure of a wedding deadline usually will be upheld. In some jurisdictions, judges increasingly are entertaining arguments that last-minute signings constitute coercion. A spouse forced to sign a wedding-morning prenup is free to argue later that his or her signing was not voluntary, but under law and precedent, the argument is not likely to prevail.
"As long as the person understood what they were doing," says Dragga, "the court is likely to uphold the prenup."
"The real problem you have with a prenup," says Dragga, "is that you have two people in love, who want to get married, and the closer you get to the wedding date . . . the likelihood is that the person who is the underdog is going to end up agreeing to what the person with the money wants."
Adds Dragga, "If the bride slips out of the rehearsal dinner to make a hushed phone call, there's a good chance she's negotiating some final prenup details. My partners and I have been in on those phone calls."
"A prenup is supposed to be fair, but what is fair today might not be considered so fair 20 years down the pike," says Marna Tucker, Washington's doyenne of divorce law. "The second problem is what is voluntary. If a smart, sophisticated businessman tells his wife-to-be a day before the wedding, 'I want you to sign these papers, trust me,' and the invitations are out and the people are coming, that does raise an issue of voluntariness."
At the divorce proceeding, says Tucker, it is up to the person benefiting from the prenup to prove that the spouse "knew what she was doing. To me such a circumstance could show an element of coercion."
Some attorneys don't buy the notion that a woman about to marry a much wealthier man is helpless.
"Power in the negotiations doesn't always follow the money," says Fairfax matrimonial lawyer Richard Colten. "Love, lust, and the urge to demonstrate eternal commitment are also powerful tools. It takes a cold heart to hold firm when the object of your yearning looks at you with teary eyes and pleads, 'What's the matter? Don't you love me? Don't you trust me?' "
Richard Shadyac Jr., of Falls Church, takes the argument a step further. He says he has seen cases in which a gold digger used the prenup to extract wealth from an older man. "What might happen," says Shadyac, "is that the woman urges the man to write a prenup, knowing that she doesn't intend to stay in the marriage one minute longer than she has to. Then whatever she gets out of it is money in her pocket."
Another attorney tells of a female client who had a quick and powerful answer to her wealthy fiancé's insistence on a prenup. "She announced there would be no premarital sex until the marital contract was consummated. That had the effect of leveling the negotiating playing field very quickly. It also sped up the process."
Provisions for how long you have to stay married to receive certain levels of benefits are not unusual. Stay married for a certain number of years, and you ensure yourself a lump-sum payout in a settlement. Divorce before that time and possibly get nothing.•
Former General Electric chief Jack Welch's prenup with his wife, Jane, had a ten-year expiration date. After he had an affair with a magazine editor and he and Jane divorced--after 13 years of marriage--a judge threw out the prenup, which promised $15 million to Jane in a divorce. Instead she got more than $100 million. Had an affair been discovered and the divorce taken place in the ninth year of marriage, Jane would have gotten $85 million less. Maybe that's why she never saw anything happen before that episode, speculates one attorney.
Sometimes the spouse who has put the time limit into the agreement uses the final year like a football player's option clause: If you want to get out of the marriage, you do it before the expiration date or pay the price. "I've had clients call me up for a divorce the month before the prenup is going to expire," says one Washington divorce lawyer. "If he takes a chance on waiting, it is going to cost more."
Although it is generally thought that prenuptial agreements were devised to protect rich husbands, their origin in law was the opposite. They were first conceived as a way to protect wealthy heiresses from gigolos.
In the 19th century, married women weren't allowed to own property. If a woman inherited property, she would lose control of it if she remarried. So the first prenuptial agreements were designed to make sure that any inheritance went to the heiress's children, not to a second husband. Otherwise her fortune became his as soon as they said the words "I do."
To this day, a signed and separately lawyered prenuptial agreement, which need not be filed anyplace except a drawer, overrides a state's community property laws and can help clarify what is separate property and what is community property over the life of a marriage.
Life would be simpler for couples, and for their attorneys, if the partners remembered what's written in the prenup. Take the case of the Maryland couple who stipulated that the house was the husband's and would be considered his separate property in a divorce settlement. Within a year, the couple went out and jointly refinanced the house, thereby commingling their money--and rendering moot what they had agreed to in the prenup.
Prenups have been the focus of at least one recent movie, Intolerable Cruelty, which starred George Clooney as the lawyer and Catherine Zeta-Jones as the wife of a client. Clooney's character is the author of an "impenetrable" prenuptial, one that had never been tossed out by a judge. In the real world, an impenetrable prenup does not exist. But barring a finding that a prenup wasn't voluntary or that the financial disclosure was fraudulent, courts usually regard the agreement favorably.
In an episode of Seinfeld, Jerry's sidekick George decides he can break up with his fiancée by telling her he will need a prenup before they get married. She laughs and announces that she is wealthier than he is.
In real life, couples are full of such surprises. A well-known Washington pundit made such a request when he got engaged, only to discover too late that his wife-to-be had twice the money he had. She gladly signed the agreement, and when she later divorced him after he cheated on her, he fared much worse than he would have without a prenup.
Prenup negotiations sometimes have surprising results. One DC case featured a prospective husband who was heir to a fortune. Trying in his own way to be generous, he agreed to start his wife out in her own business, which would be her property alone, while protecting all that was going to be his. The attorney stared at the draft of the agreement and said somewhat sadly, "There is a lot of his here, and there is a lot of hers there. But I don't see any ours."
At that, the woman--one day from being a bride--burst into tears and ran from the room. The couple postponed the marriage and went into counseling. When they finally decided to marry, they did so more as a couple. Says the lawyer, "That case made me try to ensure, in every prenup I worked on, that at least something in the marriage was jointly held, no matter how determined the parties were to keep things separate."
Another issue that sometimes gets included in prenups is where each party can take a job. Geography can get in the way of a happy marriage. In Washington, where two-professional marriages are common, it is often the case that one spouse doesn't want to be dragged to someplace like Atlanta. So some couples try to settle in advance who has to go where with whom.
