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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： C. Cheng W. Hu W. P. Tay Sch. of Electr. & Electron. Eng. Nanyang Technol. Univ. Singapore Singapore

ISBN: (纸本)9781467369985

We propose an alternating optimization algorithm for localizing a mobile non-cooperative target using a wireless sensor network. We consider the scenario where sensors receive single-bounce non-line-of-sight signals from the moving target. Each sensor is able to measure the target signal's angle-of-arrival and received signal strength. The transmit powers of the non-cooperative target at different locations are unknown, and estimated jointly with its locations and the orientations of the scatterers off which the target signals are reflected before reaching the sensors. We formulate the problem as a non-convex least squares problem, and then transform and approximate it into a form that is solvable by an alternating algorithm. We show that our algorithm converges, and simulation results demonstrate that our algorithm is able to localize the target with good accuracy.

关键词： AOA Localization RSS alternating optimization wireless sensor network RSS gene Really Simple Syndication REACTOR SAFETY STUDY Wireless sensor networks ANGLE OF ATTACK Targets optimization algorithms Moving Sensors

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Fuzzy Robust and Non-fragile Minimax Control of a Trailer-Truck Model

Fuzzy Robust and Non-fragile Minimax Control of a Trailer-Tr...

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Proceedings of the 46th IEEE Conference on Decision and Control, Pages 3678-4134

作者： Yuanwei Jing Nan Jiang Yan Zheng Georgi M. Dimirovski Faculty of Information Science and Engineering Northeastern University Shenyang Liaoning China Faculty of Engineering Computer Engg.Dept Dogus University Istanbul Turkey

ISBN: (纸本)9781424414970;1424414970

This paper deals with a fuzzy robust and non-fragile minimax control problem of a trailer-truck model. By introducing parametric uncertainty terms into the T-S model for trailer-truck systems, the fuzzy model approaches to the original system more exactly. Existence conditions are derived for the robust and non-fragile minimax control in the sense of Lyapunov asymptotic stability and formulated in the form of Linear Matrix Inequalities (LMIs). The convex optimization algorithm is used to get the minimal upper bound of the performance cost and parameter of the optimal minimax controller. Then the closed-loop system will be asymptotically stable under the condition of the worst disturbance and uncertainty. Finally, an illustrative example is used to demonstrate the better robust and non-fragile performance of the controller design.

关键词： Minimax controller design fuzzy modeling Robustness Fuzzy optimization algorithms

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On Fault Detection of NCSs Subject to Limited Communication Capacity

On Fault Detection of NCSs Subject to Limited Communication ...

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Chinese Control Conference

作者： Tao Li Wei Xing Zheng Department of Information and Communication Nanjing University of Information Science and Technology School of Computing Engineering and Mathematics University of Western Sydney

ISBN: (纸本)9781467325813

This paper addresses the problem of fault detection filter design for networked control systems (NCS) subject to limited communication capacity and a class of sensor stuck faults. Considering the communication limitations (e.g., measurement quantization, signal transmission delays, and data packet dropouts) and all possible sensor stuck faults, a unified mathematical model is first presented. Based on this framework, a full-order fault detection filter is designed such that the residual system is asymptotically stable with the prescribed attenuation level in the generalized H_∞ sense. In order to further improve the detection performance, an optimization algorithm is proposed to minimize the threshold. Finally, a spring-mass-damper system is utilized to show the effectiveness of the proposed method.

关键词： Fault Detection NCSs Communication Capacity Defect detection Fault detection filter Communication Research data packets Mathematical Model optimization algorithms

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Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent 13

Trading Computation for Communication: Distributed Stochasti...

