text:
|Numéro de publication
|Type de publication
|Numéro de demande
|Date de publication
|9 mai 2002
|Date de dépôt
|12 févr. 2001
|Date de priorité
|30 mars 2000
|Autre référence de publication
|Numéro de publication
|Stephan Wegerich, Alan Wilks, John Nelligan
|Cessionnaire d'origine
|Wegerich Stephan W., Wilks Alan D., Nelligan John D.
|Exporter la citation
|BiBTeX, EndNote, RefMan
|Citations de brevets (82), Référencé par (9), Classifications (4), Événements juridiques (3)
|Liens externes: USPTO, Cession USPTO, Espacenet
[0001] This application claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional application Ser. No. 60/93,228 filed Mar. 30, 2000.
[0002] The present invention relates to the monitoring of physical systems, processes or machines, and more particularly to systems and methods for discerning signal differentiation using an operator to detect deviations in one or more sets of signals from monitored sensors.
[0003] Generally, there is a need to detect when one or more of a set of signals from sensors monitoring a system, whether a machine, process or living system, deviates from "normal." Normal can be an acceptable functioning state, or it can be the most preferred of a set of various acceptable states. The deviation can be due to a faulty sensor, or to a change in the underlying system parameter measured by the sensor, that is, a process upset.
[0004] While threshold-type sensor alarms have traditionally been used to detect when parameters indicate that a component has strayed away from normal, acceptable or safe operating conditions, many deviations in sensor or underlying parameter values go unnoticed because threshold detection can only detect gross changes. Often such detection may not occur early enough to avoid a catastrophic failure. In particular, there is a critical need to detect when a component, as indicated by a signal or underlying parameter value, is deviating from an expected value, given its relationship to other system components, i.e., in the monitored machine, process or living system. This detection should occur even though the corresponding signal in question is still well within its accepted gross threshold limits.
[0005] A number of methods exist that try to use the relationships between sensors, signals, data or the underlying parameters that correspond thereto, to detect notable component changes that otherwise would be missed by "thresholding." Such methods are often data-intensive and computationally demanding. There is a need for accurate empirical modeling techniques that provide computationally efficient and accurate system state monitoring.
[0006] The present invention provides an improved monitoring system and method for ultrasensitive signal differentiation that achieves the detection of changes to and faults in one or more sensor signals in a set that characterizes an underlying "process or machine." The invention achieves accurate results with improved computational efficiency without relying on thresholding. Therefore, less memory and CPU power are needed to perform the necessary calculations. Also, because of the improved computational efficiency, more data "time-slices" can be processed with a given CPU speed. This is useful particularly in systems where signals or data must be sampled at a high rate, or in an application where the CPU or micro-controller is limited.
[0007] An empirical model is developed of the process or machine to be monitored, and in real-time sensor data from the monitored process or machine is compared to estimates of same from the model. The results of the comparison are statistically tested with an ultrasensitive difference test that indicates alerts on a sensor-by-sensor basis, thereby providing early warning of process upsets, sensor failures, and drift from optimal operation, long before these would be noticed by conventional monitoring techniques. According to the invention, an improved similarity operator is used in generating the estimates.
[0008] The invention provides an improved operator that can be implemented in software on a variety of systems. A typical implementation would be in a programming language like C or C++ on a Unix or Windows workstation, either as a standalone program sampling data via a National Instruments-like pc-card, or as a part or module of a broader process control software system. The program can be a Windows-like DLL or callable routine, or can comprise an entire suite of presentation screens and settings modification screens. The software can be reduced to a PCMCIA card for addition to a computer with such a port. Then, the data being input to the computation could be fed through a dongle attached to the PCMCIA card. An alternative would be to put the program into microcode on a chip. Input and output of the requisite data to a broader system would depend on the design of the overall system, but it would be well known to circuit designers how to build in the microchip version of this invention. In yet another embodiment, the computation could take place on a server remote (WAN) from the sensors that feed to it. As contemplated in another embodiment, the internet could be used to deliver real-time (or semi-real-time) data to a server farm which would process the data and send back either alarm level data or higher-level messages. In that case, it would become necessary to ensure that the asynchronous messaging "delay" of the internet was of sufficiently unobtrusive to the semi-real-time monitoring taking place over the internet/WAN. For example, bandwidth could be guaranteed so that the asynchronicity of the messaging was not any kind of delay. Alternatively, the sampling rate of the system could be slow enough that the delivery time of Internet messages was negligible in comparison.
[0009] Briefly, the present invention relates to a computationally efficient operation for accurate signal differentiation determinations. The system employs an improved similarity operator for signal differentiation. Signals or data representative of several linearly and/or non-linearly related parameters that describe a machine, process or living system are input to the inventive system, which compares the input to acceptable empirically modeled states. If one or more of the input signals or data are different than expected, given the relationships between the parameters, the inventive system will indicate that difference. The system can provide expected parameter values, as well as the differences between expected and input signals; or the system can provide raw measures of similarity between the collection of input signals and the collection of acceptable empirically modeled states.
[0010] The novel features believed characteristic of the invention are set forth in the appended claims. The invention itself, however, as well as the preferred mode of use, further objectives and advantages thereof, is best understood by reference to the following detailed description of the embodiments in conjunction with the accompanying drawings, wherein:
[0011]FIG. 1 shows a block diagram of an exemplary laboratory workbench arrangement for gathering process or machine behavior data for distillation;
[0012]FIG. 2 shows an example of an embodiment of the present invention in an on-board processor;
[0013]FIG. 3 shows an example embodiment wherein a process is shown to be instrumented with sensors having output leads;
[0014]FIG. 4 illustrates an implementation of a statistical modeling operator in accordance with the invention;
[0015]FIG. 5 shows a method for selecting training set vectors for distilling the collected sensor data to create a representative training data set;
[0016]FIG. 6 shows computation of an expected "snapshot," given the real-time actual "snapshot" of the underlying system;
[0017]FIG. 7 and FIG. 8 show snapshot numbers 1-6 as deviating signals and noise additive signals respectively with associated vector similarity values using a similarity operator in accordance with the invention;
[0018]FIGS. 9A and 9B illustrate a sensor input signal from an air conditioning condenser thermocouple showing positive drift, with FIG. 4A illustrating the residual signal resulting from the drift as deviating over time;
[0019]FIGS. 10A and 10B illustrate the residual signal generated in response to negative drift on the sensor input; and
[0020]FIGS. 11A and 11B illustrate the introduction of additive noise to the sensor input and the corresponding residual signal.
[0021] The present invention is a system, method and program product for monitoring operation of a machine, process, living system or any other such system to accurately predict individual component failures, i.e. component end of life, such that failure or process upset can be anticipated to reduce planned downtime, reduce unplanned downtime and provide for process or product quality optimization.
[0022] It is directed to the employment of an improved similarity operator within an empirical modeling engine for use in generating estimates of sensor values for a monitored process or machine based on input of the actual sensor values. Advantageously, the similarity operation carried out in the modeling according to the invention has comparatively low computational needs as compared to other similarity operators that can be used in such an empirical model-based monitoring system, such as is described in U.S. Pat. No. 5,987,399 to Wegerich et al., wherein is disclosed a similarity operator employing computationally intensive trigonometric variables. In particular a representative "training" set of data is compared against monitored signal data in a similarity engine and a statistical significance engine. Techniques for achieving ultrasensitive signal differentiation are similar to what is generically described in U.S. Pat. No. 5,764,509, directed to the application of a sequential probability ratio test ("SPRT"). Parameter data are gathered from signal sensors monitoring a system such as a machine, process or living system. The number of sensors used is not a limiting factor, generally, other than respecting computational overhead. The present invention is highly scalable. Preferably, the sensors should capture component parameters of at least some of the primary "drivers" of the underlying system. Furthermore, all sensor inputs to the system are best interrelated in some fashion (non-linear or linear).