Some women try to ensure in a prenup that if she puts her career on hold to raise children or support her husband as he moves up the ranks, in a divorce settlement she is compensated commensurate with the career she could have had. Otherwise, judges are likely to assign less monetary value to her position. Marna Tucker says that any woman contemplating a prenup should make sure her lawyer tries to include language that would elevate her nonworking status if she raises children. It is much easier to have a man agree to such things in a prenup than in a contentious divorce proceeding.
Sometimes arguments over the content of a prenuptial agreement can become the most expensive aspect of litigating a divorce. That happened in the 1989 breakup of boxing champion Sugar Ray Leonard and his wife, Juanita. According to Juanita, the prenup she had signed with Sugar Ray before their marriage--they had lived together for seven years--would have left her with nothing. Sugar Ray was wealthy by the time they married, and Juanita had little choice but to sign what was clearly a one-sided agreement. In the divorce proceedings, she claimed that Sugar Ray's adulterous behavior made the prenup invalid. The settlement eventually provided her with several million dollars out of Leonard's boxing/endorsement fortune, estimated at the time at more than $50 million. The prolonged battle fattened the pockets of lawyers on both sides.
Most people who sign prenups hope they never have to see them again. A surprising number, say family-law practitioners, forget about them, ignore the provisions, and go about their lives as if they had never been written.
As for Cindy Levine Black, "I tucked ours in a safe where Kenny keeps his basketball medals from the JCC championship he won in 1977. I was a cheerleader on that team, and the prenup is squeezed under the picture.
"For me the process of doing a prenup was like divorcing my husband before I was married," she says. "But what got me through it was that I was mad for him, and we were in love.
"We had to look at it like we were both protecting our kids as the first and foremost goal. It was something that had to be done."
Cindy's strategy for getting through the ordeal? "We did a lot of hugging. Big hugs, because I was marrying such a cute guy.
"If you are really in love, you can survive a prenup."


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Tag: Sustainability
Innovations in environmental and social behavior that enable organizations and society to operate for the long term
SSIR Digital Edition
View eBook or download PDF.
- Select Issue
Collective Impact | 96
Large-scale social change requires broad cross-sector coordination, not the isolated intervention of individual organizations.
Ten Nonprofit Funding Models | 48
For-profit executives use business models—such as "low-cost provider" or "the razor and the razor blade"—as a shorthand way to describe...
The Wall Street Takeover of Nonprofit Boards | 8
Financiers represent a growing percentage of board members at some of America's most prestigious nonprofits, resulting in poorer...
The Promise of Lean Experimentation | 4
By adopting a model from business, nonprofit organizations can launch, test, and implement new programs and services more efficiently....
Redefining Education in the Developing World | 19
A new approach that builds relevant marketplace, entrepreneurship, and health care skills is needed.
The Charity Trap | 10
Why a recent exposé on Red Cross failures in Haiti highlights unrealistic expectations for social sector organizations.
Moving Beyond Vanity Metrics | 2
To achieve greater impact, membership and advocacy organizations must find new ways to measure engagement.
Like the Vacuuming, Nonprofit Work Is Women's Work | 8
If we want the nonprofit sector to innovate, we need to acknowledge the gender gap between nonprofits and the private sector.
GiveDirectly? Not So Fast. | 17
We are mistaking an important experiment for a proven solution.
Challenging the Orthodoxies of Philanthropy | 10
Are traditional assumptions about how we "do" philanthropy preventing us from finding new and better ways of working?


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Scalloped Potatoes with Reblochon
|Reblochon cheese
|1 1⁄4 Pound, crust removed and mashed to a paste
|6 Medium, peeled and thinly sliced into 1/8 inch thick rounds
|Heavy cream
|1⁄2 Cup (8 tbs)
|3⁄4 Cup (12 tbs)
|To Taste
|White pepper
|To Taste
|Grated nutmeg
|1 Teaspoon (As Desired)
|1 Clove (5 gm), halved
|2 Tablespoon
1) Preheat the oven to 400° F.
2) Into boiling water, place the potatoes for 3 minutes.
3) Drain them thoroughly.
4) Now, in cold running water, wash them once and drain again.
5) In a saucepan, heat the cream and milk over very low heat.
6) In this, melt the reblochon cheese.
7) To this, add salt, pepper and nutmeg to taste.
8) Inside a shallow casserole, rub the cut sides of the garlic over and then spread butter evenly.
9) In this casserole, place the potatoes.
10) Over the potatoes, pour the cheese mixture.
11) Bake in the preheated oven for 30 minutes.
12) Now, reduce the heat to 325° F and bake for 1 hour more, or until the top is browned well. Check if the potatoes are completely soft.
13) Serve the Scalloped Potatoes hot with toasted bread.


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Sweet N Tender Cabbage Rolls
19 Sep 2010
|1 Large
|2 , beaten
|1⁄2 Cup (8 tbs)
|Cooked long grain rice
|2 Cup (32 tbs)
|Sliced mushrooms
|9 Ounce, drained (2 Jar)
|1 Small, chopped
|2 Teaspoon
|Dried parsley
|1 Teaspoon
|Dried oregano
|1 Teaspoon
|Dried basil
|1 Teaspoon
|1⁄2 Teaspoon
|Lean ground beef
|2 Pound
|Canned tomato sauce
|16 Ounce (2 Can)
|Packed brown sugar
|1⁄2 Cup (8 tbs)
|Lemon juice
|2 Tablespoon
|Worcestershire sauce
|2 Teaspoon
Cook cabbage in boiling water just until leaves fall off head.
Set aside 14 large leaves for rolls.
Cut out the thick vein from the bottom of each reserved leaf, making a V-shaped cut.
In a large bowl, combine eggs, milk, rice, mushrooms, onion and seasonings.
