text:
|Publication number
|Publication type
|Application number
|Publication date
|8 Dec 1998
|Filing date
|28 Jun 1996
|Priority date
|28 Jun 1996
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|Tariq Samad
|Original Assignee
|Honeywell Inc.
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|BiBTeX, EndNote, RefMan
|Patent Citations (14), Non-Patent Citations (40), Referenced by (115), Classifications (9), Legal Events (5)
|External Links: USPTO, USPTO Assignment, Espacenet
The present invention pertains to tuners for controllers, and particularly to automatic tuners for controllers. More particularly, the invention pertains to nonlinear-approximator-type of tuners. U.S. Pat. No. 5,311,421, by Masahide Nomura et al., issued May 10, 1994, and entitled "Process Control Method and System for Performing Control of a Controlled System by Use of a Neural Network," provides background information for the present invention and is hereby incorporated by reference in this description.
The invention is a technique for developing a tuner which is used for tuning or optimally guiding a controller. The tuner has a preprocessor for transforming a set of input signals into a set of normalized parameters. These parameters are inputted to a nonlinear approximator which operates on the set of normalized parameters to result in a set of normalized tuning parameters. The set of normalized tuning parameters goes to a postprocessor which scales this set of parameters into controller tuning parameters which go to the controller. The nonlinear approximator can be based on neural networks or other parametrized nonlinear structures. In essence, determining nonlinear approximator parameters actually amounts to designing or setting-up a tuner. In turn, the tuner tunes the controller having a closed loop, with appropriate controller parameters.
FIG. 1 shows a basic controller and process relationship.
FIG. 2 shows a controller with a tuner, along with a process.
FIG. 3 shows a process with a step-function input.
FIG. 4 shows the characteristics of an open loop step response of the process in FIG. 3.
FIG. 5 shows a graph with a step function having a resultant overshoot and an oscillatory settling which has to be modeled as at least a second order system with delay.
FIG. 6 reveals the closed-loop step response parameters of a system.
FIG. 7 reveals a tuner with separate inputs for process characteristic parameters and closed-loop performance parameters.
FIG. 8 is a graph of fast settling performance with is overshoot.
FIG. 9 is a graph of slow settling performance without overshoot.
FIGS. 10a and 10b show an automatic tuner for processes that can be modeled as first-order linear systems with delay.
FIG. 11 shows a neural network tuner used in conjunction with a parametrized neurocontroller.
FIG. 12 illustrates a prior art neural network tuner with a supervised learning algorithm feature.
FIG. 13 is a schematic of one framework of the present invention, not relying on supervised learning.
FIG. 14a shows an example of a tuner for a proportional-integral (PI) controller embodied as a neural network with one tuning knob input and outputs for proportional and integral gains.
FIG. 14b shows a tuner embodied as a computationally simple compositional mapping with one tuning.
FIG. 15 shows how the tuner of FIG. 14 can be used for a known linear first-order process.
FIG. 16 is a graph for slow or fast settling, dependent upon tuner adjustment input.
FIG. 17 is a schematic for developing the tuner of FIG. 15 using a nonlinear optimization algorithm.
FIG. 1 shows a simple system proportional-integral-derivative (PID) controller 12 that may be used for control of the temperature in a room. The setpoint yd for temperature is input at line 13 to controller 14 which has a u output on line 15 for controlling an airflow, such as heat or hot air flow, to process 16. The controller output goes to the furnace via a gas valve or other furnace controlling device. Process 16 is the room having its space temperature controlled. An output y on line 18 indicates the temperature of the room. Signal y is fed back to an input 20 of controller 14. In other words, controller 14 looks at the current temperature on line 18 and the setpoint (thermostat) temperature (y desired) on line 13, and uses the difference between the temperatures to calculate heating input 15 to the room or process 16.
"u" can be shown relative to various domains in a simple proportional-integral (PI) controller according to the following equations, where:
kc is the proportional gain for the controller;
ki is the integral gain for the controller;
t is time;
τ is an integration variable;
e(t) is the process output error at time t;
u(t) is the controller output at time t;
y(t) is the process output at time t;
yd (t) is the desired process output or setpoint of time t;
U(s) is the Laplace transform of u(t);
E(s) is the Laplace transform of e(t);
j is a summation index; and
l is a discrete time index. ##EQU1##
Equation (1) is stated for the continuous-time domain, equation (2) for the Laplace or frequency domain, and equation (3) for the discrete-time domain. The proportional (kc) and integral (ki) gains are set to values appropriate for the application of control system 14 to process 16. Δt is the sampling interval.
The algorithms, used for setting controller gains, for instance kc and ki, are called "tuning algorithms." The tuning algorithm is implemented in a tuner 21 of FIG. 2. The tuner puts gains kc and ki into controller 14 via line 22. To set these gains is often done by a "trial and error" approach in the related art. Current tuners in the art are not wholly satisfactory.
Inputs to tuner 21 need to indicate two types of information. The first type of information is the relevant dynamics of the process (e.g., characteristics of the room, such as its heat loss, heating-up time constant, and the delay due to ductwork from the furnace to the room). The second type of information is desired dynamics of the closed loop (e.g., time to reach the setpoint, settling time, overshoot, tolerances of overshoot, and importance of reducing control energy).
FIG. 3 shows a step function 24 of u to input 15 of process 16 and output y from line 18. FIG. 4 shows the characteristics of an open loop step response of process 16. The process is of a first order with a delay linear system which is usually sufficient for most industrial and building control processes. Such system has three process model parameters Kp, Tp and TD.
The gain is Kp =(y1 -y0)/(u1 -u0).
The time constant, Tp =t2 -t1, (which is the time that y takes to reach 63 percent of the final output). The dead time or delay is TD =t1 -t0. If Kp, Tp, TD , are changed, then the curve for y will change.
FIG. 5 shows a graph with a step function having a resultant overshoot and an oscillatory settling 25 which has to be modeled as at least a second order system with delay, relative to input 24.
FIG. 1 shows a closed-loop system 12 with a step function signal 24 to input 13. FIG. 6 reveals the closed-loop step response parameters of system 12.
Process overshoot is Ycs =(y2 -y1)/(y1 -y0)
Settling time is tST =t1 -t0
Rise time is t RT =t2 -t0 ##EQU2##
One important objective in tuning a controller is to manipulate these and other closed-loop response parameters. For example, large overshoots are typically undesirable, but often unavoidable if fast settling times are required (the settling time is the time after the setpoint change after which the process output y is within some quantity, e.g. 95%, of the desired output or setpoint Yd, with the percentage computed relative to the magnitude of the setpoint change). For fast settling, control energies will also typically be high. The response parameters have a complex relationship to each other and to the tuning parameters, and no formulae are available that can accurately characterize this relationship.
Different tuning methods take different types of tuner inputs of the relevant dynamics of the process and desired dynamics of the loop, and provide gains for different types of controllers. The present invention of tuning can be used for any kind of linear or nonlinear controller, not just a PID type. Tuner inputs can be customized as desired, and the tuner can be made robust to whatever extent desired. Robustness is the graceful tolerance of uncertainty of knowledge about the system. It is also the graceful tolerance of change or drift in the system.
Two kinds of parameters are "nominal" and "estimated". For example, Kp is the nominal or actual gain and Kp is estimated gain. Controllers are designed to take into consideration that Kp ≠Kp.
Features of the present invention are the use of one or several nonlinear approximators in the tuner, and the use of a general optimization system. The following types of nonlinear approximators are explicitly considered:
Conventional multilayer perceptron neural networks
Computationally simple, compositional sigmoidal nonlinear mappings
Radial basis function networks
Functional link networks
Cerebellar model articulation controller (CMAC) networks
Fuzzy logic models
Wavelet networks
Polynomial expressions
Specific nonlinear parametrized structures based on human intuition about controller tuning
The common aspect of these and other relevant approximator structures is that they implement nonlinear mappings the form of which can be adjusted by manipulating parameters associated with the approximator. Thus generally a nonlinear approximator, as used in this invention, can be expressed as follows:
p.sub.c =F(I; p.sub.a)
where pc is the vector of controller tuning parameters, I is the input vector to the nonlinear approximator, and pa is the parameter vector for the approximator. For example, a conventional multilayer perceptron neural network may implement the mapping F as: ##EQU3## where the hi are intermediate ("hidden unit") variables computed as: ##EQU4## Here the parameter vector pa consists of the input-to-hidden weights wki h, the hidden-to-output weights wij o, and the hidden and output bias weights bi h and bj o. i and k are summation indices, j is the element of the approximator output vector, h is the number of hidden units, and n is the number of elements of the input vector I. These expressions relate to "one-hidden-layer" neural networks. Multiple layers of hidden units can also be used.
