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The paper addresses a real-time optimization-based reference calculation integrated with a control structure for Modular Multilevel Converters (MMC) operating under normal and constrained situations (where it has reached current and/or voltage limitations, as it may occur during system faults). Firstly, a nonlinear optimization problem has been developed in which it prioritizes to satisfy the AC grid current set-points imposed by the transmission System Operator (TSO). The constrained nonlinear optimization problem is formulated based on the steady-state model of the MMC, whereby the prioritization is achieved through distinct weights defined in the Objective Function’s (OF) terms. The resultant optimization problem, however, is highly nonlinear requiring high computation burden to be solved in real-time. To cope with this issue, this paper applies a Linear Time-Varying (LTV) approximation, which permits to represent the nonlinear dynamics of the system as constant parameters, while a Linear Time-Invariant (LTI) system is used to formulate the optimization constraints. The converter’s current references are determined in real-time by solving a constrained linearized optimization problem at each control time step, which considers the TSO’s demands, the current MMC operating point and its physical limitations. Theoretical analyses comparing the responses of the linear and nonlinear optimization problems are performed to validate the accuracy of the LTV approximation. Finally, the linearized-optimization problem is integrated with the MMC controllers, evaluated under different AC and DC network conditions and compared with conventional control strategies, where it is shown that the presented method can be potentially employed to obtain the MMC current references for distinct network scenarios. [ABSTRACT FROM AUTHOR]

The aim of the paper is to define a limit performance of highly efficient battery electric quadricycles for urban mobility. The vehicle is employed for an energy efficiency competition in which urban concepts compete. A multi-physics (thermo-electro-mechanical) Tank To Wheels (TTW) model has been developed and validated. Given the information on the track route (track map and elevation) and on the throttle input command, the model computes the vehicle power demand and cruising speed. The model is validated by means of both indoor and outdoor experimental tests. The validated model is employed for the optimization of the transmission gear ratio and of the driving strategy to minimize the overall energy consumption on a given track. Design variables are related to the transmission gear ratio and to the throttle command profile. The algorithm aims to minimize the energy consumption, including constraints on the maximum current provided by the battery and on the maximum time available to complete the lap. Comparison with some common driving strategies confirmed the effectiveness of the proposed solution, with a foreseen 9% reduction of the vehicle overall energy demand. The single seater ultra-efficient electric vehicle, compared to other urban quadricycles, has a battery capacity ten times lower and a 50% longer driving range. [ABSTRACT FROM AUTHOR]

Objective: Computational modeling is increasingly used to design charging systems for implanted medical devices. The design of these systems must often satisfy conflicting requirements, such as charging speed, specific absorption rate (SAR) and coil size. Fast electromagnetic solvers are pivotal for enabling multi-criteria optimization. In this paper, we present an analytical model based on the quasi-static approximation as a fast, yet sufficiently accurate tool for optimizing inductive charging systems. Methods: The approximate model was benchmarked against full-wave simulations to validate accuracy and improvement in computation time. The coupling factor of two test coils was measured for lateral and axial displacements and the SAR was measured experimentally in a PAA phantom. Results: The approximate model takes only 11 seconds to compute a single iteration, while the full-wave model takes 5 hours to compute the same case. The maximum difference with full-wave simulations was less than 24% and the mean difference less than 2%. Adding a novel figure of merit into the multi-criterion optimization resulted in a 16% higher charging speed. The measured results of the SAR and coupling factor are within a 5 mm coil offset margin. Conclusion: The proposed approximate model succeeds as a rapid prototyping tool, enabling fast and sufficiently accurate optimization for wireless charging systems. Significance: The approximate model is the first of its kind to compute both the coupling factor and the SAR near conducting structures fast enough to enable optimization of charging speed. [ABSTRACT FROM AUTHOR]