Crumble the beef over mixture and mix well.
Place about 1/2 cup on each cabbage leaf; overlap cut ends and fold in sides, beginning from the cut end.
Roll up completely to enclose filling.
Place seven rolls, seam side down, in a 5-qt. slow cooker.
Combine the sauce ingredients; pour half over the rolls.
Top with remaining rolls and sauce.
Cover and cook on low for 7-8 hours or until a meat thermometer reads 160°.
Recipe Summary
Difficulty Level:Medium
Course:Side Dish
Method:Slow Cooked
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Sweet N Tender Cabbage Rolls Recipe


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A system and method for monitoring vibrations of a cutting tool uses a neural network for classifying signal features as break or non-break or, in another embodiment, as non-break or abnormal. A vibration signal is produced by an accelerometer, positioned to sense vibrations at the tool-workpiece interface. The signal is pre-processed to extract low frequency machining noise and detect the energy in a higher frequency band. The signal is then sampled and segments of the digitized signals are processed by digital logic into feature vectors for input to a trained neural net having two output nodes for classification. The use of a neural net provides performance improvement and economies over previously known heuristic methods of signal analysis.
1. A system for detecting tool break events while machining a workpiece comprising:
means for generating electrical signals representing vibrations at an interface between a tool and said workpiece;
analog signal processing means for producing from said signals, unipolar, signal energy-versus-time analog waveforms;
digital processor means for a) sampling and digitizing said unipolar output signals, b) screening said sampled and digitized signals for alerting spikes, and c) calculating a plurality of feature vectors for digitized signals at, preceding, and following said alerting spikes, said plurality of feature vectors including mean values, slopes of mean values, standard deviations, and slopes of standard deviations; and
a trained neural net for receiving said plurality of feature vectors at an input layer of said trained neural net, said neural net trained iteratively by applying inside data representative of a normal event and outside data representative of an abnormal event, the neural net using an adjustable bias parameter for biasing correct classification of inside and outside data during training and for forming an acceptable class boundary about said inside data and outside data, said trained neural net using said acceptable class boundary to classify said plurality of features as at least one of a break event, a non-break event, and an abnormal event.
2. The system of claim 1 further comprising break indicator means for indicating a tool break event.
3. The system of claim 1 wherein said electrical signals are ultrasonic and low frequency accelerometer signals.
4. The system of claim 1, wherein said trained neural net is classifier net.
5. A method for detecting tool break events while machining a workpiece comprising the steps of;
generating electrical signals representing vibrations at an interface between a tool and said workpiece;
producing from said signals, unipolar, signal energy-versus-time analog waveforms;
sampling and digitizing said unipolar output signals;
screening said sampled and digitized signals for alerting spikes;
calculating a plurality of feature vectors for digitized signals at, preceding, and following said alerting spikes, said plurality of feature vectors including mean values, slopes of mean values, standard deviations, and slopes of standard deviations;
inputting said plurality of feature vectors to an input layer of a neural net, said neural net trained iteratively by applying inside data representative of a normal event and outside data representative of an abnormal event, the neural net using an adjustable bias parameter for biasing correct classification of inside and outside data during training and for forming an acceptable class boundary about said inside data and outside data; and
using said acceptable class boundary for classifying said plurality of feature vectors as at least one of a break event, a non-break event, and an abnormal event.
6. The method of claim 5 further comprising the step of indicating a tool break event.
7. The method of claim 5, wherein said trained neural net is a classifier net.
This application is a continuation of application Ser. No. 08/038,440 filed 29 Mar., 1993 now abandoned.
This invention relates to a tool break detector system and to a method of monitoring cutting tool vibrations to detect tool breakage and to prevent false alarms on other vibration noise occurring during normal cutting.
Automation in the factory has advanced to a point where machining is often done without manual supervision. Tool breakage, however, has increasingly become a problem because a broken tool can damage an expensive workpiece if cutting is not quickly discontinued. In response to this problem, systems have been developed to monitor the status of the cutting tool, typically by the use of a sensor, such as an accelerometer, mounted near the tool in order to sense vibration and to automatically terminate cutting when tool breakage occurs. Tool breakage, or break event, detection is a problem of recognizing break patterns in the sensor signals and thus involves the extraction of pertinent features from the signals and then classifying the feature vector as either break or non-break. Prior art tool break detection systems handle this recognition problem through careful manual analysis of the available signals by which a set of heuristics is developed. These heuristics, or rules, are then translated into microprocessor based amplitude-time domain pattern recognition techniques for spotting those signal patterns which are found to accompany a tool break event.
An example of one such heuristic approach is U.S. Pat. No. 4,636,779, which discloses a tool break detection system wherein sampled vibration signals are digitized and processed via either digital circuitry or a programmable general purpose computer containing the logic for detecting break events. In the '779 patent, this digital circuitry or software calculates a running mean signal level of a selected number of signal samples. A transient detector compares every new sample with the running mean signal value of N previous samples to detect a transient or abrupt increase in signal level that may have its source in a major tool break event. A mean shift detector then compares the mean signal level after and before such a transient to detect a shift in mean level and thus a substantial change in background cutting noise. A mean shift persistence detector makes a check that the shift in mean level persists for a specified time period. Various other commonly assigned patents (U.S. Pat. Nos. 4,642,617; 4,636,780; 4,707,787; 4,724,524; 4,806,914; 4,849,741; 4,853,680; and 4,918,427) improve on and modify this algorithm for various reasons as explained therein, but all of these systems are alike in that expert developed heuristics are crucial to being able to discriminate normal cutting events from tool break events and these heuristics are never as accurate as one would like them to be.
