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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Yuna Sim Seungseok Sin Jina Ma Kyunam Kim Huaping Liu Intae Hwang Sangmi Moon Dept. of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju South Korea Alps Electric Korea Co. Ltd. Gwangju South Korea Oregon State University United States of America School of Electronics and Computer Engineering and Dept. of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju South Korea Dept. of IT Artificial Intelligence Korea Nazarene University Cheonan South Korea

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

The high frequency in the THz band is attracting attention as a key element of the 6G system because of its wide bandwidth and high transmission rate, but technology is needed to compensate for this due to the large path loss. Therefore, we introduce an algorithm to improve security performance by using non-terrestrial network (NTN) and reconfigurable intelligent surface (RIS). The proposed algorithm is largely composed of two parts. It is divided into the part that optimizes the base station beamforming vector using the successive convex approximation (SCA) algorithm and the part that optimizes the RIS beamforming vector using the minorization-maximization (MM) algorithm. As a result of performance analysis through simulation, the superiority of the proposed algorithm was confirmed in terms of system security performance.

关键词： Base stations Satellites Array signal processing Reconfigurable intelligent surfaces approximation algorithms Propagation losses Vectors

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WebAssembly or Container? Joint Optimization of Microservice Consolidation and Deployment towards Cost Efficient Edge-End Consortium

WebAssembly or Container? Joint Optimization of Microservice...

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International Workshop on Quality of Service

作者： Zhexiong Li Deze Zeng Ranzhao Chen School of Computer Science China University of Geosciences Wuhan China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Edge-End Consortium, with the integration of edge computing servers and end devices like IoT devices, has emerged as a promising infrastructure for on-site computing power provision. But the high heterogeneity has raised unprecedented challenges in its resource management and task scheduling. Both WebAssembly and Container provide lightweight and portable way to consolidate and deploy microservices to combat the heterogeneity problem. However, the inherent advantages and disadvantages of WebAssembly and Container make it nontrivial to decide the microservice consolidation way and the deployment site, especially in the consideration of dependency between microservices. In this paper, we investigate the problem of mixed deployment of WebAssembly and Container based dependent microservices to strike a balance between communication and deployment cost toward high overall cost efficiency. We first cast this problem into a Quadratic Integer Programming (QIP) formulation and prove it as NP-hard. We then introduce a Randomized Rounding WebAssembly and Containerized Microservice Deployment (RR-WCMD) algorithm with polynomial computation complexity and guaranteed performance efficiency. Experiment results show that RR-WCMD can significantly lower the cost by an average of 34% in comparison with state-of-theart algorithms, thanks to the joint consideration of consolidation way and deployment site.

关键词： Costs Processor scheduling Microservice architectures Quality of service Containers approximation algorithms Polynomials

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Adaptive Utility Multi-Objective Reinforcement Learning for Optimizing Batch-Fed Fermentation Processes

Adaptive Utility Multi-Objective Reinforcement Learning for ...

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Chinese Automation Congress (CAC)

作者： Ziqi Yang Dazi Li Jinglin Zhou Institute of Automation College of Information Science and Technology Beijing University of Chemical Technology Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In industrial applications, many scenarios involve multiple conflicting objectives, such as yield, raw material consumption, and environmental protection in chemical production. To address this challenge, Multi-Objective Reinforcement Learning (MORL) provides an effective solution by learning Pareto-optimal policies through multi-objective vector rewards. Despite these advances, a gap remains in accurately capturing and incorporating user preferences into the MORL framework. In response to this, a novel Adaptive Utility MORL algorithm (AU-MORL) is proposed to improve policy optimization in continuous fed-batch fermentation processes. This algorithm adaptively adjusts the utility between multiple objectives and uses reinforcement learning to generate policies aligned with user preferences. Extensive validation in the fermentation simulation environment demonstrates that the algorithm achieves a denser pareto front that more closely approximates the true front, while the resulting policies effectively align with user preferences.

关键词： Automation Reinforcement learning Production approximation algorithms Vectors Raw materials Fermentation Protection Optimization Chemicals

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Integrating Adaptive Reference Vectors with Differential Evolution Algorithm for Multi-Objective Service Composition

Integrating Adaptive Reference Vectors with Differential Evo...