This paper proposes a new decentralized data-driven load restoration (DDLR) scheme for transmission and distribution (TD) systems with high penetration of wind power. Robust DDLR models are constructed in order to handle uncertainties and ensure the feasibility of decentralized schemes. The Wasserstein metric is used to describe the ambiguity sets of probability distributions in order to build the complete DDLR model and realize computationally tractable formulation. A data-driven model-nested analytical target cascading (DATC) algorithm is developed to obtain the final load restoration result by iteratively solving small-scale mathematical models. The proposed DDLR scheme provides load restoration results with adjustable robustness, and performance efficiency is independent from the amount of data. The DDLR scheme makes full use of the available data while respecting information privacy requirements of independent operated systems, and ensures the feasibility of a decentralized load restoration strategy even in the worst-case condition. The effectiveness of the proposed method is validated using a small-scale TD system and a large-scale system with the IEEE 118-bus TS and thirty IEEE-33 DSs, showing high computational efficiency and superior restoration performance. [ABSTRACT FROM AUTHOR]

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application's behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reliability and makespan optimization of hardware task graphs in reconfigurable platforms by applying fault tolerance (FT) techniques to the running tasks based on the exploration of the Pareto set of solutions. In the presented solution, in contrast to the existing approaches in the literature, task graph scheduling, tasks parallelism, reconfiguration delay, and FT requirements are taken into account altogether. This paper first presents a model for hardware task graphs, task prefetch and scheduling, reconfigurable computer, and a fault model for reliability. Then, a mathematical model of an integer nonlinear multi-objective optimization problem is presented for improving the FT of hardware task graphs, scheduled in partially reconfigurable platforms. Experimental results show the positive impacts of choosing the FT techniques selected by the proposed solution, which is named Pareto-based. Thus, in comparison to nonfault-tolerant designs or other state-of-the-art FT approaches, without increasing makespan, about 850% mean time to failure (MTTF) improvement is achieved and, without degrading reliability, makespan is improved by 25%. In addition, experiments in fault-varying environments have demonstrated that the presented approach outperforms the existing state-of-the-art adaptive FT techniques in terms of both MTTF and makespan. [ABSTRACT FROM PUBLISHER]

Radio-wave propagation in tunnels and mines can be qualitatively understood by representing these environments as overmoded waveguides. Waveguide models are fast and intuitive, but the accurate prediction of the propagation characteristics of realistic geometries is beyond their scope. Such predictions can be derived by computationally intensive techniques, such as ray-tracing and vector parabolic equation (VPE) methods. This paper combines the two approaches, to extract surrogate models of arched tunnels in the form of rectangular waveguides. The parameters of the latter are optimized to match their propagation characteristics to those of the original arched tunnel, as predicted by the VPE method. The accuracy and limitations of these surrogate models are thoroughly evaluated. Their practical usefulness is demonstrated in an application where they are iteratively used, in lieu of the original computationally costly arched tunnel model, to optimally distribute the access points of an in-tunnel wireless communication system. [ABSTRACT FROM PUBLISHER]

Source localization based on signal strength measurements has become very popular due to its practical simplicity. However, the severe nonlinearity and non-convexity make the related optimization problem mathematically difficult to solve, especially when the transmit power or the path-loss exponent (PLE) is unknown. Moreover, even if the PLE is known but not perfectly estimated or the anchor location information is not accurate, the constructed data model will become uncertain, making the problem again hard to solve. This paper particularly focuses on differential received signal strength (DRSS)-based localization with model uncertainties in case of unknown transmit power and PLE. A new whitened model for DRSS-based localization with unknown transmit powers is first presented and investigated. When assuming the PLE is known, we introduce two estimators based on an exact data model, an advanced best linear unbiased estimator (A-BLUE) and a Lagrangian estimator (LE), and then we present a robust semidefinite programming (SDP)-based estimator (RSDPE), which can cope with model uncertainties (imperfect PLE and inaccurate anchor location information). The three proposed estimators have their own advantages from different perspectives: the A-BLUE has the lowest complexity; the LE holds the best accuracy for a small measurement noise; and the RSDPE yields the best performance under a large measurement noise and possesses a very good robustness against model uncertainties. Finally, we propose a robust SDP-based block coordinate descent estimator (RSDP-BCDE) to deal with a completely unknown PLE and its performance converges to that of the RSDPE using a perfectly known PLE. [ABSTRACT FROM PUBLISHER]