The challenge of the tool break detection problem therefore lies in the inherently noisy and variable signals in which no simple characteristic can be found which accurately discriminates tool breakage from other normal cutting events. Another challenge presented by the detection problem is the relative scarcity of break data as compared with normal cutting data. Breaks occur randomly and infrequently so that capturing break data can be a costly and involved process. A break detection system which could provide improved detection sensitivity and lower false alarm rates and could be easily adapted for use with new tool types and cutting tasks would be desirable. Furthermore, since break data is scarce and difficult and costly to obtain especially for new tool types and cutting tasks, a system which does not depend on such break data would be especially desirable.
An object of the present invention is to improve upon the performance of previous machine tool monitors.
Another object is to make effective use of multiple signal features for identifying tool break events which leads to improved accuracy and false alarm rates.
A further object of the invention is to utilize the inherent capabilities of neural networks for automated learning of decision criteria by training on data and fast algorithm development and easy updating through training.
A still further object of the invention is to provide methods for training a neural network for abnormality detection using only non-break data.
An improved machine tool break detection system according to a preferred embodiment has a means for generating electrical signals of vibrations occurring at the interface of the machine and a workpiece. An analog signal processor produces an energy-versus-time waveform from these signals. A digital processor has means for sampling and digitizing these waveforms and calculating various features of the digitized signals. These features are generated in the form of a feature vector for input to a trained neural net. The neural net can be trained using these same features using known techniques or it can be trained using feature data for non-break events only, using a novel technique called Biased Data Training. The features may be a subset of features chosen from among means, standard deviations, and slopes of these values within windows of the magnitude of the digitized signal patterns. The system may further include means for indicating break events upon the classification of a given feature input by the neural net as a break or abnormal event.
The sole FIGURE is a block diagram of the present invention.
The sole FIGURE is a block-diagram of the present invention. The machine tool monitor described in U.S. Pat No. 4,636,779, incorporated herein in its entirety, uses the same sensor and analog signal processing as the present invention. The present invention includes a novel neural network module, shown as 24 in the sole FIGURE, which has a neural net and some additional digital processing to prepare the inputs to the neural net. While neural nets are well known, they have not to the inventors' knowledge been heretofore used in monitoring for machine tool breaks.
Still referring to the FIGURE, a machine tool 10 is shown having a vibration sensor 12, which may be an accelerometer, mounted to it. Sensor 12 generates electrical signals representing vibrations at the tool-workpiece interface. The mounting location of vibration sensor 12 is determined individually for each machine tool to be monitored. The considerations are the same as for touch tool detectors and are fully explained in U.S. Pat. No. 4,631,683, incorporated herein in its entirety. Analog processor 14 filters the raw accelerometer signal output and generates an analog waveform proportional to the amplitude of vibrations in the chosen band. Modern accelerometers with built in signal conditioning are very convenient to use, inexpensive and rugged. They also operate in a frequency range that allows relatively remote placement from the cutting region while maintaining adequate signal level. Preferably the accelerometer would have a useable response from 1 Hz to 70 KHz. Sensor 12 has a broadband response that analog processor 14 bandpass filter restricts. An energy detector consisting of a full-wave rectifier and a 500 Hz low pass anti-aliasing filter produces the signal energy-versus-time analog waveform. It also has a digitally controlled gain stage to keep signals within the dynamic range of the system. Analog-to-digital converter 16 samples the waveform from processor 14 and passes the amplitude of these samples to signal pre-processor 18 of neural network module 24. In one embodiment, neural network module 24 screens the sampled signal for alerting spikes which initiate a feature extraction process by signal pre-processor 18 and subsequent network classification by neural net 20. In cases where alerting spikes are not present, the signal can be analyzed without regard to such spikes. The response time of the module has been tested and found to be fast enough to handle 250 alerting events per second. These will be discussed in more detail below. Neural net 20 classifies a given set of features (hereinafter referred to as a feature vector) as either break or non-break. Neural net 20 may be either a classifier net or an abnormality detection net to be described hereinbelow. Break events are indicated on tool break indicator 22. Tool break indicator 22 is preferably an alarm but may be any type of device for alerting an operator of a tool break, such as an alarm coupled with a visual indication such as a flashing light. The use of neural net 20 in the present invention is a significant improvement over prior art tool break detection systems. As explained in the background section, these prior art systems suffer from their reliance on expert developed heuristics which are never truly accurate and must undergo changes in order to accommodate new situations. Neural networks have the ability to handle multiple input features and can be rapidly trained and evaluated. Of the many types of neural nets which exist, the Multi-Layer-Perception (MLP) is the preferred type because of its ease of implementation and speed in recognition. A novel training method described hereinbelow in the section entitled "Biased Data Training Method", can be advantageously used to train the neural net in the present invention using only non-break cutting data. This is an enormous benefit since break data is so scarce. The MLP is a multiple layer feedforward network without feedback. The weights are typically determined by the Backpropagation algorithm or a variant thereof. The specific weight determining method is not critical to the present invention. Neural net 20 may be either a classifier as is known in the art or an abnormality detector as described in the section on Biased Data Training. The architecture of the MLP is preferably a 3-layer network with an input layer, hidden layer, and output layer. The input layer contains enough nodes to accept the feature vector as created by pre-processor 18. The output layer contains two nodes representing "non-break" and either "break" for the classifier, or "abnormal", for the abnormality detector. The hidden layer contains a variable number of nodes as determined by training trials. Two hidden nodes are found adequate for the classifier and ten for the abnormality detector.
Pre-processing of the digitized signals for feature extraction by digital processor 18 will now be described. It will be appreciated that the present invention is not limited to any type of tool, or any type of signal patterns emanating from the tool. Although one embodiment of the present invention for carbide tools will be described, the disclosed methods for identifying pertinent signal features to use is generally applicable to other types of tools. Initial selection of signal features will necessarily depend on expert experience of what have been found to be good indicators of tool break events. The key to the use of neural nets is that the expert need not understand how a particular set of signal features interact to indicate a tool break. This information is embedded in the neural net after appropriate training.
