Non-ideal Situations Research Articles

Monomer addition programs to generate constant gradient block copolymers

Equations are derived to calculate the monomer addition programs for semi-batch copolymerization reactions that allow to prepare gradient copolymers and block gradient copolymers containing arbitrary, pre-defined compositional gradients along the polymer chain. An analytical solution is given for the special case of ideal random copolymerizations, and numerical calculations are applied to discuss non-ideal situations. Three classes of addition programs are proposed and discussed with respect to time efficiency. Time-linear addition programs are shown to be insufficient to generate perfectly constant gradients over the whole compositional regions.

Organ Sales and Moral Distress

The possibility that organ sales by living adults might be made legal is morally distressing to many of us. However, powerful arguments have been provided recently supporting legalisation (I consider two of those arguments: the Consequentialist Argument and the Autonomy Argument). Is our instinctive reaction against a market of organs irrational then? The aim of this paper is not to prove that legalization would be immoral, all things considered, but rather to show, first, that there are some kinds of arguments, offered in favour of legalisation, that are, in an important sense, illegitimate, and second, that even if legalisation might not be wrong all things considered, there are good reasons for our negative moral intuitions. Moreover, identifying these reasons will help highlight some features of moral decisions in non-ideal situations, which in turn might be relevant to some other moral or policy choices.

Exact performance analysis of OS modified versions with noncoherent integration in nonideal situations

The radar signals returning from the targets being illuminated are usually accompanied by thermal noise and clutter. Constant false alarm rate (CFAR) processors are useful for detecting these targets in a background for which the parameters of the statistical distribution are not known and may be nonstationary. The ordered-statistics (OS) CFAR technique has been proven to work satisfactorily in both multiple-target and nonuniform clutter cases. Unfortunately, the large processing time taken by this scheme limits its practical uses. The modified versions of the OS processor have been proposed to replace it in these applications. They can reduce the processing time of the single-window OS detector in half without changing its useful properties. Our goal in this paper is to provide a complete detection analysis for the OS processor along with ordered-statistic greatest-of (OSGO) and ordered-statistic smallest-of (OSSO) modified versions, for M postdetection integrated pulses when the operating environment is nonideal. Analytical results of performance are presented in both multiple-target situations and in regions of clutter power transitions. The primary and the secondary interfering targets are assumed to be fluctuating in accordance with the Swerling II target fluctuation model. As the number of noncoherently integrated pulses increases, lower threshold values and consequently better detection performances are obtained in both homogeneous and multiple-target background models. However, the false alarm rate performance of OSSO-CFAR scheme at clutter edges worsens with increasing the postdetection integrated pulses. As predicted, the OSGO-CFAR detector accommodates the presence of spurious targets in the reference window, given that their number is within its allowable range in each local window, and controls the rate of false alarm when the contents of the reference cells have clutter boundaries. The OSSO-CFAR scheme is useful in the situation where there is a cluster of radar targets amongst the estimation cells.

The fusion of visual lip movements and mixed speech signals for robust speech separation

A technique for the early fusion of visual lip movements and a vector of mixed speech signals is proposed. This technique involves the initial recreation of speech signals entirely from the visual lip motions of each speaker. By using geometric parameters of the lips obtained from the Tulips1 database and the Audio–Visual Speech Processing dataset, a virtual speech signal is recreated by using audiovisual training segments as a basis for the recreation. It is shown that the visually created speech signal has an envelope that is directly related to the envelope of the original acoustic signal. This visual signal envelope reconstruction is then used to aid in the robust separation of the mixed speech signals by using the envelope information to identify the vocally active and silent periods of each speaker. It is shown that, unlike previous signal separation techniques, which required an ideal mixture of independent signals, the mixture coefficients can be very accurately estimated using the proposed technique in even non-ideal situations. For example, in the presence of speech noise, the mixing coefficients can be correctly estimated with signal-to-noise ratios (SNRs) as low as 0 dB, while in the presence of Gaussian noise, the estimation can be accurately done with SNRs as low as 10 dB.