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International Conference on Computer and Communications (ICCC)

作者： Yuhong Yan Weicheng Li Xuan Xiao Taolin Guo Shunshun Peng Hongbing Wang College of Computer and Information Science Chongqing Normal University Chongqing China School of big data and computer science Guizhou Normal University Guiyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Multi-objective service composition is increasingly crucial in distributed system engineering, especially in optimizing quality-of-service (QoS) attributes. This approach seeks to identify Pareto-optimal solutions regarding QoS attributes in distributed systems. As service requests increase, finding all Pareto-optimal solutions becomes excessively time-consuming. Although existing methods that approximate the Pareto-optimal set are scalable, they still require substantial enhancements in effectiveness. To overcome this challenge, this paper aims to refine the reference vectors to more uniformly guide the solutions towards the true Pareto-optimal set. Consequently, we propose a novel multi-objective differential evolution algorithm designed to efficiently identify solutions that demonstrate both proximity to and diversity within the Pareto-optimal set. This algorithm utilizes a reference vector-based cascade search to gather essential nondominated compositions efficiently. Additionally, it features an adaptive reference vector mechanism specifically designed to address the complexities of intricate multi-objective optimization problems. The robustness and effectiveness of our proposed algorithm have been rigorously validated using a real-world dataset, demonstrating its capability to improve service composition in distributed systems.

关键词： Adaptive systems Quality of service approximation algorithms Vectors Robustness Complexity theory Optimization Convergence Business

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Composite Community-Aware Diversified Influence Maximization with Efficient approximation

arXiv

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arXiv 2022年

作者： Guo, Jianxiong Ni, Qiufen Wu, Weili Du, Ding-Zhu The Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China The Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China The School of Computers Guangdong University of Technology Guangzhou510006 China The Department of Computer Science Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Influence Maximization (IM) is a famous topic in mobile networks and social computing, which aims at finding a small subset of users to maximize the influence spread through online information cascade. Recently, some careful researchers paid attention to diversity of information dissemination, especially community-aware diversity, and formulated the diversified IM problem. The diversity is ubiquitous in a lot of real-world applications, but they are all based on a given community structure. In social networks, we can form heterogeneous community structures for the same group of users according to different metrics. Therefore, how to quantify the diversity based on multiple community structures is an interesting question. In this paper, we propose the Composite Community-Aware Diversified IM (CC-DIM) problem, which aims at selecting a seed set to maximize the influence spread and the composite diversity over all possible community structures under consideration. To address the NP-hardness of CC-DIM problem, we adopt the technique of reverse influence sampling and design a random Generalized Reverse Reachable (G-RR) set to estimate the objective function. The composition of a random G-RR set is much more complex than the RR set used for the IM problem, which will lead to inefficiency of traditional sampling-based approximation algorithms. Because of this, we further propose a two-stage algorithm, Generalized HIST (G-HIST). It can not only return a (1 − 1/e − Ε) approximate solution with at least (1 − δ) probability, but also improve the efficiency of sampling and ease the difficulty of searching by significantly reducing the average size of G-RR sets. Finally, we evaluate our G-HIST on real datasets against existing algorithms. The experimental results show the effectiveness of our proposed algorithm and its superiority over other baseline algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： approximation algorithms

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On the optimal rank-1 approximation of matrices in the Chebyshev norm

arXiv

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arXiv 2022年

作者： Morozov, Stanislav Smirnov, Matvey Zamarashkin, Nikolai Marchuk Institute of Numerical Mathematics Russian Academy of Sciences Gubkin Street 8 Moscow119333 Russia Lomonosov Moscow State University Leninskie Gory 1 Moscow119991 Russia

The problem of low rank approximation is ubiquitous in science. Traditionally this problem is solved in unitary invariant norms such as Frobenius or spectral norm due to existence of efficient methods for building approximations. However, recent results reveal the potential of low rank approximations in Chebyshev norm, which naturally arises in many applications. In this paper we tackle the problem of building optimal rank-1 approximations in the Chebyshev norm. We investigate the properties of alternating minimization algorithm for building the low rank approximations and demonstrate how to use it to construct optimal rank-1 approximation. As a result we propose an algorithm that is capable of building optimal rank-1 approximations in Chebyshev norm for moderate matrices. © 2022, CC BY.