For carbide tools it is known that a break event is always accompanied by a spike in the signal. The spike itself is not a reliable break indicator because many other events also cause spikes. It is however, useful as a marker to initiate further analysis. The presence of a spike above a given level is considered an alert event by pre-processor 18. The signal adjacent to the spike is then processed and the extracted features passed to neural net 20 for classification. The accelerometer signal may be further processed into two signals representing the high and low frequency components, hereinafter referred to as UE (for ultrasonic energy) and LF (for low frequency) signals respectively. The UE signal consists of the envelope of the high frequency component from 20 KHz-70 KHz, and the LF signal contains the low-pass filtered component from 1 Hz to 500 Hz. It should be appreciated that the operation of the present invention is not dependent on the presence of spikes in the signals, but instead, features may be extracted throughout the signal. Limiting the neural net inputs to a window containing a spike is merely a more efficient method of operation in cases where a spike will always accompany a break. Likewise, the use of LF and UE signals as described does not limit the present invention. As will be described next, the particular signals and features selected for training the neural net are selected based on experimentation and is largely a subjective decision. Time domain characteristics are generally preferably since use of frequency domain characteristics is hampered by a low-quality power density spectra due to large amounts of noise and to the fact that system time response requirements severely limit the number of sample points. The following features may be extracted for each of the LF and UE signals:
1. mean value within window preceding spike
2. slope of the mean value within window preceding spike
3. standard deviation within window preceding spike
4. slope of the standard deviation within window preceding spike
5. amplitude of the spike
6. mean value within window following spike
7. slope of the mean value within window following spike
8. standard deviation within windows following spike
9. slope of the standard deviation within windows following spike
Data for training the neural net may be obtained by using a system such as disclosed in U.S. Pat. No. 4,636,779, and capturing files of digitized data for the UE and LF signals when a break alarm is triggered by a heuristic algorithm such as one described earlier. The files which are associated with an actual break (manually confirmed) are scanned by an expert to determine the actual location of the break event. Because not all breaks may be detected by the heuristic algorithm, the data will not constitute statistically random samples and will reflect any bias within the heuristic algorithm used. The files might constitute, for example, a 20 sec. time period. Within each such time period there would typically be many spikes. For each spike, both break and non-break, the sets of features listed above are extracted to form a feature vector set for training the neural net. Although good classification results could be obtained using all of the features listed above, it is useful to eliminate those features that either carry redundant information or do not help in classification. Lowering the number of features reduces the amount of feature extraction processing time and, more importantly, reduces the training data needed. Various methods may be used to rank the features in terms of usefulness. Some of these methods are as follows:
1. Build-up method: The feature set is "built up" by incrementally adding the one feature that results in the greatest classification improvement. In the first step, the best single feature for classification is found. Then each of the other features is tested with the first feature and the pair with the best result is kept, and so on. The earlier a feature is selected for keeping, the better it is assumed to be.
2. Strip-down method: The feature set is "stripped down" by removing one feature at a time. Starting with all features, classification performance is tested by removing one of the features, and the one resulting in the smallest performance degradation is then left out for the next strip-down step. The earlier a feature is removed, the poorer it is assumed to be.
3. Weight relevance: The method relies on the vector of weights attached to each of the input nodes of a neural network trained with all initial features. The feature associated with the smallest weight vector magnitude is assumed to be the least relevant to the classification.
4. Exhaustive search: All combinations of features are tested for best classification. This method is the most reliable for a given amount of data, but is also the most computation intensive. The method is feasible in this case only because of the small number of features involved. The best combination of features are found for networks using different numbers of input features, from one to all features. Features can then be ranked by assuming that those used by more networks are more useful.
The following table shows the ranking of the UE signal feature set using the first 3 methods:
______________________________________Ranking of UE Signal Features WeightRank Build-up Strip Down Relevance______________________________________1 6 2 62 7 4 33 5 3 14 9 1 45 8 6 26 4 5 87 2 8 58 3 7 79 1 9 9______________________________________
Using the exhaustive search method, the following best feature sets are found:
______________________________________Features found Using Exhaustive SearchNo. of features used Features selected______________________________________1 22 3, 63 3, 6, 84 1, 3, 6, 85 1, 2, 3, 6, 86 1, 2, 3, 4, 6, 87 1, 3, 6, 7, 8, 98 1, 2, 3, 4, 6, 7, 8, 99 all 9______________________________________
Using this table, if the feature usefulness is measured by the number of best networks it appears in, then the ranking of 3, 6, 8, 1, 2, 4, 9, 7, 5 results. Comparing this result with the ranking in the first table, no clear cut consensus emerges from the various methods. Probably the only situation that the various methods would agree on is when the data features are totally uncorrelated with each other, which happens rarely in practice. Nevertheless, it can be observed from both tables that certain features are consistently ranked highly, e.g., 6 and 3, while others such as 7 and 9 are consistently low.
Using the ranking tables, the feature set of 1, 2, 3, 6 and 8 may be chosen for subsequent work. The final chosen set, after adding three additional features from the LF signal, consists of the following:
1. mean UE value within window preceding spike
2. slope of the mean UE value within window preceding spike
3. standard deviation of UE within window preceding spike
4. mean UE value within window following spike
5. standard deviation of UE within window following spike
6. standard deviation of LF within window preceding spike
7. standard deviation of LF within window at the spike
8. standard deviation of LF within window following spike
It is highly desirable to have a training method which can learn on the normal events and then to distinguish any abnormal events. Such a method can be used to capture real breaks and in time, sufficient break data will be accumulated to train a classifier in the usual way. The following section describes a novel training technique which requires only normal non-break data.
Unlike conventional neural network classifiers that learn to distinguish data of different, but known classes, the neural net training method of the present invention learns to discriminate data of known classes from data that are likely to belong to previously unknown classes. The function that the network of the present invention performs can be described as abnormality, or novelty, detection; whereby abnormal, or novel, events are defined to be unlike those presented to the network during training.
For simplicity, the known data is assumed to belong to a single class although the method is applicable to multiple class data sets also.
