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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Leko, Dorijan Vasak, Mario University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Renewable Energy Systems Zagreb10000 Croatia

ISBN: (纸本)9798350335446

Battery management system (BMS) affects the safety and performance of electric vehicles by providing adequate power ranges (e.g., state of power or power profile of maximum energy) to the vehicle dynamics control. The main task of the BMS is to ensure battery longevity while maximizing the power ranges the battery can give or receive. This paper proposes a procedure for estimating the state of power (SoP) and maximum energy (ME) for a lithium-ion battery cell based on sequential linear programming and using the nonlinear dual polarization equivalent circuit model (ECM) with average power input. The estimation procedure is based on the optimization problem with linear optimization criterion and nonlinear constraints on a fixed prediction horizon. The ECM with power input enables timewise efficient on-line SoP and ME estimation for battery charging and discharging. The SoP and ME estimation is verified through a case study by using the derived ECM with power input. © 2023 IEEE.

关键词： Lithium-ion batteries

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Performance Analysis of Multivariate Autoregression Based EEG Data Compressor Circuit 3rd

Performance Analysis of Multivariate Autoregression Based EE...

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The 3rd International Conference on Frontiers in computing and systems (COMSYS 2022)

作者： Chowdhury, Md. Mushfiqur Rahman Chowdhury, Shubhajit Roy Biomedical Systems Laboratory School of Computing and Electrical Engineering Indian Institute of Technology HP Mandi India

ISBN: (纸本)9789819926794

This paper presents a Field Programmable Gate Array (FPGA)-based effective real-time data compressor circuit for lossless compression of single and multichannel EEG data. The main goal of this design is to develop real-time architecture that gives us live EEG data which can be sent to a doctor wirelessly and further which may monitor signal abnormalities in EEG when a seizure occurs. The circuit implements an efficient algorithm resulting in high compression ratio. The bio-signal compressing algorithm is based on the combination of modified multivariate autoregression method and data entropy prediction algorithm. The prediction algorithm also includes the application of low-pass FIR Filter. The algorithm has been implemented in hardware using Verilog HDL and the system has been implemented on a Xilinx Zynq Ultrascale MPSoC ZCU 104 FPGA board. The resulting compression ratio (CR) of the proposed method is 76.2% on average, in some cases, it goes up to 99% for a few channels, which is more than any other state-of-the-art algorithms. The performance of the algorithm and circuit both have been evaluated and compared with the modern peer designs. The signal parameters have been evaluated, where the signal accuracy is 92.7% on average for several datasets. The circuit has a power consumption of 0.511 micro-Watts. This FPGA-based solution uses the hardware resources effectively while achieving high-speed signal recovery. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electroencephalography

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Performance *** Comparative Analysis of Multimodal Bilinear Pooling Fusion Approaches for Deep Learning-Based Visual Arabic-Question Answering systems

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Computer Modeling in engineering & Sciences 2025年第4期143卷 373-411页

作者： Sarah M.Kamel Mai A.Fadel Lamiaa Elrefaei Shimaa I.Hassan Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer and Systems Engineering Faculty of Engineering and TechnologyBadr University in Cairo(BUC)Cairo11829Egypt Communication Systems Engineering Department Faculty of EngineeringBenha National UniversityObour11846QalyubiaEgypt

Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate *** this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in *** support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the *** the model complexity and the overall model *** fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA *** far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no ***,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA *** indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that *** the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model *** Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.

关键词： Arabic-VQA deep learning-based VQA deep multimodal information fusion multimodal representation learning VQA of yes/no questions VQA model complexity VQA model performance performance-complexity trade-off

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Dynamic Event-Triggered Leader-Following Consensus of Multiagent systems under DoS Attacks 43

Dynamic Event-Triggered Leader-Following Consensus of Multia...