As another example, a computationally simple, compositional sigmoidal nonlinear mapping may implement F as: ##EQU5## where the parameter vector pa comprises the vectors wi and the scalars ai and vij. i is a summation index and b is the number of nonlinear elements in the structure. The period (.) denotes the dot product operator. Note that this form of the nonlinear approximator, which otherwise resembles a neural network, does not require the use of transcendental functions such as exponentials or hyperbolic tangents which are costly to compute. This approximator is the approach of first choice for this invention. Again, multiple "layers" of the nonlinearities can be used.
As a third example, a radial basis function network may implement the mapping F as: ##EQU6## where the parameter vector pa comprises the vectors μi and the scalars σi and wij. i is a summation index and N is the number of Gaussian nonlinearities in the structure.
As a final example, a polynomial expansion may be used as the nonlinear approximator such as: ##EQU7## where the parameter vector pa comprises the scalars ak and bkl and n is the number of elements in the input vector I. k and l are summation indices.
FIG. 7 reveals a controller system 30 which has a nonlinear-approximator-based automatic tuner 21 having separate inputs for process characteristic parameters and closed loop performance parameters. The latter can be used to adjust closed-loop performance for slow or fast settling. Inputs 26 and 27 permit one pre-designed tuner 21 to be used for many applications or be universal for large and various classes of applications. A nonlinear approximator 28 is optimized off-line but without using supervised learning methods. Nonlinear approximator 28 may be a neural network but is not used as a nonlinear regression model (as it is in Nomura et al.--see their FIG. 33).
In addition to neural network or other nonlinear approximator 28, automatic tuner 21 can contain preprocessing and post-processing functions. These include scaling functions which are done outside of the neural network. Scaling is performed mainly for linearity purposes. Process characteristic parameters 26 (notated as pp later) and closed-loop performance parameters 27 (notated as pj later) allow one pre-designed automatic tuner 21 to be used for a broad variety of applications. No on-line or application-specific training or optimization of the neural network, or modification of other components of the automatic tuner, are needed.
A specific example is neural-network-based automatic tuner 21 for processes that can be modeled as first-order with dead time linear processes. Such processes can be modeled with three process model (or process characteristic) parameters 26: the process gain, time constant, and dead time. One closed-loop performance parameter is also assumed: a settling time knob dST (this constitutes the closed loop performance parameter at 27 in FIG. 7), which can be used to adjust closed-loop performance for slow or fast settling. There may be other closed-loop performance parameters on input 27. In this case, the neural network can be off-line optimized without using supervised learning that is used in the related art (as discussed below) using a simulation-based optimization system.
FIG. 8 shows the fast settling (i.e., low tST) performance with overshoot for a low dST input 27. FIG. 9 shows the slow settling (i.e., high tST) performance without overshoot for a high dST input 27. There may be a knob for dST that provides an analog-like variation, such as from zero to one, to automatic tuner 21.
The key feature of the present invention is the design of nonlinear approximator 28. During operation in a setup similar to FIG. 7, automatic tuner 21 can have a basic architecture, with pre-processor, non-linear approximator 28 and post-processor, as shown in figure 10a. Note in figure 10b that three process characteristic parameters are input to automatic tuner 21: estimates of process gain, time constant, and dead time (notated Kp, Tp, and Td). Output 22 of the tuner 21 consists of controller parameters (for example, for a PID controller, these would be the proportional, integral, and derivative gains) Pc. In figure 10b, Pc bias is an internal variable to automatic tuner 21 that consists of baseline controller parameters which are then modified in a context-sensitive way by neural network or a nonlinear approximator 28. Note that by intelligently structuring autotuner 21, the number of inputs required for nonlinear approximator 28 is reduced--in this case, to two. Td /Tp to line 46 and dST to line 29 are inputted to nonlinear approximator 28. An output 31, Pc, which is a control parameter, is fed into summing junction 32. A Pc bias is added at line 33 to Pc at summer 32 which outputs a pc nom, which is the vector of nominal (unscaled) control parameters, at line 34 and is inputted to scaling mechanism 35. Output 22 of scaling 35 goes to controller 14.
Tuner 21 can be used in control structure having both linear and nonlinear components. For example, tuner 21 can be used in conjunction with a parametrized neurocontroller 36, as shown in FIG. 11.
Neural network based automatic tuner 21 is optimized off line using a design framework. An "evolutionary computing" algorithm has been developed that incorporates aspects of genetic algorithms. Also, a gradient search can be enabled, with gradients numerically computed.
The flexible nature of the design framework permits one to optimize for criteria that more conventional approaches seldom permit. In particular, performance criteria need not be quadratic, or even differentiable; control structures can be arbitrarily nonlinear; and robustness can explicitly be considered. The present approach to the optimization of neural network based automatic tuner 21 does not require "learning in advance" in which input-output combinations must be first compiled using a separator optimization program, and neural network 28 then trained using supervised learning. The present approach can thus be contrasted to the approach of Nomura et al. (U.S. Pat. No. 5,311,421) in which the use of a neural network for tuning is also disclosed but in which the development of the neural network requires a supervised learning algorithm for which appropriate teaching data must be collected.
One may use supervised learning of Nomura et al. for neural network tuner 37 of FIG. 12. For tuner 37, inputs may be designated x and outputs designated Pc. dST is part of X. Pc are control parameters. Pc is compared with the desired output Pc * and the result of the comparison at comparator junction 38 is error ε, which is fed to learning algorithm 39. Algorithm 39 then provides an output to neural network 40 to adjust the weights for output adjustments. For that supervised learning, a large database of (x, Pc *) pairs need to be compiled in advance. Pc * needs to be computed for many different cases in advance. Nomura et al. involves the inputting of information with the characteristics contained therein to a neural network 40 for "learning in advance a correlation" between the information containing the characteristics and a control parameter and determining a control parameter for a controller. "Learning in advance" means the determining of optimal control parameters corresponding to different characteristics of the model of the combined controller-controlled system and then using the optimal control parameters as learning teacher data. In Nomura et al., it is also assumed that input-output combinations are available as learning data (col. 11, lines 11-17). The Cj 's in equation (10) are ideal outputs that the neural network is trained to match, and thus Cj 's are to be available for neural network training. The neural network is used as a nonlinear regression model and thus the neural network is regressed using input-output training data.
The present approach does not rely on such supervised learning. No collection of (x, Pc *) pairs is needed and no computation of (optimal) Pc *'s is required. The present neural network is developed using off-line optimization without any need for "learning in advance" in the sense of Nomura et al. No input-output training data is needed.
It should be noted that the prior art of Nomura et al., by virtue of its reliance on a supervised learning approach, is restricted to certain types of neural network tuners. Our invention eschews supervised learning entirely in favor of a simulation-based optimization approach, and an important ancillary benefit of this approach is that the tuner can comprise arbitrary nonlinear approximators, including but certainly not limited to the type of neural network structure employed in the cited prior art.
FIG. 13 shows the design framework 41 of the present invention schematically. For each parameter vector p, closed-loop simulations are run and a performance criterion value computed. No learning teacher data is required. The vector p can contain gains for a linear control structure, so that one can optimize a fixed controller for some criterion; it can contain parameters for a nonlinear-approximator-based tuner or neural network controller (e.g., parametrized neurocontroller (PNC) 36 in FIG. 11); or it can contain parameters for both the tuner and controller, so that both modules can simultaneously be optimized. The objective of the design activity is to develop tuners or controllers that can be used for a broad range of applications. Thus, numerous closed-loop simulations must be run for every choice of p. In these simulations, various parameters are varied depending on the design requirements:
Process model parameters pp which are input to the process model (PM) in the closed-loop simulation
Estimation errors between these process model parameters and the process model parameter estimates pp, of which the latter are input to the tuner and/or controller
Initial conditions, especially in the case that the closed-loop controller or process model is nonlinear
Setpoint profiles, also for nonlinear controllers or nonlinear process models
Parameters pJ that affect the cost function-the use of these parameters allow us to develop one control solution for a class of parametrized criteria