A neural network trained according to the present invention determines a decision boundary that closely defines the region occupied by the data points of a known given class. The decision boundary can be set in a controlled way to loosely or tightly bound the given data points. The boundary is typically closed but is not necessarily convex. After having formed such a decision boundary, the network can then be tested with arbitrary input vectors. Those that fall outside the decision boundary are designated by the network as different from the data used for training.
The method uses a feed forward neural network. The network inputs are the elements of the input data vector, or data features, and the output nodes represent the output classes, (e.g., normal/abnormal). The neural network is trained by backpropagation of output errors or any one of a variety of related optimization methods, e.g., gradient descent or conjugate gradient (see D.G. Luenberger, Linear & Nonlinear Programming, 2nd ed., Addison-Wesley, Reading, Mass. 1984). The present training algorithm makes three additions to the traditional backpropagation training for classification.
The first difference between classification and boundary determination training is that in the latter, one of the classes is artificially generated. Neural network training for classification needs competing classes of input data to make a reasonable decision boundary. However, in abnormality or novelty detection type problems there is often only one class of data, hereafter called the "inside" data. Therefore, a set of "outside" class data is artificially generated to provide a competing influence to the known inside points. These "pseudo" outside data points can be initially chosen so that either they are randomly distributed within a selected region of the decision space, or they can be more specifically located as discussed below.
In order to insure that the neural network classifies all of the known inside points correctly, the training error term is biased to favor the correct classification of inside points. This second difference takes advantage of the fact that the backpropagation training technique attempts to minimize the total error of the system.
The third difference is that the present method allows error parameter adjustment and multiple training cycles. Since the optimal error term bias and best set of pseudo outside points is not known at the beginning of training, the method iterates through the classification and adjustment process until completion. Completion of the method depends on finding a good boundary solution or determining that the net has converged.
In one of several possible procedures, the method begins with a neural network in some initial state, a set of input data and a net bias parameter set to zero. The N points of known inside data are exactly the points around which the boundary is to be learned. The net bias parameter is the parameter that determines the training bias for errors in the classification, i.e., an inside point classified as outside, or outside point classified as inside. The bias parameter is set to 0 for the first training iteration so the method begins training with unbiased normal classification.
Next pseudo data is generated to represent points different from the inside data. It would be ideal to place the pseudo outside points just outside of the desired boundary. However, that information is not available since that is the problem to be solved. Therefore, pseudo data is chosen so that it overlaps and/or surrounds the inside data. One reasonable choice for the initial pseudo data is to randomly generate them within a bounding hypercube defined so its hypervolume is twice that of the hypercube bounding the inside data.
A random distribution of pseudo data points can occasionally lead to an uneven distribution. In order to provide an average even distribution, the algorithm is repeated multiple times, each time the network is trained to convergence with a new set of pseudo data. This iterative technique reduces irregularities in the boundary due to local concentrations of random data.
Another way to generate an even distribution of pseudo data in a bounding hypercube is to place the points on regular intervals in each dimension. There are two problems with this method however. First, the total number of data points needed to represent the matrix increases exponentially with each additional dimension. Second, the regularity of the grid can force the boundary to have certain undesirable properties such as non-tight boundaries in concave regions and boundaries parallel to grid lines.
A modification of the randomly generated pseudo data allows retention of pseudo data points that appear to be near the boundary. The purpose of this is to push in the boundary closer to the inside points by clustering more outside points near the boundary. A simple method for finding points near the boundary is to calculate a confidence value for each pseudo data point after a particular cycle of training has converged. This confidence value is calculated by subtracting the two network output node values. A small difference denotes a low confidence and a large difference denotes a high confidence of the result. Pseudo data points resulting in low confidence values are estimated to be near the boundary and can be retained for the next training cycle.
Once the inside data is collected and the pseudo data is generated, the data points are presented to the neural network for training.
The net bias parameter can be used in several different ways. The purpose of the bias parameter is to force the error term to reflect the desired boundary state. The boundary state could be a tight boundary, a relaxed boundary, a boundary containing all inside points, or a boundary containing most of the inside points. The bias parameter can be used in different ways, such as an output error multiplier for one class whereby the error term is multiplied by a constant which is a function of the input class, or as an output target value modifier. The simplest and most effective way to modify the error term is to modify the output target values.
A neural network implementation with two output nodes corresponding to two classes provides four target values to be adjusted. These values correspond to the positive and negative target values for each of the two output nodes and for each of the two classes of inputs. For example, during training, when an inside point is presented for classification, the Inside output node is set to a target value of target (I,p), and the Outside output node is set to a target value of target (O,n). With no bias term, these values would be 1 and -1 respectively. With a bias term the output target values are calculated as:
TABLE 1______________________________________Output Target Functions______________________________________ target(I, p) = 1 + (positive insidebias) target(I, n) = -1 - (negative insidebias) target(O, p) = 1 + (positive outsidebias) target(O, n) = -1 - (negative outsidebias)______________________________________
Only the negative outside bias term is used in normal boundary training. All of the other bias terms are set to zero to set a reference against the negative outside bias, which the net bias parameter is used for. This bias term is set to positive values to force the network to get perfect accuracy on the inside data set. This works because the outside negative target value contributes more to the error term as it gets larger in absolute value.
The positive outside bias term and the negative inside bias term can be used to push the boundary in or out. These bias terms are used to modify the error generated by the outside data points. The bias values are increased to increase the outside point influence and decreased to decrease the outside point influence on the error term Generally, as the outside point influence increases, the boundary is pushed towards the inside points and as the inside point influence increases, the boundary is pushed towards the outside points.
When the network begins training, all of the target bias parameters are set to zero. As mentioned above, during normal boundary training, the net bias parameter is used to set the negative outside bias value. This value remains zero if the inside accuracy remains perfect. This is not usually the case because the outside data can overlap the inside data.