Using the finite-element model (FEM) to achieve the optimal design of electromagnetic devices is a complex and time-consuming process. Space-mapping techniques reduce computation cost by aligning two models with different granularities, namely, a coarse model and a fine model. In this paper, Kriging output space mapping (OSM), an original space-mapping technique based on adaptive nonlinear corrective projection, is proposed and compared with a conventional OSM technique. The device for optimal sizing is a five-phase linear induction motor, which is represented through two modeling levels: 1) coarse (Kriging model) and 2) fine (2-D FEM). An advantage of this new technique is its ability to provide a sufficiently accurate model for each objective and constraint function and make the coarse model converge effectively toward the fine model. [ABSTRACT FROM AUTHOR]

Discovering the 3D atomic-resolution structure of molecules such as proteins and viruses is one of the foremost research problems in biology and medicine. Electron Cryomicroscopy (cryo-EM) is a promising vision-based technique for structure estimation which attempts to reconstruct 3D atomic structures from a large set of 2D transmission electron microscope images. This paper presents a new Bayesian framework for cryo-EM structure estimation that builds on modern stochastic optimization techniques to allow one to scale to very large datasets. We also introduce a novel Monte-Carlo technique that reduces the cost of evaluating the objective function during optimization by over five orders of magnitude. The net result is an approach capable of estimating 3D molecular structure from large-scale datasets in about a day on a single CPU workstation. [ABSTRACT FROM PUBLISHER]

This paper presents a coordinated model predictive control scheme for the Mid-Columbia hydropower system. The Mid-Columbia system consists of seven hydropower plants on the Columbia River in the United States. The state-space model used in the control scheme accounts for system hydraulics, modeling time-delayed hydraulic coupling and dynamic tailrace elevations. We approximate the power generation from a hydropower plant using a piecewise planar function of turbine discharge and hydraulic head, and we demonstrate how this approximation can be written as a set of linear constraints and integrated into a quadratic program. We introduce a flow minimizing objective function that maximizes system hydraulic potential by efficiently allocating water. Compared to historical operations, the proposed control scheme reduces ramping, increases total system hydraulic head, increases system energy content, and operates the system within all elevation and flow constraints. [ABSTRACT FROM AUTHOR]

This paper describes a numerical approach to solving the mathematical structure proposed in the first part of this paper. The numerical approach employs a standard genetic algorithm (GA) embedded with an island parallel genetic algorithm (IPGA). The GA handles the decision variables of the transmission network service provider, (TNSP) while the IPGA module finds the equilibrium of the electricity market. The IPGA module uses the concept of parallel islands with limited communication. The islands evolve in parallel and communicate with each other at a specific rate and frequency. The communication pattern helps the IPGA module to spread the best-found genes across all isolated islands. The isolated evolution removes the fitness pressure of the already-found optima from the chromosomes in other islands. A stability operator has been developed which detects stabilized islands and through a strong mutation process re-employs them in exploring the search space. To improve the efficiency of the proposed numerical solution, two high performance computing (HPC) techniques are used—shared-memory architecture and distributed-memory architecture. The application of the proposed approach to the assessment of transmission augmentation is illustrated using an IEEE 14-bus example system. [ABSTRACT FROM PUBLISHER]

This paper focuses on the optimization of a linear actuator by the “output space mapping (OSM)” method. The underlying objective of this work lies in the minimization of the time required for the achievement of this optimal design. Indeed, in addition to the sole costs of optimization processes strictly speaking, the time needed for the developpement of the models is taken into account. In the context of OSM, two different finite-element models of the same actuator are used. This paper presents these modeling solutions and considers their corresponding accuracy. Results of this multi-objective optimization method are presented and compared with those obtained by the sequential simplex (SS) method based solely on the fine model. Both approaches give similar results. However, the comparison of their performances clearly shows that the OSM algorithm is an effective technique for reducing the computation time of optimization studies, even in the case of relatively simple electromagnetic structures. Hence, this approach leads to an original and effective optimization methodology. [ABSTRACT FROM AUTHOR]

Embedded multimedia devices are now a common element of our environment, such as mp3 players, handheld devices, and so on. Choosing the right main processing element is a key issue for the success of these devices, and their consumption, performance, retargetability, and development time are some of the elements that need to be analyzed and well-balanced. In this paper, we map the same multimedia application (MPEG-4 main profile) into various target platforms generally used in the embedded area. The design flow of our work starts with a single MPEG-4 encoder description which is later refined and optimized to be implemented on different platforms: an embedded platform formed by a high performance digital signal processor and an embedded processor, an application specific instruction processor, a specific hardware implemented in a field-programmable gate array for accelerating the data-flow part of the system with a soft-core for the control part, and an application specific integrated circuit. The main contribution of this paper is to illustrate a methodology that can be generalized to different data dominant applications. This paper describes a new methodology to obtain near optimal implementation from concept to silicon for all platforms and it can be extended to any hybrid HW/SW multimedia platform. We evaluate the different transformations of each platform to arrive at an optimal implementation. These higher level transformations allow achieving better results than using more precise efforts in mapping the design in the physical level. This methodology can be extended to any data dominant application. [ABSTRACT FROM AUTHOR]

