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Fusion Strategies in Multiple Particle Filtering in the Presence of Shared Unknown Static Parameters

Fusion Strategies in Multiple Particle Filtering in the Pres...

引用

Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Xiaokun Zhao Marija Iloska Mónica F. Bugallo Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The multiple particle filter (MPF) was proposed as a way to tackle online hidden states estimation for high-dimensional scenarios, where traditional PFs fail due to the curse of dimensionality. MPF works by partitioning the state into substates and assigning a separate PF to track each substate, while maintaining mutual communication between the employed filters. Like standard PFs, a common problem in MPFs is dealing with unknown static parameters in the model. However, unlike in standard PFs, estimation of these parameters in MPF requires more caution as the parameters can be shared across partitions. Such a scenario calls for fusion of information between partitions and problem becomes even more challenging when the observations in the model are interacting, i.e., when at least one measured data point is a function of more than one hidden state. In this work, we propose general fusion approaches in MPF for the estimation of static parameters shared across partitions. The proposed strategies use weights to quantify the contribution of each partition to the final estimation and can be applied to the general case with interacting observations. We demonstrate the performance of the proposed method on simulated data and showcase its benefits over several competing approaches.

关键词： Monte Carlo methods Signal processing algorithms Probability density function Particle filters Inference algorithms State-space methods partitioning algorithms State estimation Speech processing Standards

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Deep Reinforcement Learning for Collaborative Inference in Local-Serverless Edge Computing

Deep Reinforcement Learning for Collaborative Inference in L...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Dehua Chen Qinghe Dong Qian He Bingcheng Jiang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

High-dimensional parametric models and large-scale mathematical computations limit the efficiency of IoT devices. The emergence of serverless edge computing provides a solution where users can deploy models as serverless functions and delegate provisioning and scaling to the platform. However, the resource-constrained nature of edge resources leads to their inefficiency or inability to serve large neural networks. Therefore, this paper emphasizes collaborative inference between devices and serverless edge computing for DNN models. Specifically, we consider the partitioning and offloading of DNN models and propose a DRL-based strategy to learn the joint decision of model partitioning and function memory type selection to achieve a cost-optimal service that meets the SLO requirements. In addition, we introduce a simulated annealing algorithm in deep reinforcement learning that focuses on exploration in the early stage and empirical value in the later stage. Finally, experimental results show that our algorithm can satisfy various SLOs at a low service cost and outperform three benchmarking strategies.

关键词： Computational modeling Collaboration Simulated annealing Benchmark testing Deep reinforcement learning Mobile handsets Inference algorithms partitioning algorithms Edge computing Convergence

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Clustering Method and Voltage Regulation Strategy for Distribution Networks Based on Voltage Regulation Requirements

Clustering Method and Voltage Regulation Strategy for Distri...

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Intelligent Technology and Embedded Systems (ICITES), International Conference on

作者： Yunchi Qiao Xuejiao Pan Miao Mou Yanghang Ou Zhenbo Wei Xuxiang Li Weiting Xu Xinyuan Zhang State Grid Sichuan Electric Power Company Chengdu China State Grid Tianfu New Area Power Supply Company Chengdu China State Grid Chengdu Power Supply Company Chengdu China College of Electrical Engineering Sichuan University Chengdu China Economic and Technological Research Institute Chengdu China Sichuan New Type Electric Power System Research Institute Chengdu China

ISBN: (数字)9798331506292

ISBN: (纸本)9798331506308

Aiming at the problem of voltage over-limit caused by large-scale distributed energy access to distribution network, the traditional centralized voltage control method has been difficult to meet the needs of distribution network control and market development. Therefore, a distributed energy cluster voltage regulation control strategy is proposed. Firstly, in order to divide the distribution network into clusters, a k-means clustering algorithm based on electrical distance is proposed to form a distribution network voltage regulation cluster. Then, by using the methods of active power reduction and reactive power regulation, the voltage in the cluster is adjusted by combining the improved particle swarm optimization algorithm with the minimum power regulation and voltage fluctuation as the goal. Finally, the simulation is carried out in the IEEE33 node system to verify the effectiveness of the cluster voltage autonomy strategy.

关键词： Reactive power Voltage fluctuations Fluctuations Sensitivity Clustering algorithms Distribution networks Regulation partitioning algorithms Safety Voltage control

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Adaptive Personalized Federated Learning for Non-IID Data with Continual Distribution Shift

Adaptive Personalized Federated Learning for Non-IID Data wi...

