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Machine Learning Driven Digital Twin for Industrial Control Black Box System: A Novel Framework and Case Study 28

Machine Learning Driven Digital Twin for Industrial Control ...

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28th international conference on Automation and computing, ICAC 2023

作者： Siddiqui, Mustafa Kahandawa, Gayan Hewawasam, H.S. Rehman Siddiqi, Muftooh Ur Institute of Innovation Science and Sustainability Federation University Churchill3842 Australia School of Engineering and Physical Sciences Aston University BirminghamB4 7ET United Kingdom

ISBN: (纸本)9798350335859

Industrial control systems are excessively used in advanced manufacturing environments. the lack of information and data regarding the internal workings of certain systems makes virtual modelling for their Digital Twin challenging. As a result, these systems are often classified as 'black box' systems. there is minimal research found on DT models for industrial control black box systems. therefore, a novel algorithm to model the Digital Twin of the industrial control black box system in the cyber domain has been presented in this paper. Machine Learning techniques were used to develop a high-fidelity Digital Twin model of a black box system. Real-time sensor data were recorded and used to validate the proposed novel algorithm. this paper presents the proposed algorithm's effectiveness in developing a robust Digital Twin model of industrial control back box system. © 2023 ieee.

关键词： Industry 4.0

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Fault Recovery Strategy of Distribution Network with distributed Generation Based on Island Division 4

Fault Recovery Strategy of Distribution Network with Distrib...

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4th international conference on Smart Power and Internet Energy systems (SPIES)

作者： Wang, Zezhou Chen, Gang Chen, Lun Jia, Dongli Wu, Min Kang, Tianyuan Haiyan Power Supply Co Ltd Zhejiang State Grid Haiyan Peoples R China Haiyan Nanyuan Elect Power Engn Co Ltd Haiyan Peoples R China China Elect Power Res Inst Co Ltd Distribut Technol Ctr Beijing Peoples R China

ISBN: (纸本)9781665489577

In order to improve the utilization and reliability of distributed generation (distributed generation, DG) in active distribution network fault recovery, maximize the power supply of important loads and improve the power quality of the system, a distribution network reconfiguration strategy with DG based on island division is proposed. When the distribution network breaks down, firstly, the improved Kruskal algorithm is used to divide the distribution network into islands. then, the nodes that are not included in the scope of islands and the distributed generation without black start ability are connected with the system. Based on the improved genetic algorithm, the objective function is to maximize the power supply recovery of important loads and minimize the action times of switches, and the node voltage constraint, the distribution network is reconstructed under the constraints of line current constraints and network topology constraints. Finally, the effectiveness of this method is verified by an example of ieee33 node system.

关键词： distribution network reconfiguration distributed power supply Kruskal algorithm island division improved genetic algorithm

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A Novel Fusion Perception Method of Unmanned Driving Based on distributed Roadside Information 4

A Novel Fusion Perception Method of Unmanned Driving Based o...

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4th international conference on Advances in Computer Technology, Information Science and Communications, CTISC 2022

作者： Zhang, Qinqian Qian, Liang Liu, Liang Ding, Lianghui Yang, Feng Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

Multi-sensor fusion, tracking, and prediction have a long history in signal processing, control theory, and robotics. this paper proposes a trajectory tracking method based on distributed cameras. For a start, we build an innovative model that calculates the observation noise for each camera. Secondly, we can identify the abnormal data by introducing the trustworthiness function. then we use the weighted fusion method to fuse the data to minimize the variance. We will use the improved Bayesian filtering to deal with different observation noises for the final trajectory tracking. Simulation results confirm that the Root Mean Square Error (RMSE) of trajectory tracking using our method is reduced by at least 13% compared to those of single viewpoint perception. Even if several cameras are blocked, our approach can still perform best. © 2022 ieee.

关键词： Cameras

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A Layer-Based Sparsification Method For distributed DNN Training 24

A Layer-Based Sparsification Method For Distributed DNN Trai...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Hu, Yanqing Ye, Qing Zhang, Zhongyu Lv, Jiancheng Sichuan University Chengdu China

