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International Conference on Electrical Drives and Power Electronics (EDPE)

作者： Tomislav Ivaniš Marinko Kovačić Department of Electric Machines Drives and Automation Unska 3 10000 Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

Silicon carbide (SiC) power transistors have been steadily gaining popularity in power electronics applications as they have improved properties over silicon based devices, namely silicon MOSFETs and IGBTs. However, because of very high switching speed which results in lower losses, in general application their use is often a concern because of electromagnetic interference (EMI). In order to fully utilise the potential of SiC based devices, a careful design of power stage, commutation loop and gate drive is needed. Because of this reason, this paper presents a review of existing gate drive methods for performance improvement of SiC devices. A special emphasis is given to the gate drive concepts, as it is an important element for the further advancement of SiC-based converters. With improved switching, specifically active gate driving techniques a compromise between EMI and switching losses can be achieved.

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Node Importance Estimation Leveraging LLMs for Semantic Augmentation in Knowledge Graphs

arXiv

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arXiv 2024年

作者： Lin, Xinyu Zhang, Tianyu Hou, Chengbin Wang, Jinbao Xue, Jianye Lv, Hairong School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China Department of Automation Tsinghua University Beijing China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

Node Importance Estimation (NIE) is a task that quantifies the importance of node in a graph. Recent research has investigated to exploit various information from Knowledge Graphs (KGs) to estimate node importance scores. However, the semantic information in KGs could be insufficient, missing, and inaccurate, which would limit the performance of existing NIE models. To address these issues, we leverage Large Language Models (LLMs) for semantic augmentation thanks to the LLMs’ extra knowledge and ability of integrating knowledge from both LLMs and KGs. To this end, we propose the LLMs Empowered Node Importance Estimation (LENIE) method to enhance the semantic information in KGs for better supporting NIE tasks. To our best knowledge, this is the first work incorporating LLMs into NIE. Specifically, LENIE employs a novel clustering-based triplet sampling strategy to extract diverse knowledge of a node sampled from the given KG. After that, LENIE adopts the node-specific adaptive prompts to integrate the sampled triplets and the original node descriptions, which are then fed into LLMs for generating richer and more precise augmented node descriptions. These augmented descriptions finally initialize node embeddings for boosting the downstream NIE model performance. Extensive experiments demonstrate LENIE’s effectiveness in addressing semantic deficiencies in KGs, enabling more informative semantic augmentation and enhancing existing NIE models to achieve the state-of-the-art performance. The source code of LENIE is freely available at https://***/XinyuLin-FZ/LENIE. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Enhancing Long-range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-based Sentiment Analysis 31

Enhancing Long-range Dependency with State Space Model and K...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Lawan, Adamu Pu, Juhua Yunusa, Haruna Umar, Aliyu Lawan, Muhammad School of Computer Science and Technology Beihang University Beijing China National Research Institute for Teaching Materials in Information Science and Tech. China School of Automation Science and Electrical Engineering Beihang University Beijing China School of Computing University of Portsmouth Portsmouth United Kingdom Department of Information and Communication Technology Federal University Gusau Nigeria

ISBN: (纸本)9798891761964

Aspect-based Sentiment Analysis (ABSA) evaluates sentiments toward specific aspects of entities within the text. However, attention mechanisms and neural network models struggle with syntactic constraints. The quadratic complexity of attention mechanisms also limits their adoption for capturing long-range dependencies between aspect and opinion words in ABSA. This complexity can lead to the misinterpretation of irrelevant contextual words, restricting their effectiveness to short-range dependencies. To address the above problem, we present a novel approach to enhance long-range dependencies between aspect and opinion words in ABSA (MambaForGCN). This approach incorporates syntax-based Graph Convolutional Network (SynGCN) and MambaFormer (Mamba-Transformer) modules to encode input with dependency relations and semantic information. The Multihead Attention (MHA) and Selective State Space model (Mamba) blocks in the MambaFormer module serve as channels to enhance the model with short and long-range dependencies between aspect and opinion words. We also introduce the Kolmogorov-Arnold Networks (KANs) gated fusion, an adaptive feature representation system that integrates SynGCN and MambaFormer and captures non-linear, complex dependencies. Experimental results on three benchmark datasets demonstrate MambaForGCN's effectiveness, outperforming state-of-the-art (SOTA) baseline models. © 2025 Association for Computational Linguistics.

关键词： Semantics

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On Measures Resulting from the Choquet Integration 20th

On Measures Resulting from the Choquet Integration

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20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024

作者： Ontkovičová, Zuzana Torra, Vicenç Department of Computing Science Umeå University Umeå Sweden Institute of Information Engineering Automation and Mathematics Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Bratislava Slovakia

ISBN: (纸本)9783031739996

In this paper, we study measures arising as a result of the Choquet integration with respect to a particular class of measures. The initial insight is provided with several classes of additive and fuzzy measures. It can be seen that some classes are closed regarding the integration, e.g. probabilities, while some are not, such as distorted Lebesgue measures. Knowing both an integration measure and a resulting measure after the Choquet integration directly leads to a fuzzy analogue of the Radon-Nikodym derivatives. For them, a completely different possible approach to their existence is presented for a specific pair of measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Integration

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A Review on Multi-Agent Systems and JADE Applications in Microgrids

A Review on Multi-Agent Systems and JADE Applications in Mic...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Süleyman Emre Eyimaya Necmi Altin Adel Nasiri Department of Electronics and Automation TUSAS-Kazan Vocational School GaziUniversity Ankara Turkey Department of Electrical-Electronics Engineering Faculty of Technology Gazi University Ankara Turkey Electrical Engineering Department College of Engineering and Computing University of South Carolina (USC) SC USA

