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A Practical Byzantine Fault Tolerant Algorithm Based on Credit Value and Dynamic Grouping 1

2nd International Conference on Cognitive Computation and Systems, ICCCS 2023

作者： Zhai, Haonan Tong, Xiangrong College of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9789819708857

ISBN: (纸本)9789819708840

The Practical Byzantine Fault Tolerance (PBFT) consensus algorithm plays a pivotal role in the efficiency of blockchain networks. Despite several enhancements, optimized PBFT still grapples with issues, notably incomplete credit assessments, and uneven node groupings. While existing trust models predominantly focus on objectively evaluating nodes within the system, the subjective assessment of inter-node interactions remains relatively underexplored. Furthermore, post-grouping, the reduction in nodes within each group leads to a diminished capacity for Byzantine nodes. This poses a notable challenge to the algorithm’s effectiveness. In this paper, a practical Byzantine fault-tolerant algorithm (CDGPBFT) based on credit value and dynamic grouping is proposed to solve these problems. First, credit attributes are extended for nodes. The design of the node model introduces a dual judgment mechanism, including dynamic evaluation of node interactions and static system evaluation. Subsequently, nodes are categorized into three groups based on their credit scores: high, medium, and reserve node groups. Next, formulas are designed to enhance the master node election scheme and simplify the consensus process. Finally, a reward function based on node computational resources, network bandwidth, and storage capacity is proposed. Nodes that effectively complete the consensus process receive tokens as rewards, thus promoting accurate and rapid participation. Experimental validation shows that the enhanced algorithm has lower latency and communication overheads compared to PBFT, while also improving message throughput. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fault tolerance

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Fast sequence to graph alignment based on graph folding

Fast sequence to graph alignment based on graph folding

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Deng, Yanfei Kang, Jinjun Liu, Zhuang Zhu, Xiao Quan, Wei Yantai University School of Computer and Control Engineering Yantai China

ISBN: (纸本)9798350386226

Sequence-to-graph alignment is a critical component of pan-genome-based read alignment and represents the most computationally intensive step in this process. To address this challenge, we have introduced a sequence-to-graph alignment algorithm based on graph folding, which minimizes the size of the graph structure and enhances alignment efficiency while identifying the optimal path. Experiments on both simulated and real datasets demonstrate that our algorithm significantly improves the speed of sequence-to-graph alignment. The source code is available at Github: https://***/zzzerd/fgpoa © 2024 IEEE.

关键词： Graph algorithms

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Harnessing Trend Theory to Enhance Distributed Proximal Point Algorithm Approaches for Multi-Area Economic Dispatch Optimization

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computers, Materials & Continua 2025年第3期82卷 4503-4533页

作者： Yaming Ren Xing Deng College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China

The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution,particularly within multi-area interconnected power *** complexity necessitates the employment of distributed solution methodologies,which are not only essential but also highly *** the realm of computational modelling,the multi-area economic dispatch problem(MAED)can be formulated as a linearly constrained separable convex optimization *** proximal point algorithm(PPA)is particularly adept at addressing such mathematical constructs *** study introduces parallel(PPPA)and serial(SPPA)variants of the PPA as distributed algorithms,specifically designed for the computational modelling of the *** PPA introduces a quadratic term into the objective function,which,while potentially complicating the iterative updates of the algorithm,serves to dampen oscillations near the optimal solution,thereby enhancing the convergence ***,the convergence efficiency of the PPA is significantly influenced by the parameter *** address this parameter sensitivity,this research draws on trend theory from stock market analysis to propose trend theory-driven distributed PPPA and SPPA,thereby enhancing the robustness of the computational *** computational models proposed in this study are anticipated to exhibit superior performance in terms of convergence behaviour,stability,and robustness with respect to parameter selection,potentially outperforming existing methods such as the alternating direction method of multipliers(ADMM)and Auxiliary Problem Principle(APP)in the computational simulation of power system dispatch *** simulation results demonstrate that the trend theory-based PPPA,SPPA,ADMM and APP exhibit significant robustness to the initial value of parameter c,and show superior convergence characteristics compared to the residual balancing ADMM.