The decision-making process of many binary classification systems is based on the likelihood ratio (LR) scores of test patterns. This paper shows that LR scores can be expressed in terms of the similarity between the supervectors (SVs) formed by stacking the mean vectors of Gaussian mixture models corresponding to the test patterns, the target model, and the background model. By interpreting the support vector machine (SVM) kernels as a specific similarity (or discriminant) function between SVs, this paper shows that LR scoring is a special case of SVM scoring and that most sequence kernels can be obtained by assuming a specific form for the similarity function of SVs. This paper further shows that this assumption can be relaxed to derive a new general kernel. The kernel function is general in that it is a linear combination of any kernels belonging to the reproducing kernel Hilbert space. The combination weights are obtained by optimizing the ability of a discriminant function to separate the positive and negative classes using either regression analysis or SVM training. The idea was applied to both high-and low-level speaker verification. In both cases, results show that the proposed kernels achieve better performance than several state-of-the-art sequence kernels. Further performance enhancement was also observed when the high-level scores were combined with acoustic scores. [ABSTRACT FROM AUTHOR]

Accelerated life tests (ALTs) are often used to make timely assessments of the lifetime distribution of materials and components. The goal of many ALTs is the estimation of a quantile of a log-location-scale failure time distribution. Much of the previous work on planning accelerated life tests has focused on deriving test-planning methods under a specific log-location-scale distribution. This paper presents a new approach for computing approximate large-sample variances of maximum likelihood estimators of a quantile of a general log-location-scale distribution with censoring, and time-varying stress. The approach is based on a cumulative exposure model. Using sample data from a published paper describing optimum ramp-stress test plans, we show that our approach and the one used in the previous work give the same variance-covariance matrix of the quantile estimator from the two different approaches. Then, as an application of this approach, we extend the previous work to a new optimum ramp-stress test plan obtained by simultaneously adjusting the ramp rate with the lower start level of stress. We find that the new optimum test plan can have a smaller variance than that of the optimum ramp-stress test plan previously obtained by adjusting only the ramp rate. We compare optimum ramp-stress test plans with the more commonly used constant-stress accelerated life test plans. We also conduct simulations to provide insight, and to check the adequacy of the large-sample approximate results obtained by the approach. [ABSTRACT FROM PUBLISHER]

IC3 is one of the most successful algorithms for hardware model checking. Since its invention in 2010, several variants of the original algorithm have been published, proposing optimizations and/or alternative procedures for many different steps of the algorithm. In this paper, we present a thorough empirical comparison of a large set of optimizations and procedures for the steps of IC3, considering “high-level” variants/extensions to the basic algorithm, as well as “low-level” optimizations/configuration settings. We implemented each of them in the same tool, optimizing the implementations to the best of our knowledge. This enabled for a flexible experimentation in a controlled environment, and to gain new insights about their most important differences and commonalities, as well as about their performance characteristics. We conducted the experiments using as benchmarks the problems used in the last four editions of the hardware model checking competition. The analysis helped us to identify several settings leading to significant improvements with respect to a basic implementation of IC3. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach to kriging surrogate model sampling points allocation. By introducing the second (dual) kriging during the model construction, the existing sampling points are reallocated to reduce overall memory requirements. Moreover, a new algorithm is proposed for selecting the position of the next sampling point by utilizing a modified expected improvement criterion. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to numerically design a microwave (MW) shield is proposed. The strategy is based upon the interplay between a reduced model, formed by three periodical element teeth in a waveguide, and a full model composed of an entire shield, a metallic cavity, and the open space. A correlation criterion is implemented to avoid the divergence between the physical responses of the models. In order to save computational time, the evaluation of less influent design variables is included, and preoptimization is performed. The compact range method has been experimentally applied to test the optimized MW shield. A comparison between computed and measured electromagnetic leakages is presented and discussed. Measured scattering parameter S21 is lower than −30 dB over the industrial, scientific, and medical frequency band, confirming the adequacy of the strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a parallel multimodal optimization algorithm that is combined with electromagnetic transient simulation in a platform that unifies the setup, test, and execution of optimal designs for power systems. The algorithm speeds up the design of power systems as its computations can be executed independently on a highly parallelized environment. Additional speedup is achieved by using a surrogate model to estimate the objective function in regions of suspected local optima. The estimated functions can be used in the subsequent stages of postoptimization studies, such as sensitivity analyses. Comparative studies, in terms of computation time, are conducted against sequential execution of the proposed algorithm. The optimal design of a VSC-HVDC transmission is described to demonstrate the capabilities of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