关键词： approximation algorithms

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Flexible Beam Coverage Optimization for Movable-Antenna Array

Flexible Beam Coverage Optimization for Movable-Antenna Arra...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Dong Wang Weidong Mei Boyu Ning Zhi Chen National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China (UESTC) Chengdu China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Fluid antennas (FAs) and movable antennas (MAs) have attracted increasing attention in wireless communications recently. As compared to the conventional fixed-position antennas (FPAs), their geometry can be dynamically reconfigured, such that more flexible beamforming can be achieved for signal coverage and/or interference nulling. In this paper, we investigate the use of MAs to achieve uniform coverage for multiple regions with arbitrary number and width in the spatial domain. In particular, we aim to jointly optimize the MAs’ weights and positions within a linear array to maximize the minimum beam gain over the desired spatial regions. However, the resulting problem is non-convex and difficult to be optimally solved. To tackle this difficulty, we propose an alternating optimization (AO) algorithm to obtain a high-quality suboptimal solution, where the MAs’ weights and positions are alternately optimized by applying successive convex approximation (SCA) technique. Numerical results show that our proposed MA-based beam coverage scheme can achieve much better performance than conventional FPAs.

关键词： Geometry Wireless communication Fluids Array signal processing Interference Minimax techniques approximation algorithms Global communication Antennas Optimization

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Inverse Extremal Problem for an Anti-Tumor Therapy Model

Inverse Extremal Problem for an Anti-Tumor Therapy Model

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International Seminar Day on Diffraction

作者： Andrey Kovtanyuk Christina Kuttler Kristina Koshel Alexander Chebotarev Mathematical Department Technische Universität München München Germany Far Eastern Federal University Far Eastern Center for Research and Education in Mathematics Vladivostok Russia Amur State University Blagoveshchensk Russia

ISBN: (数字)9798331511722

ISBN: (纸本)9798331511739

An optimal control problem for a model of tumor growth is studied. In a given subdomain, it is required to minimize the density of tumor cells, while the drug concentration in tissue is limited by given minimal and maximal values. On the basis of the obtained estimates of the solution to the controlled system, the solvability of the control problem is proved. The problem is reduced to an optimal control problem with a penalty. An algorithm for solving the op-timal control problem with a penalty is constructed and implemented. The efficiency of the algorithm is illustrated by a numerical example.

关键词： Drugs Diffraction Optimal control Medical treatment approximation algorithms Numerical models Tumors Convergence

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A Q-learning-based Multi-timescale Resilience Enhancement Approach for Power Grids with High Renewables

A Q-learning-based Multi-timescale Resilience Enhancement Ap...

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Power Science and Technology (ICPST), IEEE International Conference on

作者： Yanting Huang Qing Zhong Akang Wang Shunjiang Lin Chaoyi Peng Shunbo Lei School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Shenzhen China School of Electric Power Engineering South China University of Technology Guangzhou China Shenzhen Research Institute of Big Data Shenzhen China Power Dispatch and Control Center China Southern Power Grid Guangzhou China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Power grid resilience is critically undermined by extreme weather events, especially when coupled with uncertain renewable energy generation, which can lead to a severe power outage. When facing such outages, it is essential for system operators to restore damaged lines in a sequence that maximizes the overall power supply. Identifying the optimal restoration sequence can be cast as a multi-stage problem where at each stage a single line is first restored in an hourly basis and then numerous power flow problems within the timescale of minutes are optimized under the resulting network topology. Evaluating the economic effect of restoring a specific line entails the optimization of power flow problems with time-varying renewables, lacking a closed-form expression. To address this, we employ the Q-learning algorithm, utilizing a well-designed Q-function to approximate the effect of restoring each line and embedding such a function into a training process. Simulations on the IEEE 33-Bus and IEEE 39-Bus Systems confirm the efficacy of the proposed approach, showcasing significant enhancements in resilience and recovery capabilities.

关键词： Renewable energy sources Q-learning Heuristic algorithms Power system dynamics approximation algorithms Power grids Optimization

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ON APPROXIMATING THE DYNAMIC AND DISCRETE NETWORK FLOW PROBLEM

arXiv

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arXiv 2024年

作者： Manna, Bubai Roy, Bodhayan Suppakitpaisarn, Vorapong Department of Mathematics Indian Institute of Technology Kharagpur India Graduate School of Information Science and Technology The University of Tokyo Japan

We examine the dynamic network flow problem under the assumption that the flow consists of discrete units. The dynamic network flow problem is commonly addressed in the context of developing evacuation plans, where the flow is typically treated as a continuous quantity. However, real-world scenarios often involve moving groups, such as families, as single units. We demonstrate that solving the dynamic flow problem with this consideration is APX-hard. Conversely, we present a PTAS for instances where the base graph is a path with a constant number of nodes. We introduce a 'ready time' constraint to the minsum bin packing problem, meaning certain items cannot be placed in specific bins, develop a PTAS for this modified problem, and apply our algorithms to the discrete and dynamic flow problem. Copyright © 2024, The Authors. All rights reserved.

关键词： approximation algorithms

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