ISBN: (纸本)9798350319934

With the increasing size of Deep Neural Networks (DNNs) and datasets, DNN training will consume a lot of time. Various distributed strategies have been utilized to speed up the training process. Nevertheless, the frequent communication between different computational nodes dramatically limits the scale of the cluster. Currently, many gradient compression methods (e.g., Sparsification, Quantization, Low-Rank) have been proposed to optimize the transmission process. Since each layer needs to be processed and compressed respectively before transmission, a rash of time-consuming tensor operations is demanded. To alleviate this issue, we propose a layer-based sparsification method, which stems from the observation that the learning efficiency and convergence speed of different layers of DNN is different during the training process. Instead of compressing all gradients in an iteration, some layers are preferentially selected using a well-designed sliding window, which largely avoids unnecessary tensor operations. the sliding window is adjusted according to the dynamic characteristics of the parameter during the DNN training. Extensive experiments on one Text task and two Image Classification tasks are conducted to validate the efficiency of the proposed method in the same environment configuration, where the gradient compression adopts the layer-based sparsification and the other three comparative state-of-the-art methods. the total training time, communication time, and model accuracy are collected and analyzed. Sufficient experimental results demonstrate that both training and communication time can be greatly reduced, which is especially up to 60% in some cases. Additionally, despite slightly decreased within 1% model quality, the results indicate that the improvement in throughput can be as high as 50%. © 2022 ieee.

关键词： Deep neural networks

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A Virtual Power Plant Construction Method Considering Dynamic Aggregation of Discrete distributed Resources 4

A Virtual Power Plant Construction Method Considering Dynami...

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4th international conference on Smart Power and Internet Energy systems (SPIES)

作者： Du, Yang Guo, Lingyu Yang, Xingang Xiong, Xianghong Yang, Zhongguang Yu, Jie Hou, Haoyu Ge, Xiaolin State Grid Shanghai Municipal Elect Power Co Elect Power Res Inst Shanghai Peoples R China Shanghai Univ Elect Power Elect Power Engn Shanghai Peoples R China

ISBN: (纸本)9781665489577

With the rapid development of distributed resources, the dispersion of their distribution, the variability of their operation, and the random fluctuation of their power pose a great challenge to the optimal operation of the system. therefore, a virtual power plant construction model considering dynamic aggregation of discrete distributed resources is proposed to realize the coordinated and optimal operation of discrete distributed resources. First, to address the problem that complementary characteristics of multi-type distributed resources are difficult to model, the aggregate energy based on landscape theory is established to quantify the matching relationship between distributed resources. Second, asymmetric robust intervals are used to meticulously describe the stochastic output of renewable energy, and the complex robust model is transformed into a mixed-integer linear programming model. Finally, a dynamic aggregation model of a virtual power plant is established by considering the two objectives of maximizing aggregate energy and minimizing cost, as well as tracking scheduling instructions and the physical feasibility constraints of cross-space aggregation. the proposed virtual power plant construction method is verified to be reasonable and feasible by combining the improved ieee 39-bus example.

关键词： virtual power plant landscape theory aggregation distributed resource robust optimization asymmetric uncertainty intervals

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Gray Wolf Optimization Algorithm to Enhance Data Aggregation in Wireless sensor Network 5

Gray Wolf Optimization Algorithm to Enhance Data Aggregation...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Loganathan, Sathyapriya Victoria Jancee, B. Senkamalavalli, R. Subbu Lakshmi, T.C. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India East Point College of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Francis Xavier Engineering College Department of Information Technology Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350321425

Wireless sensor networks (WSNs) have become increasingly popular as distributed systems using small, lightweight nodes with limited resources for monitoring environmental conditions or measuring physical parameters. To minimize energy utilization, data aggregation can be employed to reduce the data quantity forward to the base station (BS). To solve these issues, a Gray wolf optimization algorithm (GWOA) is proposed to enhance data aggregation in WSN. Increasing the network lifetime, node reliability, and throughput with the selection of best cluster heads (CHs) utilizing the Gray Wolf Optimization (GWO) algorithm is the major objective of this research. this approach selects the CH by applying the GWO algorithm established on the node remaining energy, available bandwidth, and distance. the GWO-based clustering protocol minimizes dead nodes and energy utilization and raises the clustering rounds and throughput. the simulation results demonstrate that the increases the node reliability and network throughput. © 2023 ieee.

关键词： Energy efficiency

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Development of optical sensor for determination of perchlorate ions 4

Development of optical sensor for determination of perchlora...

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4th ieee international conference on Design and Test of Integrated Micro and Nano-systems, DTS 2022

作者： Messaoud, Najib Ben Dridi, Cherif Abbas, Mohammed Nooredeen Sousse Tunisia International Iberian Nanotechnology Laboratory Inl Av. Mestre Jose Veiga Braga4715-12330 Portugal Electroanalytical Laboratory National Research Centre Cairo Egypt

ISBN: (纸本)9781665497831

An optical sensor for perchlorate anions (CIO4-) comprising a structure of Cobalt phthalocyanine derivative (Co(II)Pc-PAA) modified glass electrode has been developed. the immobilization of Co(II)Pc-PAA membrane was confirmed by contact angle measurements. the response was determined by the increase in the absorbance signal as a function of exposure to buffer solutions containing different concentrations of CIO4- anions. the optical sensor showed a linear response to CI04- from 10-10M to 10-2 M, with a limit of detection of 4.81 Χ10-10M, one of the lowest achieved up until now. the proposed sensor has demonstrated a good specificity to CIO4- when compared to common interferents anions such as CI-and NO3-. To the best of our knowledge, this is the first optode system for the detection of CIO4-, which provides a promising sensor for the specific and sensitive determination of CIO4- in environmental water samples. © 2022 ieee.

