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arXiv 2023年

作者： Hu, Yujiao Jia, Qingmin Yao, Yuao Lee, Yong Lee, Mengjie Wang, Chenyi Zhou, Xiaomao Xie, Renchao Yu, F. Richard Future Network Research Center Purple Mountain Laboratories Nanjing211111 China Department of Computer Science Northwestern Polytechnical University Xi’an710029 China School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430070 China Department of Computer Science Peking University Beijing100091 China State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Systems and Computer Engineering Carleton University Ottawa Canada

The fiercely competitive business environment and increasingly personalized customization needs are driving the digital transformation and upgrading of the manufacturing industry. IIoT intelligence, which can provide innovative and efficient solutions for various aspects of the manufacturing value chain, illuminates the path of transformation for the manufacturing industry. It's time to provide a systematic vision of IIoT intelligence. However, existing surveys often focus on specific areas of IIoT intelligence, leading researchers and readers to have biases in their understanding of IIoT intelligence, that is, believing that research in one direction is the most important for the development of IIoT intelligence, while ignoring contributions from other directions. Therefore, this paper provides a comprehensive overview of IIoT intelligence. We first conduct an in-depth analysis of the inevitability of manufacturing transformation and study the successful experiences from the practices of Chinese enterprises. Then we give our definition of IIoT intelligence and demonstrate the value of IIoT intelligence for industries in fucntions, operations, deployments, and application. Afterwards, we propose a hierarchical development architecture for IIoT intelligence, which consists of five layers. The practical values of technical upgrades at each layer are illustrated by a close look on lighthouse factories. Following that, we identify seven kinds of technologies that accelerate the transformation of manufacturing, and clarify their contributions. The ethical implications and environmental impacts of adopting IIoT intelligence in manufacturing are analyzed as well. Finally, we explore the open challenges and development trends from four aspects to inspire future researches. Copyright © 2023, The Authors. All rights reserved.

关键词： Environmental impact

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance.

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Securing the digital Frontier: An Analysis of Cybersecurity Strategies and Obstacles

Securing the Digital Frontier: An Analysis of Cybersecurity ...

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Computational, Communication and Information technology (ICCCIT), International Conference on

作者： V. Saravanan Indumathi. G. S Sathish Kumar James V.Samuthira Pandi J. Shakila S. Banumathi Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of MBA St Joseph's College of Engineering OMR Chennai BNY Mellon Center Pittsburgh USA Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of CSE Jeppiaar Engineering College Rajiv Gandhi Salai Chennai Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

In an era where digital connectivity is ubiquitous, the imperative to safeguard cyberspace has never been more pressing. This research delves into the multifaceted landscape of cybersecurity, examining the strategies employed by governments, businesses, and individuals to protect sensitive information and critical infrastructure from cyber threats. By leveraging a mixed-methods approach, which includes a review of existing literature, case studies, and expert interviews, the research provides a nuanced understanding of the cybersecurity ecosystem. Key findings reveal that while technological advancements have bolstered defense mechanisms, including sophisticated malware, advanced persistent threats (APTs), and insider threats, continues to outpace traditional security measures. Moreover, the study underscores the critical role of human factors, such as user error and inadequate training, as significant vulnerabilities. The research concludes with a set of recommendations aimed at enhancing cybersecurity posture, including the adoption of a proactive, adaptive defense strategy, increased investment in cybersecurity education and awareness, and the fostering of international cooperation to combat transnational cybercrime. The insights gleaned from this analysis are intended to inform policymakers, industry leaders, and cybersecurity professionals, equipping them with the knowledge to fortify the digital frontier against evolving threats.

关键词： Training Reviews Pressing Malware Critical infrastructure Information and communication technology computer security Protection Interviews Investment

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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International Conference on Information technology (INCIT)

作者： Sakorn Mekruksavanich Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual’s behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained.

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Secrecy Wireless Information and Power Transfer in Ultra-Dense Cloud-RAN with Wireless Fronthaul

Secrecy Wireless Information and Power Transfer in Ultra-Den...

