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international conference on Automation, computing and Renewable systems, ICACRS 2022 - Proceedings

International Conference on Automation, Computing and Renewa...

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1st ieee international conference on Automation, computing and Renewable systems, ICACRS 2022

ISBN: (纸本)9781665460842

The proceedings contain 251 papers. The topics discussed include: optically controlled microwave sensor for biomedical applications;deep learning network for object detection under the poor lighting condition;a brief analysis on machine learning classifiers for intrusion detection to enhance network security;a brief review on melanoma diagnosis models using machine learning techniques;equilibrium optimizer with deep learning model for autism spectral disorder classification;automated intracranial hemorrhage detection and classification using rider optimization with deep learning model;heart disease prediction and classification using machine learning and transfer learning model;review of machine learning algorithms for autism spectrum disorder prediction;novel approach to non-invasive blood glucose monitoring based on visible laser light;a machine learning based approach for breast cancer prediction;covid-19 infection segmentation using deep learning techniques;retinal fundus image retrieval and classification using optimal deep learning model;breast cancer segmentation by k-means and classification by machine learning;an extensive review of machine learning techniques for EEG signal processing;hybrid machine learning based false data injection attack detection and mitigation model for waste water treatment plant;twitter sentiment analysis with machine learning;hybrid particle swarm optimization with deep learning driven sarcasm detection on social media;design of kernel extreme learning machine based intelligent crop yield prediction model;and reliable densely connected network with machine learning based diabetic retinopathy grading approach.

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An Energy-Efficient Metaheuristic for Offloading Security-Critical Tasks in Mobile Edge computing with Integrated DPUs

An Energy-Efficient Metaheuristic for Offloading Security-Cr...

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intelligent Technology and Embedded systems (ICITES), international conference on

作者： Shaoying Hong Jiayin Zhu Yiqi Yuan Jin Sun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China International Joint Audit Institute Nanjing Audit Technology Nanjing China

In mobile edge computing (MEC) paradigm, for the security-critical computation tasks offloaded from the mobile device, the MEC server needs to decrypt the encrypted task data before task execution, leading to heavy computation load on the CPUs of the MEC server. The integration of data processing unit (DPU) into the MEC server can release CPUs from data decrpytion for the purpose of reducing computation and energy overheads. This paper studies a task offloading problem in a MEC system with integrated DPUs, which aims at reducing the total energy consumption under the deadline constraint on task completion time. We formulate the studied problem as an combinatorial optimization model and propose a group mapping-based cuckoo search (GMCS) metaheuristic algorithm to explore high-quality energy-efficient solutions to the formulated model. The proposed GMCS introduces a group mapping operator and a greedy-based task offloading strategy for converting cuckoo individuals into offloading solutions and evaluating the objective values of the converted solutions, respectively. We provide a theoretical analysis to justify that the mapping operator can improve the diversity of the converted solutions and in turn the metaheuristic’s searching capability. We create various testing instances for a MEC system equipped with DPUs to demonstrate the proposed GMCS produces high-quality solutions to the studied offloading problem, with reduced energy consumption compared with baseline algorithms.

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Towards the Integration of Mixed Reality and Machine Learning for Older Adults Personalized Smart Home and Health Monitoring

Towards the Integration of Mixed Reality and Machine Learnin...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), ieee international conference on

作者： Atieh Mahroo Marco Sacco Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) National Research Council of Italy (CNR) Lecco Italy Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

This contribution proposes a multi-modal system for older adults' health monitoring within the smart home framework that integrates Mixed Reality, a network of IoT sensors, wearable health sensors, and users' feedback to collect data and monitor older adults' health. It provides a comprehensive framework for older adults' health monitoring and safe exercise sessions to enhance their physical and cognitive well-being. The proposed system allows older adults to control the smart home features and benefit from assistive services via a mixed reality application. The system explores exploiting machine learning models to provide personalized physical and cognitive training routines to enhance older adults' quality of life, autonomy, and general well-being. It leverages historical data coming from wearable sensors, smart mixed reality glasses, IoT sensors, and users' feedback to predict the users' health status and activity level and suggest the proper exercise routine accordingly.

关键词： Training Mixed reality Smart homes Machine learning Sensor systems Older adults Biomedical monitoring intelligent sensors Wearable sensors Monitoring

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Research on Parameter Identification Method for Robotic Manipulators Joint Friction Model Based on PINN

Research on Parameter Identification Method for Robotic Mani...

