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IEEE Annual Conference on Decision and Control

作者： Giuseppe Notarstefano Department of Engineering Universita del Salento via Monteroni 73100 Lecce Italy

ISBN: (纸本)9781479978878

A new challenge for learning algorithms in cyberphysical network systems is the distributed solution of big-data classification problems, i.e., problems in which both the number of training samples and their dimension is high. Motivated by several problem set-ups in Machine Learning, in this paper we consider a special class of quadratic optimization problems involving a "large" number of input data, whose dimension is "big". To solve these quadratic optimization problems over peer-to-peer networks, we propose an asynchronous, distributed algorithm that scales with both the number and the dimension of the input data (training samples in the classification problem). The proposed distributed optimization algorithm relies on the notion of "core-set" which is used in geometric optimization to approximate the value function associated to a given set of points with a smaller subset of points. By computing local core-sets on a smaller version of the global problem and exchanging them with neighbors, the nodes reach consensus on a set of active constraints representing an approximate solution for the global quadratic program.

关键词： dimensions Set of points incoming data data classification active constraint quadratic programming distributed algorithms Quadratic Peer-to-peer network Distribute learning algorithms optimization algorithms

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Multi-Objective Hardware-Software Co-optimization for the SNIPER Multi-Core Simulator 10

Multi-Objective Hardware-Software Co-Optimization for the SN...

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IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Chis, Radu Vintan, Lucian Tech Univ Dept Comp Sci Cluj Napoca Romania Lucian Blaga Univ Comp Sci Elect Engn Dept Sibiu Romania

ISBN: (纸本)9781479965694

Modern complex microarchitectures with multicore systems like CPUs, APUs (accelerated processing units) and GPUs require hundreds or thousands of hardware parameters to be fine-tuned to get the best results regarding different objectives like: performance, hardware complexity (integration area), power consumption, temperature, etc. These are only a few of the objectives needed to be taken into consideration when designing a new multicore system. Exploring this huge design space requires special tools like automatic design space exploration frameworks to optimize the hardware parameters. Although the microarchitecture might be very complex, the performance of the applications is also highly dependent on the degree of software optimization. This adds a new challenge to the DSE process. In this paper, using the multi-objective design space exploration tool FADSE, we tried to optimize the hardware and software parameters of the multicore SNIPER simulator running SPLASH-2 benchmarks suite. We optimized the hardware parameters (nr cores, cache sizes, cache associativity, etc.) and software parameters (GCC optimizations, threads, and scheduler) values that have been varied during the DSE process and shown the impact of these parameters on the optimization's multi-objectives (performance, area and power consumption). Furthermore, for the best found Pareto configurations the temperatures will be computed so that in the end we will have a 4-dimensional objective space.

关键词： Design Space Exploration Multi-objective optimization algorithms Sniper Multi-Core Simulator SPLASH-2 benchmarks

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Localization of a moving non-cooperative RF target in NLOS environment using RSS and AOA measurements

Localization of a moving non-cooperative RF target in NLOS e...

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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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A MRF Shape Prior for Facade Parsing with Occlusions

A MRF Shape Prior for Facade Parsing with Occlusions

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Mateusz Kozinski Raghudeep Gadde Sergey Zagoruyko Guillaume Obozinski Renaud Marlet Universite Paris-Est LIGM (UMR CNRS 8049) ENPC

ISBN: (纸本)9781467369657

We present a new shape prior formalism for the segmentation of rectified facade images. It combines the simplicity of split grammars with unprecedented expressive power: the capability of encoding simultaneous alignment in two dimensions, facade occlusions and irregular boundaries between facade elements. We formulate the task of finding the most likely image segmentation conforming to a prior of the proposed form as a MAP-MRF problem over a 4-connected pixel grid, and propose an efficient optimization algorithm for solving it. Our method simultaneously segments the visible and occluding objects, and recovers the structure of the occluded facade. We demonstrate state-of-the-art results on a number of facade segmentation datasets.

关键词： optimization algorithms Occlusion Parsing occluding segmentation Segmentation

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A new measurement matrix optimal algorithm based on SVD

A new measurement matrix optimal algorithm based on SVD

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2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015)

作者： Fei Zhong Yue Zhao Shuxu Guo College of Electrical and Information engineering Changchun Institute of Technology Changchun 130012 China College of Electronic Science and Engineering Jilin University Changchun Jilin 130012 P. R. China

This paper proposes a new compressed sensing (CS) measurement matrix optimal algorithms based on singular value decomposition (SVD). New measurement matrix can be obtained by using SVD for the decomposition of Gaussian measurement matrix. Simulation results prove that using the new measurement matrix can not only greatly improve the robustness and stability of CS algorithm, but also have better behaviors on image quality recovery. Moreover, this method is suitable for the further study of other random measurement matrix.

关键词： compressed sensing (CS) measurement matrix singular value decomposition (SVD) measurement matrix Singular value decomposition Swine vesicular disease optimization algorithms

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Silhouette-based 3D Model for Zebrafish High-throughput Imaging

Silhouette-based 3D Model for Zebrafish High-throughput Imag...

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International Conference on Image Processing Theory, Tools and Applications

作者： Yuanhao Guo Wouter J. Veneman Herman P. Spaink Fons J. Verbeek Imaging and BioInformatics Section LIACS Leiden University Department of Animal Sciences and Health Institute of Biology Leiden University

ISBN: (纸本)9781479986385

Good estimations of volume and surface area are important to biological systems measurement. In this paper we develop a 3D reconstruction from evenly sampled axial views in order to enable the volume and surface area measurement. We develop this system for high throughput applications with the zebrafish model system. The VAST BioImager is specifically developed for this purpose and with this system the axial views can be produced. Silhouettes derived from the axial sequence are shape priors which can be directly used to solve the camera calibration problem that is required for the accurate 3D reconstruction. Nonlinear optimisation algorithms have shown to be suitable for the further development of the reconstruction problem. The method proposed in this paper can be included in a measurement pipeline that is used in all kinds of high throughput applications in the zebrafish field. From the 3D reconstruction features can be derived that will contribute to the phenotyping of zebrafish.