The stochastic economic lot scheduling problem (SELSP) considers the make-to-stock production of multiple standardized products on a single machine with limited capacity and set-up costs under random demands, random set-up times, and random production times. The SELSP is an NP-hard inventory problem. Current solutions for the SELSP can be classified as analytic or heuristic. In both approaches, however, the computation time needed to obtain an optimal solution is still unsatisfactory. In this paper, the SELSP is first formulated as a fixed-sequence base-stock (FSBS) system with quantity-limited lot-sizing policy. An algorithm combining artificial bee colony (ABC) approach and ordinal optimization (OO) theory, abbreviated as ABCOO, is then proposed to find a good enough base-stock level of the FSBS system using reasonable computation time. The proposed algorithm combines the advantage of multidirectional search in ABC with the advantage of goal softening in OO. Finally, the ABCOO algorithm is used to solve an SELSP involving 12 products and three queuing models. Test results obtained by the ABCOO algorithm are compared with four lot-sizing policies and three meta-heuristic methods. The base-stock level obtained by the ABCOO algorithm is excellent in terms of solution quality and computational efficiency. Furthermore, a time series forecasting technique is used to predict the variant demand rates needed to resolve time-lag problems of the ABCOO algorithm. Tests of the forecasting technique confirm that it considerably improves the performance and enables the proposed algorithm real-time applications. [ABSTRACT FROM PUBLISHER]

To handle significant variability in loads, renewable energy generation, as well as various contingencies, (two-stage) robust optimization method has been adopted to construct unit commitment models and to ensure reliable solutions. In this paper, we further explore and extend the modeling capacity of two-stage robust optimization and present two new robust unit commitment variants: the expanded robust unit commitment and the risk constrained robust unit commitment model. We derive structural properties, demonstrate the connection to the popular scenario based stochastic unit commitment models, and present a customized column-and-constraint generation method. Numerical experiments on those models are performed using practical data sets, which illustrate their modeling strength, economic outcomes, and the algorithm performance in solving those models. [ABSTRACT FROM PUBLISHER]

This paper presents a simulation model based on the diagonalization algorithm for the study of the short-term operation of a pool-based oligopolistic electricity market with multi-period auctions. A detailed producer's profit maximization model as well as a detailed non-convex independent system operator (ISO) market clearing algorithm are used. A multi-period auction is assumed, where generators, under conditions of perfect information (regarding system load and competitor offers), submit stepwise energy and reserves offer curves and, subsequently, the ISO clears the market. An innovative correction of the residual demand curve (RDC) is introduced to account for the effect of some of the non-convexities of the ISO market clearing. Convergence of the diagonalization algorithm is enhanced using reinforcement learning (RL), through which the knowledge on the success of the producers' past behavior is used to adjust the supply offers. [ABSTRACT FROM PUBLISHER]