[0023] Turning to FIG. 1, a block diagram of an exemplary laboratory workbench arrangement 100 is shown for gathering process or machine behavior data for distillation. In this example, the monitored system is depicted as a machine prototype 102 and may be, for example, a combustion engine, an electric motor, a pump, a compressor, a refrigerator, and so on. It is understood that, as further indicated herein, the monitored system may be any machine, living system or system carrying out a process. In this example, the machine 102 is termed a prototype, but importantly, its function is to generate sensor data that is substantially the same as the actual parameter values expected in a production model of the machine, as would be measured by the same sensors. Of course, the training may be in situ wherein the prototype is a production model itself, and ideally, not different in any way from other production models. In addition, when sufficient data has already been accumulated, that previously accumulated data may be used as the training data source, the prototype machine being a virtual machine derived from production machines contributing data to the accumulation.
[0024] The machine 102 may be connected to and controlled by a control system 104, generally comprising a microcontroller- or microprocessor-based digital system with appropriate analog digital and digital/analog inputs and outputs as are known to those skilled in the art. Machine 102 is instrumented with sensors monitoring machine components or reactions thereto (e.g., chamber temperature or pressure) and providing resultant sensor values along outputs 106. During training, the machine 102 is operated through an expected range of operations, and data acquisition system 108 records values of all sensors 106 with which machine 102 is instrumented. Additionally, control signals from control system 104 may also be recorded by data acquisition system 108, and may be used as "sensor signals" that correlate with the other sensor signals.
[0025] Data acquired by data acquisition system 108 can, accordingly, be processed using a computer module 110 for producing a distilled training data set representing the operational ranges of machine 102, using the preferred training method, or other appropriate such methods as may be known in the art.
[0026] The monitoring system described herein includes an empirical modeling engine and a statistical decision-making engine supported by a suite of software routines for data preconditioning, training, and post-decision reporting. This system is modular and can be applied separately depending on the requirements of the particular monitoring application. Typically, process monitoring equipment employs sensors having some common characteristics. A sensor data set is acquired as being representative of the normal or desired operation range of the system. The parameters monitored by sensors should be chosen for the model to be correlated, either linearly or nonlinearly. Generally, multiple sensor inputs may be necessary, however, the described algorithms may apply to single sensor applications by using signal decomposition of the single sensor signal into components that can be treated as multiple, correlated inputs for modeling and monitoring. The identification of small deviations in signals from normal operation is provided as indicative of the status of the sensor's associated physical parameter.
[0027] The inventive monitoring system can be adapted to provide not just monitoring of existing sensor data, but also generation of derived "virtual" sensor data based on other actually-monitored sensors. Thus, an example 120 of an embodiment of the present invention in an on-board processor is shown in FIG. 2, wherein a system, machine or process, represented by machine 122, is controlled by a control system 124 that is located on the machine. Machine 122 is instrumented with sensors for some of the physical or logical parameters of interest that may be controlling the machine, and the outputs for these sensors are shown as output conductors 126, which feed into the control system 124. These are also fed to a processor 128 located within or on the machine, disposed to execute a computing program for monitoring sensor signals and an optional computing program for generating a set 130 of virtual signals on output conductors 126. The processor is connected to a local memory 132, also on or in the machine 122, which stores data comprising the training set distilled to represent the expected operational states of the machine 122. Memory 132 can also advantageously store programs for execution by the processor 128. Virtual signals 130, if included, previously generated by the processor 128 are provided to the control system 124, in lieu of genuine sensor values. Generation of virtual sensor estimates using the improved similarity operator of the present invention can be more fully understood with reference to copending patent application Ser. No. 09/718,592 of Wegerich, filed Nov. 22, 2000, and entitled "Inferential Signal Generation for Instrumented Equipment and Process." Virtual signals may be generated as a cost saving measure to reduce sensor count or for unmonitorable physical or logical parameters of the machine 122.
[0028] Processor 128 can also be a part of the control system 124, and in fact can be the processor on which the control system routines are executed, in the event the control system is a digital computed control system. Ideally, the processor 128 and memory 132 are powered by the same power source as the control system 124. However, under certain circumstances, it may also be preferable to provide for a processor 128 and memory 132 independent from another processor and or memory (not shown) of the control system 124, in order to provide for example virtual signals 130 in a timely fashion, as though they were truly instrumented parameters. For example, processor 128 may operate at a higher clock speed than the control system processor.
[0029]FIG. 3 shows an example embodiment 140, wherein a process 142 is shown to be instrumented with sensors having output leads 144. These leads 144 provide sensor signals to a control system 146 that controls the process. These signals are also provided to a remote communications link 148, which is disposed to communicate digital values of the sensor signals to a second remote communications link 150, located at a physically remote place. A processor 152 is provided, which may comprise a computing system and software, that uses the sensor signals received by link 150 to monitor the process 142 for sensor failures, process upsets or deviations from optimal operation and optionally generate at least one virtual sensor signal indicative of an inferred physical parameter of process 142. A memory 154 is provided to store training set data representative of the expected operational behavior of the process 142, according to the distillation method described above.
[0030] Furthermore, a display 156 may be provided at the remote location for displaying data descriptive of the process 142, i.e., sensor signals 144 and any virtual signals derived therefrom or both. The virtual signals generated by processor 152 can also be transmitted from link 150 back to link 148 and input over leads 158 to control system 146 for advantageous control of the process. Data from original sensor signals and or virtual sensor signals (if included) can also be transmitted to a third remote communications link 160, located at yet a third distant place, for display on display 162, thereby providing valuable information concerning the process to interested parties located at neither the physical process site nor at the monitoring system site.
[0031] The remote communications links can be selected from a variety of techniques known in the art, including internet protocol based packet communication over the public telecommunications infrastructure, direct point-to-point leased-line communications, wireless or satellite. More specifically, remote links 148,152 and 160 may be internet-enabled servers with application software for accumulating, queuing and transmitting data as messages, and queues for receiving and reconstituting data arriving as messages. Alternatively, communications can be synchronous (meaning in contrast to asynchronous, message-based communications) over a wireless link.
[0032] The embodiment of the invention shown in FIG. 3 allows computation of signals using computing resources that are located geographically distant from the system being monitored and/or controlled. One benefit of remote monitoring is that the monitoring and analysis resources may be shared for a multitude of systems, where the memory 154 may hold multiple training sets characterizing the various monitored systems, processes and machines or distributed combinations thereof. Another benefit is that results may be displayed and also potentially used in further analysis by interested parties located distant from the system being monitored.
[0033] A preferred method of employing the improved similarity operator of the present invention is shown in FIG. 4, which illustrates an implementation of the operator according to a method 170 for monitoring a process or machine instrumented with sensors. In a training step 172 a model is derived that captures the normal operating ranges for the sensors. Upon initiating the software system at step 174, training is determined to occur either on-line or not in step 176. Previously archived data 180 may also be used to provide data characteristic of normal or acceptable operation, or data characteristic of normal or acceptable operation may be obtained from the real-time operation of the monitored system 178. According to the method 170, training patterns (the D matrix) are chosen at step 184 to generate the empirical model at 186, as further described below. The training set is a matrix of vectors in which each vector represents one of the normal operating states of the system.
[0034] Real time monitoring stage 190 is indicated at step 188, whereupon the model generated at 186 is employed in the steps 192 for estimation. Real-time data is acquired in 192 and an estimate of what the sensor values should be is generated based thereon in view of the model. For each snapshot of data acquired in 192, the similarity operator of the invention is used to compare the actual real-time sensor values to vectors of sensor values in the training set.
[0035] The differences between the expected values and real-time sensor signals, i.e., the residual signals formed in 192, are sent to a decision-making engine in the process deviation decision-making step 182. The decision-making engine continuously renders a reliable decision at 194 based on a SSCADI index calculated at 198 over a moving window of successive residual observations, determining whether or not the residual signals reveal a statistically relevant abnormality.