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Annual Conference on Neural Information Processing Systems

作者： Tianbao Yang NEC Labs America Cupertino CA 95014

ISBN: (纸本)9781632660244

We present and study a distributed optimization algorithm by employing a stochastic dual coordinate ascent method. Stochastic dual coordinate ascent methods enjoy strong theoretical guarantees and often have better performances than stochastic gradient descent methods in optimizing regularized loss minimization problems. It still lacks of efforts in studying them in a distributed framework. We make a progress along the line by presenting a distributed stochastic dual coordinate ascent algorithm in a star network, with an analysis of the tradeoff between computation and communication. We verify our analysis by experiments on real data sets. Moreover, we compare the proposed algorithm with distributed stochastic gradient descent methods and distributed alternating direction methods of multipliers for optimizing SVMs in the same distributed framework, and observe competitive performances.

关键词： Communication Research optimization algorithms ascent Stochastic Distribute Coordinates Dual Multiplying circuits tradeoffs Star networks observational data

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A Fast Adaptive Guided Filtering Algorithm for Light Field Depth Interpolation

A Fast Adaptive Guided Filtering Algorithm for Light Field D...

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IEEE International Symposium on Circuits and Systems

作者： Jie Chen Lap-Pui Chau Nanyang Technological University Singapore 639798

ISBN: (纸本)9781479934331

Light field camera provides 4D information of the light rays, from which the scene depth information can be inferred. The disparity/depth maps calculated from light field data are always noisy with missing and false entries in homogeneous regions or areas where view-dependant effects are present. In this paper we proposed an adaptive guided filtering (AGF) algorithm to get an optimized output disparity/depth map. A guidance image is used to provide the image contour and texture information, the filter is able to preserve the disparity edges, smooth the regions without influence of the image texture, and reject the data entries with low confidence during coefficients regression. Experiment shows AGF is much faster in implementation as compared to other variational or hierarchical based optimization algorithms, and produces competitive visual results.

关键词： AGF light regions ANGPTL6 gene Light image profile Light fields image texture data entry Filtering algorithms optimization algorithms

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Modified Particle Swarm optimization for Search Missions

Modified Particle Swarm Optimization for Search Missions

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40th Southeastern Symposium on System Theory (SSST 2008)

作者： Ryan R. Pitre Electrical Engineering Department University of New Orleans New Orleans LA USA

ISBN: (纸本)9781424418060

There are many applications that can benefit from a well planned search. Whether the search objective is a lost hiker, a stolen vehicle on the interstate, or enemies on the battlefield, some assumptions must be made about the search objective before the search can begin. These assumptions focus the search in areas that have a relatively high likelihood of finding the targets of interest. A common approach to mission planning is to apply an optimization algorithm and obtain a good solution based on these assumptions. In general, the mission planner uses simulated targets to emulate the expected target behavior in order to evaluate candidate search paths. In practice, a major drawback is that the prior distribution of targets is only used to evaluate the search paths rather than to guide the optimization algorithm in generating the search paths. This paper introduces a method that explicitly exploits the sampled target distribution to create search paths, which naturally improves the results since the search paths directly depend on the time varying target locations. Results from a realistic cooperative path planning scenario show that explicit usage of target distributions can improve the performance of particle swarm optimization.

关键词： Particle swarm optimization search path optimization algorithms Targets Paths Searches mission planning Prior distribution Byways

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Optimal Scoring Rule Design under Partial Knowledge

arXiv

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arXiv 2021年

作者： Chen, Yiling Yu, Fang-Yi Harvard University United States George Mason University United States

This paper studies the design of optimal proper scoring rules when a principal has partial knowledge of an agent’s signal distribution. Recent work [24] characterizes the proper scoring rules that maximize the increase of an agent’s payoff when the agent chooses to access a costly signal to refine a posterior belief from his prior prediction, under the assumption that the agent’s signal distribution is fully known to the principal. In our setting, the principal only knows about a set of distributions where the agent’s signal distribution belongs. We formulate the scoring rule design problem as a max-min optimization that maximizes the worst-case increase in payoff across the set of distributions. We propose an efficient algorithm to compute an optimal scoring rule when the set of distributions is finite, and devise a fully polynomial-time approximation scheme that accommodates various infinite sets of distributions. We further remark that widely used scoring rules, such as the quadratic and log rules, as well as previously identified optimal scoring rules under full knowledge [24], can be far from optimal in our partial knowledge settings. © 2021, CC BY.

