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International Conference on Computer Engineering and Applications (ICCEA)

作者： Ruihong Zhu Hanyan Huang Shan Xie School of Systems Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Constrained Bayesian Optimization (CBO) is an effective class of algorithms for solving optimization problems where both the constraint function and the objective function are expensive black-box functions. However, most current CBO algorithms are myopic, considering only the immediate gain from a single sampling without taking into account long-term rewards. Non-myopic CBO, employing an approximate dynamic programming (ADP) approach called "rollout" to simulate the impact up to the next h steps, can be used to address such problems. Nevertheless, obtaining the exact solution using this method incurs significant computational costs, with each sampling suggestion sampling taking several hours. In this paper, a computationally efficient non-myopic CBO is proposed, which can provide a sampling suggestion within a matter of minutes. Firstly, the variance adjustment parameters are introduced to control the exploration and exploitation behavior of Constrained Expected Improvement (CEI), a myopic CBO method. Secondly, the ADP approach "rollout" is used to determine the optimal variance adjustment parameters that maximize long-term rewards. Finally, the results of several numerical experiments and an antenna design problem indicate that the proposed algorithm outperforms the compared myopic CBOs in terms of convergence speed and accuracy.

关键词： Accuracy Monte Carlo methods Heuristic algorithms Closed box approximation algorithms Linear programming Computational efficiency

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A Nearly Time-Optimal Distributed approximation of Minimum Cost k-Edge-Connected Spanning Subgraph

arXiv

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arXiv 2022年

作者： Dory, Michal Ghaffari, Mohsen University of Haifa MIT United States

The minimum-cost k-edge-connected spanning subgraph (k-ECSS) problem is a generalization and strengthening of the well-studied minimum-cost spanning tree (MST) problem. While the round complexity of distributedly computing the latter has been well-understood, the former remains mostly open, especially as soon as k ≥ 3. In this paper, we present the first distributed algorithm that computes an approximation of k-ECSS in sublinear time for general k. Concretely, we describe a randomized distributed algorithm that, in Õ(k(D + k√n)) rounds, computes a k-edge-connected spanning subgraph whose cost is within an O(log n log k) factor of optimal. Here, n and D denote the number of vertices and diameter of the graph, respectively. This time complexity is nearly optimal for any k = poly(log n), almost matching an Ω( D + √n/k) lower bound. Our algorithm is the first to achieve a sublinear round complexity for k ≥ 3. We note that this case is considerably more challenging than the well-studied and well-understood k = 1 case—better known as MST—and the closely related k = 2 case. Our algorithm is based on reducing the k-ECSS problem to k set cover instances, in which we gradually augment the connectivity of the spanning subgraph. To solve each set cover instance, we combine new structural observations on minimum cuts with graph sketching ideas. One key ingredient in our algorithm is a novel structural lemma that allows us to compress the information about all minimum cuts in a graph into a succinct representation, which is computed in a decentralized fashion. We hope that this succinct representation may find applications in other computational settings or for other problems. © 2022, CC BY.

关键词： approximation algorithms

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Classification of Cognitive Activities EEGs via Singular Spectrum Analysis and Joint Approximate Diagonalization of Eigen-Matrices Algorithm with Ensemble Classifier

Classification of Cognitive Activities EEGs via Singular Spe...

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IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)

作者： Xiangyu Luo Bingo Wing-Kuen Ling Faculty of Information Engineering Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331523008

ISBN: (纸本)9798331523015

Cognition involves the recognition and perception of external objects to solve creative problems and adapt to the environment, forming the foundation of human intelligence. However, neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) lead to cognitive impairment. Transcutaneous electrical nerve stimulation (TENS) is a recognized therapy for enhancing cognitive performance. This paper proposes using singular spectrum analysis (SSA) and the joint approximate diagonalization of eigen-matrices (JADE) algorithm with an ensemble classifier to classify electroencephalograms (EEGs) before and after TENS application during various cognitive activities. Initially, subjects wear a headband to acquire single-channel EEGs before and after TENS is applied to their radial nerves. Empirical mode decomposition (EMD) is then used to suppress underlying EEG trends. Subsequently, SSA and the JADE algorithm perform blind source separation (BSS) of EEGs from electromyograms (EMGs) and electrocardiograms (ECGs). JADE is a fourth-order based independent component analysis (ICA) algorithm. The tunable Q-factor wavelet transform (TQWT) decomposes EEGs into subbands from which features are extracted. Finally, an ensemble classifier, combining random forest and least squares support vector machine (LS-SVM), classifies the data. The computer numerical simulation results demonstrate that our proposed method achieves higher classification accuracy compared to existing methods.

