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Multi-attribute Graph Based Mixed Integer Linear Programming Model for a Job Shop Scheduling Problem Considering intelligent Vehicle Transport

Multi-attribute Graph Based Mixed Integer Linear Programming...

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2022 international conference on Machine Learning, Control, and robotics, MLCR 2022

作者： Zhang, Hui Huang, Min School of Economics and Management Jiangsu University of Science and Technology Zhenjiang China College of Information Science and Engineering Northeastern University Stat Key Laboratory of Synthetical Automation for Process Industries Shenyang China

ISBN: (纸本)9781665454599

Nowadays, intelligent vehicles have be used for transporting jobs in many manufacturing plants;thus, routing vehicles properly is very important for processing jobs. This paper studied a job shop scheduling problem considering intelligent vehicle transport. The problem is difficult to be handled because it needs to optimize the route of a vehicle and the processing sequences of jobs on machines simultaneously. To address this problem, a multi-attribute directed graph is proposed, in which a determined vehicle path associated with the attributes of job and machine is defined as a node and an undetermined vehicle path is defined as an arc;thereby, a mixed integer linear programming model is established. Numerical experiments show the effectiveness of the proposed model. © 2022 ieee.

关键词： intelligent vehicle highway systems

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Indigenous IoT and Software Framework for Implementing Digital Twin Over Indian Region Towards Sustainable Development 1

Indigenous IoT and Software Framework for Implementing Digit...

引用

1st international conference on Computing, Sciences and Communications, ICCSC 2024

作者： Agarwal, Nikunj Singla, JaiG. Dhaka, Vijaypal Singh School of Computing and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur India Space Applications Centre ISRO Signal and Image Processing Area Gujarat Ahmedabad India

ISBN: (纸本)9798350353648

Digital Twin is a digital replication of physical things. This technology has been in works for over a decade, and has been implemented in various regions. This study shows the integration of Internet of Things(IoT) with the Digital Twin technology seamlessly within the Indian ecosystem to lead towards sustainable development. The Digital Twin model utilizes the data collected through an indigenous IoT solution equipped with LoRa technology and Node MCU. However, with the diverse geographic properties of the Indian subcontinent, it poses a significant challenge to collect comprehensive data of the entirety of the region. This paper presents an approach to develop an indigenous and integrated solution to simplify the complexities of integrating data from the sensors spread across the region into one simple framework. This paper further present show this Digital Twin model can play a huge role in showcasing data received from indigenous IoT devices to nroduce more accurate and actionable insishts. © 2024 ieee.

关键词： Sustainable city

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Application of the Continuous Fourier Transformation in signal Theory 3

Application of the Continuous Fourier Transformation in Sign...

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3rd ieee international conference on System Analysis and intelligent Computing, SAIC 2022

作者： Kharkevych, Yurii Mukhin, Oleg Hrabova, Uliana Makarchuk, Andriy Kal'chuk, Inna Kharkevych, Galyna Lesya Ukrainka Volyn National University Faculty of Information Technology and Mathematics Lutsk Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Kyiv Ukraine Lesya Ukrainka Volyn National University Faculty of International Relations Lutsk Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

There are a number of methods in systems analysis that are used to solve a wide range of applied problems, including digital signal processing. The method of discrete Fourier transformation is one of the widely used methods. The method of discrete Fourier transformation allows us to analyze, convert and synthesize signals. It is not possible using analog processing. The discrete Fourier transformation is particularly used to work with digital signals processing. However, sometimes it is necessary to work with continuous signals, so it is impossible to use the Fourier discrete transformation. In such cases, we use the so-called continuous Fourier transformation, which is a continuous analogue of a discrete transformation and has a similar content. Due to the specifics of the continuous Fourier transformation as a method for solving classical problems of systems analysis, there is a need to study the number of its properties, including its convergence. The relation between the smoothness of the summing Abel-Poisson function, which models the studied signal, and the convergence rate of its continuous Fourier transformation has been established. Since the summing Abel-Poisson functions are harmonic, as well as the signals that occur in real conditions, the results obtained in this paper take into account not only the smoothness of the function, but also its periodicity. Having taken into account this aspect, we could indicate the method of signal recovery, which gives better approximate properties than those previously studied in foreign and domestic scientific works. © 2022 ieee.

关键词： systems analysis

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MMV based EMG signal Reconstruction for Remote Rehabilitation

MMV based EMG Signal Reconstruction for Remote Rehabilitatio...