关键词： optimization algorithms

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ARTIFICIAL INTELLIGENCE FOR SCIENCE: THE EASY AND HARD PROBLEMS

arXiv

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arXiv 2024年

作者： Battleday, Ruairidh M. Gershman, Samuel J. Department of Psychology Center for Brain Science Harvard University United States

A suite of impressive scientific discoveries have been driven by recent advances in artificial intelligence (AI). These almost all result from training flexible algorithms to solve difficult optimization problems specified in advance by teams of domain scientists and engineers with access to large amounts of data. Although extremely useful, this kind of problem solving only corresponds to one part of science—the "easy problem." The other part of scientific research is coming up with the problem itself—the "hard problem." Solving the hard problem is beyond the capacities of current algorithms for scientific discovery because it requires continual conceptual revision based on poorly defined constraints. We can make progress on understanding how humans solve the hard problem by studying the cognitive science of scientists, and then use the results to design new computational agents that automatically infer and update their scientific paradigms. © 2024, CC BY-NC-ND.

关键词： optimization algorithms

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Efficient Convex algorithms for Universal Kernel Learning

arXiv

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arXiv 2023年

作者： Talitckii, Aleksandr Colbert, Brendon Peet, Matthew M. Department of Mechanical and Aerospace Engineering Arizona State University TempeAZ85281-1776 United States

The accuracy and complexity of machine learning algorithms based on kernel optimization are determined by the set of kernels over which they are able to optimize. An ideal set of kernels should: admit a linear parameterization (for tractability);be dense in the set of all kernels (for robustness);be universal (for accuracy). Recently, a framework was proposed for using positive matrices to parameterize a class of positive semi-separable kernels. Although this class can be shown to meet all three criteria, previous algorithms for optimization of such kernels were limited to classification and furthermore relied on computationally complex Semidefinite Programming (SDP) algorithms. In this paper, we pose the problem of learning semiseparable kernels as a minimax optimization problem and propose a SVD-QCQP primal-dual algorithm which dramatically reduces the computational complexity as compared with previous SDP-based approaches. Furthermore, we provide an efficient implementation of this algorithm for both classification and regression – an implementation which enables us to solve problems with 100 features and up to 30,000 datums. Finally, when applied to benchmark data, the algorithm demonstrates the potential for significant improvement in accuracy over typical (but non-convex) approaches such as Neural Nets and Random Forest with similar or better computation time. Copyright © 2023, The Authors. All rights reserved.

关键词： optimization algorithms

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Research on the Multi-population Differential Evolution Algorithm and the Performance

Research on the Multi-population Differential Evolution Algo...

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International Conference on Mathematics, Modeling and Simulation Technologies and Applications

作者： Renquan Huang Jing Tian Juanjuan Wang Junmin Kang School of Economy and Finance Xi'an International Studies University

ISBN: (纸本)9781713800361

The original differential evolution algorithm(DE) is a single-population differential evolution algorithm(SPDE).DE converges very quickly,and takes the advantage of *** improved DE has a better performance,but there are premature problems in optimizing complex *** multi-population differential evolution algorithm(MPDE) is proposed to overcome premature problems in this *** optimal substitution strategy(OSS) and the elite immigration strategy(EIS) are studied to maintain the diversity of *** simulation concludes that MPDE converges faster than SPDE in optimizing the ultra-high dimensional problems,and the EIS is superior to the ***,the efficiency of DE is more effective than that of MPDE when the algorithms *** shows that multi-population strategy is a feasible and effective way to the premature problems of DE.

关键词： Multi-population differential evolution algorithm optimization algorithm Optimal substitution strategy Elite immigration strategy optimization algorithms differential evolution algorithms evolutionary algorithm MT ENVIRONMENTAL IMPACT STATEMENTS open source software Office of the Supervising Scientist

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