We consider a system subject to shocks that arrive according to a nonhomogeneous pure birth process (NHPBP). As a shock occurs, the system has two types of failures. Type-I failure (minor failure) is rectified by a general repair, whereas type-II failure (catastrophic failure) is removed by an unplanned replacement. The probabilities of these two types of failures depend on the number of shocks since the last replacement. We consider a policy with which the system is replaced at the nth type-I failure, or at any type-II failure. The aim of this paper is to determine the optimal policy n^\ast, the number of minor failures up to replacement that minimizes the expected cost rate of the system subject to NHPBP shocks. The model is a generalization of the existing models, and is more applicable in practice. We present some numerical examples, and show that several classical models are the special cases of our model. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of minimizing overkills and re-probes in wafer probe testing is formulated as a multiobjective optimization problem. Overkill is a measure of good dies that were considered bad and re-probe is an additional manual probe testing to save overkills. The goal is to provide an optimal setting of threshold values for engineers to decide whether to carry out a re-probe after the two times of automatic probe testing. A two-stage algorithm is proposed to take advantage of particle swarm optimization (PSO) and optimal computing budget allocation (OCBA) for solving a good enough setting that minimizes overkills and re-probes within a reasonable computational time. A crude model based on a shorter stochastic simulation with a small number of test wafers is used as a fitness evaluation in a PSO algorithm to select N good enough settings. Then, we proceed with the refined OCBA to search for a good enough setting. The two-stage algorithm is applied to a real semiconductor product, and the threshold values obtained by the proposed algorithm are promising in the aspects of solution quality and computational efficiency. We have also demonstrated the computational efficiency of our algorithm by comparing with the genetic algorithm and evolution strategy. [ABSTRACT FROM PUBLISHER]

One of the challenges in video rate control lies in determining a quantization parameter (Qp) that will be used for both the rate-distortion (R-D) optimization process and the quantization of transform coefficients. In this paper, we attempt to achieve effective rate control with a different approach. By modeling the relationships of distortion, texture bits, non-texture bits, and Qp, we derive the Qp required for both R-D optimization and quantization through Lagrangian optimization. From experiments with several video sequences, we found that our rate control scheme is capable of effective rate control with only a few model updates during encoding. The proposed rate control scheme adapts quickly to the characteristics of the source data and is particularly effective at controlling the rate of videos with high and unpredictable motion content. [ABSTRACT FROM AUTHOR]

Existing radio frequency (RF) integrated circuit (IC) design automation methods focus on the synthesis of circuits at a few GHz, typically less than 10 GHz. That framework is difficult to apply to RF IC synthesis at mm-wave frequencies (e.g., 60–100 GHz). In this paper, a new method, called efficient machine learning-based differential evolution, is presented for mm-wave frequency linear RF amplifier synthesis. By using electromagnetic (EM) simulations to evaluate the key passive components, the evaluation of circuit performances is accurate and solves the limitations of parasitic-included equivalent circuit models and predefined layout templates used in the existing synthesis framework. A decomposition method separates the design variables that require expensive EM simulations and the variables that only need cheap circuit simulations. Hence, a low-dimensional expensive optimization problem is generated. By the newly proposed core algorithm integrating adaptive population generation, naive Bayes classification, Gaussian process and differential evolution, the generated low-dimensional expensive optimization problem can be solved efficiently (by the online surrogate model), and global search (by evolutionary computation) can be achieved. A 100 GHz three-stage differential amplifier is synthesized in a 90 nm CMOS technology. The power gain reaches 10 dB with more than 20 GHz bandwidth. The synthesis costs only 25 h, having a comparable result and a nine times speed enhancement compared with directly using the EM simulator and global optimization algorithms. [ABSTRACT FROM PUBLISHER]

Groupwise nonrigid image registration is a powerful tool to automatically establish correspondences across sets of images. Such correspondences are widely used for constructing statistical models of shape and appearance. As existing techniques usually treat registration as an optimization problem, a good initialization is required. Although the standard initialization—affine transformation—generally works well, it is often inadequate when registering images of complex structures. In this paper we present a more sophisticated method that uses the sparse matches of a parts+geometry model as the initialization. We show that both the model and its matches can be automatically obtained, and that the matches are able to effectively initialize a groupwise nonrigid registration algorithm, leading to accurate dense correspondences. We also show that the dense mesh models constructed during the groupwise registration process can be used to accurately annotate new images. We demonstrate the efficacy of the approach on three datasets of increasing difficulty, and report on a detailed quantitative evaluation of its performance. [ABSTRACT FROM PUBLISHER]