Each closed-loop simulation results in one elementary cost function Ji being computed. The results of all the simulations conducted for one vector p are composed into an overall cost function J which is returned to the optimization algorithm:
J=f.sub.J (J.sub.1, J.sub.2, . . . , J.sub.N)
where N is the number of closed-loop simulations performed (typically 1000).
The above paragraphs describe the general framework of the design system. For this project, one has made specific choices for optimization runs. Note these below:
Process models are first order with dead time. The process gain Kp is normalized to 1.0, the process time constant Tp to 10.0. The process dead time Td varies so that the constraint in the next statement holds:
Estimates of Kp, Tp, and Td are input to the neural network controller or tuner. These estimates are perturbations of the true model parameters as discussed below. Td is limited to the range 0,2Tp !.
The parameter estimate for a parameter pi is bounded uniformly within (1-r)pi, (1+r)pi !, where r is a parameter. r is a function of the criterion parameter dST which can vary between 0 and 1. This function is a linear function so that the minimum value of r, achieved when dST is 0, is rmin ; and the maximum value, corresponding to dST equal to 1, is rmax. Thus, dST controls the robustness of the control, in addition to being a settling time knob as discussed below.
The only criterion parameter employed is dST. The elementary criterion is:
J.sub.i =a.sub.0 d.sub.ST t.sub.ST.sup.s +a.sub.1 (1-d.sub.ST)y.sub.os.sup.s +a.sub.2 (ΣΔu.sup.2).sup.s
Here tST s is the scaled settling time for the simulation, yos s is the scaled maximum fractional overshoot relative to the setpoint change, and (ΣΔu2)s is the scaled sum of control moves over the simulation period. dST, tST s, yos s and Δu all of course vary with each closed-loop simulation, but for clarity one has not explicitly indicated the dependence on case i in this equation. The weighting factors used for most experiments are a0 : 0.75, a1 : 0.2, a2 : 0.05.
The overall cost function J is simply the sum of the individual Ji's: ##EQU8## The scaled settling time tST s is based on the computed settling time tST as follows. First, the maximum and minimum expected values are computed for tST :
t.sub.ST.sup.max =A.sub.Tp.sup.max T.sub.p +A.sub.Td.sup.max T.sub.d
t.sub.ST.sup.min =A.sub.Tp.sup.min T.sub.p +A.sub.Td.sup.min T.sub.d
and then tST s is linearly scaled within these limits, and bounded outside: ##EQU9## A similar approach is followed for calculating yos s from yos. A fractional overshoot of 0.0 (i.e., no overshoot) maps to a yos s value of 0.0, and a fractional overshoot of 20 percent or more (relative to the setpoint change) maps to 1.0, with the scaling in between again being linear. For the squared sum of control moves, these bounds are 1.0 and 3.0.
The above has specified in some detail how the optimization algorithm evaluates a parameter vector. This scheme has been followed for neural network tuner designs for different controllers. Parameter choices for two types of controllers--PI/PID and "higher order linear controllers" (HOLC) are shown below:
______________________________________DesignParameter PID/PI Tuner HOLC Tuner______________________________________r.sub.min 0 0r.sub.max 0.25 0.5A.sub.Tp.sup.min 0.2 0.1A.sub.Tp.sup.max 2.0 2.0A.sub.Td.sup.min 2.0 1.0A.sub.Td.sup.max 8.0 6.0______________________________________
Thus, both the PID/PI tuner and the HOLC tuner are designed to allow robustness/performance tradeoffs to be done by simply adjusting one knob (dST).
An outline of the evolutionary computing algorithm used for the design of the neural network tuners (the parameter vector is notated as w rather than p) is provided:
1. Generate an initial set or population of vectors w1, . . . , wN and evaluate cost function values Ji =J(wi).
Compute J.sub.max =.sub.i.sup.max J.sub.i, w.sub.max =.sub.i.sup.argmax J.sub.i.
2. Select a parameter vector w' at random (uniform distribution) from population
3. Select the perturbation standard deviation σw randomly from between specified lower and upper bounds, σw min and σw max
4. Generate a perturbation vector δw ˜N(0, σw 2)
5. Generate a parameter vector w"←w'+δw and evaluate J(w")
6. If J(w")≦Jmax then Replace wmax with w' Set w' to w" Recompute Jmax and wmax Goto Step 5 {Reapply perturbation}
7. Goto Step 2
According to the present invention, one can design a tuner by employing a nonlinear optimization algorithm in conjunction with a closed-loop simulation system. The nonlinear optimization algorithm attempts to minimize the value of the cost function J(p) by appropriately adjusting the nonlinear approximator parameters p. FIGS. 14a and 14b show two similar nonlinear-approximator-based tuners 21. In FIG. 14a, the nonlinear approximator is implemented as a multilayer perceptron neural network. The parameters to be optimized in this case are w1, . . . , w14. In FIG. 14b, the nonlinear approximator is implemented as a computationally simple compositional mapping--the figure shows the mathematical formulation of the mapping. Because of its computational simplicity, this is a preferred embodiment. The parameters to be optimized in this case are w1, w2, . . . , wb, a1, a2, . . . , ab, v11, v21, . . . , vb1, v12, v22, . . . , vb2. It should be noted that the implementations of FIGS. 14a and 14b have very similar features, and in both cases output KC and KI as functions of dST and the respective parameters. However, the supervised learning algorithm described in Nomura et al. cannot directly be used for the approximator of FIG. 14b. Once the appropriate parameters are determined so as to minimize J(p), tuner 21 can be applied to real systems (provided that the constraints assumed in the design hold for the real system).
The optimization involved in tuner design may seem complex and time consuming. However, once designed, tuner 21 can be easily used in a variety of different applications on simple computational platforms. FIG. 15 shows how tuner 21 is used for system control. Tuners can be designed and used in different embodiments, depending on the nature of user-control of closed-loop performance desired, and the types of systems and controllers the tuner will be used with.
In one example embodiment of a fixed first order linear process, PI controller, for settling time tuning, one can describe how this invention can be applied to design a nonlinear-approximator-based tuner 21 for PI controller 14 with a "settling time" tuning knob 29 when the processes for which tuner 21 is designed are assumed to be adequately characterized by first-order linear dynamics which are accurately known. A system is illustrated in FIG. 15. The process model is expressed in the Laplace domain as: ##EQU10## where Kp is the process gain and Tp the time constant, the values for which are known (through experiments or process knowledge). Tuner 21 has one tuning knob 29, dst, which can be varied over a range 0.0 to 1.0 to make the process response to a setpoint change be fast or slow. For fast settling, one is relatively tolerant of overshoot in the response, and control action. For slow settling, however, one wants overshoot to be minimized and control action not to be aggressive. FIG. 16 shows curve 42 of y when dst is low and curve 43 when dst is high for setpoint change 24. A possible cost function 44 (FIG. 17) for this embodiment is the following: ##EQU11## In this expression, E(•) denotes the expectation operator, and dst ε 0,1! denotes that the expectation is to be taken over a (uniformly distributed) range from 0 to 1 of values of dst. tst represents the settling time of the closed-loop simulation, yos the overshoot in the response, and Δumax the maximum control move encountered. The functions fst (.), fos (.), and fum (.) are scaling functions so that the effect of the three terms is in accordance with the relative importance of each of these features. Note that since dst is an input to tuner 21 which outputs PI gains that influence the closed-loop behavior (and hence yos, tst, and Δumax ), the value of the expression (1) is a function of parameters p of the nonlinear approximator.
Once a tuner 21 is designed to minimize J(w) in equation (1), it can directly be used to control the target process. The control performance can be adjusted through the use of the tuning knob dst 29. When dst is low (near 0), setpoint changes will be rapidly tracked. When dst is high (near 1), fast setpoint tracking is no longer important, but output overshoot and the maximum actuator move will be minimized. Of course, since tuner design 21 is based on a specific process model, the tuner performance will largely be dependent on the accuracy of the model. For cases where the model is not precisely known, tuners can be optimized for robust performance.
Equation (1) cannot be analytically solved in most cases of practical interest. However, it can be approximated by Monte Carlo simulations to as high a degree of accuracy as required. This simulation approximation is guided by a nonlinear optimization algorithm 45 of FIG. 17. Of course, high precision is gained at the expense of computing time.
For the embodiment of an uncertain first order with delay linear process, PID controller, settling time tuning, one no longer assumes that the process is precisely known. Instead, tuner 21 is designed to optimize performance over a range of process models. One extends the process model of the embodiment of a fixed first order linear process and PI controller, by adding a delay or dead-time: ##EQU12## where Td is the process dead time. The three process parameters are assumed to lie within known spaces K (for Kp), T (for Tp), and Θ (for Td). In addition, one is now interested in a tuner for a PID controller in interactive form. The controller transfer function is: ##EQU13## where U(s) is the controller output (Laplace transformed), Ti is the integral time, τd the derivative time, α the rate amplitude (a known constant), and E(s) the error (Laplace transformed).
In this case, one can adopt the following cost function: ##EQU14## Here, instead of minimizing an expectation, one is interested in minimizing the worst case performance. The maximum is computed over spaces for dst, Kp, Tp, and Td. The robustness associated with a tuner that is optimized for Cost Function (2) implies a loss of nominal performance. If in fact the process model parameter estimates were known more precisely than one assumes here, then better control could be achieved. This suggests that a tuning knob could be added that allows the user of the tuner to make the robustness versus nominal performance tradeoff on a case-by-case basis without reoptimization. The next embodiment description discusses this aspect of the invention.