When the inside data goes below 100% accuracy the net bias parameter is increased as a function of the accuracy. As the accuracy decreases, the net bias change increases. Once the net has reached perfect inside accuracy again, the net bias is reduced a smaller amount. The result of these adjustments is a net bias parameter that gets large in early training iterations and generally converges to a smaller value as the net finds a good boundary in later iterations.
In order to end the training loop, the trained neural network must pass the exit condition. There are two possible conditions for detecting the end of training, determining an acceptable boundary and detecting when the weights have stabilized.
The method of boundary convergence used in the present invention employs a minimum accuracy criteria. After a net has been trained for a fixed number of cycles to determine a reasonable bias value and outside accuracy, the net is allowed to train until it meets a minimum inside and outside accuracy.
The stability of the neural network could also be used to detect algorithm completion. The neural network tends to stabilize to some degree early in training. However, after an initial reasonable solution has been found, the boundary can change radically without much change in the network weights. Thus, it is very difficult to detect network convergence by just looking at the net weights. This is particularly difficult if the random pseudo data approach is used, since the net is constantly changing to compensate for different outside data sets. After many trials to determine if the weights converged, it was determined that weight convergence was not generally a good measure of training completion.
The boundary learning algorithm would work in the following way on an illustrative example. A two-dimensional circular region is derived from a random set of points within a circle about the origin of radius equal to 70. If 100 points of that data are taken as the input to the method then N=100. The net bias parameter is set to 0.0. The pseudo data bounding hypercube has dimensions of 200 on each side, centered on the origin. The method generates 100 points of pseudo outside data randomly distributed in the range of -100 to 100 in each dimension. The net is then trained to convergence on the 200 training points. The net is tested and the inside and outside accuracy values of the trained network are used to determine if the net should be trained again. Assume for example that the outside accuracy was 60% and the inside accuracy was 95%. The net bias parameter is then increased. Since the inside accuracy was not perfect, the cycle is repeated. The new training data generated includes the N original inside points and pseudo data points. The M pseudo data points can be generated in the several ways described hereinabove. The cycle continues until all of the inside points are classified correctly, and the outside accuracy is greater than some threshold. The resulting net is saved as the output of the algorithm.
A variation to this approach is to first generate random pseudo points which are located on a bounding hypercube surface which points by definition are outside the region of the inside data points. This bounding hypercube could be defined for example by the extremum (maximum and minimum) values contained in the inside data set. After the first training iteration, a loose boundary will have formed around the inside data points. A new set of pseudo outside points are generated within the bounding hypercube and are classified by the network. Those pseudo points that are classified as inside are discarded, and the rest kept for another training iteration. In this variation, pseudo data points that would have occupied the inside data region are removed, thus reducing the dependence on the net bias parameter. The net bias parameter is not necessary because the net accuracy on the inside points will be 100% if pseudo data in the inside region are removed. Again the procedure is repeated until a convergence criteria is met. The prior set or sets of pseudo data may be retained for training during each iteration.
Another variation to this approach is to generate data on a bounding hypercube volume in a regular matrix fashion. This ensures an even distribution of pseudo data points, but the number of such points increases very rapidly with the dimension of the input vectors.
Yet another extension of this method is to allow multiple inside classes, in which case the network will have an output node for each inside class plus the one for the outside class. Additional bias parameters can be introduced either as multipliers for the error terms in equation (1), or as additional output target values similar to those listed in Table 1 (target (I, P1), target (0,P1), target (I, P2), etc.) .
Whereas in straight classification, the number of hidden nodes should be kept to a minimum to avoid over training (a term that refers to getting much greater accuracy in training than in testing), a greater number of hidden nodes is generally needed in Biased Data Training. This can be intuitively understood by envisioning in decision space, the classification boundaries. In separating known classes, relatively few simple decision planes or surfaces are needed; but in abnormality detection, the data clusters of the known class(es) must be well bounded on all sides. The flexibility needed to form arbitrary shaped and bounded surfaces generally require more adjustable parameters in the network. The hidden nodes and associated weights provide these parameters.
While specific embodiments of the invention have been illustrated and described herein, it is realized that modifications and changes will occur to those skilled in the art. It is therefore to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit and scope of the invention.
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-- Total revenues of $1.8 billion, up 3.4%
-- Adjusted diluted earnings per share of $0.86, up 12%
-- Cash flow from operations improved to $329 million
-- 2008 adjusted EPS guidance increased to $3.17 - $3.22 per diluted share
-- Agreement in principle reached regarding previously disclosed
NID-related matter
MADISON, N.J., Oct. 21 /PRNewswire-FirstCall/ -- , 2008-- Quest Diagnostics Incorporated (NYSE: DGX), the world's leading provider of diagnostic testing, information and services, announced that for the third quarter ended September 30, 2008, adjusted income from continuing operations increased to $0.86 per diluted share, before $0.03 per share associated with a non-cash charge to write down an equity investment, and $0.02 per share associated with the impact of hurricanes. Including these items, net income from continuing operations was $160 million, or $0.81 per diluted share. For the third quarter 2007, net income was $150 million, or $0.77 per diluted share.
Third quarter revenues increased 3.4% to $1.8 billion. The impact of hurricanes reduced revenues by approximately half a percent. Clinical testing revenues increased by 3.0% compared to the prior year. Revenue per requisition increased 2.3% and clinical testing volume, measured by the number of requisitions, increased 0.7%.
"During the quarter, we grew revenues, increased adjusted earnings per
share 12% and generated $329 million in cash," said Surya N. Mohapatra,
Ph.D., Chairman and Chief Executive Officer. "Our strong cash flows and
prudent capital management position us well for these turbulent times. We
are successfully executing our plan, focusing on providing a differentiated
service to our customers, a
|SOURCE Quest Diagnostics Incorporated
Copyright©2008 PR Newswire.