[0036] The described embodiment uses a platform with an operating system for real-time monitoring 190 and sampling of the real-time sensor data via a National Instruments feed, with a monitor including an estimation module 192 with a windowing environment to provide feedback in the form of alarms 194 or other high-level messages 196 to an operator of equipment. The program embodying the invention can be written in C or in LabView, a product available from National Instruments Company. The operator portion itself comprises a callable Windows DLL that the LabView software calls. The software may be written as components, including: (1) a training component 172 (for selecting the D matrix from a set previously obtained of "normal" input data), (2) an estimation module component 192 for modeling (to provide the similarity operations both on matrix D and on the real-time input vector, and also to provide an estimated output or an output of the degree of similarity of the vector to the states recognized in the D matrix), and (3) a statistical test component 198 (which tests data such as using SPRT on the similarity measurement or residual between the input and the estimate).
[0037] Monitoring begins by providing signal data as vectors, each with as many elements as there are sensors. A given vector represents a "snapshot" of the underlying system parameters at a moment in time or time correlated. Additional pre-processing can be done, if necessary for time correlation to insert a "delay" between cause and an identified effect between sensors. That is to say, for example, if sensor A detects a parameter change that will be reflected at sensor B three "snapshots" later, the vectors can be reorganized such that a given snapshot contains a reading from sensor A at a first moment, and a corresponding reading from sensor B three snapshots later. Each snapshot is representative of a "state" of the underlying system. Methods for time-correlating in this way are known to those skilled in the art.
[0038] Turning to FIG. 5, a method for selecting training set vectors at step 184 is graphically depicted for distilling the collected sensor data to create a representative training data set. In this simple example, five sensor signals 202, 204, 206, 208 and 210 are shown for a process or machine to be monitored. Although the sensor signals 202, 204, 206, 208 and 210 are shown as continuous, typically, these are discretely sampled values taken at each snapshot. As indicated hereinabove, snapshots need not be ordered in any particular order and so, may be ordered in chronological order, parametric ascending or descending order or in any other selected order. Thus, the abscissa axis 212 is the sample number or time stamp of the collected sensor data, where the data is digitally sampled and the sensor data is temporally correlated. The ordinate axis 214 represents the relative magnitude of each sensor reading over the samples or "snapshots."
[0039] In this example, each snapshot represents a vector of five elements, one reading for each sensor in that snapshot. Of all the collected sensor data from all snapshots, according to this training method, only those five-element snapshots are included in the representative training set that contain either a global minimum or a global maximum value for any given sensor. Therefore, the global maximum 216 for sensor 202 justifies the inclusion of the five sensor values at the intersections of line 218 with each sensor signal 202, 204, 206, 208, 210, including global maximum 216, in the representative training set, as a vector of five elements. Similarly, the global minimum 220 for sensor 202 justifies the inclusion of the five sensor values at the intersections of line 222 with each sensor signal 202, 204, 206, 208, 210. Collections of such snapshots represent states the system has taken on. The pre-collected sensor data is filtered to produce a "training" subset that reflects all states that the system takes on while operating "normally" or "acceptably" or "preferably." This training set forms a matrix, having as many rows as there are sensors of interest, and as many columns (snapshots) as necessary to capture all the acceptable states without redundancy.
[0040] Turning to FIG. 6, the training method of FIG. 5 is shown in a flowchart. Data collected in step 230 has N sensors and L observations or snapshots or temporally related sets of sensor data that comprise an array X of N rows and L columns. In step 232, an element number counter i is initialized to zero, and an observation or snapshot counter t is initialized to one. Two arrays "max" and "min" for containing maximum and minimum values respectively across the collected data for each sensor, are initialized to be vectors each of N elements which are set equal to the first column of X. Two additional arrays Tmax and Tmin for holding the observation number of the maximum and minimum value seen in the collected data for each sensor, are initialized to be vectors each of N elements, all zero.
[0041] In step 234, if the sensor value of sensor i at snapshot t in X is greater than the maximum yet seen for that sensor in the collected data, max(i) is updated to equal the sensor value and Tmax(i) stores the number t of the observation in step 236. If not, a similar test is done for the minimum for that sensor in steps 238 and 240. The observation counter t is incremented in step 242. In step 244, if all the observations have been reviewed for a given sensor (t=L), then t is reset and i is incremented (to find the maximum and minimum for the next sensor) in step 246. If the last sensor has been finished (i=N), step 248, then redundancies are removed and an array D is created from a subset of vectors from X.
[0042] First, in step 250, counters i and j are initialized to one. In step 252, the arrays Tmax and Tmin are concatenated to form a single vector Ttmp having 2N elements. These elements are sorted into ascending (or descending) order in step 254 to form array T. In step 256, holder tmp is set to the first value in T (an observation number that contains a sensor minimum or maximum). The first column of D is set equal to the column of X corresponding to the observation number that is the first element of T. In the loop starting with decision step 258, the ith element of T is compared to the value of tmp that contains the previous element of T. If they are equal (the corresponding observation vector is a minimum or maximum for more than one sensor), it has already been included in D and need not be included again. Counter i is incremented in step 260. If they are not equal, D is updated to include the column from X that corresponds to the observation number of T(i) in step 262, and tmp is updated with the value at T(i). The counter j is then incremented in step 264. In step 266, if all the elements of T have been checked, then the distillation into training set D has finished, step 266.
[0043] Once the D matrix has been determined, in the training phase 172, the preferred similarity engine may begin monitoring the underlying system (122, 142 or 178) and through time, actual snapshots of real sensor values are collected and provided to the similarity engine. The output of the similarity engine can be similarity values, expected values, or the "residual" (being the difference between the actual and expected values).
[0044] One or all of these output types are passed to the statistical significance engine, which then determines over a series of one or more such snapshots whether a statistically significant change has occurred as set forth hereinbelow. In other words, it effectively determines if those real values represent a significant change from the "acceptable" states stored in the D matrix. Thus, a vector (Y) of expected values:
{right arrow over (y)} out ={overscore (D)}·{right arrow over (W)}
[0045] (i.e., estimates) is determined for the sensors. The expected state vector (Yout) is equal to contributions from each of the snapshots in D, which contributions are determined by the weight vector W. The multiplication operation (·) is the standard matrix/vector multiplication operator. W has as many elements as there are snapshots in D. W is determined by:
[0046] D is again the training matrix, and D′ is the standard transpose of that matrix (i.e., rows become columns). Yinput includes the real-time or actual sensor values from the underlying system, and therefore it is a vector snapshot.
[0047] The similarity operator {circle over (x)} can be selected from a variety of mechanisms for providing a measure of the similarity of one snapshot as compared to another. One general class of similarity operators performs an element for element comparison, comparing a row of the first matrix with a column containing an equal number of elements from the second matrix. The comparison yields a "similarity" scalar value for each pair of corresponding elements in the row/column, and an overall similarity value for the comparison of the row to the column as a whole, e.g., for one snapshot to another.
[0048] The similarity operator has been improved in the present invention to define the similarity si between ith elements as:
[0049] where:
[0050] Max(range) is the maximum value of that "sensor" across the matrix (typically across the transpose of the D matrix), that is the maximum value of x in a given column of x in the above matrix, or in other words the maximum value for that corresponding sensor in the training set;
[0051] Min(range) is the minimum value of that "sensor" in the same manner;
[0052] Xi=the ith component of the row of the first matrix (x matrix above);
[0053] Mi=the ith component of the column of the second matrix (m matrix above);
[0054] ρ=an analysis parameter that can be manually varied for enhanced analysis and is defaulted to 1; and
[0055] λ=a sensitivity analysis parameter that also can be manually varied for enhanced analysis and is defaulted to 1.