关键词： Support vector machines Q-factor Accuracy approximation algorithms Feature extraction Electroencephalography Blind source separation Classification algorithms Random forests Spectral analysis

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Autonomous Drone Navigation in Dynamic and Uncertain Environments

Autonomous Drone Navigation in Dynamic and Uncertain Environ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M K Lodi Sudeshna Sarkar Atul Dadhich Department of Management School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India Department of Management ARKA JAIN University Jamshedpur Jharkhand India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Autonomous drone navigation in dynamic and uncertain environments is an crucial research subject matter in the area of robotics. Drones are more and more being utilized in various programs, together with seek and rescue, bundle transport, and inspection of infrastructure. In these packages, autonomous navigation is vital to make sure safe and green operation of the *** principle assignment in autonomous drone navigation is to make decisions and plan trajectories in actual-time while considering the dynamic and uncertain nature of the surroundings. Dynamic environments talk over with those in which the gadgets, obstacles or terrain are converting constantly, as an example, trees swaying within the wind, transferring motors, or production on the ground. uncertain environments, alternatively, are those wherein the drone has limited or incomplete information approximately the surroundings, making it difficult to appropriately are expecting the country of the *** deal with these demanding situations, researchers are developing algorithms that allow drones to navigate in dynamic and unsure environments. those approaches normally involve a aggregate of belief, planning, and control algorithms. belief algorithms use sensors, inclusive of cameras and lidar, to understand the surroundings and hit upon limitations. making plans algorithms use this records to generate a trajectory that avoids collisions and reaches the desired vacation spot.

关键词： Uncertainty Navigation Heuristic algorithms Production approximation algorithms Real-time systems Trajectory Planning Sensors Drones

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Joint OMA-NOMA Cell-Free Massive MIMO with Limited Fronthaul

Joint OMA-NOMA Cell-Free Massive MIMO with Limited Fronthaul

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chi Y. Nguyen Tung T. Vu Hien Quoc Ngo Michail Matthaiou Centre for Wireless Innovation (CWI) Queen’s University Belfast Belfast UK School of Engineering Macquarie University Sydney Australia

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

We consider a joint orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) cell-free massive multiple-input multiple-output (CFmMIMO) system with limited fronthaul capacity. In this system, some users (UEs) are grouped to be served by access points (APs) in NOMA mode, while other UEs are served in OMA mode. We formulate a mixed-integer nonconvex problem of optimizing the power control and AP-group association to maximize the sum spectral efficiency (SE) in the considered system. This problem is subject to minimum SE requirements of each UE, per-AP transmit power, and limited fronthaul capacity. We propose an algorithm based on the successive convex approximation (SCA) optimization technique to obtain a stationary-point solution for the formulated problem. Numerical results demonstrate that the proposed joint optimization approach increases significantly sum SE compared to other heuristic baseline schemes, especially under a tight fronthaul capacity limitation. Also, the joint OMA-NOMA CFmMIMO system provides remarkably higher 95%-likelihood sum SE compared to a CFmMIMO system using only the OMA scheme, especially up to 42% when the coherence interval is short.

关键词： NOMA Spectral efficiency Simulation Power control Coherence Massive MIMO approximation algorithms Global communication Optimization

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Hybrid Beamforming Design for Wideband Near-Field ISAC with Delay Alignment Modulation

Hybrid Beamforming Design for Wideband Near-Field ISAC with ...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xuanrui Zhang Wan Xiang Yawen Chen Wei Zheng Zhaoming Lu Xiangming Wen Beijing Key Laboratory of Network System Architecture and Convergence Beijing China Beijing Laboratory of Advanced Information Networks Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

This paper studies a multi-user integrated sensing and communication (ISAC) system with delay alignment modulation (DAM) in the near-field. We aim to optimize multi-path hybrid beamforming so as to maximize the signal-to-noise ratio (SNR) of the target echo while ensuring communication users' signal-to-interference plus-noise ratio (SINR) requirements and transmit power budget. To this end, we propose an alternating optimization algorithm using successive convex approximation (SCA) technology, penalty convex-concave procedure (PCCP) and second-order cone programming (SOCP). Simulation results demonstrate the effectiveness of the proposed algorithm and show the impact of channel model mismatch and the distance dimension introduced by the near-field model on the near-field DAM-ISAC system.

关键词： Array signal processing Dams Simulation Signal processing algorithms Modulation Interference Integrated sensing and communication approximation algorithms Delays Signal to noise ratio

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Dual Ensemble Kalman Filter for Stochastic Optimal Control

Dual Ensemble Kalman Filter for Stochastic Optimal Control

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IEEE Conference on Decision and Control

作者： Anant A. Joshi Amirhossein Taghvaei Prashant G. Mehta Sean P. Meyn Department of Mechanical Science and Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign Department of Aeronautics and Astronautics University of Washington Seattle Department of Electrical and Computer Engineering University of Florida Gainesville

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, stochastic optimal control problems in continuous time and space are considered. In recent years, such problems have received renewed attention from the lens of reinforcement learning (RL) which is also one of our motivation. The main contribution is a simulation-based algorithm – dual ensemble Kalman filter (EnKF) – to numerically approximate the solution of these problems. The paper extends our previous work where the dual EnKF was applied in deterministic settings of the problem. The theoretical results and algorithms are illustrated with numerical experiments.