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ieee/ASME (AIM) international conference on advanced intelligent Mechatronics

作者： Di Luo Zhiqin Cai Da Jiang Haijun Peng Jianghuai Advance Technology Center Hefei China Department of Engineering Mechanics Dalian University of Technology Dalian China National Engineering Laboratory for Wheeled Vehicle China North Vehicle Research Institute Beijing China Department of Engineering Mechanics and State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian China

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

In the research and application of robotic manipulator systems, the friction phenomenon poses challenges to system stability and control precision. To further improve the parameter identification accuracy in traditional friction modeling for robotic manipulators, this paper proposes a friction model parameter identification method based on the Physics Informed Neural Network (PINN). The proposed method takes the relative velocity and normal pressure in the motion of the robotic manipulators as the information input items, with the friction and model parameters as outputs. The network parameters and identification parameters are updated according to the Adam method, achieving a more precise identification of friction parameters. It comprehensively considers friction mechanism information and data information to jointly construct the objective optimization function. Through simulation comparisons with noisy/noise-free data, the PINN method is validated to have higher identification accuracy than Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), with an average reduction of $30 \%$ and $50 \%$ in the identification error rates for noise-free and noisy data, respectively.

关键词： Parameter estimation Accuracy Error analysis Friction Noise Manipulators data models

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Voltage Regulation in Polymer Electrolyte Fuel Cell systems Using Gaussian Process Model Predictive Control

Voltage Regulation in Polymer Electrolyte Fuel Cell Systems ...

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ieee/RSJ international conference on intelligent Robots and systems (IROS)

作者： Xiufei Li Miao Yang Miao Zhang Yuanxin Qi Zhuowei Li Senbin Yu Yuantao Wang Linpeng Shen Xiang Li Department of Energy Sciences Lund University Lund Sweden College of Computing and Data Science Nanyang Technological University Singapore Shenzhen International Graduate School Tsinghua University Shenzhen China The University of Nottingham China Gala Sports China Beijing University of Technology China Nantong University China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study presents a novel approach using Gaussian process model predictive control (MPC) to stabilize the output voltage of a polymer electrolyte fuel cell (PEFC) by regulating hydrogen and airflow rates. Two Gaussian process models capture PEFC dynamics, accounting for constraints like hydrogen pressure and input change rates to reduce predictive control errors. The performance of the physical model and Gaussian process MPC in handling constraints and system inputs is compared. Simulations show that the proposed Gaussian process MPC maintains the voltage at 48 V while adhering to safety constraints, even with workload disturbances from 110-120 A. Compared to traditional MPC with detailed system models, Gaussian process MPC has similar overshoot and slower response time but requires less system information and no underlying true system model.

关键词： Atmospheric modeling Hydrogen Fuel cells Gaussian processes Predictive models Safety Time factors Polymers Voltage control Predictive control

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OMEx-DriveTrain: An Open, Modular Experimental Platform for the Electric Powertrain

OMEx-DriveTrain: An Open, Modular Experimental Platform for ...

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ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Daniel Fruhner Robert Kleinert Carsten Wolff Friedbert Pautzke Heinrich Zöllner Shubhangi Sisodiya Vipul Shrikant Deshpande Dortmund University of Applied Sciences and Arts Dortmund Germany Bochum University of Applied Sciences Bochum Germany

ISBN: (纸本)9781665426060

The automotive industry is characterized by a complex and highly distributed development network. As a result, among other things, the coordination of the strongly interlinked process landscape makes it difficult to ensure the development quality of the integrated system in a given time. Model-based systems engineering and agile methods might help to increase the speed of development, but the combination of hardware and software development must be kept synchronized. To overcome this problem, the goal of the OMEx-DriveTrain project is to develop an open, modular and experimental platform for the electric powertrain to develop, integrate and verify complex products in the field of electromobility. In this paper, the realization of the open, modular and experimental platform will be introduced.

关键词： Industries conferences data acquisition Mechanical power transmission Software Hardware Synchronization

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Explainable Cancer Segmentation Through Classification

Explainable Cancer Segmentation Through Classification

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ieee international conference on intelligent Computer Communication and Processing (ICCP)

作者： Andrei Dugăeşescu Cătălin-Mihail Chiru Mihai Nan Mihai Trăscău Adina Magda Florea Computer Science Department National University of Science and Technology POLITEHNICA Bucharest Romania

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

Lung cancer detection remains a challenging task due to the complex nature of medical imaging and the shortage of specialized radiologists. This paper addresses the challenges of early detection of lung cancer using advanced machine learning techniques for CT image analysis. Given the limitations of current semantic segmentation methods and the lack of interpretability, we propose an innovative approach combining classification techniques with explanatory methods to enhance both precision and transparency. We developed a novel data preprocessing pipeline, a multi-stage CNN classifier training model, and a segmentation technique using concatenated Grad-CAM heatmaps. Our evaluations demonstrate significant improvements in tumour detection and model explainability, providing more effective support for radiologists. These advancements are reflected in increased segmentation accuracy, as evidenced by comparative analyses and qualitative assessments.