[0056] Accordingly, vector identity yields si=1.0 and complete dissimilarity yields si=0.0. Typically si falls somewhere between these two extremes.
[0057] Overall similarity of a row to a column is equal to the average (or some other suitable statistical treatment) of the number N (the element count) of si values:
[0058] or alternatively the similarity S of a row to a column can also be calculated:
[0059] that is, the average of the θi, divided by ρ, raised to the power of λ, and subtracted from one. Accordingly, the S values that are summed from θi or si are the S values depicted in the result matrix above. This operator, having been described generically above, works as follows in the determination of W. The first factor, D′{circle over (x)}D, is referred to herein as matrix G, i.e.,
{overscore (G)}={overscore (D)} ′ {circle over (x)}{overscore (D)}
[0060] So, for example, with D and its transpose for 4 sensors a, b, c, d and for n training set snapshots are:
[0061] then the matrix G is:
[0062] where, for example, element g12 in the matrix G is computed from row 1 of D-transpose (a1, b1, c1, d1) and column 2 of D (a2, b2, c2, d2) as either:
[0063] where the elements si and θi are calculated as:
[0064] The resulting matrix G is symmetric around the diagonal, with ones on the diagonal. To further calculate W, the computation of (D′{circle over (x)}Yinput) is performed in a similar fashion, except Yinput is a vector, not a matrix:
[0065] The G matrix is inverted using standard matrix inversion, and the result is multiplied by the result of D′{circle over (x)}Yinput. The result is W:
[0066] These weight elements can themselves be employed, or expected values for the sensors can be computed, applying the training matrix D to the weight vector W:
[0067] Turning to the analysis or tuning parameters λ and ρ, while either value can remain set to its default value of 1, analysis parameter ρ can be varied to all values greater than or equal to 1. The sensitivity analysis parameter λ can be varied to any value and, preferably, to any positive value and more particularly, can be selected in the range of 1-4.
[0068] The computation results of the expected sensor values is subtracted from the actual sensor values to provide a "residual". The residual is also a vector having the same number of elements as the sensor-count, with each element indicating how different the actual value is from the estimated value for that sensor.
[0069] So, for example, set forth below is an example of C code for the overall functionality of the inventive similarity operator based upon a modeling technique for the calculation of the similarity between two vectors.
[0070] First user specified selectable parameters include:
[0071] D: Training matrix; M rows by N columns, where M is the number of variables in the model and N is the number of training vectors chosen from a training data set.
[0072] λ: Non-linearity exponent, i.e., an analysis or coarse tuning parameter.
[0073] ρ: Scaling factor, i.e., an analysis or fine tuning parameter.
[0074] Then, inputs include:
[0075] D: The two dimensional training matrix.
[0076] M: The number of variables or rows in the D matrix.
[0077] N: The number of training vectors or columns in the D matrix.
[0078] lambda: OP Non-linearity (coarse) tuning parameter.
[0079] pRow: Scaling (fine) tuning parameter.
[0080] Outputs are defined as:
[0081] R: A range vector defined externally to avoid dynamic memory allocation.
[0082] G: The model matrix G calculated as set forth below.
[0083] Before real-time monitoring can begin, the global range R for each of the i=1,2, . . . , M variables in the model must be calculated using a representative data set as described hereinabove with reference to FIGS. 5 and 6. If X is a data matrix with rows corresponding to observations and columns to variables, then the range is calculated for each variable as follows.
"R i=max(X(n, i))−min(X(n, i)), over all n"
[0084] for (column=0; column<M; ++column)
{ min = X[column]; max = X[column]; for (row=1; row<N; ++row) { if (X[row*M + column] < min) min = X[row*M + column]; if (X[row*M + column] > max) max = X[row*M + column]; } R[column] = max − min; }
[0085] Then after determining the ranges Rl for each variable, the OP operator model matrix C can be constructed from an appropriate training matrix D. The model matrix is an N by N square matrix calculated using the OP operator, as follows:
"G=D′{circle over (x)} SSCOP D, (where,{circle over (x)}SSCOP is the SSCOP operator)"
[0086] The SSCOP operator measures the similarity between vectors. When calculating C, the similarities between all pairs of column vectors in D are calculated. The SSCOP operator uses the ranges Ri, non-linearity analysis parameter λ, and the scaling analysis parameter ρ to calculate similarity measurements. Performing the following procedure carries out the similarity between two vectors, d1 and d2 in D using SSCOP.
[0087] If D contains N column vectors, each including M elements,
D=[d 1 |d 2 | . . . |d N]
d k =[d 1(t)d 2(t) . . . d M(t)]
[0088] then vector similarity is measured as follows:
[0089] i) Calculate the elemental similarity angles θi for the ith sensor and each pair of elements in d1 and d2.
[0090] ii) Calculate the elemental similarity measurements.
[0091] iii) Next, calculate the overall vector similarity.
first, if (si<0), then si=0
[0093] These steps are used to calculate the similarity between all combinations of vectors in D to produce G.
[0094] So, for example:
[0095] Given a data set X, the Range of each variable is
[0096] Setting ρ=2, λ=3, and D is
[0097] the resulting model matrix G is then
[0098] and the inverse is (condition number equals 91.7104):
// Next, calculate G from Dt SSCOP D for (row=0; row<N; ++row) { for (column=0; column<N; ++column) { G[row*N + column] = 0; for (i=0; i<M; ++i) { if (D[i*N + row] > D[i*N + column]) { theta = (D[i*N + row] − D[i*N + column]) / R[i]; } else { theta = (D[i*N + column] − D[i*N + row]) / R[i]; } s = 1 − pow(theta, lambda) / pRow; if (s < 0) s = 0; G[row*N + column] += s; } G[row*N + column] /= M; } }
[0099] A statistical significance test is applied to the elements of the residual, or to the residual as a whole. More particularly, a test known as the sequential probability ratio test ("SPRT") is applied to the residual for each sensor. This provides a means for producing an alarm or other indicator of statistically significant change for every sensor in real-time.
[0100] The SPRT type of test is based on the maximum likelihood ratio. The test sequentially samples a process at discrete times until it is capable of deciding between two alternatives: H0μ=0; and HA:μ=M. It has been demonstrated that the following approach provides an optimal decision method (the average sample size is less than a comparable fixed sample test). A test statistic, ψt, is computed from the following formula:
[0101] where ln(•) is the natural logarithm, fHs( ) is the probability density function of the observed value of the random variable Yi under the hypothesis Hs and j is the time point of the last decision.
[0102] In deciding between two alternative hypotheses, without knowing the true state of the signal under surveillance, it is possible to make an error (incorrect hypothesis decision). Two types of errors are possible. Rejecting H0 when it is true (type I error) or accepting H0 when it is false (type II error). Preferably these errors are controlled at some arbitrary minimum value, if possible. So, the probability of a false alarm or making a type I error is termed α, and the probability of missing an alarm or making a type II error is termed β. The well-known Wald's Approximation defines a lower bound, L, below which one accepts H0 and an upper bound, U beyond which one rejects H0.
[0103] Decision Rule: if ψt<L, then ACCEPT H0;
[0104] else if ψ, <U, then REJECT H0; otherwise, continue sampling.
[0105] To implement this procedure, this distribution of the process must be known. This is not a problem in general, because some a priori information about the system exists. For this purpose, the multivariate Normal distribution is satisfactory.
[0106] For a typical sequential test
[0107] where M is the system disturbance magnitude and {overscore (Y)} is a system data vector.
