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检索条件"任意字段=International Workshop on Multilevel Optimization - Algorithms and Applications"

共 1021 条记录，以下是881-890 订阅

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Single-snapshot DoA estimation using adaptive elastic net in the complex domain

Single-snapshot DoA estimation using adaptive elastic net in...

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international workshop on Compressed Sensing Theory and its applications to Radar, Sonar and Remote Sensing (CoSeRa)

作者： Muhammad Naveed Tabassum Esa Ollila Dept. of Signal Processing and Acoustics Aalto University Aalto Finland

ISBN: (纸本)9781509029211

The elastic net (EN) is a popular regularization and variable selection method that overcomes the shortcomings of Lasso such as poor recovery in the face of high mutual coherence. In this paper, we develop an efficient algorithm to solve the weighted EN criterion for complex-valued measurements applying the cyclic coordinate descent approach. We illustrate that usage of smartly chosen adaptive (i.e., data-dependent) weights revamps the algorithm to overcome the shortcomings of naive (non-weighted) EN and enhances the exact recovery. Usefulness of the proposed algorithm is illustrated for compressed beamforming (CBF) with the single-snapshot DoA (direction-of-arrival) estimation, in a demanding multi-source scenario that contains closely spaced sources having large variation in source powers. Accurate DoA's estimation performance and error plummet by the proposed algorithm validates its application and advocates its potential usage in other signal processing problems.

关键词： Signal processing algorithms Charge coupled devices Coherence optimization Estimation Compressed sensing Direction-of-arrival estimation

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A Novel Genetic Simulated Annealing Algorithm for the Resource-Constrained Project Scheduling Problem

A Novel Genetic Simulated Annealing Algorithm for the Resour...

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international workshop on Intelligent Systems and applications, ISA

作者： Xiaoguang Yu Dechen Zhan Lanshun Nie Xiaofei Xu Sch. of Comput. Sci. & Technol. Harbin Inst. of Technol. Harbin

A novel hybrid meta-heuristic algorithm, entitled as RCPSPGSA, is proposed for solving the resource-constrained project scheduling problem (RCPSP) in this paper. The algorithm incorporates the simulated annealing algorithm (SA) into genetic algorithm in order to improve local searching performance and boost up evolution capability. In each evolution iteration GA generates a new temporary population, and after that SA is used for improving every individual in it and at the mean time the next gap population is generated. For the sake of keeping the same convergence direction and speed of GA and SA, the cooling procedure occurs at the end of each evolution iteration. Simulation experiments are performed on the standard project instance sets of PSPLIB, and orthogonal experiment method is introduced to solve the parameter selection problem. Parameter combinations selected by this method are proved to be outperformed. Experimental results show that RCPSPGSA improves solution quality for J30, J60, J90 sets and not bad for J120.

关键词： Simulated annealing Scheduling algorithm Computational modeling Processor scheduling Computer science Ant colony optimization Computer simulation Genetic algorithms Convergence Cooling

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Dual-head marking performance optimisation via evolutionary solutions

Dual-head marking performance optimisation via evolutionary ...

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IEEE international workshop on Electronic Design, Test and applications (DELTA)

作者： J. Koh Tiong Sieh Kiong I.B. Aris S. Mahmoud College of Engineering Universiti Tenaga Nasional Selangor Malaysia Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia

This paper presents a new approach to optimise the performance of a multi-head marking system in terms of its marking speed. This processing method named as MMA (molecular marking optimisation algorithm) adopts the use of genetic algorithm. The advantage of the 'self evolving' nature of the genetic algorithm has been considered to discover the most relevant combination of features for each diagnosis considered. The knowledge acquired by the process is interpreted and mapped into vectors, which are kept in the database and used by the system to guide its reasoning process. The representation approach has been implemented via computer program in order to achieve optimised marking performance. Also, the performance of the new operators for evolutionary approaches to the time-based problem has been discussed in the paper

关键词： Biological cells Genetic algorithms Genetic mutations optimization methods Educational institutions Spatial databases Magnetic heads Bidirectional control Conferences Electronic equipment testing

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Pattern Recognition with Embedded Systems Technology: A Survey

Pattern Recognition with Embedded Systems Technology: A Surv...