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ieee international conference on robotics and Biomimetics

作者： Ruihui Zhao Haoxiang Zhang Yehao Ma Robotics Institute Ningbo University of Technology Ningbo China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

To overcome the limitations of low compression rates and extended reconstruction times in the Single Measurement Vector (SMV) model for electromyography (EMG) signal processing, this paper presents a compression and reconstruction algorithm based on the Multiple Measurement Vector (MMV) model. The EMG signals undergo preprocessing, and the optimal combination of the Discrete Sine Transform (DST) sparse basis and the BPBDMtx measurement matrix is selected. signal reconstruction is then performed using the Orthogonal Matching Pursuit (MMV-OMP) algorithm. Experimental results show that the proposed MMV-based compression sensing algorithm significantly outperforms the traditional SMV model in terms of reconstruction quality, compression rate, and reconstruction speed. This method offers a novel pathway for efficient EMG signal reconstruction and has broad potential for applications in sensor technology and remote rehabilitation.

关键词： Telemedicine Matching pursuit algorithms signal processing algorithms Transforms Reconstruction algorithms Robot sensing systems Electromyography Vectors signal reconstruction Sparse matrices

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An Effective Defense Method based on Hash Authentication against Mode-switching Attack of Ormon PLC 7

An Effective Defense Method based on Hash Authentication aga...

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7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Gao, Jianlei Li, Yaobing Li, Yun Gong, Tianyu Xu, Fengjuan Zhao, Qian Jiang, Hao Jia, Jiongxuan China Industrial Control Systems Cyber Emergency Response Team Institute of Baozhang Beijing China

ISBN: (纸本)9781665478571

Industrial control system (ICS) integrating different software and hardware is not only to collect, obtain and calculate data information, but also to control the machine or production process so that it can run automatically. Ormon PLCs are widely used in critical manufactures and have adopted factory interface network service (Fins) protocol to exchange message. The recent articles indicate that Ormon PLCs used Fins Ethernet protocol were exposed to mode-switching malicious network attack. However, the traditional detection methods based on function codes and Snort detection rules could not get an ideal result. In this paper, we propose a method that can overcome these problems in dealing with mode-switching attack by an external hash authentication device embedding asymmetric encryption. This security device built into a hash authentication mechanism, which is responsible for identifying and distinguishing the interaction information message between the upper computer and PLC interaction information. In order to validate our method, some experiments have been done to show that defend method can disrupt successfully the communication link between the attacking computer and PLC device to defend this malicious cyber attacks efficiently. © 2022 ieee.

关键词： Network security

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A Divider-less General PLL Lock Assist and Automatic Frequency Calibration System for Millimeter-Wave Sub-Sampling Phase-Locked Loops 29

A Divider-less General PLL Lock Assist and Automatic Frequen...

引用

29th ieee international conference on Electronics, Circuits and systems (ieee ICECS)

作者： Kurth, Patrick Nickel, Philipp Hecht, Urs Gerfers, Friedel Tech Univ Berlin Mixed Signal Circuit Design Einsteinufer 17 D-10587 Berlin Germany

ISBN: (数字)9781665488235

ISBN: (纸本)9781665488235

In this paper, a truly divider-less, robust and highly configurable lock assist and automatic frequency calibration (AFC) for sub-sampling phase-locked loops (SSPLL) is demonstrated. The design uses a secondary, low-frequency ring-oscillator charge-pump PLL and exploits shifted-phase sub-sampling to obtain frequency information. A fractional ratio between the reference clock of the 56 GHz SSPLL and the sample clock of the AFC is leveraged to achieve sub-harmonic and arbitrary slow frequency detection. Apart from the analog front-end with a high-speed track-and-hold and a simple low-speed, low-resolution analog-to-digital converter, the entire lock detection, frequency processing and calibration is implemented as digital logic.

关键词： Sub-Sampling Phase-Locked Loop Automatic Frequency Calibration Lock Assist

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG signals 47

Deep Learning Networks for Human Knee Abnormality Detection ...

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47th international conference on Telecommunications and signal processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment. © 2024 ieee.

关键词： Learning systems

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Smart Plant Application of Autonomous Decentralized systems with the Introduction of Edge Nodes 15

Smart Plant Application of Autonomous Decentralized Systems ...