[0108]FIG. 7 shows deviating signals for snapshots numbered 1-6 with associated vector similarity values using the inventive similarity operator. These six separate examples show vector-to-vector similarity graphically depicted. In each snapshot chart, the x-axis is the sensor number, and the y-axis is the sensor value. Each snapshot comprises 7 sensor values, that is, seven elements. The two lines in each chart show the two respective snapshots or vectors of sensor readings. One snapshot may be from real-time data, e.g., the current snapshot, and the other snapshot may be from the D matrix the comprises the model. Using the inventive similarity operator described above, element-to-element similarities are computed and averaged to provide the vector-to-vector similarities shown in the bar chart in FIG. 7. It can be seen that the similarity comparison of snapshot chart #6 renders the highest similarity scalar, as computed using the similarity operator of the present invention. These similarity scalars are used in computing estimates as described above, with regard to the generation of W.
[0109]FIG. 8 shows noise additive signals for snapshots numbered 1-6 with associated vector similarity values using the inventive similarity operator. The output of the similarity engine can be similarity values, expected values, or a "residual," i.e., the difference between the actual and expected values. A more typical set of examples of sensor data with noise are depicted in six snapshot charts, this time comprising 40 sensor elements. Each chart again contains two lines, one for each of two snapshots being compared using the similarity operator of the present invention, these charts are far more difficult to compare visually with the eye. The similarity operator scalar results are shown in the bar chart of FIG. 8, which provides an objective measure of which vector-to-vector comparison of the set of six such comparisons actually has the highest similarity. Again, the similarity values can be output as results (e.g., for classification purposes when comparing a current snapshot to a library of classified snapshots) or as input to the generation of estimates according to the equations described above, which ultimately can be used for monitoring the health of the monitored process or machine.
[0110]FIGS. 9A and 9B illustrate a sensor input signal from an air conditioning condenser thermocouple showing positive drift, with FIG. 9A illustrating the residual signal resulting from the drift as deviating over time. In FIG. 9A, it can be seen that the drifted "actual" sensor signal and the estimated signal generated using the similarity operator of the present invention generate a difference or "residual" that grows in FIG. 9B as the estimate and actual diverge. The estimate clearly follows the original pre-drift sensor value that the drifted sensor was based on.
[0111]FIGS. 10A and 10B illustrate an example of a residual signal generated in response to negative drift on the sensor input. Because the drift is in the opposite direction, it is negative and the estimate again follows what the sensor should have been. Note that the residual shown in FIG. 10B is actually growing in the negative direction.
[0112]FIGS. 11A and 11B illustrate an example of the introduction of additive noise to the sensor input and the corresponding residual signal. Note that the failing sensor is actually getting noisier, and the similarity estimate remains close to the pre-failed sensor signal on which the noisy signal was based. The residual in FIG. 11B swings wider and wider, which would be easily detected in the SPRT-based statistical decision engine and alarmed on.
[0113] Thus, the present invention provides a superior system modeling and monitoring tool for ultra sensitive signal differentiation accurately detecting system parametric deviations indicative of more subtle underlying system or system component changes, whether the system is a machine, a process being carried out in a closed system, a biological system or any other such system. The tool differentiates between anomalous results caused by defective sensors and component related failures, allowing adjustment therefor. Further, the signal differentiation sensitivity of the tool far exceeds prior art thresholding results using less memory and computer power to consider larger volumes of data, interactively if desired, irrespective of available computer power.
[0114] It will be appreciated by those skilled in the art that modifications to the foregoing preferred embodiments may be made in various aspects. The present invention is set forth with particularity in the appended claims. It is deemed that the spirit and scope of that invention encompasses such modifications and alterations to the preferred embodiment as would be apparent to one of ordinary skill in the art and familiar with the teachings of the present application.
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Bead, acrylic with rubberized coating, green / black / white, 28x19mm puffed oval with stripes. Sold per pkg of 20.
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Lightweight acrylic puffed oval beads feature black and white stripes. These beads with a rubber coating are silky soft and warm to the touch. Due to the acrylic aroma, it is recommended to air out the beads before use. Testing for colorfastness is recommended. Colors, shapes, sizes and designs may vary in each lot.


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Extreme Violence in the Service of God: Coming Soon to a Theater Near You
Roy Assaf stars in 'God's Neighbors,'a Tarantino-esque film directed by Meni Yaesh, follows a band of violent Hasidim who terrorize the non-observant.
A white tablecloth, a bottle of wine, a kiddush cup. In the opening scene of "God's Neighbors," the camera follows the wine as it is poured into the cup and the familiar blessing over the wine is recited.
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Link to a file copy of the New Indiana Parenting Time Guidelines, effective 2013.
Changes to the Indiana Parenting Time Guidelines were approved by the Indiana Supreme Court. The approved Guidelines will go into effect on March 1, 2013. Above are links to the new Guidelines.
Existing parenting time orders in effect on March 1, 2013 shall be enforced according to the parenting time guidelines that were in effect on the date the parenting time order was issued. Also, changes to the Indiana Parenting Time Guidelines does not alone constitute good cause for amendment of an existing parenting time visitation order; however, a court or parties to a proceeding may refer to these guidelines in making changes to a parenting time order after the effective date of the guidelines. The commentary to the new Guidelines suggests that parents who agree that current changes to the Indiana Parenting Time Guidelines are in their child's best interests should file their written agreement with the court for approval.
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Diet to Go
A leading meal delivery diet with a new approach to its healthy menu offerings.
- Established: 1991
- Founder: Hilton Davis
- Headquarters: Lorton, VA
- Accessibility: Online
- Diet Type: Food delivery, low-fat, low-carb, vegetarian
- Gender: Male and female
It's no wonder we struggle to maintain our weight. With busy work, family and social commitments, our chaotic lifestyles leave us little time to plan and prepare healthy meals. As a result, weight loss and health become less of a priority. Diet to Go offers the solution to finding the balance between your hectic life and desire to eat nutritious meals. Diet to Go is a delivery service that offers delicious meals, either via a fresh delivery or frozen delivery.
With the Diet to Go meal delivery plans, there are three options to choose from. Whether you choose the low-fat, low-carbohydrate or vegetarian meal plan, there is a 100 percent guarantee that you will be satisfied with your food. Diet to Go has been helping people lose weight for over 20 years and the plans are customizable to fit your schedule and budget. The goal of Diet to Go is to provide fresh and healthy food in the proper portion sizes that is delicious and makes weight loss easy. Within the three different meal plans, you can also choose to do fresh local pickup if it is available in your area, or simply have it delivered via FedEx. The low-fat meal plan is the most popular of the meal plans while the low-carb plan is slightly more expensive.PRO
- Provides a structured eating plan for busy people
- Meets broad nutritional standards
- Supports efforts for weight loss
- Offers convenience
- Broad selection in menu choices, including vegetarian and low-carb
- Shipping available nationwide for frozen meals
- Competitively priced
- Fresh service limits any need for grocery shopping
- Variety of pricing structures to meet your schedule and budget
- You won't feel hungry
- May not be cost effective for entire families
- Must provide own fresh vegetables with the frozen delivery option
- Fresh delivery not available in all areas
This Virginia-based company has cut out all the usual diet work of counting calories and carbs and simply delivers meals to your home that meet your dietary needs, be it reduced-fat, low-calorie, low-carb or even vegetarian.
Diet to Go offers meals that meet the guidelines of the American Heart Association, American Cancer Society, American Diabetes Association and the American Dietetic Association, maintaining standards with low-fat, low-sodium and low-cholesterol meals.