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international workshop on Database and Expert Systems applications

作者： Juan-Carlos Perez-Cortes Jose-Luis Guardiola Alberto-Jose Perez-Jimenez Instituto Tecnológico de Informática Universidad Politécnica de Valencia Valencia Spain

Pattern Recognition (PR) tasks are natural candidates for embedded systems, since they usually interact with humans and other complex processes in the real world. Often regarded as the part of Artificial Intelligence (AI) closer to perception, a typical PR application reacts to external events that the system perceives through physical sensors or input devices and produces a response using actuators or information display subsystems. Being usually far from trivial, very demanding from the computational point of view, and requiring a fast reaction time, PR algorithms constitute a real challenge to the embedded system designer. In this talk, some of the main application domains and optimization approaches proposed to deal with these relevant issues, along with many open problems and paths to improvement, are presented.

关键词： Pattern recognition Embedded system Artificial intelligence Humans Intelligent sensors Sensor systems and applications Intelligent actuators Displays Embedded computing Algorithm design and analysis

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Recent advances on data-driven services for smart energy systems optimization and pro-active management

Recent advances on data-driven services for smart energy sys...

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Metrology for Living Environment (MetroLivEn), IEEE international workshop on

作者： Tancredi Testasecca Marilena Lazzaro Elissaios Sarmas Stathis Stamatopoulos Dipartimento di Ingegneria Università degli Studi di Palermo Palermo Italy Research & Innovation Department Engineering Ingegneria Informatica S.p.A. Palermo Italy Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

optimization and proactive management of energy systems are crucial for achieving sustainability, efficiency and resilience in future smart energy networks. Data-driven approaches offer promising solutions for tackling the complex and dynamic challenges of energy systems, such as uncertainty, variability, and heterogeneity. Meanwhile, recent advances in decreasing hardware costs and improving data accessibility have allowed for the collection of high-quality data, leading to the development of more accurate and robust data-driven models of different energy systems. In this study, a comprehensive overview of current and future trends in data-driven optimization for smart energy systems is presented. After introducing the motivation and the background of this research field, the potential applications and benefits of optimization in various domains is discussed, such as electric vehicles charge, district heating networks and energy districts. Subsequently this review focuses on different methods and techniques for data-driven optimization and proactive management, ranging from scientific models to machine learning algorithms. Finally, the novel European project, DigiBUILD, is introduced, where different case studies are tested in several pilots, including electric vehicle charging management for increasing renewable energy source consumption, district heating network operative costs optimization and building energy and comfort management.

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A Hybrid Method of GA and ACO for 2-D Entropic Automatic Thresholding of Digital Images

A Hybrid Method of GA and ACO for 2-D Entropic Automatic Thr...

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international workshop on Intelligent Systems and applications, ISA

作者： Xiaohong Shen Yulin Zhang Fangzhen Li School of Control Science and Engineering Shandong University Jinan China School of Computer Science and Technology Shandong Economics University Jinan China

Thresholding is an important approach of image segmentation. One of the criteria to select the threshold is two- dimensional (2-D) entropic thresholding. However this method is time-consuming. A hybrid method of GA and ACO is introduced to the 2-D entropic thresholding instead of the exhaustive search. In the proposed method, the ant which represents the threshold vector is at the same time the chromosome. The search space is equally divided into several blocks each marked by pheromone. To reflect the collaboration of ant colony, the 2-D entropy of the ant and the pheromone of its block are both used to construct the fitness function. The best threshold vector is obtained by the genetic evolution of ant colony. Experiments show the proposed method obviously reduced the search time and its accuracy, stability and search efficiency are better than that of the 2-D entropic algorithm by using GA or ACO.

关键词： Digital images Entropy Ant colony optimization Image segmentation Genetic algorithms Biological cells Stability Convergence Two dimensional displays Automatic control

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Novel methods and results in training universal multi-nested neurons

Novel methods and results in training universal multi-nested...

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IEEE international workshop on Cellular Nanoscale Networks and their applications, (CNNA)

作者： R. Dogaru F. Ionescu P. Julian M. Glesner Dept. of Applied Electronics and Information Engineering Polytechnic University of Bucharest Romania Department of Electrical and Computer Engineering John Hopkins University Technical University of Darmstadt Institute of Microelctronic Systems Germany

This paper presents state of the art methods for training compact universal CNN cells (or neurons) to represent arbitrary local Boolean functions. The design tools are analyzed and optimized such that they are capable to provide fast solutions for cells with more than 4 inputs. In particular, it is proved statistically that any arbitrary Boolean function with n=5 inputs (corresponding to a von Neumann CNN neighborhood) admits multinested cell realizations thus confirming a conjecture that was previously proven only for n<5. Several hints are also provided regarding the choice and the influence of various parameters of the design algorithms on the quality of the solution and the speed of finding it.