In an embodiment of an uncertain first order linear process, multiple lead-lag controller, for settling time and robustness tuning, the controller is not of the PID variety but consists of three lead lag terms: ##EQU15## A second tuning knob, r, is added that also varies between 0 and 1. The parameter spaces over which tuner 21 operates are now a function of r: ##EQU16## For simplicity, one may revert to the simple delay-less process model.
For r=0, the expectation is evaluated over small spaces K and T in this case, and one is interested in maximizing performance at the expense of robustness. As r is increased, the ranges increase in accordance with prespecified functions K(r) and T(r). Note that the effect of r can be incorporated into dst itself-the slower settling times associated with high dst values are consistent with increased robustness. Thus, Cost Function (3) could be simplified by removing r and making K and T dependent on dst.
In the embodiments discussed so far, tuner 21 input has not included any information on process 16 itself. The tuners can be used for different processes to the extent that they fall within the robustness spaces K, T, and Θ above. The next two embodiments are described in which the tuner is also provided process parameter estimates as inputs, and is thereby generically applicable to a considerably larger space of processes. For the first embodiment, the generic feature is obtained simply by scaling controller gains.
In a process-generic tuner, first order process model, one may assume the design conditions of the embodiment of a first order linear process with a PI controller, and let the tuner be designed for specific nominal values of Kp and Tp. Let these values be Kp nom and Tp nom, respectively. For a given setting of dst, let the gains output by the tuner be Kc nom and Ki nom. The same tuner outputs can be used for a different process (one that permits a first order approximation) with a gain of Kp new and a time constant of Tp new by adapting the gains as follows: ##EQU17##
Scaling formulae can be derived for all cases where both the controller and the process model are linear. Similar formulae can also be derived for some cases where the controller is nonlinear and the process model is linear. Such scaling formulae can compensate for arbitrary Kp and Tp values. In cases where the process model contains additional parameters, the nominal values above need to be functions of model parameters.
One may extend the embodiment of the process-generic tuner, first order process model, by adding a delay (dead time) to the process model. To design a tuner that will work for all processes that can be modeled as a first-order linear system with delay, provided that the delay does not exceed some constant factor β of the time constant Tp, one can provide a process model input to the tuner. This input is ##EQU18##
The design cost function (1) is now extended as: ##EQU19## So the tuner design is performed for fixed values of Kp and Tp and with Td varying between 0 and βTp. Once the design is complete-a weight vector w is determined that minimizes Cost Function (4)-the resulting tuner can then be used for any process that can be modeled as first order with dead time, the only constraint imposed being that the dead time is no greater than β times the process time constant. For such use, estimated values of process gain, time constant, and dead time need to be input to the tuner. These are used to compute Tr and also to effect scalings such as those defined above. Since Cost Function (4) does not address robustness, these parameters must be accurately known.
In a state feedback controller, having a nonlinear process model and input disturbance rejection tuning, one assumes that the process is nonlinear, with model structure known: ##EQU20## y(t)=x1 (t) In this model, the xi are state variables and γ is a parameter. For a control structure, one adopts a linear state-feedback control law:
u(t)=a.sub.1 Δx.sub.1 (t)+a.sub.2 Δx.sub.2 (t)+a.sub.3 Δx.sub.3 (t)
where Δxi =xi.sup.(d)-xi, the difference between the desired state value and the measured value. Thus, the tuning problem in this case is to estimate appropriate values of the controller parameters a1, a2, and a3. This is to be done on the basis of two knobs: One is a performance criterion setting ddr that allows the user to tradeoff control action versus fast disturbance rejection for step input disturbances. The other is an estimate γ of the model parameter γ. By incorporating this latter parameter, one allows the same controller and tuner to be used over a range of processes of structure similar to equation (5) without being limited to process models with γ equal to some specific nominal value. The fact that we use an estimate of γ instead of the actual value implies that the tunings will have designed-in robustness to errors in parameter estimation. A suitable cost function for this embodiment is the following: ##EQU21## Values of γ for which this tuner is designed, range from 1 to 5. The estimation error for tuning input purposes is up to �δ, and is a function of ddr so that greater robustness is demanded for slow rejection time. The step input disturbances d that one is interested in rejecting have amplitudes between -dmax and dmax. tdr is the time to reject the input disturbance and Σ(Δu) is the sum-squared control action. The expectation must also be computed over a space X0 of initial conditions x(0).
For the embodiment of a state feedback controller having a nonlinear process model and input disturbance tuning as above, one can also employ a nonlinear controller of known structure, for example:
u(t)=a.sub.1 x.sub.1 (t)+a.sub.2 x.sub.2 (t)+a.sub.3 x.sub.3 (t)+a.sub.4 x.sub.1 (t)+a.sub.5 x.sub.1.sup.2 (t)
x.sub.3 (t)+a.sub.6 x.sub.2 (t)x.sub.3 (t)
In this embodiment the tuner outputs six parameters a1 -a6. Cost Function (6) can again be used.
For a PD controller, integrating linear process, easy-to-compute response feature inputs, rise time tuning, the following may be implemented.
The process model is: ##EQU22## The controller is: ##EQU23##
Tuner outputs are proportional gain Kc and derivative gain Kd. The tuner input consists of simple features computed from the process output after exciting the process at time t0 with a pulse of height H and width W. The process output is measured at times t0 +nW/4, for n=0, 1, . . . , 10. Ten features are computed simply as y(t0 +nW/4)-y(t0) for n=1, 2, . . . , 10. Neither Tp nor Kp are assumed to be known in this embodiment. However, appropriate values for H and W do depend on the process, and some general notions of the time constant and gain are needed.
In all the above embodiments, the key piece of the tuner is a nonlinear approximator. Various nonlinear approximators can be used in this context, including multilayer perceptron neural networks, computationally simple sigmoidal compositions, radial basis function networks, functional link networks, CMAC networks, fuzzy logic models that employ fuzzification and/or defuzzification and/or membership functions, wavelet networks, polynomial expansions, and specific nonlinear parametrized structures.
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text:
Tikcro Adds New Cancer Immune Modulators Targets
Dec 15, 2015, 16:05 ET from Tikcro Technologies Ltd.
TEL AVIV, Israel, Dec. 15, 2015 /PRNewswire/ -- Tikcro Technologies Ltd. (OTCQB: TIKRF) today announced that it has extended an ongoing research and license agreement with Yeda Research and Development Company Ltd., the technology transfer arm of the Weizmann Institute of Science in Israel, for additional targets for cancer immune modulators generated from a certain lab at the Weizmann Institute of Science.
Under this extension, further research and development will be performed for new antibodies addressing ligands of the PD pathway PD-L1, PD-L2 and the TIGIT checkpoint. Tikcro will fund this research at such lab in the Weizmann Institute of Science to develop antibodies to be selected and verified in pre-clinical trials. The license consideration due from Tikcro to Yeda includes royalties from net sales, sub-license fees and fixed fees linked to clinical and commercial sales milestones.
"We extend this research and license agreement to further pursue the development of new pathway-specific antibody drug-candidates. This extension is triggered by exciting pre-clinical results reached in 2015," commented Aviv Boim, CEO of Tikcro. "Going into 2016, we intend to further validate additional antibodies for these promising immune therapy targets."
About Tikcro Technologies:
Tikcro Technologies Ltd. (OTCQB: TIKRF) supports early stage development in growth areas, with a focus on biotechnology projects originated in Israeli academic centers. Tikcro is currently engaged with development of certain antibodies selected and verified in pre-clinical trials with a focus on antibodies binding to cancer immune modulators CTLA-4 and PD1 pathways. For more information about Tikcro, visit Tikcro's website at www.tikcro.com.
Safe Harbor Statement
Certain of the statements contained herein may be considered forward-looking statements that involve risks and uncertainties including, but not limited to, risks related to the Company's ability to raise financing and the risks related to early stage biotechnology projects, including, but not limited to, obtaining required licenses at reasonable commercial terms, the development, testing, regulatory approval and commercialization, intellectual property rights, competition, exposure to intellectual property claims and dependence on key suppliers and personnel. Such risks and uncertainties are set forth in the Company's SEC reports, including the Company's Forms 20-F. Actual results may materially differ. Results of operations in any past period should not be considered indicative of the results to be expected for future periods. The Company undertakes no duty to update any forward-looking information.
SOURCE Tikcro Technologies Ltd.