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International Workshop on Quality of Service

作者： Sisi Chen Weijie Liu Xiaoxi Zhang Hong Xu Wanyu Lin Xu Chen Sun Yat-sen University The Chinese University of Hong Kong The Hong Kong Polytechnic University

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Federated Learning (FL) has surged in popularity, allowing machine learning models to be collaboratively trained using decentralized client data, all while upholding privacy and security standards. However, leveraging locally-stored data introduces challenges related to data heterogeneity. While many past studies have addressed this non-IID problem, they often overlook the dynamic nature of each individual client’s data or disrupt its continuous shift. In this paper, our emphasis is on the challenges posed by temporal data distribution shift alongside non-IID data across clients, a more prevalent yet complex situation in real-world FL. We propose to analytically capture the evolving nature of each local data distribution, by modeling them as a time-varying composite of multiple latent Gaussian distributions. We then employ the expectation maximization (EM) algorithm to deduce the distribution model parameters based on the prevailing observed training data, ensuring that the learned mixture proportion weights mirror a consistent trajectory. Additionally, by embedding an adaptive data partitioning method into the EM algorithm and using each partition to train a distinct sub-model, we realize an intuitive and novel personalized FL paradigm. This refines the FL training by exploiting the heterogeneity and temporal shifts of clients’ datasets. We derive analytical results to guarantee the convergence of our training method. Comprehensive tests across diverse datasets and distribution configurations also underscore our enhanced efficacy compared to several state-of-the-art.

关键词： Training Adaptation models Federated learning Training data Quality of service Data models partitioning algorithms

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Multi-population artificial bee colony algorithm based on Lagrange interpolation

Multi-population artificial bee colony algorithm based on La...

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Communication Technology and Information Technology (ICCTIT), International Conference on

作者： Yang Cao Xingyu Wang Zhonghua Han Shenyang Jianzhu University Shenyang City Liaoning Province China

ISBN: (数字)9798331528973

ISBN: (纸本)9798331528980

The artificial bee colony (ABC) algorithm, inspired by bees’ foraging, solves optimization problems well but converges slowly due to its search equation’s bias towards exploration. This paper proposes the MPABCLI based on Lagrange interpolation. It divides individuals into sub-populations by fitness, uses different variation strategies, limits candidate parents’ range, and adds Lagrangian interpolation local search (LSLI) to balance exploration and exploitation. Tests on CEC2014’s 30 functions show that for 10D, 30D and 50D problems, MPABCLI outperforms other ABC algorithms, with better performance and accuracy for global numerical optimization.

关键词： Interpolation Evolutionary computation Artificial bee colony algorithm Search problems Communications technology partitioning algorithms Computational efficiency Resource management Information technology Optimization

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An improved parallel optimization algorithm for cascade support vector machines

An improved parallel optimization algorithm for cascade supp...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Keyang Luo Yiwei Fan School of Mathematics and Statistics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331527259

ISBN: (纸本)9798331527266

Support Vector Machines (SVMs) are widely used machine learning algorithms, recognized for their strong performance across various classification tasks. However, their computational complexity, particularly with large datasets, poses significant challenges. Parallel learning offer partial relief, with the Cascade SVM being a prominent approach that partitions data into smaller subsets to manage computational load. Despite its advantages, the current Cascade SVM algorithm suffers from accuracy loss and inefficient convergence. In this paper, we present an improved parallel optimization algorithm for the Cascade SVM. Specifically, we design a novel exchange system between support vectors (SVs) and non-SVs, combining with screening rules to ensure the preservation of all global SVs, which significantly improves classification accuracy and accelerates convergence. Experimental results on synthetic and real-world datasets demonstrate that the proposed algorithm achieves accuracy comparable to that of a standard SVM, while offering substantial speed improvements over the traditional multi-pass Cascade SVM, particularly in large-scale and high-dimensional settings. Our method offers a robust and efficient solution for large-scale SVM training.

关键词： Support vector machines Training Accuracy Machine learning algorithms Multi label classification Vectors partitioning algorithms Standards Optimization Convergence

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Indoor Localization based on Hyperbolic positioning with Multiple feature information fusion

Indoor Localization based on Hyperbolic positioning with Mul...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Xueping Chen Liangbo Xie Mengmeng Xiao Yi Song Mu Zhou School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

A hyperbolic positioning algorithm based on the fusion of multiple feature information combining two kinds of radio frequency identification (RFID) signal information, i.e., phase and RSSI difference, is proposed in this paper. Specifically, a partition strategy based on RSSI difference is established. Then, the partition strategy is integrated into the hyperbolic positioning algorithm for localization. Experimental results show that the proposed algorithm achieves a median positioning error of 9.07 cm.