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IEEE Conference on Wireless Communications and Networking

作者： Ji Wang Xinxin Ma Le Zheng Kai Yang Zhao Chen Qiaoqiao Xia Department of Electronics and Information Engineering Central China Normal University Wuhan China School of Information and Electronics Beijing Institute of Technology Beijing China Department of Computer Science Tongji University Shanghai China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

This paper studies the secrecy wireless information and power transfer problem in ultra-dense cloud radio access network (UD-CRAN) with wireless fronthaul, which is a promising framework for future Internet of Things (IoT). The transmission schemes of wireless fronthaul and access links are jointly designed, while addressing the characteristics of ultra-dense network such as base station diversity and high probability of line-of-sight transmission. Specifically, we employ the idea of block diagonalization to deal with the fronthaul interference, which support multi-stream fronthaul transmission for each remote radio head (RRH). We then jointly optimize the power allocation in the fronthaul and the resource allocation in the access link which includes beamforming for information and energy transmission, on/off of RRHs, and user-RRH association. In order to solve the formulated mixed integer non-convex optimization problem, we leverage the sparsity of beamforming vectors brought by the ultra-dense RRHs. We then solve the reformulated problem by employing the successive convex approximation approach. Finally, numerical results are presented to demonstrate the effectiveness of the proposed scheme.

关键词： Wireless communication Cloud computing Array signal processing Line-of-sight propagation Communication system security Resource management Internet of Things

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EEG based Emotion Recognition: A Tutorial and Review

arXiv

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arXiv 2022年

作者： Li, Xiang Zhang, Yazhou Tiwari, Prayag Song, Dawei Hu, Bin Yang, Meihong Zhao, Zhigang Kumar, Neeraj Marttinen, Pekka Qilu University of Technology Shandong Academy of Sciences Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Software Engineering College Zhengzhou University of Light Industry Zhengzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Aalto University Finland School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Engineering Medicine Beijing Institute of Technology Beijing China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Deemed University Punjab Patiala India

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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Lag synchronization in an unidirectional ring of memristive neurons

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The European Physical Journal Special Topics 2025年 1-12页

作者： Vijayan, Vijeesh Natiq, Hayder Momani, Shaher Pham, Viet-Thanh Perc, Matjaž Center for Research SRM Easwari Engineering College Chennai India Center for Cognitive Science Trichy SRM Medical College Hospital and Research Center Trichy India Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Computer Technology Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Baghdad Iraq Nonlinear Dynamics Research Center (NDRC) Ajman University Ajman United Arab Emirates Department of Mathematics Faculty of Science University of Jordan Amman Jordan Faculty of Electronics Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Community Healthcare Center Dr. Adolf Drolc Maribor Maribor Slovenia University College Korea University Seoul Republic of Korea

In recent years, the study of neuronal models has provided significant insights into brain dynamics and neurological disorders. Map-based neuronal models, such as the Rulkov map, have gained considerable popularity due to their computational efficiency and ability to replicate complex neuronal dynamics. We thus here study the collective dynamics of an unidirectional ring network composed of three memristive Rulkov maps, with particular emphasis on synchronization patterns and their dependence on coupling types. By employing electrical and memristive/field couplings, we analyze the emergence of complete synchronization, lag synchronization, and phase synchronization under varying coupling strengths. Our findings highlight how diffusive-based synaptic pathways modulate synchronization and collective behavior in the network. The presented results also offer new perspectives on the role of coupling functions in shaping neuronal synchronization, and they reveal their deeper implications for understanding pathological brain states and for designing neuromorphic systems.