关键词： Optical sensors

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Gesture Recognition using IR sensor camera 4

Gesture Recognition using IR sensor camera

引用

4th international conference on Power, Energy, Control and Transmission systems, ICPECTS 2024

作者： Kumar, B. P. Pradeep Subburam, S. Fathima, S.K. Sivasankari, S.S. Saritha, G. Surendiran, J. Atria Institute of Technology Dept. of Cs and Ds Karnataka Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Dept. of It Tamil Nadu Chennai India Sona College of Technology Dept. Of. Cse Tamil Nadu Salem India Dayananda Sagar University Dept of Ece Karnataka Bengaluru India Sri Sairam Institute of Technology Dept. of Ece Tamil Nadu Chennai India Hkbk College of Engineering Dept. of Ece Karnataka Bengaluru India

ISBN: (纸本)9798331508845

In the dynamic field of pattern recognition, there's a notable shift towards human skeleton recognition, particularly in computer vision. Recognizing skeletal gestures presents a challenging task, especially with homogeneous backgrounds adding complexity. Human posture recognition is a crucial aspect within gesture recognition. the rise of low-cost devices like Kinect, along with its Software Development Kit (SDK), offers an opportunity to tackle issues conventional cameras face. this paper explores using Kinect's human skeleton data for posture recognition in a monitoring framework. It investigates six depth distances, each linked to six gesture types derived from human skeletal movements. Results show that Kinect, powered by neural networks, accurately detects six key postures: Start, Stop, Forward, Right, Left, and Normal. this emphasizes the transformative role of neural networks in HCI, revolutionizing gesture recognition for innovative and intuitive interfaces. © 2024 ieee.

关键词： Musculoskeletal system

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Proceedings of ieee 2022 4th international conference on Advances in Electronics, Computers and Communications, ICAECC 2022

Proceedings of IEEE 2022 4th International Conference on Adv...

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4th ieee international conference on Advances in Electronics, Computers and Communications, ICAECC 2022

ISBN: (纸本)9781665402392

the proceedings contain 52 papers. the topics discussed include: design and development of interdigital band pass filter for L-band wireless communication applications;classification of nutrient deficiencies in plants using recurrent neural network;an ml-based citizen-driven traffic violation reporting android app;YouTube assistant for quick navigation and content moderation;analysis of leakage current mechanism in supercapacitor with experimental approach;frequency control and small signal stability improvement with fuzzy control based single and two area power systems;DSTATCOM based closed loop controlled wind power plant with self-excited induction generator for controlling terminal voltage against load disturbances;mitigation of harmonics in distributed system with D-GCSC fed loads using closed loop control of DSTATCOM;detection of malicious activity for mobile nodes to avoid congestion in wireless sensor network;and defects determination and diagnosis in bare dice for high reliable hybrid microcircuits.

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FNotify: A Low-Latency and Scalable Publish/Subscribe System using RDMA 24

FNotify: A Low-Latency and Scalable Publish/Subscribe System...

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作者： Xu, Yusen Shen, Sijie Han, Mingcong Chen, Rong Institute of Parallel and Distributed Systems Seiee Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350319934

Publish/subscribe systems are widely used as messaging middleware in various scenarios, such as online business, financial trading, and social media. However, the network overhead intro-duced by the pub/sub system can severely impact application performance. Especially in the latency-sensitive applications of the data center, optimizing the latency of publishing or delivering messages of the pub/sub system is particularly important. RDMA network has gained popularity in relevant applications due to its low latency, high bandwidth and ability to bypass the CPU. Never-theless, it is still challenging to design and optimize the pub/sub system using RDMA. through an in-depth analysis of RDMA features, we design and implement FNotify, a high-performance RDMA-accelerated pub/sub system. FNotify optimizes latency and CPU load using in-memory message management and one-sided RDMA primitives for message collection. FNotify also proposes a topic-based partitioning scheme to achieve good scal-ability. We conducted a comprehensive evaluation and analysis on an 8-node RDMA cluster to verify the validity of our design by comparing the performance of FNotify with Apache Kafka (a state-of-the-art pub/sub system) in a typical pub/sub application scenario and various micro-benchmarks. Experimental results show that FNotify improves overall performance by 1.5× to 3.9× and reduces end-to-end latency by more than 20× compared to Apache Kafka. © 2022 ieee.

关键词： Middleware

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