Solution approaches to chance constrained programming (CCP) have been recently developed and applied in many areas for optimization under uncertainty. Due to the nonlinear model with multiple uncertain variables as well as multiple output constraints, CCP has not been directly applied to optimal power flow (OPF) under uncertainty. The objective of this paper is twofold. First, we introduce the CCP approach to OPF under uncertainty and analyze the computational complexity of the chance constrained OPF. Second, the effectiveness of implementing a back-mapping approach and a linear approximation of the nonlinear model equations to solve the formulated CCP problem is investigated. Load power uncertainties are considered as multivariate random variables with correlated normal distribution. Based on both the nonlinear and the linearized model, results of a five-bus system and the IEEE 30-bus test system are presented to demonstrate the scope of chance constrained OPF. [ABSTRACT FROM PUBLISHER]

This paper proposes a new methodology to evaluate system generating capacity reliability indices considering time-dependent power sources and loads. Based on sequential Monte Carlo simulation (MCS) and the cross-entropy (CE) method, the basic idea is to find an optimal distortion for the equipment transition rates using the CE concepts. A chronological simulation is then carried out using these newly found optimal reference parameters. This process suitably modifies the chronological evolution of the system in order to improve its statistical efficiency and convergence properties. As a result, the computational efforts of the sequential simulation can be greatly reduced while retaining many of its main advantages. Comparisons with the chronological, pseudo-chronological, and quasi-sequential MCS are carried out using the IEEE-RTS-96 (Reliability Test System) and modifications of this system that include renewable sources. [ABSTRACT FROM PUBLISHER]

In this paper, a synthesis of design methodologies for electromagnetic devices made with soft magnetic composite (SMC) material is presented. To realize efficient and low-cost SMC devices, it is necessary to benefit from interesting isotropic SMC material properties such as 3-D flux circulation path. In this case, the best approach is to carry out a topological structure research and perform a global optimization using physical models. However, the iterative optimization process can be computationally intensive and some compromises should be found between accuracy and computation time. One way is to use coarse models with important simplifying hypotheses but the optimal solution is often not valid. In this case, one can perform a limited number of finite element (FE) simulations to compute some correction coefficients and improve optimization convergence to a valid optimal solution. Another way is to perform the device optimization by using directly FE models with low mesh density and several simplifications of the device geometry. Such design approaches are illustrated by three concrete realizations with SMC material. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the contingency-constrained single-bus unit commitment problem. The proposed model explicitly incorporates an n - K security criterion by which power balance is guaranteed under any contingency state comprising the simultaneous loss of up to K generation units. Instead of considering all possible contingency states, which would render the problem intractable, a novel method based on robust optimization is proposed. Using the notion of umbrella contingency, the robust counterpart of the original problem is formulated. The resulting model is a particular instance of bilevel programming which is solved by its transformation to an equivalent single-level mixed-integer programming problem. Unlike previously reported contingency-dependent approaches, the robust model does not depend on the size of the set of credible contingencies, thus providing a computationally efficient framework. Simulation results back up these conclusions. [ABSTRACT FROM PUBLISHER]

This paper proposes a new way to achieve robotic tasks by two-dimensional (2-D) visual servoing. Indeed, instead of using classical geometric features such as points, straight lines, pose, or a homography, as is usually done, the luminance of all pixels in the image is considered here. The main advantage of this new approach is that it requires no tracking or matching process. The key point of our approach relies on the analytic computation of the interaction matrix. This computation is based either on a temporal luminance-constancy hypothesis or on a reflection model so that complex illumination changes can be considered. Experimental results on positioning and tracking tasks validate the proposed approach and show its robustness to approximated depths, low-textured objects, partial occlusions, and specular scenes. They also showed that luminance leads to lower positioning errors than a classical visual servoing based on 2-D geometric visual features. [ABSTRACT FROM AUTHOR]

With the thermal effect, improper analog placements may degrade circuit performance because the thermal impact from power devices can affect electrical characteristics of the thermally-sensitive devices. There is not much previous work that considers the desired placement configuration between power and thermally-sensitive devices for a better thermal profile to reduce the thermally-induced mismatches. This paper first introduces the properties of a desired thermal profile for better thermal matching of the matched devices. It then presents a thermal-driven analog placement methodology to achieve the desired thermal profile and to consider the best device matching under the thermal profile while satisfying the symmetry and the common-centroid constraints. Experimental results based on real analog circuits show that the proposed approach can achieve the best analog circuit performance/accuracy with the least impact due to the thermal gradient, among existing works. [ABSTRACT FROM PUBLISHER]