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An Evolutionary Enhanced Particle Filter-Based Fusion Localization Scheme for Fast Tracking of Smartphone Users in Tall Complex Buildings for Hazardous Situations

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IEEE SENSORS JOURNAL 2024年第5期24卷 6799-6812页

作者： Jamil, Harun Jian, Yang Cent South Univ Sch Automation Dept Control Sci & Engn Changsha 410083 Hunan Peoples R China Cent South Univ Sch Automation Dept Elect Engn Changsha 410083 Hunan Peoples R China

Precise indoor tracking of smartphone users in intricate multistory structures is an instructive challenge, particularly in dangerous scenarios. To precisely identify users, we present the enhanced particle filter-weighted differential evolution (EPF-WDE) scheme in this study. This novel fusion-based approach uses smartphone sensors such as accelerometers, gyroscopes, magnetometers, barometers, and Bluetooth low-energy (BLE) beacons with their coordinates information. The proposed approach manages nonlinear and multidimensional systems with a focus on convergence. We incorporate the weighted differential evolution (WDE) method, which is well known for its global optimization capabilities, into the resampling stage of the EPF to improve convergence by providing multiple key smartphone-based sensor information as input. Our findings demonstrate notable gains in accuracy and convergence. Scenario 1 uses the evolutionary particle filter with four inputs, obtaining an astounding root mean square error (RMSE) of 0.099 compared to Scenario 2 and the basic particle filter (PF) with an RMSE of 0.14 and 0.29, respectively. These results establish our method as a superior alternative for practical applications, especially in emergency scenarios and tall, complex buildings where accurate user localization is critical.

关键词： Location awareness Sensors Sensor fusion Kalman filters Particle filters Sensor systems and applications Fingerprint recognition algorithm convergence Bluetooth low-energy (BLE) beacons data fusion enhanced particle filter(EPF) pedestrian dead reckoning (PDR) smartphone sensors weighted differential evolution (WDE)

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Evaluating the convergence of tabu enhanced hybrid quantum optimization

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QUANTUM INFORMATION PROCESSING 2023年第5期22卷 205-205页

作者： Blanzieri, Enrico Pastorello, Davide Cavecchia, Valter Rumyantsev, Alexander Maltseva, Mariia Univ Trento Dept Informat Engn & Comp Sci Via Sommar 9 I-38123 Povo TN Italy Trento Inst Fundamental Phys & Applicat Via Sommar 14 I-38123 Povo TN Italy CNR Inst Mat Elect & Magnetism Via Cascata 56-c I-38123 Povo TN Italy RAS Inst Appl Math Res Karelian Res Ctr 11 Pushkinskaya Str Petrozavodsk 185910 Russia Petrozavodsk State Univ 33 Lenina Pr Petrozavodsk 185000 Russia Univ Bologna Dept Math Piazza di Porta San Donato 5 I-40126 Bologna Italy

In this paper, we introduce the tabu enhanced hybrid quantum optimization metaheuristic approach useful for optimization problem-solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model. The results of numerical evaluation of the algorithm on quantum hardware as well as on a classical semiconductor hardware model are also demonstrated.

关键词： Quantum annealing Hybrid approach Tabu search algorithm convergence Ising model

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A multiion particle swarm optimization algorithm based on repellent and attraction forces

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第5期33卷 e5979-e5979页

作者： Zhu, Shufang Zhou, Shangbo Shang, Jiaxing Wang, Limin Qiang, Baohua Chongqing Univ Coll Comp Sci Shazheng Rd 174 Chongqing 400030 Peoples R China Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing Peoples R China Guilin Univ Elect Comp Images & Graph Guilin Peoples R China

Particle swarm optimization (PSO) is an iterative computational methods which is used for obtaining the solutions of practical optimization problems. PSO is however, prone to be ended up obtaining a local optimum. Various strategies are proposed in the related literature to address this issue. Such strategies, however, often reduce the convergence speed of the algorithms. This article proposes a multiion particle swarm optimization (MION-PSO) algorithm which incorporates three strategies to balance the exploration and exploitation abilities of the algorithm. To improve the exploration abilities, the particles are regarded as ions with repellent/attraction forces among them. The second strategy is a multiion strategy (MIS) in which the population is divided into many subswarms, namely, particle group. Using MIS, the optimal solution within each group is then used to purposefully guide the updates of other individuals. To delete the useless particles without the capability of updating historical best location for a specific period and have a misleading impact on others, without we employ a particle elimination strategy. Twenty-three benchmark functions and eight baseline algorithms are employed to test the performance of MION-PSO method. The experimental results show that MION-PSO method significantly outperforms the baseline algorithms in terms of convergence, and further exhibits high capability in finding the optimal solutions especially on unimodal functions.