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43rd Chinese Control Conference, CCC 2024

作者： Ma, Ji Chen, Xiangyong Wu, Yue Sun, Ji Liu, Jianhua Linyi University The Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong School of Automation and Electrical Engineering Linyi276005 China

ISBN: (纸本)9789887581581

In this paper, the leader-following consensus of multi-agent systems (MASs) under denial-of-service (DoS) attacks over undirected networks is studies. First, introduce an estimator under DoS attacks to design a dynamic event-triggered communication protocol, which avoids continuous communication between agents. Then, with the presence of the estimator between the triggering instants can reduce the state shift. In contrast to the conventional method, the existence of dynamic threshold need fewer triggering times. Under this control strategy, the systems only need the states in triggering instants according to estimated states. Finally, verify the effectiveness of the proposed approach by a simulation example. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Denial-of-service attack

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Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML 2nd

Edge Intelligence for Wildlife Conservation: Real-Time Hornb...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Hing, Kong Ka Behjati, Mehran Saleh, Vala Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Electrical Engineering Shiraz University of Technology Shiraz Iran Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real-time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno-canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel-Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real-world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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Velocity control of a Stephenson III six-bar linkage-based gait rehabilitation robot using deep reinforcement learning

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Neural computing and Applications 2025年第7期37卷 5671-5682页

作者： Kapsalyamov, Akim Brown, Nicholas A. T. Goecke, Roland Jamwal, Prashant K. Hussain, Shahid School of Information Technology and Systems University of Canberra CanberraACT2617 Australia School of Clinical Sciences Queensland University of Technology BrisbaneQLD Australia School of Systems and Computing UNSW CanberraACT Australia Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan City Kazakhstan

Lower limb rehabilitation robots can help to improve the locomotor capabilities of patients experiencing gait impairments and help medical workers by reducing strain on them. However, since commercially available exoskeletons are expensive and there is a lack of number of physiotherapists many patients are still not able to get proper rehabilitation training. The closed-loop linkage mechanisms have recently drawn much attention in the realization of gait rehabilitation robots. Such mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. In this work, we have proposed a fully operational one degree-of-freedom mechanism which can generate complex naturalistic lower limb trajectories. Although in theory, it is assumed that the constant speed applied at the input crank is sufficient to control the system, in reality, the external forces exerted by human legs and the inertia of the links can greatly alter the rotational velocity at the crank, which may negatively affect the training process. Therefore, we have explored the performance of a deep reinforcement learning-based control algorithm designed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Exoskeleton (Robotics)

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Collective Citizen Energy Investment Models for Solar Power: Case Study Croatia

Collective Citizen Energy Investment Models for Solar Power:...

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2023 International Conference on Future Energy Solutions, FES 2023

作者： Herenčić, Lin Capuder, Tomislav Kirac, Mislav Čačić, Goran University of Zagreb Faculty of Electrical Engineering and Computing Department of Energy and Power Systems Zagreb Croatia Zagreb Croatia

ISBN: (纸本)9798350332308

Cost-competitiveness of decentralized renewable energy sources (RESs) has opened opportunities for citizens' ownership of RES and for the development of new business models. Citizen energy investment models based on cooperative principles have gained increased attention lately due to promises to support local economic welfare, boost local economy, increase social cohesion, help in grid optimization, and increase citizens' support for new RES projects. In this paper, we provide an overview of collective citizen energy investment concepts and describe the six types of models being developed in Croatia. We provide an overview of the initiatives together with the challenges, threats, and opportunities found when concepts are faced with the real-life practice in Croatia. We show that even though citizens' ownership of RES (mainly individual solar PV systems) is strongly increasing, the current developments of collective (group) citizen energy investment models are predominantly related to sporadic research projects and enthusiasts' initiatives, meaning still no wider-scope investments in RES have been conducted this way. Following the findings, we provide recommendations for removing the barriers and speeding up the development of such projects in the future. © 2023 IEEE.