Diet to Go works to help you lose weight without the complications of meal planning and cooking. The meal plans are based on research and eliminate the need to count calories. If you find there is a meal that you don't care for, you can fill out a substitution form to have a different meal sent on your next order. For the low-fat menu option, you can choose from a meal plan that offers 1,200 or 1,600 calories daily. These meals are low in sodium and include all of your fruits, vegetables and condiments. Some of the low-fat menu items you can choose from include:
- Harissa Salmon
- BBQ Chicken
- Chicken Parmesan
- Curry Shrimp
- Italian Meatballs
The low-fat vegetarian meal plan is great for those that prefer to leave meat out of their diet. This type of menu helps keep your eating healthy while being satisfying and giving you the essential protein your body needs. As with the low-fat meal plan, the vegetarian plan offers 1,200 and 1,600 calorie options. The vegetarian meal plan does contain dairy and eggs so it wouldn't be suitable for those that eat a vegan diet. These meals are low in sodium and include fresh and natural ingredients. Some of the vegetarian meal options you have to choose from are:
- Vegetarian Burrito
- Black Bean Chili
- Chana Masala
- Eggplant Parmesan
- Armenian Stew
The last meal plan option is the low-carb plan. This plan offers five weeks of menus of freshly prepared food with no additives. Instead of counting calories each day, these meals are measured by their level of net carbs. With this meal plan, you will consume 30 net carbs each day, receive all of the sides and condiments you need and be following an Atkins style low-carb program. Diet to Go takes all of the guess work out of losing weight. Some of your sample meals with the low-carb program are listed below.
- Chicken Italiano
- Herbed Catfish
- Chicken Alfredo
- Beef Pot Roast
- Cod Loin with Lemon Herb Sauce
If you are receiving the fresh-prepared meal, you'll find little need to go to the grocery store, as the meals come accompanied by generous portions of fruits, vegetables, smoothies, bread or rice, and other sides.
If you are receiving the frozen meal service, you will want to compliment each meal with the healthy sides of your choosing.EXERCISE
Diet to Go focuses on the meal portion of a healthy lifestyle so there are no specific exercise recommendations provided. There are some articles available through the Diet to Go blog that can help guide your fitness and exercise to work well with the meal plan for maximum results.CONCLUSION
As with any meal delivery service, it may be a financial extravagance for some. However, compared to its competitors and for the quality of the product, Diet to Go is reasonably priced.
Whether using Diet to Go to help you manage your own weight loss goals, or seeking it as a solution to provide your family more balanced and varied meals, this meal delivery program provides a well-structured, tasty, variety that supports your goals.Common Misspellings
diettogo, diet 2 go, diet-2-go, deit to go, diets to goAuthor
A sampling of feedback and experiences shared by our community.
The food was way better than what I had anticipated. I'm really glad that DTG is very affordable. I can finally experience a meal replacement program that consists of real food. -- Jemay Cinco
I was not disappointed. I real like the meal selections. Works great for me. -- FITGURL
I have really enjoyed just about every single meal I have had this week … I lost 1.5 pounds in 3 days and I love the convenience. I would recommend to anyone sitting on the fence. -- Jenny
Well-balanced meals that are extremely convenient. I can't figure out how they manage to keep the vegetables so fresh, though they do. -- Lori Jo Vest
Featured Diets
Related Diets: Biggest Loser Meal Delivery Plan, Bistro MD, Chefs Diet, DineWise, Medifast , Nutrisystem, The Fresh Diet
User Feedback(Page 1 of 9, 171 total comments)
Many of the meals had at least one ormore items that were like mush. The worst was tge seafood sandwich. I ordered week five meals and even updated my selectons then they sent me meals I hate from week four. This was a waste of time and money.
Diets to Go does not work for weight loss. The portions are not tightly controlled. We originally loved the service because it was easier than cooking. BUT, the food quality declined quickly. They started using a lot of processed food in the meals. The chicken reminded me of those Tyson pre-cooked chicken breast with tasteless sauce. Processed foods contribute to weight gain. I was also disappointed in the vegetables (worse than canned) and the packaging was horrible! They used cheap plastic that is less than healthy. And, the meals were clearly pre-frozen, even though they claim it is fresh. More concerning was the lack of customer service. DTG also frequently made errors in the orders, and each time you call you will reach the same two agents (Mary was one of them). They sound as if they are working in a basement. I spoke with a manager once, but she was even more unprofessional. She took it personally when we expressed concern about their error in charging our card above the agreed amount. In retrospect, it should have been a red flag when my gym stopped carrying their food because of the number of complaints, but I failed to do my research and settled. There are too many other options, like BistroMD, Biggest Loser, and Hello Fresh. They might have been a good company once, but they are certainly not anymore.
Thanks so much for your very helpful review of DTG. I ordered DTG from www.couponchief.com because they offered good discount and was so excited when the whole package arrived. The food was way better than what I had anticipated. I'm really glad that DTG is very affordable. I can finally experience a meal replacement program that consists of real food. Thanks again for pointing me in the right direction
I started with the low-cal option for several weeks, and got the pickup choice, actually the lunch and dinner was very tasty. I was not disappointed. I real like the meal selections. Works great for me. I did not want the delivery choice which is a different menu. I reside in San Francisco so I am not going to complain about the price.
Diet to go is dangerous if you have IBS. My wife used this for 2 weeks and it totally plugged her up. Had to go to the hospital. Thought it was mostly her, but after 2 meals myself (so they wouldn't go to waste), I didn't have a bowel movement in the morning for the first time in years. So there is something about the meals. Dangerous if you don't have a good digestive track.
I have the perspective of someone who picks up the meals at a location because the Diet To Go headquarters is close by. I have really enjoyed just about every single meal I have had this week (minus the fruit ambrosia - eck!). Your meals are slightly customizable depending on what they are cooking that week, but you need to make your changes before 12pm on Monday. My vegetables have been fresh, the meats have been tasty, but the fruit has kind of been disappointed. Their fruit medley cup is, as other reviewers noted, just a Dole Fruit Cup dumped into a non-descript plastic cup. I also had a disappointed pineapple crisp. It was just canned pineapple with some oatmeal and cinnamon on top. Despite the minor issues, I am planning to stick with this program for the next few weeks. I lost 1.5 pounds in 3 days and I love the convenience. I would recommend to anyone sitting on the fence.
They don't give the option of half up, half down, so I'll just have to point out that I eventually started trying other meal delivery services to see if anything else was better. I like most of the breakfast options, even if they are basically a muffin and chobani yogurts (avoid eggs at all cost), and i like most of the lunches. However, the dinners had a batting average of about 25% I liked. I asked if I could just get the lunches for dinner and was told no. They tasted the most like frozen meals from the store.
It was convenient to have the food delivered, but most of it came out soggy once heated. Also the "fruit" included is merely cups of fruit in syrup (which you can buy for like 50 cents at Walmart) and many of the "veggies" in a meal were cans of V8 (again, you can buy at Walmart for cheap). Not worth the money. You can eat a Lean Cuisine or Healthy Choice meal for each meal every day for cheaper and it would be better in taste and texture.
diet to go has its tasty meals depending on your taste, i however have issues with the way they pack their meals, in cardboard sleeves and dry ice that breaks up inside the cooler.this is dangerous especially if u have small kids in the house.it comes with all these warnings telling u to put on gloves because direct contact with it will burn u, does this sound like something u would want in your house?? and they get sassy when u tell them about it.
The food is absolutely horrible. I barely made it through the first week of food. Actually I didn't make it. I would take a few bites and throw the rest away and end up eating cereal for my dinner. When I placed my first order, I also put a hold on week 3 and 4 so that I would have time to determine whether or not I wanted to continue. When week 2 showed up, I took the container and threw the entire thing into the dumpster. I didn't even want the food in my freezer. Then week 3 showed up! I called customer service and they claimed they didn't have a hold on record for my account. I explained that I did it the same night I placed the first order. Too bad for me. They wouldn't do anything about it. Week 3 also ended up in the dumpster and I cancelled my account.
I guess you can lose weight because you won't want to eat any of it.