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

The microgrid systems include distributed energy resources such as photovoltaic systems, wind energy conversion systems, and synchronous generators, energy storage devices, and loads. In order to achieve a smooth operation of all these resources and energy sutainability, a proper control mechanism is needed that considers elements of the system and optimization goals. In this control mechanism, each unit’s situation is important and therefore, a communication system within the microgrid must be established. Thanks to this communication, units are aware of each other's situation and can determine actions. This communication is provided by agents assigned as message carriers between units in a microgrid. Multi-agent systems have emerged as a promising approach to realize and optimize energy management in microgrids. In this study, agent and multi-agent system structures used in microgrids are introduced and communication platforms are examined. Java Agent Development Environment (JADE), one of the platforms used to design multi-agent-based systems, is reviewed and its use in microgrids is described.

关键词： Reviews Decentralized control Microgrids Synchronous generators Distributed power generation Smart grids Energy management

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UGotMe: An Embodied System for Affective Human-Robot Interaction

arXiv

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arXiv 2024年

作者： Li, Peizhen Cao, Longbing Wu, Xiao-Ming Yu, Xiaohan Yang, Runze School of Computing Macquarie University Australia School of Computer Science and Engineering Sun Yat-sen University China Department of Automation Shanghai Jiao Tong University China

Equipping humanoid robots with the capability to understand emotional states of human interactants and express emotions appropriately according to situations is essential for affective human-robot interaction. However, enabling current vision-aware multimodal emotion recognition models for affective human-robot interaction in the real-world raises embodiment challenges: addressing the environmental noise issue and meeting real-time requirements. First, in multiparty conversation scenarios, the noises inherited in the visual observation of the robot, which may come from either 1) distracting objects in the scene or 2) inactive speakers appearing in the field of view of the robot, hinder the models from extracting emotional cues from vision inputs. Secondly, real-time response, a desired feature for an interactive system, is also challenging to achieve. To tackle both challenges, we introduce an affective human-robot interaction system called UGotMe designed specifically for multiparty conversations. Two denoising strategies are proposed and incorporated into the system to solve the first issue. Specifically, to filter out distracting objects in the scene, we propose extracting face images of the speakers from the raw images and introduce a customized active face extraction strategy to rule out inactive speakers. As for the second issue, we employ efficient data transmission from the robot to the local server to improve real-time response capability. We deploy UGotMe on a human robot named Ameca to validate its real-time inference capabilities in practical scenarios. Videos demonstrating real-world deployment are available at https://***/HumanoidVLE/ © 2024, CC BY.

关键词： Anthropomorphic robots

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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Multi-Agent Reinforcement Learning for Wireless Networks Against Adversarial Communications

Multi-Agent Reinforcement Learning for Wireless Networks Aga...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zefang Lv Yifan Chen Liang Xiao Helin Yang Xiangyang Ji Department of Information and Communication Engineering Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University Department of Automation Tsinghua University

Based on the efficient and reliable exchange of learning messages containing both the policy selection experiences such as the learning parameters and observations among the learning agents, multi -agent reinforcement learning (RL) has to address adversarial communications that send fake learning messages to learning agents with the goal of decreasing the RL rewards or even failing the learning tasks. In this paper, we propose a multi-agent RL (MARL) communication framework for wireless networks against adversarial communications, in which each learning agent chooses the cooperative agents to share learning messages based on the agent reputation that indicates the probability to send fake learning messages. By comparing with the learning history, each learning agent authenticates the received learning messages before integrating them in the RL task state formulation and the learning parameter update for robust task learning. The learning factor that increases with the correlation between the local and the shared observation is calculated to update the task Q-values and neural network weights based on the shared learning parameters. As a case study, the multi-agent deep Q-network within our proposed MARL communication framework is implemented in the UAV swarm video transmission system and the performance gain over the benchmark is provided in the simulation results based on 5-UAV swarm against an attacker that sends fake observations and neural network weights.

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Code Clone Detection Using Boosting Algorithms 3

Code Clone Detection Using Boosting Algorithms

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3rd International Conference on Advanced Research in computing, ICARC 2023

作者： Thanoshan, M.V. Banujan, Kuhaneswaran Kumara, B.T.G.S. Prasanth, S. Li, Zhenni Paik, Incheon Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technologies Belihuloya Sri Lanka Guangdong University of Technology School of Automation Guangzhou510006 China University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Japan

ISBN: (纸本)9798350347371

To increase programming productivity, developers often copy and paste the source code with or without changing it. However, they may also introduce significant downsides in the long run, including complicating the software and raising maintenance costs. The activity of duplicating the code is known as code cloning. They are classified into four types - Type-1, Type-2, Type-3, and Type-4. In this paper, the author presents a machine-learning approach for detecting code clones of all kinds except for Type-2. Abstract Syntax Trees are used to extract features from the methods. A distance combination approach combines two feature vectors of a pair of methods and their class labels. Once the dataset is finalised, a machine- learning approach is utilised to classify the clone type. Moreover, boosting classifiers like XGBoost, CatBoost, LightGBM, Gradient Boosting and AdaBoost are evaluated for the highest classification accuracy. From the results obtained, LightGBM outperformed all the other classifiers with the highest F1 score of 0.81. This study would motivate future researchers to focus on identifying the Type-2 clones and extracting novel features in determining the clone types. © 2023 IEEE.

关键词： Adaptive boosting

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