subset: commoncrawl


text:
The Creative Life
Hi y'all!
I'm Rachel Fontenot, an artist, poet and surface designer who is passionate about color and pattern, poetry, the great outdoors and art history. I also love to crochet and knit, scrapbook and make handmade cards. All of which you will find on this blog! My artwork and patterns are happy, peaceful and full of color as are all the things my happy hands find to create. I'm a native of the colorful and eclectic city of New Orleans where I currently live with my wonderful hubby of 20+ years marriage. Welcome!
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Recent Posts
Monthly Archives: February 2012
all the colors in my pencil box = rainbow magic
I heart rainbows… and I proudly admit it! I'm a child of the 70's and rainbows were all the rage back then and I've never been able to shake my love for all the bright and wonderous colors of the rainbow. … Continue reading
Posted in My Artwork, Surface Pattern Design 3 Comments
Cezanne Doodle
Paul Cezanne (1839–1906) is one of my favorite artist. He was a French Post-Impressionist painter whose artwork was key in the transition from the 19th century classical ways of approaching art to a new and radically different world of art in the 20th century. Cezanne … Continue reading
Posted in Doodle A Day, My Artwork Leave a comment
Doodle A Day
In 2010 I started to do a sketch a day. I bought a Flexi-Sketch sketchbook which has 300 pages and comes in a variety of sizes. It has 110lb drawing paper which holds ink, marker, pencil and light watercolor well. Perfect … Continue reading
Posted in Doodle A Day, My Artwork 2 Comments


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11:52 AM - December 8, 2010 by MMChrisS
Should EA be worried about any potential NFL lockout?
Chris Sanner: I think the short-term interests of the Madden brand would be relatively unaffected by any potential lockout. You are hearing a lot on both sides about whether there will even be a lockout, so it's a full wait and see on that issue altogether. However, if a lockout occurs, I'd think Madden sales would slip -- probably modestly -- but Madden can make up for much of the potential slip in the short term by putting out a great product to a NFL-hungry audience.
The real problem lies with the long-term interests of the Madden franchise. What sport has not taken a hit from a lockout on the popularity front? It's very possible that the NFL could be set back quite a bit, especially if the 2011 season never happens. That will have a very tangible effect on Madden's sales long term if the NFL brand cools significantly. At that point, EA might have to ask if the NFL license is worth the exclusive cost they are paying for it.
Robert Kollars: I don't think that EA should worry too much. Even if there is a lockout, the NFL and Madden fanbase will still be there. It may even cause fans of the real NFL to want to grab a copy of Madden even more. If you can't watch the NFL on TV, at least you can try to replicate it on a console.
And while what I am about to propose simply will not happen, especially after the Elite fiasco, if the NFL is locked out, I would love to see Madden go without a release next year. I can only imagine the improvements the developers could make to this game if they had a full two-year developmental cycle.
Read More - OS Roundtable: Should EA Be Worried About a Potential NFL Lockout?