Coherent control of second harmonic generation using spectrally phase coded femtosecond waveforms

We report the demonstration of coherent control of second harmonic generation (SHG) with over three orders of magnitude contrast in the SHG yield. Under conditions of large group-velocity mismatch and narrow phase matching bandwidth, we show that SHG can serve (in the perturbative limit) as a nonlinear optical but classical analogue for quantum mechanical two-photon transitions in atomic media, with advantages in efficiency and ease of implementation. Our coherent control scheme results in a spectral phase correlator for ultrashort pulse waveforms, which may have applications in novel high-bandwidth optical communication schemes. We demonstrate a set of different waveforms with similar strong control yields by introducing families of binary phase codes taken from communication theory into the coherent control process. The response to these waveforms under certain nonideal situations is also studied.

The nonequilibrium mesoscopic SNS transistor

We describe an SNS junction with a control voltage applied in parallel to the interfaces within the framework of the quasiclassical theory. This voltage allows to control and even reverse the supercurrent through the junction. Extending our previous work (Phys. Rev. Lett. 81 (1998) 1682), we will discuss limitations and performance of the device in non-ideal situations in connection to recent experiments (Nature 397 (1999) 43).

A Joint Maximum Likelihood Estimation Procedure for the Hyperbolic Cosine Model for Single-Stimulus Responses

Joint maximum likelihood estimation (JMLE) for the hyperbolic cosine model (Andrich & Luo, 1993) is extended to the situation in which the units of items are allowed to vary. Four issues were considered in developing this JMLE procedure: (1) the indeterminacy caused by the unit parameter of each item, (2) a necessary constraint to eliminate this indeterminacy, (3) the initial estimates of the unit and location parameters of the items, and (4) a practical correction procedure to account for inconsistency in parameter estimates. Four estimation cycles were designed to address these issues. Results from two sets of simulation studies (ideal and non-ideal situations) indicated that the algorithm yielded reasonably accurate recovery of the parameters.

Finite-time performance of a quadratically constrained MVDR beamformer

The finite-time behavior of a quadratically constrained version of the minimum-variance, distortionless-response line-array beamformer is examined. An entity known as the weight ratio is defined and it is demonstrated that there are simple expressions that relate the weight ratio to the field scenario which explicitly take into account the finite-time misadjustment phenomenon. It is further demonstrated that holding the weight ratio to a value of about 0.5 produces an adaptive beamformer with a beneficial combination of robustness to nonideal situations (e.g., sensor misalignment, pointing errors, correlated interferers, misadjustment effects, etc.) while retaining the ability to adapt quickly in highly dynamic fields.

Robust implicit self-tuning regulator: Convergence and stability

A family of implicit self-tuning regulators (STR) is presented, based on Lyapunov analysis techniques for the control of a class of multi-input and multi-output (MIMO) dynamical systems. Linearity in the parameters is assumed to hold, but the ‘estimation error’ is considered to be nonzero; this allows control of a larger class of systems and also has the effect of producing a robust controller. Moreover, the certainty equivalence (CE) principle is not used in the STR design, overcoming a major problem in adaptive control. The structure of the STR is naturally derived using a tracking error/passivity approach. The role of persistency of excitation (PE) is explored, and is used to show the boundedness of parameter estimates when gradient-based parameter tuning of STR is performed in nonideal conditions. New on-line tuning algorithms for implicit STR are derived that are similar to the ε-modification approach for the case of continuous-time systems and that include a modification to the adaptation gain and a correction term to the standard gradient-based tuning. These improved parameter tuning algorithms guarantee tracking as well as bounded parameter estimates in nonideal situations, so that PE is not needed. The notions of a passive STR, a dissipative STR and a robust STR are introduced. Finally, this paper provides a comprehensive theory in the development of identification, prediction and adaptive control schemes for discrete-time systems based on Lyapunov analysis.