关键词： Multi-area economic dispatch problem proximal point algorithm trend theory

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Shared Latent Embedding Learning for Multi-View Subspace Clustering

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IEICE Transactions on Information and Systems 2024年第1期E107.D卷 148-152页

作者： Liu, Zhaohu Song, Peng Mu, Jinshuai Zheng, Wenming School of Computer and Control Engineering Yantai University Yantai264005 China Southeast University Nanjing210096 China

Most existing multi-view subspace clustering approaches only capture the inter-view similarities between different views and ignore the optimal local geometric structure of the original data. To this end, in this letter, we put forward a novel method named shared latent embedding learning for multi-view subspace clustering (SLE-MSC), which can efficiently capture a better latent space. To be specific, we introduce a pseudo-label constraint to capture the intra-view similarities within each view. Meanwhile, we utilize a novel optimal graph Laplacian to learn the consistent latent representation, in which the common manifold is considered as the optimal manifold to obtain a more reasonable local geometric structure. Comprehensive experimental results indicate the superiority and effectiveness of the proposed method. © 2024 Institute of Electronics, Information and Communication, Engineers, IEICE. All rights reserved.

关键词： Embeddings

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Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

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Big Data Mining and Analytics 2024年第1期7卷 142-155页

作者： Zhongyin Xu Xiujuan Lei Mei Ma Yi Pan School of Computer Science Shaanxi Normal UniversityXi'an 710119China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated *** Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization *** main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix *** intervals and state changes are then used to encode logD and solubility for subsequent *** the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,*** models are compared with the baseline models with respect to their abilities to generate molecules with specific *** show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

关键词： molecular optimization transformer Matched Molecular Pairs(MMPs) logD solubility

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An enhanced neuro-adaptive PID sliding mode control for robot manipulators:promoting sustainable automation

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Neural Computing and Applications 2025年第9期37卷 6877-6898页

作者： Elmogy, Ahmed Alhemaly, Nagah El-Ghaish, Hany Elawady, Wael Computer Engineering Deptartment Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Computers & Control Engineering Department Tanta University Tanta31527 Egypt

The control of robot manipulators presents significant challenges, primarily due to their complex, nonlinear dynamics. Another major difficulty arises from environmental and operational disturbances. Numerous control frameworks and approaches have been proposed in the literature to address these issues. However, many of these approaches are discontinuous or non-adaptive, which negatively affects their performance and makes them unsuitable for real-time applications. This paper proposes a novel integrated control scheme that combines adaptive control with an improved continuous second-order sliding mode control (CSOS), utilizing generalized artificial neural networks (GANN) for enhanced performance in robot manipulators. The proposed control method consists of two key components: An adaptive proportional-integral-derivative (PID) controller and an adaptive CSOS-based control module (CSOSSD-APID), designed to deliver superior transient and steady-state performance. In this approach, the adaptive CSOS benefits from GANN’s strong noise handling capabilities and its ability to estimate uncertainties effectively. This integration significantly enhances the robustness of robot manipulator control across various tracking tasks, using a single pre-trained GANN model with fine-tuned weights tailored to each task. Numerical simulations demonstrate the effectiveness and versatility of the proposed control scheme, particularly in managing highly time-varying trajectories while contributing to more sustainable and efficient automation practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adaptive control systems

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A Semantic-Based Approach for Self-Configuring Smart Home Systems Using Feedback-Based Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 17729-17741页

作者： Ran, Weizhi Nantogma, Sulemana Yang, QingZhuo Zhang, Shangyan Wang, Yan Xu, Yang University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Xi'an University of Architecture and Technology School of Information and Control Engineering China