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MOVIE SYNOPSIS [ General Audiences ] With a good performance of Jason Contini (see cast), this film directed by Wyatt Weed was premiered in cinema in 2014. The movie "Four Color Eulogy" comes from USA. You can watch Four Color Eulogy online on TV cable or theaters with original audio in English at different dates. This film premiered in theaters in 2014.
Rating: 5 by 10 users./
Runtime: 100 minutes
Jason Contini, Jessica Laney, Nicholas J. Hearne, John Contini, Amy Loui, Zachary Allen Farmer, Taylor Pietz, Whit Reichert, Dean Christopher, Jonathan Hey, James Anthony, Ryan Scott Foizey, Don McLendon, Chuck Brinkley, Mike Dowdy and William Gilomen.
100-minute film (full movie) directed by Wyatt Weed.
The original audio of Four Color Eulogy has dialogues in Dolby Digital, it was produced in USA.
It premiered in cinemas or television in 2014.
Screenplay written by Nicholas J. Hearne, Jason Contini, John Contini and Wyatt Weed.
Wyatt Weed.
Jason Contini, David Whatley, Scott Stoltz, Nicholas J. Hearne and Gayle Gallagher.
This film was produced and / or financed by Archlight Studios and Pirate Pictures.
Without commercial breaks, the full movie Four Color Eulogy has a duration of 100 minutes; the official trailer can be watched in streaming on the Internet. You can watch this full movie free in EnglishDolby Digital in movie TV channels, renting the DVD or with VoD services (Video On Demand player, Hulu) and PPV (Pay Per View, Netflix).
See the page of this full movie, Four Color Eulogy in FULLTV Movies.


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Gender pay gap
Do gender pay gap reporting obligations apply to my organisation? What employees do we include in the calculations? What counts as "ordinary pay" and "bonus pay"? Where do we publish the final figures? We answer five questions HR professionals have been asking about the new gender pay gap reporting duty.
The first "snapshot date" for gender pay gap reporting is 5 April 2017, with larger employers having to publish their first gender pay gap report no later than 4 April 2018. We look at what organisations need to consider when writing a gender pay gap report.
The run-up to April is typically a busy time of year for HR professionals, with new employment legislation due to come into force. 2017 is no exception, with the most significant development being the introduction of the gender pay gap reporting duty for larger employers. However, there are a number of other key changes affecting all employers, regardless of their size.
With the gender pay gap reporting Regulations coming into force in April, it is critical that HR teams can understand and explain gender pay headline figures and the reasons for pay gaps.
Recent research shows that the gender pay gap has significantly reduced for younger workers, but that the gap remains wide once women become mothers. Ed Stacey, head of employment at PwC Legal, questions whether or not gender pay gap reporting will tackle the problem.
New rules on gender pay gap reporting simply compel employers to publish details of their pay gap on an annual basis, taking action is merely voluntary. Gillian MacLellan, employment partner with law firm CMS, looks at how employers can tackle their pay gap.
Although gender pay gap reporting legislation does not come into force until early 2017, employers may have to collect gender pay gap data from as early as April 2016. HR professionals can use this timeline to get ready for their reporting obligations.
Significant employment law changes are anticipated for 2017, amid the ongoing uncertainty resulting from the Brexit referendum.
A new duty requiring big employers to publish data on their gender pay gaps looks set to come into effect in April 2017 after revised draft Regulations were published by the Government.
Gender pay and equal pay are frequently confused. However, there are key differences between the two and employers need to be aware of their obligations in relation to both.
Legal guidance: HR and legal information and guidance relating to the gender pay gap.
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Blend 4 - Tobacco E-Juice by 503 e-Liquid (30ml)
Blend 4 is a tobacco cigarette e-liquid packed full of flavor. This bold RY4 variant may be the best you ever try. Recommended for heavy smokers trying to make the switch.
What You Get:
- 1x 30ml E-Liquid in Glass Bottle with Dripper
Blend 4 is a 65pg/35vg blend.
We Also Recommend


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Moving your legal career out of Australia: Why is it so difficult?
I noticed the announcement a couple of weeks ago in Lawyers Weekly that Michael Cripps was leaving as General Counsel of IAG to join international law firm Clyde & Co in Shanghai. Good luck to him. There is nothing particularly … Continue reading
Posted in Headhunting, Law, Law jobs, Lawyer, Lawyers, Legal recruitment, Online headhunting, Recruitment Tagged head hunting, Law, law career, Lawyers, legal career, legal jobs, Legal recruitment, recruitment, twosteps Leave a comment
Legal Career Tips for Young Lawyers – should you stay or should you go?
I have read many comments on blogs this week, quite a few on linkedin, about young lawyers who are trying to make decisions about their legal careers at a very early stage, mainly driven by redundancy, apathy, hard work and … Continue reading
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The Office of Student Life and the New Student Days Committee welcomes you and your family to Hastings College! We are excited you have selected Hastings College and can't wait for you to arrive.
New Student Days kicks off Saturday, Aug. 26, 2017, with the first day of classes being Aug. 28.
New Student Days Schedule
You can download the schedule as a .pdf here »
Or view the schedule as a Google doc here »
If you have any questions, feel free to contact the Student Life Office at 402.461.7305.
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|29 Apr 2012
windows ultimate 32 bit
make partitions with windows 7 or xp
My OS is windows XP and I make partitions with this OS
But now I want to instsl windows 7
Which of the following is better?
1) I instal windows 7 without any changes in my drivers
2) I delete current partitions and make new partitions , then instal windows 7
|My System Specs
|29 Apr 2012
Windows 7 Home Premium SP1, 64-bit
Use choice 2.
Back up your data and boot from the installation disc.
When you come to the partitioning screen, choose "drive options, advanced" and delete all partitions.
You do NOT have to delete partitions and make new partitions BEFORE you install. Do that DURING the install by using "drive options, advanced".
There is a pretty good chance the Windows disk will have all the drivers you need, but you might go to your motherboard manufacturer's web site (Asus) and download at least the LAN driver so you will be able to establish an Internet connection after you install Windows 7.
|My System Specs
|29 Apr 2012
Microsoft Windows 10 Pro Insider Preview 64-bit
One more thing ... while making new partitions, make it sure that you are not making more that four primary partitions in a single disk. If you do, the disk will be converted to a dynamic one, and you will not be able to use it properly.
If you want to format logical partitions ... you may use Partition Wizard Bootable CD , the instructions are here : Create Partition help of partition software - Partition Wizard. .
|My System Specs
|30 Apr 2012
Follow these illustrated steps to Clean Install Windows 7.
Be sure to back up your data first.
If you save the XP partition it will auto-configure a Dual Boot menu to choose between OS's. Later we can help you remove XP. Dual Boot Installation with Windows 7 and XP
|My System Specs
|Thread Tools
|Similar help and support threads
Can I make partitions on SSHD?
Hello, Just want to ask whether the SSHD can be partitions (like normal hard drive) or not and whether this process (partition the SSHD) will cause problems? Many Thanks :):):) Arshad
|Hardware & Devices
Windows 7 Ultimate Install, Can't make partitions
I got the BSOD a bit ago, and I don't really remember why, but I decided this might be a good time to clear everything (being inbetween semesters) and upgrade to Windows 7 Ultimate from Windows 7 Home. I unfortuanetly have only had problems since then. I know having some system specs would help,...
|Installation & Setup
All of my Partitions disappeared and I cannot make them active.
Hello all, I am new to these forums so please forgive me if I put this post in the wrong area or something of that nature. I'm running a 2 month old Toshiba Satellite PC, when suddenly all of my Partitions, except for my C: drive disappeared. My computer also failed to recognize my CD/DVD Rom...
|General Discussion
How to make partitions to reinstall Windows when windows does not boot
So here's the story: I was just getting some work done last night on my PC, when suddenly the power went out. After a while when the power came back on, my computer would not boot. It was stuck on the Windows loading screen. So I restarted and did diagnostic (pressed F9 before booting), turns out...
|Installation & Setup
How to make more partitions so that every partition has its own OS?
I want to make 3 partitions. The first one will contain win7, the seond one xp, and the third ubuntu.
|Installation & Setup
How do I make 9 Partitions & Install Win 7 ?
Hi, This may sound crazy but I have to do it to separate my various kinds of data. I have a 500 GB Hard Disk. I want to make 9 partitions of various sizes & Install Windows 7 64 bit ( Ultimate ) in the first partition. I installed this Hard Disk as slave to my current Hard Disk &...
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Good intentions
December 23, 2008
I had good intentions to take some nice photos of Maximilien and make them into Christmas cards but then we all got sick. It started out with me getting a sinus infection and strep which kept me out of work for two days. Then Julien came down with some mystery illness. And he never gets sick so that was really weird and that threw us for a loop. Then things were looking up and we were all feeling better and then Max went to spend the night at his grandmother's house and he came home very sick (which I think he had been saving up from the sick bank that is his creche) with pink eye, a double eye infection and a cold. I started to feel better and then last night I spent the better half of the night trekking back and forth from the bathroom and our bedroom (13m2 of hallway, people!) . And again, stuck at home today (while the tea house remained closed two days before Christmas) with a baby with pink eye who wouldn't stop kissing me and a scary rumbly stomach that sounded like something from that toilet scene in Dumb and Dumber.
And here I am... thinking about tomorrow. Christmas eve.
is it just me or has this year just flown by?
I apologize to my family back home for the lack of Christmas photos of Maximilien. My camera is on charge tonight with good intentions of getting some action shots of Max opening presents tomorrow.
So, here I am wishing you happy and healthy holidays from our family to yours.