关键词： VAST BioImager Zebrafish 3D model Shape carving Volume and surface area measurement three-dimensional displays Zebrafish optimization algorithms surface area AXIAL

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A NOVEL GLOBAL optimization ALGORITHM AND ITS APPLICATION TO AIRFOIL optimization

A NOVEL GLOBAL OPTIMIZATION ALGORITHM AND ITS APPLICATION TO...

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ASME Turbo Expo: Turbine Technical Conference and Exposition

作者： Yang, B. Xu, Q. He, L. Zhao, L. H. Gu, Ch. G. Ren, P. Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200030 Peoples R China Shanghai Elect Power Generat R&D Ctr Shanghai Peoples R China

ISBN: (纸本)9780791845578

In this paper, a novel global optimization algorithm has been developed, which is named as Particle Swarm optimization combined with Particle Generator (PSO-PG). In PSO-PG, a particle generator was introduced to iteratively generate the initial particles for PSO. Based on a series of comparable numerical experiments, it was convinced that the calculation accuracy of the new algorithm as well as its optimization efficiency was greatly improved in comparison with those of the standard PSO. It was also observed that the optimization results obtained from PSO-PG were almost independent of some critical coefficients employed in the algorithm. Additionally, the novel optimization algorithm was adopted in the airfoil optimization. A special fitness function was designed and its elements were carefully selected for the low-velocity airfoil. To testify the accuracy of the optimization method, the comparative experiments were also carried out to illustrate the difference of the aerodynamic performance between the optimized and its initial airfoil.

关键词： optimization optimization algorithms Airfoils

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Real-World Dynamic optimization Using An Adaptive-mutation Compact Genetic Algorithm

Real-World Dynamic Optimization Using An Adaptive-mutation C...

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IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

作者： Uzor, Chigozirim J. Gongora, Mario Coupland, Simon Passow, Benjamin N. De Montfort Univ Ctr Computat Intelligence Leicester Leics England De Montfort Univ DIGITS Leicester Leics England

ISBN: (纸本)9781479945153

While the interest in nature inspired optimization in dynamic environments has been increasing constantly over the past years, evaluations of some of these optimization algorithms are based on artificial benchmark problems. Little has been done to carry-out these evaluation using a real-world dynamic optimization problems. This paper presents a compact optimization algorithm for controllers in dynamic environments. The algorithm is evaluated using a real world dynamic optimization problem instead of an artificial benchmark problem, thus avoiding the reality gap. The experimental result shows that the algorithm has an impact on the performance of a controller in a dynamic environment. Furthermore, results suggest that evaluating the algorithm's candidate solution using an actual real-world problem increases the controller's robustness.

关键词： control system synthesis dynamic programming genetic algorithms performance index stability vibration control PID controller design adaptive-mutation compact genetic algorithm compact optimization algorithm controller performance controller robustness dynamic environment nature inspired optimization real-world dynamic optimization problems torsional mass-spring damper system Microcontrollers Microcontrollers control system synthesis Genetic Algorithm (GA) vibration control performance indicators dynamic programming optimization algorithms Controllers Benchmark testing controller design

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Periodic and event-triggered communication for distributed continuous-time convex optimization

Periodic and event-triggered communication for distributed c...

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American Control Conference

作者： Kia, Solmaz S. Cortes, Jorge Martinez, Sonia Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781479932740

We propose a distributed continuous-time algorithm to solve a network optimization problem where the global cost function is a strictly convex function composed of the sum of the local cost functions of the agents. We establish that our algorithm, when implemented over strongly connected and weight-balanced directed graph topologies, converges exponentially fast when the local cost functions are strongly convex and their gradients are globally Lipschitz. We also characterize the privacy preservation properties of our algorithm and extend the convergence guarantees to the case of time-varying, strongly connected, weight-balanced digraphs. When the network topology is a connected undirected graph, we show that exponential convergence is still preserved if the gradients of the strongly convex local cost functions are locally Lipschitz, while it is asymptotic if the local cost functions are convex. We also study discrete-time communication implementations. Specifically, we provide an upper bound on the stepsize of a synchronous periodic communication scheme that guarantees convergence over connected undirected graph topologies and, building on this result, design a centralized event-triggered implementation that is free of Zeno behavior. Simulations illustrate our results.

关键词： Algorithm design and analysis Convergence Convex functions Cost function Privacy Topology Control of networks optimization algorithms

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An Accelerated Dual Gradient-Projection Algorithm for Embedded Linear Model Predictive Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2014年第1期59卷 18-33页

作者： Patrinos, Panagiotis Bemporad, Alberto IMT Inst Adv Studies Lucca I-55100 Lucca Italy

This paper proposes a dual fast gradient-projection method for solving quadratic programming problems that arise in model predictive control of linear systems subject to general polyhedral constraints on inputs and states. The proposed algorithm is well suited for embedded control applications in that: 1) it is extremely simple and easy to code;2) the number of iterations to reach a given accuracy in terms of optimality and feasibility of the primal solution can be tightly estimated;and 3) the computational cost per iteration increases only linearly with the prediction horizon.

关键词： Computational methods optimization algorithms predictive control for linear systems

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