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Concept Housewares - Metal Wall Wine Rack
Weekly Deal: $43.68
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Save space on your floor and counters with a Wall Mounted Wine Rack by Concept Housewares. The wine rack measures 20"W x 12"D x 12"H and holds eight wine bottles and six glasses. Made of metal, the wall mounted wine rack has a Black Matte finish to complement any décor. Easy to assemble, the wine rack comes with instructions and all the required hardware. Ships via UPS/FedEx ground.
(W = side to side) (D = front to back) (H = top to bottom)
20"W x 12"D x 12"H
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Orchid Event Solutions (OES) is seeking an Account Manager based in our Salt Lake City office. OES is a full service provider of online event registration services, software, on-site registration support and convention housing management for association, trade show, direct selling, corporate, faith based and government clients. Primary Responsibilities: Coordinate hotel room blocks, reservations and convention registration for convention and exposition events for national and international customers. Manage all aspects of housing and registration events including working relationships with clients, hotels partners and convention attendees. The ideal candidate will possess a solid mix of organizational skills, analytical ability, and customer service skills. We are looking for a self-starter capable of working independently as well as in teams. Job Duties: ??? Works with meeting planners and hotels to maintain multiple hotel room blocks and registration activities for events. ??? Works with clients to understand their housing and registration needs and translates those needs into operations within company standards. ??? Responsible for software system set-up for both housing and registration events. ??? Ensures a positive experience for clients and event attendees from event open date through end date. ??? Controls hotel room inventory and conference registration for assigned accounts. ??? Maintains strong grasp of account contracts to ensure all efforts are made to fulfill client and hotel obligations. ??? Collects housing fee and registration revenue on assigned accounts. ??? Facilitates conflict resolution between hotels, convention delegates, and meeting planners. ??? Generates convention performance reports. ??? Travels to various events for on-site support. ??? Achieves departmental production standards as set forth by the Director of Housing Operations and the Director of Registration Operations. ??? Other duties as assigned.
Relocation costs may be covered.
??? Must be able to work effectively with the public and possess excellent oral and written communication skills. ??? Strong working knowledge of Microsoft Excel and the ability to learn new programs specific to the department. ??? Ability to work within company standards using checklists to achieve operational goals. ??? Great organization skills and ability to adapt in a fast-paced work environment. ??? Must be able to travel throughout the year and work evenings, weekends and holidays based on client and company demands.
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Katie Dolar
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Phyllo-Topped Seafood Pie
- Course Main Dishes
- Prep. Time 30 mins
- Cooking Time 35 - 40 mins
- Yields 6 servings
Entertain with elegance by topping this pot pie with an attractive, but easy, phyllo crust. The gently poached seafood enveloped in a rosé sauce will make your dinner party extra-special. A lightly dressed green salad on the side is the perfect accompaniment.
- Course Main Dishes
- Prep. Time 30 mins
- Cooking Time 35 - 40 mins
- Yields 6 servings
Preheat oven to 425 °F (220 °C). Butter a 13 x 9-inch (33 x 23 cm) baking dish.
Cut salmon into 1-inch (2.5 cm) chunks. Trim hard side muscle off scallops. Set both aside.
In large skillet, melt butter over medium heat. Add garlic and sauté for about 2 min. Pour in stock and wine and bring to a simmer. Add salmon, scallops and shrimp; poach, stirring gently, for 3 to 5 min or until just slightly firm and opaque. Using a slotted spoon, transfer fish and seafood to prepared dish; set aside.
Whisk flour into cream and gradually stir into pan. Bring to a simmer over medium heat, whisking constantly. Cook, whisking, for about 5 min or until sauce is thickened. Stir in tomatoes and chives. Season to taste with salt and pepper. Pour over seafood in dish; gently stir to combine.
Lay one sheet of phyllo on work surface; keep remaining covered with plastic wrap. Brush phyllo lightly with butter. Place another sheet on top; brush with butter. Place over filling in dish, tucking edges. Working with one sheet at a time, brush remaining phyllo with butter; scrunch up gently into a rippled bundle and place on dish.
Bake for about 20 min or until phyllo is golden brown and filling is bubbling. Let stand for 5 min before serving. Garnish with chives.
To prepare this dish ahead, keep poached seafood and sauce separate, let cool, cover and refrigerate for up to 1 day. Reheat the sauce over medium-low heat, stirring often, until hot and bubbling then proceed with the recipe.
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100 Good Reasons
Dairy products made from 100% Canadian milk make breakfast easy.see all the reasons


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We all have an Achilles' heel when it comes to dieting. Some people can stay on their weight loss plans until they get a whiff of peanut butter — and then they dive into the jar until they find themselves scraping the sides. Others can resist all temptation unless they're confronted with creamy, cold, sweet ice cream — and then there's no stopping.
But for some dieters, that pretty bread basket spells DANGER! Comforting, chewy bread tastes so good, and seems so innocent – and then you find yourself smoothing the fifth layer of butter on the fifth slice of toast and realize: You derailed from your diet.
Here are our top tips for managing your beloved bread when you're on a diet:
Wheat-Free and Gluten-Free Options
Whether it's celiac disease, a sensitivity or your personal taste, you may need or want to set a goal of avoiding wheat and other substances containing gluten.
Try it and see if you feel fine with just one serving with a healthy option such as Ener-G Light Gluten Free Tapioca Loaf or even make-your-own Bob's Red Mill Wonderful Gluten-Free Bread Mix.
No Bread Options
But is going gluten-free the answer for you — or do you feel that you do better bypassing bread completely?
If you decide to ban bread, try these tips:
- Lettuce wraps make a low-carb, low-calorie substitute for bread while adding vitamins, fiber and minerals. Try a sturdy choice such as Romaine lettuce.
- Slice raw zucchini the long way and make a "sandwich" that's full of nutrition and crunch.
- Use low-carb crackers made of cheese such as Kitchen Table Bakers Aged Original Parmesan Crisps.
- Try gluten-free crackers that are all-natural, such as Foods Alive Organic Flax.
Diet Diva
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