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M: 2 mile walk with Luke
T: nothing
W: 1 hour yard work- pruning the crepe myrtle
Th: 2 mile walk, 1 hour yard work- raking
F: 1 hour yard work- chopping out crepe myrtle into small enough pieces for the garbage men to take away
S: 1 mile walk
S: 2 mile walk alone
Grandpa was in town this week, so I was privileged enough to take 2 walks by myself. I was feeling better too, so I got some much needed Spring yard cleaning work done.
Sunday, March 20, 2011
Weekly Summary
Posted by Katie Jones at 6:22 PM
Labels: Katie Jones, outdoors, workouts while pregnant
Subscribe to: Post Comments (Atom)


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[IMAGE 1]Recently, there's been a lot of talk about this kid from San Clemente named Travis Mellem and some kind of contract with No Fear. Travis is a real-good surfer who all of a sudden has become the sort of kid that people point out when he's surfing his heats-he's the new mini-man. He's also a real funny, real nice kid-enjoy his interview. -AC
How old are you?
Where do you come from?
San Clemente, California.
Who are your sponsors?
No Fear, Becker, Sticky Bumps, Surf One, Reef, and Oakley.
What's your local break?
Who are your favorite surfers in San Clemente?
Probably Shea Lopez.
Just cause he's really good and fluid and doing pretty good in contests. He's from Florida, but lives here now. He's just basically fun to watch.
So how was your trip you took to Puerto with TransWorld?
It was fun getting beat up and stuff. It was fun, I got a couple of little barrels.
[IMAGE 2]Are you looking forward to travelling more?
Yeah, I want to. I want to see everyone's culture's and learn more.
What are your plans for the future?
My goal is to make it pro and make the top 44. Take a few years off school and see where I can go with it.
Do you ever feel pressure to succeed, to do well in contests?
Yeah in bigger contests, you just want to good to make the sponsors happy and just get your name out there. We have a couple here and there.
What do you like to do when you're at home?
I like spearfishing and skateboarding around.
Doesn't spear fishing get crazy, do you ever see sharks or anything like that?
No, I've never seen a shark, but I've seen a couple of hugeseals-that's fun. They're a lot bigger under water.
What's the dumbest thing that's happened to you?
Probably going to Puerto with Blair. No I'm joking. I don't know.


text: Tourists relying on their trusty travel guide will miss out on this destination located within the historic Fort George of Chennai, Tamil Nadu. And while Marina Beach is nearby and captures most of the foreign visitor's attention, St Mary's Church has a fascinating story to tell. Locals, taxi drivers and car services can easily get you to the proper location past the imposing security gates at the fort entrance.


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I spent most of last summer studying abroad in London. While plans to move back seem to be growing more difficult by the day, (thank you, economic downturn, feel free to end anytime now), I flip through my pictures on a weekly basis and dream of having unlimited access to Topshop and Portobello Market.
Though it's been months since I arrived back, I am left contemplating the differences between American and European fashion sense. I'm not going to turn this into some big anti-American rant because really, we're not that bad, but you have to hand it to those kids overseas — they sure know how to pull themselves together in the morning.
Aside from possessing the ability to eat normal portions of food, Europeans have a knack for throwing on outfits in a way that simultaneously screams effortless and spot-on trend.
When I was in London, I felt decently comfortable with my style. Brits are pretty quirky, so it was OK to look somewhat intentionally mismatched — which is a great thing when one suitcase of clothes needs to last a month and a half.
In Paris, however, I constantly felt intimidated by the people surrounding me. Practicality is thrown out the window. The sight of a woman zooming around on a moped in 5-inch heels never failed to flabbergast me. I mean, my God — I refuse to even wear heels to walk from my apartment on Dawson Street up to Hemingway's!
Sure, not everyone in Europe is running around in some straight-off-the-runway couture gown, but for the most part everyone is at least dressed. You don't see people — not even the teenagers — loafing around in sweats. They're in clothes that are well-made and tailored to perfection.
Here in America, it's no secret that we're a little late in the game when it comes to fashion trends. We scramble to get into trends four years after they make a European debut. We'll buy whichever iteration we can get the cheapest, sacrificing fit and quality in a desperate attempt to appear "in-the-know."
In America, we feel guilty about everything — possibly a lasting complex over that whole stomping-in-and-taking-the-Native-Americans'-land thing. We feel guilty about getting what we want. We feel like if something brings us pleasure, we shouldn't have it. We feel guilty about free time, as if having a few hours to ourselves each day is something to be embarrassed about, like it must mean we don't work hard enough.
We feel guilty about wearing the same outfit three times in the same month. The French, on the other hand, will buy one solid outfit a season and wear it to shreds. The quality is impeccable, of course. Sometimes I think it would be nice to feel satisfied by buying one designer outfit a season, rather than spending money left and right to always feel "updated".
Perfection, too, seems to be an American preoccupation. After all, does the poster child of Europe have enough plastic surgery to make Joan Rivers jealous? No, not at all. Los Angeles, a land of nose jobs and breast implants, has no foreign counterpart.
Instead, the beauty regimens of Europeans focus far more on natural beauty. They eat and drink to take care of their skin. They can run out to the farmer's market to pick up their evening's produce without a hint of makeup on and their hair in a lopsided knot. Yet you can bet they'll look way better than shellacked Americans making their way to the grocery store to buy over-processed food in overpriced outfits.
While I think we could all benefit from a hefty dose of European nonchalance, I'm still a big fan of America. I have a tremendous deal of pride in a country that gave birth to Ralph Lauren and Marc Jacobs. I would, however, like to take this opportunity to give a big shout out to Sweden. To the masterminded country behind H & M: If it weren't for you, my closet would probably be empty.