关键词： Training Heating systems Adaptation models Accuracy Semantic segmentation Lung cancer data models Cams Medical diagnostic imaging Tumors

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Combined Longitudinal-Lateral Dynamic Modeling and Control via an Integrated Physics-data-Based Approach

Combined Longitudinal-Lateral Dynamic Modeling and Control v...

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ieee/ASME (AIM) international conference on advanced intelligent Mechatronics

作者： Wenpeng Wei Zhaoyu Qiu Xiaoyuan Zhu Guodong Yin Tianyi He School of Mechanical Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Department of Mechanical and Aerospace Engineering Utah State University Logan UT USA

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

This paper presents an Integrated Physics-dataBased (IPDB) modeling and control scheme of the combined longitudinal-lateral vehicle dynamics. A nonlinear bicycle vehicle model is used to derive the linear parameter-varying (LPV) system representation, where four vehicle motion variables are considered as scheduling parameters. Taking advantage of kernels from LPV representation, the combined longitudinal-lateral dynamics are further expressed by the data snapshots of states, inputs, and scheduling parameters, which formulate the IPDB model. After that, the IPDB model is used to design a state-feedback gain-scheduling tracking controller to follow a reference trajectory. For validation purposes, the proposed modeling and control method is implemented on a QCar experimental platform. First, the IPDB model of coupled longitudinal-lateral dynamics is derived from experimental data and is further validated with excellent model accuracy under various driving conditions. Furthermore, an IPDB model-based gain-scheduling controller is synthesized and compared with the baseline Stanley controller in the experiment to track a given trajectory. The experimental results demonstrate that the IPDB model-based controller renders better tracking control performance.

关键词： Accuracy Mechatronics Tracking Optical variables measurement Dynamic scheduling data models Trajectory

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Implementing an intelligent Energy Management Framework for IoT Edge Devices

Implementing an Intelligent Energy Management Framework for ...

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ieee UP Section conference on Electrical Computer and Electronics (UPCON)

作者： Rajeev Kanday Ramandeep Singh School of Computer Science and Engineering Lovely Professional University Punjab

Cloud computing is a well-established model for creating service-centric systems in a traditional scenario. However, when it comes to analyzing and delivering results for a vast quantity of data, cloud computing is constrained by ultra-low latency, high bandwidth, security, and real-time analytics. These constraints can be overcome via with the hybrid design offered by fog and edge computing. The fog and edge based systems reduce the processing time, save operating expenses, and boost security. In edge computing, computational resources are distributed at the network's edge In order to build and run edge computing platforms, additional challenges have been created by the operational size of the system. computing at the edge decreases latency, increases security, and enhances privacy by offloading tasks from cloud data centers to edge routers and edge nodes. There has been far less attention paid to energy efficiency in edge computing than in cloud data centers due to the complex relationships between edge devices, edge servers, and cloud data centers. Testing the energy efficiency of edge devices, edge servers, and cloud data centers is essential to achieving energy efficiency with edge computing. The purpose of this paper is to provide a general overview of the background and development of edge computing. Our discussion then moved to the key areas involved in edge computing. The final section of the paper discusses the challenges and opportunities associated with edge computing.

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Information Encryption Security System Based on Chaos Algorithm 7

Information Encryption Security System Based on Chaos Algori...

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7th international conference on Cyber Security and Information Engineering, ICCSIE 2022

作者： Liu, Xiya Intelligent Manufacturing College Tianjin Sino-German University of Applied Sciences Tianjin China

ISBN: (纸本)9781665455749

Chaotic cryptography is structurally related to the concepts of confusion and diffusion in traditional cryptography theory. Chaotic cryptography is formed by the inevitable connection between chaos theory and pure cryptography. In order to solve the shortcomings of the existing research on information encryption security system, this paper discusses the realization technology of information security, the design principles of encryption system and three kinds of chaotic mapping systems, and discusses the selection of development tools and programmable devices. And the information encryption security system based on chaos algorithm is designed and discussed, and the randomness test of three groups of encrypted files is carried out by the proposed algorithm and the AES (advanced Encryption Standard) algorithm. Experimental data show that the uniformity of P-value value of chaos algorithm is 0.714 on average. Therefore, it is verified that the information encryption security system using chaos algorithm has high security. © 2022 ieee.

关键词： Cryptography

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