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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Nandhini, R. Radha, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819767090

The main aim of this survey is to provide wider insight about the use of MEMS (Micro-Electro-Mechanical-System) technology in various interdisciplinary fields. The areas include IoT (Internet-Of-Things) for smart automations, agricultural field for performing precision farming. Automotive sector to predict failures, environmental aspects to detect factors affecting ecosystem, food control to measure the quality. Healthcare for identifying the disease in prior stages, robotics to carry out multiple operations. Machine Learning algorithms play vital role in classifying and extracting the necessary feature from the raw data. Deep Learning techniques like ANN (Artificial Neural Network) is employed for estimation of optimal input and output parameters. CNN (Convolutional Neural Network) for representing the designed structure in binary pixelated image form. The various types of sensors, energy harvesters, transducers and actuators which are designed using MEMS technology and their predominant role in recent advancements has been discussed in brief. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automotive industry

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Multi-View Few-Shot Reasoning for Emerging Entities in Knowledge Graphs

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IEEE Transactions on Big Data 2025年第3期11卷 1321-1333页

作者： Yan, Cheng Zhao, Feng Tao, Xiaohui Zhu, Xiaofeng Huazhong University of Science and Technology Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Wuhan430074 China University of Southern Queensland School of Mathematics Physics & Computing Toowoomba4350 Australia University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China

A knowledge graph (KG) is a form of representing knowledge of the objective world. With the expansion of knowledge, KGs frequently incorporate new entities, which often possess limited associated data, known as few-shot features. Addressing the missing knowledge for these emerging entities is crucial practically, but there are significant challenges due to data scarcity. Previously developed methods based on knowledge graph embedding (KGE) and graph neural networks (GNNs) focusing on instance-level KGs are confronted with challenges of data scarcity and model simplicity, rendering them inapplicable to reasoning tasks in few-shot scenarios. To tackle these issues, we propose a multi-view few-shot KG reasoning method for emerging entities. The primary focus of our method lies in resolving the problem of link prediction for emerging entities with limited associated triples from multiple perspectives. Distinct from previous methods, our approach initially abstracts a concept-view KG from the conventional instance-view KG, enabling the formulation of commonsense rules. Additionally, we employ the aggregation of multi-hop subgraph features to enhance the representation of emerging entities. Furthermore, we introduce a more efficient cross-domain negative sampling strategy and a multi-view triple scoring function based on commonsense rules. Our experimental evaluations highlight the effectiveness of our method in few-shot contexts, demonstrating its robustness and adaptability in both cross-shot and zero-shot scenarios, significantly outperforming existing models in these challenging settings. © 2015 IEEE.

关键词： Knowledge graph

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Quantifying Bytes:Understanding Practical Value of Data Assets in Federated Learning

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Tsinghua Science and technology 2025年第1期30卷 135-147页

作者： Minghao Yao Saiyu Qi Zhen Tian Qian Li Yong Han Haihong Li Yong Qi School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China School of Cyber Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China

The data asset is emerging as a crucial component in both industrial and commercial *** valuable knowledge from the data benefits decision-making and ***,the usage of data assets raises tension between sensitive information protection and value *** an emerging machine learning paradigm,Federated Learning(FL)allows multiple clients to jointly train a global model based on their data without revealing *** approach harnesses the power of multiple data assets while ensuring their *** the benefits,it relies on a central server to manage the training process and lacks quantification of the quality of data assets,which raises privacy and fairness *** this work,we present a novel framework that combines Federated Learning and Blockchain by Shapley value(FLBS)to achieve a good trade-off between privacy and ***,we introduce blockchain in each training round to elect aggregation and evaluation nodes for training,enabling decentralization and contribution-aware incentive distribution,with these nodes functionally separated and able to supervise each *** experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.

关键词： Federated Learning(FL) blockchain fairness

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A Frog-Shaped UWB MIMO Antenna Design for 5G

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Progress in Electromagnetics Research C 2025年 151卷 101-112页

作者： Yao, Shanhua Yang, Tianchu Qiu, Xiaorong Li, Xiang School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China School of Computer Science Huainan Normal University Huainan232001 China

In this paper, a frog-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna is proposed for 5G applications in the n77, n78, n79, and 6 GHz bands with a compact antenna structure of 31 × 55 × 1.5 mm3. The designed antenna consists of two frog-shaped monopole antennas and a floor from which a part has been removed, and the operating bandwidths range from 3.05 to 13.38 GHz, which meets the design criteria for UWB. The T-shaped floor with two T-shaped slots impedes the flow of coupling currents and improves the isolation of the antenna. This results in an isolation of less than −17 dB over the entire operating bandwidth and less than −20 dB in the 5G band. In addition, the envelope correlation coefficient (ECC) is less than 0.007, the diversity gain (DG) more than 9.96, the total active reflection coefficient (TARC) less than −30 dB, and the channel capacity loss (CCL) less than 0.34 bit/s/Hz. The measured and simulated results agree with each other, demonstrating the antenna’s potential application in 5G communication systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Bandwidth