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items |
How's your distance? lol sounds like you're close.
A very fine device. The better your diaphragm the better the darts fly (since I've played baritone in bands I am literally lethal with this thing...if I deeply inhale with a VERY sudden & full exhale, the dart buries itslef inside & the fletch comes off XD). This 36" 40 caliber model comes with 12 darts with white fletches (must attach yourself), a quiver consisting of 2 parts (a ring with 16 holes for darts & a guard to prevent pricking yourself, a mouthpiece (very its wider to obviously catch more air), a muzzle piece, and a soft foam grip.
The barrel is very straight, and for those like me who can blow hard will find their shots very accurate and fairly consistent. To fire, drop the dart into the mouthpiece & push with a finger until it is fully inside the barrel (tilt up as well,the dart might fall out if you tilt it downwards & its not secure). Then aim by lookig at the target (you will see double-vision of the muzzle, have the target be between yet above the "two" muzzles to be accurate, changing height to compensate for distance & gravity), then blow into the barrel.
If you wonder which is best for you, I recommend these as followed...
12" for self-defense, dart-board shooting, and for ninja/assassin lovers.
36" for target, petit-game(like frogs & mice), and standard dense-combat (dense being a large yet thick region making movement difficult, like the jungle/forest or a post-apocalypse urban setting.
48" for advanced target shooting, petit/small game (as in from frogs & mice to squirrels/chipmunks, rats, & small birds) hunting, or long-range dense combat.
Either way, I am satisfied with this & will buy both the 12" one & a dart board for my room.
This product is not a toy. In the hands of anyone with a good strong breath this is as lethal as a blade or gun is. Do not give to children unless they are well-trained, can be trusted, and have ADULT supervision. If you are an adult, yes you can leave it somewhere for display but leave few or no darts for kids to get, they can hurt eachother even without the gun if they have the dart.
: Only
Current Price:
more to your cart to reduce the price to


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1. Re: Report keeps getting corruptedgdeepak Nov 12, 2013 4:43 AM (in response to jcarrier)
We faced that issue before, And it was later discovered that when we opened the reports during data load or deployment of the essbase cube (mainly deployment), we got that error.
So check if some activity is being performed on the source whenever u encounter that error. We shouldnt open the reports during that time.
Our essbase application is stabilized now and we havent deployed it since months and we didnt get that error in reports again.
2. Re: Report keeps getting corruptedjcarrier Nov 12, 2013 2:47 PM (in response to gdeepak)
We hadn't opened this report until our scheduled batches that included it failed, due to the same NullPointerException. So I don't think it could have been corrupted in that way.
3. Re: Report keeps getting corruptedgdeepak Nov 13, 2013 5:14 AM (in response to jcarrier)
I actually meant, we are not supposed to open the reports and not supposed to run the reports during the time changes are being done on the source. So, are you sure that no changes were being done on source during the time your batch was running?
4. Re: Report keeps getting corruptedjcarrier Nov 13, 2013 2:42 PM (in response to gdeepak)
Yes, this report is part of our standard close process, and doesn't change. We also run this report overnight at about 2am, so we wouldn't have any users accessing the report while it's running.
Based on the timeline of events, it seems to be breaking in between being run. After we got this error the first time and replaced the report, it ran successfully. Then, when the next month end close process started and we ran the report, it failed consistently again. We replaced it again and it ran fine. We're going to rebuild the report from scratch and see if that makes a difference, but we're open to any additional solutions.
Thank you,
5. Re: Report keeps getting corruptedgdeepak Nov 14, 2013 4:20 AM (in response to jcarrier)
I meant someone accessing the data source ie essbase or HFM or planning during the time reports are run. As you say it is 2am, may be there wont be users accessing the datasouce but, do you have any kind of scripts or data syncs running on data source at the same time?
6. Re: Report keeps getting corruptedjcarrier Nov 14, 2013 2:17 PM (in response to gdeepak)
No we schedule our reports around all of our scripts and processes so nothings running at the same time. Nothing else is running at the same time as this report.


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Brooklyn Quilts!, our 2016 quilt show was a great success. Thank you to all of you who worked hard to make it happen and to all those who supported our guild and came.
Brooklyn Quilts!
Brooklyn Quilters Guild 2016 Quilt Show
April 2-3, 2016
Saturday 10am – 6pm
Sunday, 10am – 4pm
Marriott at the Brooklyn Bridge, Grand Ballroom
333 Adams Street, Brooklyn, NY 11201
Handicapped Accessible
For more information:
Visit our Facebook page: Brooklyn Quilters Guild


text:
Diana Build Guide by dzijo
Not Updated For Current Season
This guide has not yet been updated for the current season. Please keep this in mind while reading. You can see the most recently updated guides on the browse guides page.
Thank You!
Your votes and comments encourage our guide authors to continue
creating helpful guides for the League of Legends community.
Diana Build
Ability Sequence
Not Updated For Current Season
The masteries shown here are not yet updated for the current season, the guide author needs to set up the new masteries. As such, they will be different than the masteries you see in-game.
Chapter 1
Start at Blue Buff. Take care of that and then go on Wolves, then Wraiths and then Golems. After that u are lvl 3. U can gank before if someone pushed too much, but in practive now start ganks on lane taht is best for gank. Gank as much as u can. When u can't just go in jungle and farm. After u reach lvl 6 then gank a lot. Just use Q on enemy, then ulti and then W and E at the same time... Enemies have no chance of escaping. In teamfights try to harras from behind with Q, and go in when AD carry or AP carry goes to close and it is hit by Q. Then just E to pull him to you and W to survive until your alies come fully in figth. When u take care of carry just go on most squishy targets. And remeber to use your W couse it's a great shield and it can save u many times.


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2015 Performances
Wed Nov 25
Girl Band
The Observatory
Wed Dec 2
Girl Band
Dada Dallas
A Girl Band setlist is a compilation of all the songs Girl Band has played live in concert. Many Girl Band concerts have the same setlist, and sometimes Girl Band setlists will vary from night to night. We've gathered together 2017 Girl Band tour setlists as well as past Girl Band setlists to give you a better idea of what you can expect at your local Girl Band show. If you have a Girl Band song list you would like to upload, please check back as that functionality will be added soon, as well as setlists for Girl Band.


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Hey, I just bought this 8-hour charger with 4 AA 2500 NiMH kit today and at first everything seemed fine, but then I noticed how hot the batteries were getting and so I took the charger and batteries out from the wall outlet (it was around 8 hours when I did this, btw.)
Is it normal for these to get hot? It says WARM on the instructions, but these were hot, and I'm just scared to even put them in my camera.
I have a Canon A510 -- should I just order the Canon charger and battery set from dell or someplace? What NiMH AA batteries/battery charger would you recommend?