Modelling and financial analysis tools for PV grid-connected systems

Integration of photovoltaic (PV) technology in buildings must always be realized by taking into account rational criteria in PV system design. This paper presents analytical models to estimate the energy and economic performances of PV grid-connected systems in non-ideal situations, i.e. considering spatial constraints (orientations, possible shadowing), technical characteristics and energy interchanges with the electricity grid. It offers a useful tool for the PV system designer, avoiding otherwise complex performance calculations.

Discrete-time model reference adaptive control of nonlinear dynamical systems using neural networks

A multilayer discrete-time neural net (NN) controller is presented for the direct model reference adaptive control of a class of multi-input multi-output (MIM0) nonlinear dynamical systems. The nonlinear dynamical system is assumed to be controllable and its state vector is available for measurement. The NN controller exhibits learning-while-functioning features instead of learning-then-control so that control is immediate with no explicit learning phase needed. The tracking error between the output of a nonlinear plant and an ideal linear model converges within a very short time. Persistence of excitation (PE) is not needed, linearity in the parameters is not required, and certainty equivalence is not used. This overcomes several limitations of standard adaptive control. The novel weight tuning paradigm for the NN controller is based on the well-known delta rule but includes a modification to the learning rate parameter plus a correction term. It guarantees tracking as well as bounded NN weights in non-ideal situations so that a persistency of excitation conditions on the internal signals is not needed. It is found that the constant learning rate parameter at a given layer should decrease with the number of hidden-layer neurons present in that particular layer; this is a major drawback often documented in the NN control literature. This drawback is easily overcome by employing a projection algorithm at each layer. Theoretical analysis is justified through extensive simulation studies.

Orthogonal Projection Approach Applied to Peak Purity Assessment

The orthogonal projection approach (OPA), a stepwise approach based on an orthogonalization algorithm, is proposed. The performance of OPA for the assessment of peak purity in HPLC-DAD is described and compared with that of SIMPLISMA. The occurrence of artifacts in both approaches under nonideal situations is discussed.

Multilayer discrete-time neural-net controller with guaranteed performance

A family of novel multilayer discrete-time neural-net (NN) controllers is presented for the control of a class of multi-input multi-output (MIMO) dynamical systems. The neural net controller includes modified delta rule weight tuning and exhibits a learning while-functioning-features. The structure of the NN controller is derived using a filtered error/passivity approach. Linearity in the parameters is not required and certainty equivalence is not used. This overcomes several limitations of standard adaptive control. The notion of persistency of excitation (PE) for multilayer NN is defined and explored. New online improved tuning algorithms for discrete-time systems are derived, which are similar to sigma or epsilon-modification for the case of continuous-time systems, that include a modification to the learning rate parameter plus a correction term. These algorithms guarantee tracking as well as bounded NN weights in nonideal situations so that PE is not needed. An extension of these novel weight tuning updates to NN with an arbitrary number of hidden layers is discussed. The notions of discrete-time passive NN, dissipative NN, and robust NN are introduced. The NN makes the closed-loop system passive.

Information extraction and backaction in photon number measurements

We analyse schemes for linear measurements of the photon number of a light beam and address how much information it is possible to extract from a quantum detector in single or repeated measurements, how much information is destroyed in non-ideal situations, and how much backaction is imposed for the extracted information.

Performance analysis and improvement in model reference adaptive control

The purpose of this paper is to address the issue of performance by using two additional criteria to assess performance in the ideal and nonideal situations. They are the mean square tracking error criterion and the L/sub /spl infin// tracking error bound criterion. We use these criteria to examine the performance of a standard model reference adaptive controller and motivate the design of a modified scheme that can have an arbitrarily improved nominal performance in the ideal case and in the presence of bounded input disturbances. It is shown that for these cases the modified scheme can provide an arbitrarily improved zero-state transient performance and an arbitrary reduction in the size of possible bursts that may occur at steady state. As in every robust control design, the nominal performance has to be traded off with robust stability and therefore the improvement in performance achieved by the proposed scheme is limited by the size of the unmodeled dynamics, as established in the paper.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Nonlinear regression technique for parameter extraction from field-emission data