关键词： algorithm convergence attraction multiion particles swarm optimization repellant

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A note on the complex and bicomplex valued neural networks

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APPLIED MATHEMATICS AND COMPUTATION 2023年第1期445卷

作者： Alpay, Daniel Diki, Kamal Vajiac, Mihaela Chapman Univ Schmid Coll Sci & Technol One Univ Dr Orange CA 92866 USA

In this paper we first write a proof of the perceptron convergence algorithm for the complex multivalued neural networks (CMVNNs). Our primary goal is to formulate and prove the perceptron convergence algorithm for the bicomplex multivalued neural net-works (BMVNNs) and other important results in the theory of neural networks based on a bicomplex algebra.(c) 2023 Elsevier Inc. All rights reserved.

关键词： Bicomplex algebra Bicomplex analysis Complex-valued neural networks Bicomplex-valued neural networks Activation functions Perceptron algorithms algorithm convergence

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Assessment of convergence of the algorithm for Determining the Upper Bound of Dynamic Error on the Example of Acceleration Sensors 20

Assessment of Convergence of the Algorithm for Determining t...

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20th International Conference on Research and Education in Mechatronics (REM)

作者： Tomczyk, Krzysztof Cracow Univ Technol Fac Elect & Comp Engn Krakow Poland

ISBN: (纸本)9781538692578

This paper presents the theoretical basis and an assessment of the convergence of the iterative algorithm intended for determining the upper bound of dynamic error produced by accelerometers as an example of measuring sensors. Voltage output and charge output accelerometers are considered here. The theoretical basis refers to the accelerometer models, a reference model applied for the error calculation, and an algorithm for determining the signals that yield the upper bound of error. Signals that have one constraint regarding the magnitude and are limited in time are taken into consideration. The convergence of the iterative algorithm for several selected values of the model parameters for both accelerometers is assessed. The tests were carried out in the program MatCad and can be successfully extended to other types of measurement sensors.

关键词： upper bound of dynamic error accelerometers algorithm convergence

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A Spectral Clustering-Based Multi-Source Mating Selection Strategy in Evolutionary Multi-Objective Optimization

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IEEE ACCESS 2019年 7卷 131851-131864页

作者： Wang, Shuai Zhang, Hu Zhang, Yi Zhou, Aimin Wu, Peng Changzhou Univ Sch Mech Engn Changzhou 213164 Peoples R China Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent Beijing 100074 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai 200062 Peoples R China

In evolutionary multi-objective optimization, it has been illuminated that guide search with neighboring solutions improve the quality of new trial solutions and accelerate algorithms convergence by the regularity property of the continuous multi-objective optimization problems (MOPs). Very recently, clustering learning-based mating strategies have been popular for establishing reproduction operators with neighboring solutions. However, the existing mating strategies may be more reasonable with the full consideration and utilization of the regularity property. The current mating restrictions excessively emphasize algorithm convergence and ignore population diversity. In addition, the selected clustering algorithms in mating restrictions are not conducive for solving MOPs, which have complex Pareto sets (PSs) and nor Pareto fronts (PFs). To solve above problems and address both the algorithm convergence and the population diversity of multi-objective evolutionary algorithms (MOEAs), the spectral clustering based multi-source mating selection strategy (SMMS) is designed to detect regularity properties and balance population diversity while accelerating algorithm convergence. Consequently, a spectral clustering based multi-source mating selection multi-objective evolutionary algorithms is proposed, teamed SMMEA. SMMEA is applied to a number of test suites with a complex PS or PF, and compared with six state-of-the-art MOEAs. The results demonstrate that the proposed algorithm outperforms over the other approaches.

关键词： algorithm convergence clustering algorithm evolutionary algorithm mating restriction strategy multi-objective optimization population diversity

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Synchronization Estimation and Optimization Control of Random Opportunistic Networks 18

Synchronization Estimation and Optimization Control of Rando...