关键词： Location awareness Accuracy Antennas and propagation partitioning algorithms Indoor environment Radiofrequency identification

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Introducing ML to IMA Technology – System Perspective

Introducing ML to IMA Technology – System Perspective

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Conference on Digital Avionics Systems (DASC)

作者： Zain Iqbal Matthias Lehmann Bastian Luettig Recep Ayyildiz Tobias Bobrzik Tullio Vardanega Zamira Daw University of Padova Italy University of Stuttgart Germany

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

Machine learning (ML) technologies hold substan-tial promise for revolutionizing the aerospace industry, offering advancements in health system monitoring, autonomous systems, and drone-based operations. However, their integration into aerospace systems introduces significant safety challenges, necessitating stringent regulatory guidance to ensure technology reliability and safety. Depending on input data complexity, high-performance hardware like Graphical Processing Unit (GPU)s, TPUs, and Application-Specific Integrated Circuit (ASIC)s are crucial for ML applications, yet aviation's stringent safety standards restrict their usage. This paper focuses on the critical aspects of system architecture, particularly in the context of lever-aging the well-established Integrated Modular Avionics (IMA) for ML execution. Challenges in effectively integrating ML within IMA systems are discussed, along with various ML integration strategies, including embedding ML in applications, hosting models on the same hardware, or utilizing external hardware for specialized tasks. Case studies are explored to understand practical implementations and future research directions to enhance ML integration within the IMA framework, emphasizing the evolving role of TinyML in enabling efficient, low-power ML implementations in aerospace applications.

关键词： Adaptation models Tiny machine learning Atmospheric modeling Systems architecture Aerospace electronics Hardware Robustness partitioning algorithms Standards Aerospace industry

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Enhancing Security through Modularization: A Counterfactual Analysis of Vulnerability Propagation and Detection Precision

Enhancing Security through Modularization: A Counterfactual ...

引用

IEEE International Workshop on Source Code Analysis and Manipulation

作者： Mohammad Mahdi Abdollahpour Jens Dietrich Patrick Lam University of Waterloo Victoria University of Wellington

ISBN: (数字)9798331528508

ISBN: (纸本)9798331528515

In today's software development landscape, the use of third-party libraries is near-ubiquitous; leveraging third-party libraries can significantly accelerate development, allowing teams to implement complex functionalities without reinventing the wheel. However, one significant cost of reusing code is security vulnerabilities. Vulnerabilities in third-party libraries have allowed attackers to breach databases, conduct identity theft, steal sensitive user data, and launch mass phishing campaigns. Notorious examples of vulnerabilities in libraries from the past few years include log4shell, solarwinds, event-stream, lodash, and equifax. Existing software composition analysis (SCA) tools track the propagation of vulnerabilities from libraries through dependencies to downstream clients and alert those clients. Due to their design, many existing tools are highly imprecise―they create alerts for clients even when the flagged vulnerabilities are not exploitable. Library developers occasionally release new versions of their software with refactorings that improve modularity. In this work, we explore the impacts of modularity improvements on vulnerability detection. In addition to generally improving the nonfunctional properties of the code, refactoring also has several security-related beneficial side effects: (1) it improves the precision of existing (fast and stable) SCAs; and (2) it protects from vulnerabilities that are exploitable when the vulnerable code is present and not even reachable, as in gadget chain attacks. Our primary contribution is thus to quantify, using a novel simulation-based counterfactual vulnerability analysis, two main ways that improved modularity can boost security. We propose a modularization method using a DAG partitioning algorithm, and statically measure properties of systems that we (synthetically) modularize. In our experiments, we find that modularization can improve precision of Software Composition Analysis (SCA) tools to 71%, up from 35%

关键词： Codes Source coding Phishing Software algorithms Wheels Libraries Software partitioning algorithms Security Software development management

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Distributed Reactive Power Optimization for Active Distribution Network Based on Network Partition

Distributed Reactive Power Optimization for Active Distribut...

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Electrical Power Systems and Intelligent Control (EPSIC), International Conference on

作者： Jinzhou Zhu Qingren Jin Xinlei Cai Lei Yu Zijie Meng Tong Liu Zhenfan Yu Power Dispatching and Controlling Center of Guangdong Power Grid Company Limited Guangdong Power Grid Company Limited Guangzhou China Electrical Power Research Institute China Southern Power Grid (CSG) Guangzhou China

ISBN: (数字)9798350366075

ISBN: (纸本)9798350366082

Active distribution networks (ADNs) serve as a linchpin in contemporary power systems, tasked with accommo-dating the rising penetration of renewable energy sources while ensuring efficient and reliable operation. This paper proposes a network partition-based framework for distributed reactive power optimization in active distribution networks, with the minimization of network losses during the operation of active distribution networks as the optimization objective. This paper constructs a distributed reactive power optimization model that includes both continuous and discrete devices, such as on-load tap changer (OLTC) and capacitor banks (CB). Moreover, a solution method based on the alternating optimization process (AOP) and alternating direction method of multipliers (ADMM) is introduced. Case study simulations validate the effectiveness of the proposed reactive power optimization algorithm.

关键词： Reactive power Renewable energy sources Capacitors Convex functions partitioning algorithms Power system reliability Active distribution networks Reliability On load tap changers Optimization

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