The standard method for obtaining field emission parameter estimates from current–voltage data is to transform the Fowler–Nordheim equation [L. W. Nordheim, Proc. R. Soc. London Ser. A 119, 626 (1928)] with approximate elliptical functions such that a linear regression technique can be used to obtain the emission area and enhancement factor estimates for a given work function. We present here a method for obtaining the parameter estimates that avoids bias caused by the functional approximations and the data transformation required for the previous method. The parameter bias due to several nonideal measurement situations is also considered. Finally, the method is applied to experimental data.

Evaluation of a predictive curve-fitting method for processing data from flow systems: Part 1. Flow system with a mixing chamber

This paper describes the evaluation of a curve-fitting predictive approach to processing data from a flow system with a well-stirred mixing chamber. The method utilizes data from the leading edges of response peaks to predict the response that would be measured if sufficient sample were used to give a steady-state response. Advantages of the method under optimal conditions include a 10-fold reduction of dependency on sample volume relative to a peak-height method, a 20- to 65-fold reduction of dependency on flow-rate relative to a peak-area method, and extended linear ranges in situations involving nonideal detector response and situations involving slow reaction kinetics. Limitations include a requirement for larger sample volumes and degraded error coefficients for flow-rate relative to a peak-height method and chamber volume relative to a peak-area method. The new approach is judged to offer complementary features relative to peak-height and peak-area methods.

Luminosity enhancement by focusing and colliding beams in a plasma.

The potential gain of luminosity by both focusing and colliding {ital e}{sup +}{ital e{minus}} beams in an underdense plasma is investigated. We first suggest the possibility of creating the plasms by the beam self-induced tunneling ionization'' of a gas. We then study the beam optics in the continuous focusing environment provided by the plasma, and the subsequent {ital e}{sup +}{ital e{minus}} beam-beam interaction in the same plasma. When applied to a range of beam parameters based upon the present Stanford Linear Collider conditions, we find that a gain by more than a factor of 10 in luminosity is possible. The sensitivity of various nonideal situations is discussed.

On the adsorption of polymer solutions on random surfaces: the annealed case

ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTOn the adsorption of polymer solutions on random surfaces: the annealed caseDavid Andelman and Jean Francois JoannyCite this: Macromolecules 1991, 24, 22, 6040–6042Publication Date (Print):October 1, 1991Publication History Published online1 May 2002Published inissue 1 October 1991https://doi.org/10.1021/ma00022a022RIGHTS & PERMISSIONSArticle Views65Altmetric-Citations32LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (288 KB) Get e-Alerts Get e-Alerts

Adaptive process control using pattern-based performance feedback

Two adaptive control strategies using pattern-based performance feedback are studied. Both strategies are based on an analysis of patterns exhibited in the recent history of the controller error and focus on adaptation opportunities that arise as a result of sustained set-point changes. The strategies consider a two-parameter adaptation where model gain and time constant are updated after each pattern analysis. The first method uses a vector quantizing neural network for the pattern analysis task. The network is constructed using exemplar controller error patterns developed by deliberately mismatching parameters of a PI controller's internal model against those of an ‘ideal’ simulated system. Once on-line, the network then receives controller error response patterns from the actual application and outputs controller model parameter updates. The second method uses a rule-based pattern analysis to determine features of the controller error response pattern. These pattern features are evaluated relative to user-specified desired features, and rules and procedures again produce PI controller model parameter updates. Both methods are compared in setpoint tracking demonstrations to determine qualitative robustness for non-ideal situations such as measurement noise, constraint of the manipulated variable, model order mismatch and unmeasured oscillatory disturbances. Results reveal that rule-based feature analysis has the benefit of being time independent but the disadvantage of not being able to handle non-ideal situations without a cumbersome rule base. Although the neural network approach requires specification of a time window for pattern analysis, the method proves to be easier to implement and more robust when confronted with non-ideal operating situations.