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A High-Fidelity Virtual Liver Model Incorporating Biological Characteristics

SSRN

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SSRN 2023年

作者： Zhang, Xiaorui Zhang, Wenzheng Sun, Wei Song, Aiguo Xu, Tong Engineering Research Center of Digital Forensics Ministry of Education Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science & Technology Nanjing210044 China Wuxi Research Institute Nanjing University of Information Science & Technology Wuxi214100 China School of Automation Nanjing University of Information Science & Technology Nanjing210044 China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and Control School of Instrument Science and Engineering Southeast University Nanjing210096 China University of Southern California Los AngelesCA United States

Flexible tissue modeling plays an important role in the field of telemedicine. It is related to whether the soft tissue deformation process can be accurately, real-time and vividly simulated during surgery. However, most existing models lack the unique biological characteristics. To solve this problem, a high-fidelity virtual liver model incorporating biological characteristics, such as the viscoelastic, anisotropic and nonlinear biological characteristics, is proposed based on the finite element model. When simulating the resection of diseased livers, the model undergoes a plastic deformation process where the model loses elasticity due to excessive deformation, but does not reach the cutting fracture threshold. To improve the fidelity of the liver model, this study is the first to introduce the viscoplasticity of biological tissues to the liver model. The 3DMax2020 and OpenGL4.6 were used to build a liver surgery simulation platform, and the PHANTOM OMNI manual controller was used to sense the feedback force during the operation. The proposed model was verified from three aspects of accuracy, fidelity and real-time performance. The experimental results show that the proposed virtual liver model can enhance visual perception ability, improve deformation accuracy and fidelity. © 2023, The Authors. All rights reserved.

关键词： Simulation platform

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Impact Evaluation of Driving Style on Electric Vehicle Battery based on Field Testing Result

Impact Evaluation of Driving Style on Electric Vehicle Batte...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ka Seng Chou Davide Aguiari Rita Tse Su-Kit Tang Giovanni Pau Faculty of Applied Sciences Macao Polytechnic University Macao SAR China Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna Bologna IT Autonomous Robotics Research Center Technology Innovation Institute UCLA Samueli Computer Science University of California Los Angeles

Monitoring electric vehicles' battery status and forecasting their state of health is still an open challenge. To determine how and why a battery degrades over time, we have extensively monitored a Nissan Leaf's battery pack for more than one year. Collecting more than 4.5 million samples via a custom monitoring connected device to investigate how different driving behaviors affect battery aging. In addition, the best driving behaviors based on the battery's optimal temperature are revealed, including speed, acceleration and brake pedal pressure, and horsepower.

关键词： Temperature sensors Temperature measurement Aging Electric vehicles Batteries Behavioral sciences Brakes

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PTC-FOZNN: A Strictly Predefined-Time Convergent Fractional-Order Recurrent Neural Network for Solving Time-Variant Quadratic Programming

PTC-FOZNN: A Strictly Predefined-Time Convergent Fractional-...

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International Conference on Control and Automation (ICCA)

作者： Yi Yang Weibing Li Jianshu Zhou Junda Huang Jinfei Hu Richard M. Voyles Xin Ma Department of Mechanical and Automation Engineering Multi-Scale Medical Robotics Center The Chinese University of Hong Kong Hong Kong China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China School of Engineering Technology Purdue University West Lafayette IN USA

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This study presents a Predefined-Time Convergent Fractional-Order Zeroing Neural Network (PTC-FOZNN) as an innovative solution for Time-Invariant Quadratic Programming (TIQP) and Time-Variant Quadratic Programming (TVQP) problems. This model represents a noteworthy integration of fractional calculus into recurrent neural networks for cyclic motion planning of robots. The PTC-FOZNN diverges from the conventional Zeroing Neural Network (ZNN) by implementing a conformable fractional derivative that complies with the Leibniz rule-a standard often unmet by existing fractional derivatives. Additionally, it employs a novel predefined-time stabilizer as its activation function. When compared to the traditional ZNN, the PTC-FOZNN, configured with $0 < \alpha\leq 1$ , exhibits enhanced convergence speed and greater positional accuracy for both TIQP and TVQP tasks. Through meticulous theoretical and empirical validation, including simulation trials with a Franka Emika Panda robotic arm, the PTC-FOZNN model has proven its effectiveness in precise trajectory tracking and computational efficacy.

关键词： Recurrent neural networks Trajectory tracking Kinematics Manipulators Planning Quadratic programming Task analysis

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