As urban populations grow, smart home technology has become a key enabler for enhancing energy efficiency, comfort, and convenience in residential environments. However, existing smart home implementations often struggle to adapt to complex and dynamic user needs due to issues like device heterogeneity, rigid pre-set solutions, and limited interoperability. This paper presents a novel semantic-based self-configuring framework that captures the functional, operational, state, and characteristic perspectives of smart home devices, facilitating seamless interconnection and interoperability across heterogeneous devices. The proposed framework uses service entity, that is semantically modeled to act as a linker and mediator between applications and devices to enable dynamic scheduling and control based on application requests and user preferences. To enable continuous adaptation and learning, the proposed approach formulates the self-configuration problem as a Markov Decision Process (MDP), with parameters instantiated using semantic information and rules. A novel Feedback-based Reinforcement Learning approach is then proposed to adjust the reward function and state transition parameters based on user feedback. The performances of the proposed approach against a bench-marked fixed and rule-based mechanisms are demonstrated through simulation. © 2014 IEEE.

关键词： Smart homes

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Social NSTransformers: Low-Quality Pedestrian Trajectory Prediction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5575-5588页

作者： Jiang, Zihan Ma, Yiqun Shi, Bingyu Lu, Xin Xing, Jian Goncalves, Nuno Jin, Bo Suzhou215000 China Northeast Forestry University College of Computer and Control Engineering Harbin150000 China University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal

This article introduces a novel model for low-quality pedestrian trajectory prediction, the social nonstationary transformers (NSTransformers), that merges the strengths of NSTransformers and spatiotemporal graph transformer (STAR). The model can capture social interaction cues among pedestrians and integrate features across spatial and temporal dimensions to enhance the precision and resilience of trajectory predictions. We also propose an enhanced loss function that combines diversity loss with logarithmic root mean squared error (log-RMSE) to guarantee the reasonableness and diversity of the generated trajectories. This design adapts well to complex pedestrian interaction scenarios, thereby improving the reliability and accuracy of trajectory prediction. Furthermore, we integrate a generative adversarial network (GAN) to model the randomness inherent in pedestrian trajectories. Compared to the conventional standard Gaussian distribution, our GAN approach better simulates the intricate distribution found in pedestrian trajectories, enhancing the trajectory prediction's diversity and robustness. Experimental results reveal that our model outperforms several state-of-the-art methods. This research opens the avenue for future exploration in low-quality pedestrian trajectory prediction. © 2024 IEEE.

关键词： Stars

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Neuroadaptive Containment control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Progress on the Fault Diagnosis Approach for Lithium-ion Battery Systems:Advances,Challenges,and Prospects

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Protection and control of Modern Power Systems 2024年第5期9卷 16-41页

作者： Hanxiao Liu Luan Zhang Liwei Li School of Control Science and Engineering Shandong UniversityJinan 250061China School of Computer Science and Technology Shandong UniversityQingdao 266237China

Because of their advantages of high energy and power density,low self-discharge rate,and long lifespan,lithium-ion batteries(LIBs)have been widely used in many applications such as electric vehicles,energy storage systems,smart grids,***,lithium-ion battery systems(LIBSs)frequently malfunction because of complex working conditions,harsh operating environment,battery inconsistency,and inherent defects in battery ***,safety of LIBSs has become a prominent problem and has attracted wide ***,efficient and accurate fault diagnosis for LIBs is very *** paper provides a comprehensive review of the latest research progress in fault diagnosis for ***,the types of battery faults are comprehensively introduced and the characteristics of each fault are ***,the fault diagnosis methods are systematically elaborated,including model-based,data processing-based,machine learning-based and knowledge-based *** latest research is discussed and existing issues and challenges are presented,while future developments are also *** aim is to promote further researches into efficient and advanced fault diagnosis methods for more reliable and safer LIBs.

关键词： Battery management system battery safety fault diagnosis lithiumion battery system

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