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Minimum Initial Marking Estimation in Labeled Petri Nets Using Minimum Token Number Prediction

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IEEE Transactions on Automatic Control 2025年第5期70卷 3296-3302页

作者： Yue, Hao Xu, Yakun Hu, Hesuan Wu, Weimin Li, Lingxi College of Computer Science and Technology Qingdao266580 China Xidian University School of Electro-Mechanical Engineering Xi'an710071 China Nanyang Technological University School of Computer Science and Engineering College of Engineering 639798 Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Zhejiang University Institute of Cyber-Systems and Control Hangzhou310027 China Purdue University Elmore Family School of Electrical and Computer Engineering College of Engineering IndianapolisIN46202 United States

This article proposes an approach to addressing the problem of minimum initial marking (MuIM) estimation for labeled Petri nets (LPNs). We introduce the important concept of a label synthesis net for LPNs and develop a method for predicting the minimum number of tokens. By using this prediction method, we develop an algorithm that has polynomial complexity in the length of the observed label sequence for estimating MuIMs. An illustrative example is provided to show the effectiveness and efficiency of our proposed approach. Moreover, experimental results demonstrate its advantage over existing work. Finally, we provide a comparison of some representative studies in the literature for MuIM estimation. © 1963-2012 IEEE.

关键词： Discrete event simulation

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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

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computer Modeling in engineering & Sciences 2025年第4期143卷 289-334页

作者： Xiangyang Cao Yaojie Zheng Hanbin Xiao Min Xiao School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Sanya Science and Education Innovation Park Wuhan University of TechnologySanya572024China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan430063China

This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.

关键词： Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells

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ResilioMate: A Resilient Multi-Agent Task Executing Framework for Enhancing Small Language Models

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IEEE Access 2025年 13卷 86892-86911页

作者： Xiong, Yubing Huang, Mingrui Liang, Xuechen Tao, Meiling East China Jiaotong University School of Transportation Engineering Jiangxi Nanchang330013 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China

Recent advances in large language models (LLMs) have been limited by their processing requirements and vulnerability to adversarial assaults, whilst short language models (SLMs) struggle with performance consistency in complex tasks. This research introduces ResilioMate, a resilient multi-agent framework that enhances SLMs by utilizing distributed cognitive burden distribution, dual-scale memory systems, and collaborative bias prevention strategies. The method employs dynamic task decomposition across specialized agents (e.g., Assistant, Checker) to minimize computational costs and combines short-term trajectory tracking with long-term self-reflective optimization for adaptive execution. At its core, the LeptoConnect model series (1.8B/7B parameters) is created using hybrid attention distillation and dynamic curriculum learning to enable cross-domain competence while retaining SLM efficiency. ResilioMate accomplishes three critical improvements: (1) The 1.8B LeptoConnect model attains 81.6% of GPT-4’s performance in knowledge graph construction through parameter-efficient fine-tuning with structured weight matrices;(2) LeptoConnect-7B achieves a score of 41.3 in database operations, compared to GPT-4’s 32.0, through collaborative cognitive load allocation;(3) A bias-interception network effectively suppresses adversarial propagation while achieving code correction performance of ROUGE-L’s 42.86. The framework’s dual-scale memory architecture reduces computational redundancy by 26.4% through real-time task tracking and multi-agent knowledge refinement. These developments illustrate ResilioMate’s efficacy in bridging the performance gap between SLMs and LLMs, offering a scalable solution for the deployment of efficient language agents in real-time and peripheral computing environments. © 2013 IEEE.

关键词： Computational grammars

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Adopting Graph Neural Networks to Understand and Reason About Dynamic Driving Scenarios

IEEE Open Journal of Intelligent Transportation Systems

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IEEE Open Journal of Intelligent Transportation Systems 2025年 6卷 579-589页

作者： Su, Peng Xiang, Conglei Chen, Dejiu KTH Royal Institute of Technology Faculty of Engineering Design Department Stockholm100 44 Sweden KTH Royal Institute of Technology Electrical Engineering and Computer Science School Stockholm100 44 Sweden