关键词： Optimal control Reinforcement learning Aerospace electronics approximation algorithms Kalman filters Lenses

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Central Finite-Difference Based Gradient Estimation Methods for Stochastic Optimization

Central Finite-Difference Based Gradient Estimation Methods ...

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Simulation Winter Conference

作者： Raghu Bollapragada Cem Karamanli Stefan M. Wild Operations Research and Industrial Engineering The University of Texas at Austin Austin TX USA Applied Math. and Comp. Res. Division Lawrence Berkeley National Laboratory Beckeley CA USA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This paper presents an algorithmic framework for solving unconstrained stochastic optimization problems using only stochastic function evaluations. We employ central finite-difference based gradient estimation methods to approximate the gradients and dynamically control the accuracy of these approximations by adjusting the sample sizes used in stochastic realizations. We analyze the theoretical properties of the proposed framework on nonconvex functions. Our analysis yields sublinear convergence results to the neighborhood of the solution, and establishes the optimal worst-case iteration complexity $\left(\mathscr{O}\left(\varepsilon^{-1}\right)\right)$ and sample complexity $(\theta(\epsilon^{-2}))$ for each gradient estimation method to achieve an $\varepsilon$ -accurate solution. Finally, we demonstrate the performance of the proposed framework and the quality of the gradient estimation methods through numerical experiments on nonlinear least squares problems.

关键词： Accuracy Heuristic algorithms Estimation approximation algorithms Complexity theory Optimization Finite difference methods Convergence

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VNF-Based Service Provision in Software Defined LEO Satellite Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第9期20卷 6139-6153页

作者： Jia, Ziye Sheng, Min Li, Jiandong Zhou, Di Han, Zhu Xidian Univ State Key Lab ISN Xian 710071 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

Low earth orbit (LEO) satellite networks will play important roles in the sixth generation (6G) communication system. Software defined network technique is a novel approach introduced to the LEO satellite networks to improve the resource flexibility and efficiency, forming the software defined LEO satellite networks (SDLSNs). How to efficiently allocate the resources of SDLSN to provide services for the terrestrial users is a key issue. Hence, in this work, we explore the service provision for SDLSN via virtual network functions (VNFs) orchestration on the software defined time-evolving graph. In view of the scarce, intermittent and unstable satellite-to-satellite (S2S) links, the problem is formulated to minimize the S2S resource consumption while satisfying the terrestrial tasks, which is in the form of integer linear programming. Since the problem is intractable by exhaustive search, we design a branch-and-price algorithm based on the coupling of Dantzig-Wolfe decomposition, column generation, and branch-and-bound to efficiently acquire the optimal solution. Further, to obtain a faster solution for practical usage, we further design an approximation algorithm for the subproblem and leverage the beam search to accelerate the pruning for the search tree. Finally, extensive simulations are conducted and the numerical results validate the effectiveness of the proposed schemes.

关键词： Wireless communication Particle beams Analytical models Satellites Software algorithms Low earth orbit satellites approximation algorithms Low earth orbit (LEO) satellite networks virtual network function (VNF) satellite resource allocation branch-and-price

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Research on the Application of Fractional-Order Hyperchaotic Systems in Image Encryption

Research on the Application of Fractional-Order Hyperchaotic...

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IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Yanling Sun Baoxing Liu Department of Science Shenyang Jianzhu University Shenyang China Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Aiming at the security and reliability issues of color images in the process of transmission and storage, an image encryption method based on deformed fractional-order Chen hyperchaotic system diffusion combined with improved Arnold and Zig-Zag double scrambling is proposed. First, the color image is divided into three channels: R, G, and B. Arnold scrambling algorithms with different keys are used to scramble the three channels respectively. Secondly, the chaotic sequence generated iteratively by the deformed fractional Chen hyperchaotic system is used to diffuse the image, Zig-Zag scrambling operations are interspersed in it to ensure better encryption results. Simulation shows that with the improved encryption algorithm, the correlation coefficient in the horizontal, vertical and diagonal directions of the image is approximately 0, and the values of UACI and NPCR are close to ideal values. Experimental results show that the method in this paper has good statistical performance and high security. This ensures the robustness requirements of the image during transmission.

关键词： Correlation coefficient Color approximation algorithms Robustness Encryption Image storage Information entropy Resilience

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