关键词： Neurons Cellular neural networks Boolean functions Hypercubes Equations Design optimization Algorithm design and analysis Logic devices Reconfigurable logic Modems

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Identification of hybrid systems using stable spline kernels

Identification of hybrid systems using stable spline kernels

引用

IEEE workshop on Machine Learning for Signal Processing

作者： Gianluigi Pillonetto Dept. of Information Engineering University of Padova

All the approaches for identification of hybrid systems appeared in the literature assume known the model complexity. Widely used models are e.g. piecewise ARX with a priori fixed orders. In addition, the developed algorithms are typically tested only on quite simple systems, e.g. with ARX subsystems of order 1 or at most 2. This is a significant limitation for real applications. Here, we propose a new regularized technique for identification of piecewise affine systems, which we dub the hybrid stable spline algorithm (HSS). HSS exploits the recently introduced stable spline kernel to model the submodels impulse responses as zero-mean Gaussian processes, embedding information on submodels predictor stability. Using the Bayesian interpretation of regularization, the problem of classifying and distributing the data to the subsystems is cast as marginal likelihood optimization. An approximated optimization is performed by a Markov chain Monte Carlo scheme. Then, the stable spline algorithm is used to reconstruct each subsystem. Numerical experiments show that HSS can identify high-order piecewise affine systems, without having exact information on ARX subsystems order.

关键词： Splines (mathematics) optimization Kernel Random variables Indexes Bayes methods Markov processes

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Programming Hierarchical TS Fuzzy Systems

Programming Hierarchical TS Fuzzy Systems

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international workshop on Genetic and Evolving Systems, GEFS

作者： Yuehui Chen Lizhi Peng Ajith Abraham School of Information Science and Engineering Jinan University Jinan Shandong China School of Computer Science and Engineering Chung-Ang University Seoul South Korea

In this paper, we focus on an evolutionary algorithm to design hierarchical or multilevel fuzzy system (architecture and parameters) automatically. This research work presents an automatic way of evolving hierarchical Takagi-Sugeno fuzzy systems (TS-FS). The hierarchical structure is evolved using probabilistic incremental program evolution (PIPE) with specific instructions. The fine tuning of the if-then rule's parameters encoded in the structure is accomplished using particle swarm optimization (PSO). The proposed method interleaves both PIPE and PSO optimizations. Except for the randomly initialized hierarchical structure and parameters, we further explore the embedding of a clustering algorithm to speed up the learning algorithm. The new method results in a smaller rule-base and good learning ability. The proposed hierarchical TS-FS is evaluated by using Mackey-Glass chaotic time-series forecasting problem. When compared to other hierarchical TS-FS the proposed approach exhibits competing results with high accuracy and smaller size of hierarchical architecture

关键词： Fuzzy systems Function approximation Clustering algorithms Input variables Fuzzy control Takagi-Sugeno model Associative memory Binary trees Genetics optimization methods

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A Personnel Rating optimization Algorithm

A Personnel Rating Optimization Algorithm

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IEEE international workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Jianyu Yuan Chunyan Yan Zhiwei Ye School of Computer Science Hubei University of Technology China

ISBN: (纸本)9781665426060

In view of the enterprise personnel management (EPM), a novel nature-inspired optimization algorithm called personnel rating optimization (PRO) is proposed in this paper. The design of PRO mimics the workflow of management activities from EPM to achieve the aim for bolstering the team's competence level. Built on the mechanisms of PRO, including personal promotion, swarm interaction, and coordinated management, it could quickly lock onto the solution region in high-dimensional space. In this study, we analyzed the essential similarities between PRO and EMP about management strategies, then presented the detailed procedures and mathematical model applied in PRO. A set of various numerical benchmark functions have been conducted on PRO and some other state-of-art natural computation algorithms including whale optimization algorithm (WOA), differential evolution algorithm (DE), genetic algorithm (GA) and particle swarm optimization (PSO). Experimental results show that PRO has high precision and good stability, and the premature phenomenon is effectively restrained.

关键词： Sociology Transforms Benchmark testing Search problems Stability analysis Personnel Problem-solving

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