With advances in Deep Neural Networks (DNN), Automated Driving Systems (ADS) enable the vehicle to perceive their surroundings in dynamic driving scenarios and perform behaviors by collecting operational data from sensors such as LiDAR and cameras. Current DNN typically detect objects by analyzing and classifying unstructured data (e.g., image data), providing critical information for ADS planning and decision-making. However, advanced ADS, particularly those required to perform the Dynamic Driving Task (DDT) autonomously, are expected to understand driving scenarios across various Operational Design Domains (ODD). This capability requires the support for a continuous comprehension of driving scenarios according to operational data collected by sensors. This paper presents a framework that adopts Graph Neural Networks (GNN) to describe and reason about dynamic driving scenarios via analyzing graph-based data based on collected sensor inputs. We first construct the graph-based data using a meta-path, which defines various interactions among different traffic participants. Next, we propose a design of GNN to support both the classification of the node types of objects and predicting relationships between objects. As results, the performance of the proposed method shows significant improvements compared to the baseline method. Specifically, the accuracy of node classification increases from 0.77 to 0.85, while that of relationships prediction rises from 0.74 to 0.82. To further utilize graph-based data constructed from dynamic driving scenarios, the proposed framework supports reasoning about operational risks by analyzing the observed nodes and relationships in the graph-based data. As a result, the model achieves a MRR of 0.78 in operational risks reasoning. To evaluate the practicality of the proposed framework in real-world systems, we also conduct a real-time performance evaluation by measuring the average process time and the Worst Case Execution Time (WCET). Com

关键词： Graph neural networks

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Multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning 24th

Multiple-Round Aggregation of Abstract Semantics for Secur...

引用

24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhang, Jiao Li, Xiong Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615445

Federated Learning (FL) provides a valuable framework that allows for the collaborative training of models across distributed networks while maintaining the privacy of the data involved. The concept of secure aggregation is crucial in preserving both the privacy and integrity of data within FL systems. However, the deployment of secure aggregation encounters significant barriers. Conventional secure aggregation methods are primarily designed for single-round operations, which can become prohibitively expensive in scenarios that require multiple rounds. Additionally, these methods often struggle in large-scale environments characterized by heterogeneous data and models. This paper introduces the Multiple-Round Aggregation of Abstract Semantics (MRAAS), a novel scheme designed to address the aforementioned limitations in secure aggregation within heterogeneous FL environments. The MRAAS scheme enhances security and reduces the need for extensive data exchanges by aggregating abstracted model semantics instead of raw model gradients, facilitating secure, multiple rounds of aggregation. Key innovations of MRAAS include an advanced abstraction technique, robustness against participant drop-out, and the use of recyclable secrets. These innovations collectively enhance system resilience and reduce the necessity for frequent cryptographic renewals. Our theoretical evaluations focus on the security features of MRAAS. Empirical validations conducted through comparative analyses utilizing two public datasets confirm the superiority of MRAAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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TriCvT-DTI: Predicting Drug-Target Interactions Using Trimodal Representations and Convolutional Vision Transformers

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3451-3464页

作者： Maroua, Azouz Tian, Gang Wang, Rui Zhou, Jiehan Shandong University of Science and Technology Qingdao266510 China Shandong University of Science and Technology School of Computer Science and Engineering Qingdao266510 China Shandong University of Science and Technology College of Energy and Mining Engineering Qingdao266510 China Shandong University of Science and Technology Qingdao China Docent at University of Oulu Oulu Finland

Predicting interactions between drugs and their targets is vital for drug discovery and repositioning. Conventional techniques are slow and labor-intensive, while deep learning algorithms offer efficient solutions. However, deep learning often focus on single drug representations or simplistic combinations, leading to suboptimal feature representation. Moreover, the prevalent use of convolutional neural networks (CNNs) in drug image representation neglects the necessity for both local and global drug information in Drug-Target Interaction (DTI) tasks. To address these challenges, we propose TriCvT-DTI, a novel approach that combines molecular images, chemical sequence features, and graph representations of drugs to comprehensively capture structural, spatial, and functional aspects. TriCvT-DTI introduces a bidirectional multi-head attention mechanism for interactive feature learning between drugs and targets, enhancing performance by modeling complex relationships. By using Convolutional Vision Transformers (CvTs), TriCvT-DTI can effectively extract structural and spatial features from drug images. We evaluate our model on three datasets: Human, C. elegans, and Davis, and we compare it with state-of-the-art methods. Then we train TriCvT-DTI with uni-modality and bi-modality to compare then extract the impact of each modality on TriCvT-DTI. Experimental results demonstrate that TriCvT-DTI outperforms existing methods on both balanced and unbalanced datasets. Moreover, it presents impressive generalization capabilities on the Drug-Target Interaction (DTI) task. © 2013 IEEE.

关键词： Molecular imaging

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