Reviewers are being too picky and do not understand how these things work. This is the third diet delivery service I have tried and it is awesome! You sacrifice fresh for frozen . Yes, but, it is fresh frozen food and not processed like grocery frozen meals. they give desserts, beverages and snacks= no need to go to the grocery. The whole point is to lose weight an dif you eatthe 1200 calroe a day plan, you will lose weight and easily without the shopping, prepping, cooking, weighing and portioning of food.You pay for the convenience and the bonus is most meals taste great! They let you sub meals for ones you like which is unheard of in diet delivery and can be done bc options are frozen. If you are serious about losing weight, don't listen to the whiners. This plan works. I've lost close to 10 lbs in two weeks with light daily walking. Down from 180 to 171 lbs.
diet to go is okay, they tend to drown everything in cheese, saying thats its nutritious value of cheese, well when your waking up 3am with stomach cramps from cheese, thats of no consolation. they put everything else in a little cup but refuse to put the cheese on the side.
Diettogo food really sucks - stay waaaay away.
The food, with very few exceptions, is awful.
There's a reason that you only see positive reviews. All others are removed.
I hate to be disagreeable, especially in light of all the positive reviews, but I am in the middle of my first week of Diet To Go, and it's going to be very hard to finish out the week. I do not like the food. At all. It is peculiar, to say the least. A Reuben sandwich prepared with French Toast bread? A "Mandarin Pudding" that is separated into 3 substances: mandarin oranges, watery juice, and a squishy orange something? I have had a couple of items that were ok, but definitely nothing to rave about.
All but one of the reviews in this article were positive; however, several of the reviews had a thumbs down icon. Why? Is that why Diet To Go got only a 70% rating. I don't think that is fair to the Diet To Go company or to your readers.
Diet to go has received good reviews. After wks. of contemplating, came across a 30% offer & wanted to try it. Sales was very helpful & ordered the 3meal a day, 7 day menu. Upon recv. my 1st order, I was missing 2 meals. I called & Jo in C.S. said she'd refund me $10.00 & email confirmation. Was really disappointed, but the meals were very tasty. I sent a "contact us" form & "feedback" form asking for a goodwill gesture on their part of another week @ the 30%. Was ready to jump on board & become a long-term customer. She said she checked w/her supervisor & they will either give me $10.00 back or another week for 30% but not both. I proceed to tell her, the money returned is a given, cause I didn't get product. I read to there info on the website that states, "our 100% satisfaction guarantee, if for any reason you are unhappy with your order or our service, we'll make it right or refund your money. It's that easy." All I'm asking for is my $10.00 back & another week @ the same discounted rate. She then became very aggressive & stated, "I never said I'd do both. Don't put words in my mouth". I never thought or said she said that. Trying to get the name of the Customer Service manager or anyone in upper management was like pulling teeth. I was given the owners name of Hilton Davis & a supervisors name of Laura Salmon. Customer service reps are not suppose to be hostile. The food is really, really good! My hopes are dashed at continuing on with this company. Someone needs to design instruction on Workplace Bullying for their Customer Service Dept. Feel sad about it all. :(
I've gotten several weeks of Diet-to-Go meals and not only are they healthy, they're delicious, too. Well-balanced meals that are extremely convenient. I can't figure out how they manage to keep the vegetables so fresh, though they do. I usually do the low-carb program and it's loaded with green veggies and lean protein. I love when you come home and there's a bit DTG cooler waiting for you. They ship it with dry ice, so it stays frozen and goes right into your fridge or freezer. I highly recommend it to anyone who's looking for healthy, pre-prepared meals. (Great for singles and busy professionals. Throw them in the microwave and they're done. Easy-peasy!)
Forgot to choose the thumbs up the first time and didn't see a place to edit :)
I have had several opportunities to try diet-to-go throughout the past year and each time I have been extremely satisfied. The shipping was perfect and very fast/reliable. It arrives in a cooler so even if it's dropped off before I get home it's still good to go. The meals are quite tasty. I love the stuffed shells with cheese and the turkey barbecue melts (I must say these are my favorite). It does get me out of my comfort zone because it does pair foods that I would not normally do so. Each meal comes with at least one side (some come with more than one). I think the only con that I can think of is that I would prefer more veggies- as I usually have to supplement with another vegetable so I have two servings. Also- I have only made them in the oven and they are quick and painless. My future wish- a gluten free option.


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How to Set Up Your Own eJunkie Store (video)
Where should you sell your digital products and/or services online? The easiest solution is to setup paypal 'buy now' buttons. But using this simple solution once people pay, you have to manually send them the digital product they purchased.
If you want a more automated sales process and if you are looking to build a relatively small, simple ecommerce store I recommend using ejunkie in combination with your paypal account. You can set up your own store for as little as $5 and securely sell your digital products. I personally use e-junkie to sell my ecourses, ebooks, virtual event tickets and even my most common web design services. You can take a look at my store here.
Generally when I recommend eJunkie to clients, they have quite a few questions about how to actually set up the store, so I decided to walk you all through the process as part of my Summer Circle Mastermind group series.
In this webinar training I discuss the following:
- How to Sign Up to eJunkie
- Create Your Product 'Buy Now' Page
- Upload your digital downloads (pdf, mp3s, etc)
- Add a buy now button to your web page
- Add products to eJunkie Store
- Setup Affiliate links using eJunkie
- Setup Discount codes using ejunkie
This training is one of 6 from my summer circle mastermind group (watch the replays here).
Enjoy the video above. If you'd like to sign up to ejunkie through me, here is my link to their services.
If you are an author or write and would like to learn more about promoting or creating your own books, ebooks and magazines online be sure to check out my Books.Buzz.Business Webinar Conference.
Ready to Start Her Up?
Are you ready to start your own online business? Click here to grab my Start Her Up Guide; an information packed resource that can help you get started and learn how to build your web business on a floss string budget in 3 steps - Make it! Market it! Monetize it!


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Fontmell and Melbury Downs
This section of the page features an image gallery, so if you're using a screen reader you may wish to jump to the main content.
Open expanses of flowery downland and far-reaching views
This area of natural beauty was bought in memory of Thomas Hardy, to protect the landscape in which his novels are set. Fontmell Down and Harding's Down offer stunning views that stretch for miles across the Blackmore Vale.
From the car park at the top of Spread Eagle Hill, you can embark on many circular walks across the chalk downland. It's a fantastic place to spot butterflies and orchids, which thrive here.
Head north towards Compton Down and Melbury Hill and you will find one of the best displays of glow-worms during June and July (if the weather is good).
Melbury Hill, meanwhile, marks the high point of a spur, offering sweeping views across much of the downs.
The Melbury Beacon is located 863 feet above sea level and reaching it rewards walkers with wonderful views to the north of the Saxon town of Shaftesbury and across to the surrounding counties.
Nearby Melbury Down is a dry valley that cuts the ridge of Cranborne Chase and straddles the counties of Dorset, Hampshire and Wiltshire.
Come and explore
There is so much to see and do at these special sites. From wonderful walks and wildlife spotting (including glow-worms) to 50 things fun, there is more to Fontmell and Melbury than fantastic views.
The risk of ticks
We'd like to make you aware of the presence of ticks at these sites. These tiny creatures lurk in the grassland and attach themselves to passing animals and humans. We'd advise you to read this leaflet from Public Health England so you're aware of the risks.


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What travellers think
Travellers that stayed here think this hostel is best for
- Relaxing
- Unique atmosphere
- Nature-lovers
- Sightseeing
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The hostel Richterswil is located in a small town on Lake Zurich, yet close to the city of Zurich.
Travellers that stayed here think this hostel is best for
Simply excellent hostel, the best i have ever stayed in.
Sat 19 Sep 2015
The breakfast was amazing, the private shower with a closed door nice, and the view is beautiful. It is a short walk from the train station and is easy to find if you just walk north along the Lake Zurich.
Wed 09 Sep 2015
Good location for relax even a bit far from city but very convenience by S-Bahn. Next time I'll stay longer here.
Fri 05 Jun 2015


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Shrimp Southwestern Pizza Dip
Source: dLife
Creamy, spicy layered dip with shrimp and black beans.