关键词： Solar energy

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Probabilistic Distributed Intrusion Detection For Zero-Trust Multi-Access Edge computing 36

Probabilistic Distributed Intrusion Detection For Zero-Trust...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Sharma, Rahul Chan, Chien Aun Leckie, Christopher The University of Melbourne School of Computing and Information Systems Australia The University of Melbourne Department of Electrical and Electronic Engineering Australia

ISBN: (纸本)9781665477161

With the advent of Multi-Access Edge computing (MEC) in 5G, there is a shift in the core processing and deployment of applications to the edge of the network. This enables applications requiring ultra low latency response times and high bandwidth but also exposes them to new cyber attacks. One of the proposed ways to strengthen application security in MEC is to use Zero Trust Strategies (ZTS). However, despite its strong authentication and authorisation capabilities, it is still vulnerable to attacks and requires further mechanisms to detect anomalies, especially across multiple clusters. Collaborative Intrusion Detection systems (CIDS) have been proposed for detecting sophisticated coordinated attacks but often fall short in detecting focused stealthy attacks. In this paper, we present a novel tree-based Probabilistic Distributed CIDS to detect stealthy and volumetric anomalies in service-to-service interactions deployed across multiple edge clusters with a Zero Trust Network setup. We perform our evaluation based on detection accuracy and network overhead and outline its performance improvement against a traditional Distributed CIDS and a previously proposed Hybrid CIDS. Based on an evaluation using telemetry data from real-world application clusters, our novel CIDS resulted in improvements in detection accuracy of 99.4% and network overhead of up to 51.81% when detecting anomalies in service path interactions, HTTP methods, service endpoint interactions, and unusual variance in numerical values comprising request size, response size, and traffic processing duration. © 2023 IEEE.

关键词： Intrusion detection

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Magnetic Flux Servoing for Precise Localization Based on Gradient Tensor Contractions 19

Magnetic Flux Servoing for Precise Localization Based on Gra...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Martinovic, Dean Orsag, Matko University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems Zagreb10000 Croatia

ISBN: (纸本)9798350320695

This paper focuses on bridge inspections performed by intelligent unmanned aerial vehicles (UAV). For this, small data loggers are placed by the UAVs at the bridge, which have to be removed later. In our previous work we presented the magnetic localization method SRIOD, which not only detects the center of the data logger but also its orientation. In this work we proof SRIOD in a real application and let a robotic arm locate a magnetic target. The hardware involved is presented including the control strategy for the robot. The motions of the robot and the magnetic target are captured with 12 Optitrack cameras in order to validate the localization quality. © 2023 IEEE.

关键词： Antennas

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Charging Control of A Vanadium Redox Flow Battery Integrated with Solar PV Plant 32

Charging Control of A Vanadium Redox Flow Battery Integrated...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Hajihosseini, Mojtaba Car, Mateja Aleem, Zeeshan Lešić, Vinko University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Renewable Energy Systems Croatia Vestas Arhus Denmark

ISBN: (纸本)9798350399714

Renewable energy sources such as solar and wind power are increasingly being used as alternative energy sources globally to create a low carbon society. For the next generation grid with significant share of distributed energy sources, energy storage technologies are essential. Vanadium redox flow batteries (VRFBs) have many advantages over other energy storage technologies. In the context of the solar plant operation, these batteries can significantly improve ancillary services of frequency regulation and voltage control to support the grid. This paper develops a VRFB model to apply a stand-alone solar system for residential applications. Both maximum power point tracking and voltage control modes are applied to the solar system. A bidirectional DC-DC converter is designed for management of the 5 kWh battery system. The vanadium redox flow battery (VRFB) is used to control the unpredictability of energy supply, serving both as an energy source and a consumer. Introduction of the VRFB and corresponding energy management system enables smoothening and energy shift of solar output, enhances the system reliability with real-time monitoring, operation, regulation, stable and effective operation, and overall system performance optimization. Described system is validated by simulations in MATLAB/Simulink. © 2023 IEEE.

关键词： Maximum power point trackers

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