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15th international conference on Advanced Computer Theory and Engineering, ICACTE 2022

作者： Wang, Jingyang Zou, Tao Yang, Zhijia Wang, Hongrui Key Laboratory Of Networked Control System Institutes For Robotics And Intelligent Manufacturing Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China Guangzhou University College Of Mechanical And Electrical Engineering Guangzhou China State Key Laboratory Of Robotics Key Laboratory Of Networked Control System Institutes For Robotics And Intelligent Manufacturing Shenyang Institute Of Automation Chinese Academy Of Sciences Shenyang China University Of Chinese Academy Of Sciences Beijing China

ISBN: (数字)9781665481809

ISBN: (纸本)9781665481809

This paper illustrates the application of an autonomous decentralized system introducing edge nodes in a smart factory. The nodes include atomic nodes, the most fundamental autonomic units constituting an autonomous decentralized system that are capable of independently extracting information from the data field and processing that information internally and simultaneously transmitting the processing results and other internal information to the data field proactively in a broadcast mode, where the transmitted information circulates in the data field. The group-based data field enables the sharing of information by atomic nodes. Grouped management nodes are the managers of the whole group, and edge nodes are the terminal intelligent control units of an intelligent autonomous decentralized system. Improvements in system decentralization, fault tolerance characteristics and flexibility of the system can lead to homogenization, autonomous control and autonomous coordination in complex manufacturing environments, as well as excellent online fault tolerance, online expansion and online maintenance of the system. In addition, it enhances the ever-changing and evolving control requirements by deconstructing the complexity in the system. © 2022 ieee.

关键词： Fault tolerance

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Facial Emotion Recognition using YOLO based Deep Learning Classifier 1

Facial Emotion Recognition using YOLO based Deep Learning Cl...

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1st international conference on Electronics, Communication and signal processing, ICECSP 2024

作者： Vanamoju, Sri Vaibhav Mohan Dev Vineetha, Moturi Veda Tekchandani, Hitesh Joshi, Pallavi Shukla, Praveen Kumar Khanna, Anita Electronics and Communication Koneru Lakshmaiah Education Foundation Hyderabad India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Amrita Vishwa Vidyapeetham Mysuru Department of Computer Science Mysuru India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Bilaspur India

ISBN: (纸本)9798350364590

Understanding facial expressions is key to successful communication. It's like decoding a secret language that helps us know if someone is happy, sad, angry, surprised, or something else altogether. Harnessing facial emotion recognition (FER) in machines holds the potential to revolutionize human-computer interaction, improve customer service experiences and provide invaluable support in medical diagnosis. Machines can comprehend human's needs more effectively by reading the emotions from their facial expression and therefore this results in a more natural conversation between them and the machine. Various machine and deep learning methodologies have been implemented for FER. In this research work, the authors propose the state-of-the-art YOLO (You Only Look Once) based deep learning classifier for FER. The YOLO algorithm is a single-shot approach that efficiently classifies objects in a single pass. It achieves this by using a single neural network to predict bounding boxes and target class probabilities by utilizing the entire image as input. YOLOv8 is a recent iteration of the YOLO object detection algorithm is popular for its speed and accuracy which offers improvements over prior versions, making it a suitable choice for real-time intended task of FER. With the proposed classifier, the authors achieved test accuracy of 72.56% on the FER-2013 dataset in a seven-class FER task. © 2024 ieee.

关键词： Adversarial machine learning

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Garbage classification recognition based on improved YOLOv8 model

Garbage classification recognition based on improved YOLOv8 ...

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signal processing and intelligent Computing (SPIC), international conference on

作者： Lijuan Qin Xiaoyu Tang Feng Zhang School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The use of intelligent garbage classification methods and devices for waste sorting is set to become the future trend in waste management. With the development of high-precision image recognition algorithms, research in intelligent garbage classification has gradually matured. The core lies in utilizing image recognition technology to analyze garbage image information, thereby surpassing the limitations of manual sorting and achieving higher standards of speed and accuracy. This paper proposes a target detection scheme based on the introduction of the C2f_RepGhost module into YOLOv8, aiming to construct a model system for efficient garbage identification and classification. Thanks to its streamlined design and real-time response characteristics, this model can operate efficiently on platforms such as smart mobile devices, enabling real-time intelligent automatic classification of various types of waste.

关键词： Waste management Accuracy Image recognition Computational modeling Streaming media Real-time systems Data models Usability Standards Sorting

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