1 each cooking spray
1 lb Shrimp, mixed species, raw, small
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16 oz Salsa, chunky
0 1/2 cup fresh chopped green onion
1 cup low fat shredded monterey jack cheese
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Gorham News
Local news for Gorham, ME continually updated from thousands of sources on the web.
Bayside could be location for future Portland dorms
19 hrs ago | The Free Press | Comment?
Our Opinion: The Phoenix thinks the Gorham campus should go, we need it to stay
Tuesday Sep 29 | The Free Press | Comment?
Cree scores maiden victory in NJSS Green Acres
Friday Sep 18 | HarnessLink.com | Comment?
Gorham man dies in motorcycle crash
Wednesday Sep 16 | Bangor Daily News | Comment?
Portland to mark 9/11 anniversary
Friday Sep 11 | Sun Journal | Comment?
Updated: Tue Oct 06, 2015 03:31 pm
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The Byzantine Economy
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Preview (read now) Add to my own site Give this ebook to a friend Add to my wishlist Author's page Publisher's page
moreThis is a concise survey of the economy of the Byzantine Empire from the fourth century AD to the fall of Constantinople in 1453. Organised chronologically, the book addresses key themes such as demography, agriculture, manufacturing and the urban economy, trade, monetary developments, and the role of the state and ideology. It provides a comprehensive overview of the economy with an emphasis on the economic actions of the state and the productive role of the city and non-economic actors, such as landlords, artisans and money-changers. The final chapter compares the Byzantine economy with the economies of western Europe and concludes that the Byzantine economy was one of the most successful examples of a mixed economy in the pre-industrial world. This is the only concise general history of the Byzantine economy and will be essential reading for students of economic history, Byzantine history and medieval history more generally. less
Cambridge University Press; September 2007
286 pages; ISBN 9780511352720
Read online, or download in secure PDF format
Title: The Byzantine Economy
Author: Angeliki E. Laiou; Cécile Morrisson
286 pages; ISBN 9780511352720
Read online, or download in secure PDF format
Title: The Byzantine Economy
Author: Angeliki E. Laiou; Cécile Morrisson
Buy, download and read The Byzantine Economy (eBook) by Angeliki E. Laiou; Cécile Morrisson today!
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REDWOOD CITY, Calif., Aug. 2, 2013 /PRNewswire/ -- AcelRx Pharmaceuticals, Inc. (Nasdaq: ACRX), a specialty pharmaceutical company focused on the development and commercialization of innovative therapies for the treatment of acute and breakthrough pain, today announced that it will release second quarter 2013 financial results after market close on Monday, August 12, 2013. AcelRx management will host an investment-community conference call at 4:30 p.m. eastern time (1:30 p.m. pacific time) on August 12, 2013 to discuss the financial results and provide a corporate update.
Investors who wish to participate in the conference call may do so by dialing (800) 860-2442 for domestic callers, (866) 605-3852 for Canadian callers or (412) 858-4600 for international callers. Those interested in listening to the conference call live via the internet may do so by visiting the Investors page of the company's website at www.acelrx.com and clicking on the webcast link on the Investors home page.
A webcast replay will be available on the AcelRx website for 90 days following the call by visiting the Investor page of the company's website at www.acelrx.com.
About AcelRx Pharmaceuticals, Inc.
AcelRx Pharmaceuticals, Inc. is a specialty pharmaceutical company focused on the development and commercialization of innovative therapies for the treatment of acute and breakthrough pain. AcelRx's lead product candidate, Zalviso, is designed to solve the problems associated with post-operative intravenous patient-controlled analgesia which has been shown to cause harm to patients following surgery because of the side effects of morphine, the invasive IV route of delivery and the complexity of infusion pumps. AcelRx has announced positive results from each of th
|SOURCE AcelRx Pharmaceuticals, Inc.
Copyright©2012 PR Newswire.
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Chapman Delts
Why join a fraternity?
Fraternities are values-based organizations offering young men many challenges and opportunities that a traditional college education may not provide. Through joining a fraternity, men are given the opportunity to take advantage of leadership opportunities helping him to grow, and to become part of something larger than himself. A member of a fraternity has an immediate support group of lifelong friends and networking opportunities. Joining a fraternity is more than just something you do in college. It is a commitment and benefit for a lifetime.
Why join Delta Tau Delta at Chapman?
Chapman Delts have been an integral part of the Chapman University community for over 10 years. We have made a point in striving to better our chapter and our members whenever possible. We have the pleasure of holding the highest fraternity cumulative GPA on campus and we were given the Fraternity of the Year award last year. Through becoming a part of Delta Tau Delta at Chapman, you will be surrounding yourself with men who are committed to lives of excellence.
Follow Us!
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Latest News
Delts Win Skit 2015!
Posted by Lijah Vann Gardner on Thursday August 13th, 2015
Shoutout to our brothers who have worked hard on out 2015 Greek Skit! We won 1st place! Well worth the work!
Fraternity Rush is Back!
Posted by on Wednesday September 10th, 2014
We are excited to be hosting a list of some new events this semester for rush. Below you will find a schedule of our rush events for the week, and I hope to see you all there! Please feel free to like... Read More


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The NewsDemon Blog
Limited Time Super Unlimited USENET Deal from NewsDemon
By Newsgroup Usenet February 2nd, 2012
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The relief agency gathered his information and proposed an Offer in Compromise to the IRS, based upon his net worth and future income potential. After it was initially declined, they appealed the decision and settled at a reduced amount of $3754.00, a reduction of almost $144,000.
This type of drastic tax relief is not uncommon under hardship circumstances. A single mom from Sterling, VA owed over $52,000 to the IRS. She was connected with another tax relief center in her local area. Since she desperately needed a fresh start, the agency submitted the necessary forms to the IRS emphasizing her recent job loss, and lack of ability to pay. The IRS reluctantly accepted $10 to settle all of her back taxes.
The Tax Relief Hotline is just one of several managed by CareConnect USA. The group initially created the helplines for America's social workers to assist their clients with money troubles. As of September 2009, over 80,000 workplace break rooms feature the CareConnect USA poster with free help lines for employees and their families. Other hotlines assist with debt relief , collection complaints, foreclosure assistance, free bankruptcy advice, discount prescriptions, and ID theft recovery. Many non-profit and civic organizations find the hotlines a useful resource for their troubled families. For more information on all the help lines visit www.careconnectusa.org
# # #
CareConnect USA distributes free tools for counselors and HR managers. CareConnect USA helps families and employees find resources for financial assistance.


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Daemen College : Academics & Research : Division of Health & Human Services : Education : Graduate Programs : Masters in Special Education : Thomas Reynolds Center for Special Education and After-School Programs
The Thomas Reynolds Center for Special Education and After-School Programs' mission statement is to effectively prepare teacher candidates to teach research based learning strategies that assist students with disabilities in mastering academic content within the Common Core standards.
For the 13th consecutive year, the Thomas Reynolds Center continues to provide academic assistance in an after-school environment to elementary school students with disabilities who attend partner schools. For the 2015-2016 academic school year, The Thomas Reynolds Center will partner with Amherst Central School District (specifically students attending Windermere and Smallwood Elementary). The after-school program runs from Monday, October 19, 2015 through Thursday, April 21, 2016. Participating student receive instruction in the areas of reading, writing, mathematics and student empowerment. Participating students also receive small group homework help with the goal of teaching students skills working towards becoming independent.
That's the best way to really get a feel for what Daemen is all about! Daemen is located in suburban Amherst, New York just minutes away from the city of Buffalo. To schedule a weekday individual interview and tour the campus, email us email@example.com or call 800.462.7652.
Faculty accompanied students to Mexico City and Cuernavaca for the January inter-semester term.
Founders WalkwayPaving the path to Rosary Hall, the walkway allows donors to permanently inscribe a brick memorializing a special loved one, occasion or dedication.
©2014 Daemen College : 4380 Main Street, Amherst, NY 14226 : 800.462.7652