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18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Wang, Chao Yan, Xin Chen, Maosen Wuhan Univ Technol Dept Comp Sci Wuhan Hubei Peoples R China

ISBN: (纸本)9781728128658

Algebraic connectivity and Fiedler vector are extremely important global state parameters of the network. Algebraic connectivity determines the convergence speed of distributed algorithms in the network, and Fiedler vector determines the importance of each node in the network. At present, distributed algorithms for estimating algebraic connectivity are limited to fixed network systems, and the convergence of estimation algorithms and optimization of convergence are not studied in depth. Aiming at the above problems, this paper applies the algebraic connectivity estimation algorithm to the random opportunistic network, and then analyzes the convergence of the algorithm and deduces the convergence speed formula of the algorithm, and finally optimizes the convergence of the algorithm.

关键词： opportunistic networks algebraic connectivity algorithm convergence convergence optimization Network dynamics

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Matrix completion via capped nuclear norm

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IET IMAGE PROCESSING 2018年第6期12卷 959-966页

作者： Zhang, Fanlong Yang, Zhangjing Chen, Yu Yang, Jian Yang, Guowei Nanjing Audit Univ Sch Technol Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Dept Comp Sci & Technol Nanjing Jiangsu Peoples R China

Matrix completion is to recover a low-rank matrix from a subset of its entries. One of the solution strategies is based on nuclear norm minimisation. However, since the nuclear norm is defined as the sum of all singular values, each of which is treated equally, the rank function may not be well approximated in practice. To overcome this drawback, this study presents a matrix completion method based on capped nuclear norm (MC-CNN). The capped nuclear norm can reflect the rank function more directly and accurately than the nuclear norm, Schatten p-norm (to the power p) and truncated nuclear norm. The relation between the capped nuclear norm and the truncated nuclear norm is revealed for the first time. Difference of convex functions' programming is employed to solve MC-CNN. In the proposed algorithm, a key sub-problem, i.e. a matrix completion problem with linear regularisation term, is solved by using the active subspace selection method. In addition, the algorithm convergence is discussed. Experimental results show encouraging results of the proposed algorithm in comparison with the state-of-the-art matrix completion methods on both synthetic and real visual datasets.

关键词： image segmentation singular value decomposition convergence matrix completion capped nuclear norm low-rank matrix recovery nuclear norm minimisation singular values rank function MC-CNN Schatten p-norm truncated nuclear norm convex function programming linear regularisation term active subspace selection method algorithm convergence synthetic visual datasets real visual datasets Berkeley Segmentation Dataset

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Synchronization Estimation and Optimization Control of Random Opportunistic Networks

Synchronization Estimation and Optimization Control of Rando...

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第十八届分布式计算及其应用国际学术研讨会

作者： Chao Wang Xin Yan Maosen Chen The Department of Computer Science Wuhan University of Technology

Algebraic connectivity and Fiedler vector are extremely important global state parameters of the *** connectivity determines the convergence speed of distributed algorithms in the network,and Fiedler vector determines the importance of each node in the *** present,distributed algorithms for estimating algebraic connectivity are limited to fixed network systems,and the convergence of estimation algorithms and optimization of convergence are not studied in *** at the above problems,this paper applies the algebraic connectivity estimation algorithm to the random opportunistic network,and then analyzes the convergence of the algorithm and deduces the convergence speed formula of the algorithm,and finally optimizes the convergence of the algorithm.

关键词： opportunistic networks algebraic connectivity algorithm convergence convergence optimization Network dynamics

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A Discrete-Time Projection Neural Network for Solving Degenerate Convex Quadratic Optimization

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2017年第1期36卷 389-403页

作者： Zhang, Zican Li, Chuandong He, Xing Huang, Tingwen Southwest Univ Sch Elect & Informat Engn Chongqing 400715 Peoples R China Texas A&M Univ Qatar Dept Math POB 23874 Doha Qatar

This paper presents a discrete-time neural network to solve convex degenerate quadratic optimization problems. Under certain conditions, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to an optimal solution. Compared with the existing continuous-time neural networks for degenerate quadratic optimization, the proposed neural network in this paper is more suitable for hardware implementation. Results of two experiments of this neural network are given to illustrate the effectiveness of the proposed neural network.

关键词： Degenerate quadratic optimization Discrete-time neural network Convex optimization algorithm convergence

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