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Modelling of advanced sensor based Distributed Control System for Real- Time Prediction and Minimization of Power Loss in Industrial Automation systems 4

Modelling of Advanced Sensor based Distributed Control Syste...

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4th International conference on Expert Clouds and applications, ICOECA 2024

作者： Sengeni, D. Mano Jemila, M.R. Priya, G. Soorya Krishnan, T. Santhana Rathinasabapathi, G. Kannan, Sathish CK College of Engineering and Technology Department of Electronics and Communication Engineering Chellangkuppam Tamil Nadu Cuddalore607003 India Dr. N. G.P. Institute of Technology Department of Biomedical Engineering Tamil Nadu Coimbatore641048 India Loyola Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Mevalurkuppam Chennai600123 India Department of Electrical and Electronics Engineering Tamil Nadu Thandalam Chennai602105 India Panimalar Engineering College Department of Mechanical Engineering Tamil Nadu Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350385793

This research study analyses the integration of a Distributed Control System (DCS) to optimise the efficiency and reliability of industrial automation, with a special emphasis on reducing power loss. Leveraging real-time sensor data and machine learning models, including Decision Trees, Artificial Neural Networks, and Random Forests, the research provides a proactive strategy to anticipating and avoiding anomalous current and voltage patterns. The DCS, armed with predictive maintenance procedures, emerges as a solid framework for reducing energy usage, assuring system stability, and limiting possible quality losses. Evaluation using performance ratings and confusion matrices reveals the efficiency of the proposed system in properly recognising and correcting deviations from normal operating circumstances. The results contribute not only to the progress of distributed control systems in industrial automation but also provide a realistic approach to real-time monitoring and predictive maintenance, supporting sustainability and operational excellence in manufacturing settings. This study sets a platform for future initiatives seeking to construct intelligent, adaptable systems for increased industrial automation performance. © 2024 IEEE.

关键词： Predictive maintenance

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Measurement of Aspheric Surfaces Using an Arcuate Region Scanning Method by 2D Laser Displacement sensor

Measurement of Aspheric Surfaces Using an Arcuate Region Sca...

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2021 International conference on advanced Technologies and applications of Modern Industry, ATAMI 2021

作者： Lan, Menghui Li, Bing Wei, xiang Wu, xiuyuan State Key Laboratory for Manufacture Systems Engineering Xi'An Jiaotong University No. 99 Yanxiang Road Yanta District Shaanxi Xi'an710054 China Xi'An Jiaotong University Suzhou Academy No.99 Renai Road Dushu Lake Higher Education Zone Suzhou Industrial Park Jiangsu215123 China International Joint Research Laboratory for Micro/Nano Manufacturing and Measurement Technologies Xi'An Jiaotong University Shaanxi Xi'an710049 China

This study proposes an effective measurement of aspheric surfaces using arcuate region scanning method by 2D laser displacement sensor. In a global coordinate system, the point cloud data of each arcuate region in aspheric surface are obtained successively, coordinated with the rotation of the rotating platform and the transverse movement of the 2D laser displacement sensor in a fixed step size. When scanning different areas, it is necessary to calculate the scanning angle gap and included angle according to different scanning length of arc. Then the aspheric surface error could be calculated through the reconstruction consequence. It can be used for in-situ measurement in the processing stage of aspheric mirrors. We present an experiment compared with the measurement results of PGI to verify the validity of this measurement method. © Published under licence by IOP Publishing Ltd.

关键词： Scanning

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Enhancing Indoor Mobile Robot Localization through the Integration of Multi-sensor Fusion Algorithms 1

Enhancing Indoor Mobile Robot Localization through the Integ...

引用

1st International conference on Robotics, Engineering, Science, and Technology, RESTCON 2024

作者： Yatigul, Rut Pudchuen, Noppadol Blattler, Aran Jitviriya, Wisanu King Mongkut's University of Technology North Bangkok Department of Production and Robotics Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Department of Instrumentation and Electronics Engineering Bangkok Thailand

ISBN: (纸本)9798350348842

This paper presents an innovative approach that combines visual odometry, Inertial Measurement Unit (IMU), and wheel odometry, using the Extended Kalman Filter and Unscented Kalman Filter to enhance mobile robot localization and mapping. The primary goal is to improve localization accuracy and robustness while providing a cost-effective alternative to traditional SLAM methods that rely on expensive LIDAR and RGBD camera systems. By integrating visual odometry, IMU data, and wheel odometry our method not only enhances precision and robustness of the mobile robot localization system but also reduces the financial burden associated with high-end sensor hardware. This fusion creates a reliable solution, particularly suited for resource-constrained environments. Our research contributes to the democratization of SLAM technologies, making them more accessible to a wider range of applications. The results presented in this report showcase the potential of our approach. This paper highlights the pivotal role of multi-sensor fusion framework which significantly enhances localization accuracy and robustness. © 2024 IEEE.

关键词： Extended Kalman filters

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5th International conference on Neural Computing for advanced applications (NCAA)

作者： Zhang, Jing Wang, Jiankun Xu, Lu Zhou, Ting Gu, Junwei Wang, Yu Zhuhai Wanda Intelligent Commercial Technol Co Lt Zhuhai 519000 Peoples R China Xi An Jiao Tong Univ Dept Mech Engn Xian 710049 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Traditional collaborative filtering techniques deal with no time-effect data. For recommender systems, as user preferences change over time, it can-make the preferences of recommendation results change. In addition, the data explosion makes the model process more and more data over time, which greatly increases the computational cost. To solve the above problems, a similarity index time-effect collaborative filtering algorithm based on Attentional Double BP Network (SITCF-ADBP) is proposed. Firstly, three time-effect functions are proposed for processing evaluation metrics of user preferences changed over time. Secondly, after clustering the training samples using K-Nearest Neighbor (KNN), a set of similarity index screening principles is specified, and only one sample with high similarity is retained to reduce the input of the model. Finally, a Double BP Network incorporating a channel attention mechanism is designed to process the adequately processed data. Experiments were conducted on the MovieLens datasets (ml-1m) and (ml-100k), wherein our proposed approach was benchmarked against several state-of-the-art (SOTA) deep learning methods. The obtained results indicate that the model can effectively cope with the time-effect and data explosion of the recommender system.

关键词： Collaborative Filtering Similarity Index Time-effect Attention Mechanism

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Direct Printing of Liquid Metal Matrix Composites for Flexible Embedded Circuits 12th

Direct Printing of Liquid Metal Matrix Composites for Flexib...

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12th International conference on advanced Materials and Engineering Materials, ICAMEM 2023

作者： Wang, Bing Chen, xiaoliang State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Xi’an Jiaotong University Shaanxi Xi’an710049 China

ISBN: (纸本)9789819735297

Flexible circuits are crucial for applications such as wearable electronics, soft robotics, and human–machine interaction. However, achieving high conductivity and stable electrical performance under large deformations remains a challenge. Here, we propose an embedded composite conductive material circuit manufactured through direct printing. Firstly, we modified conductive material by adding Cu particles to liquid metal (LM), resulting in a new composite conductive material with higher viscosity and adhesion. Subsequently, the composite conductive material is directly scraped and printed onto a substrate with micro-pillar structures, forming a high-stability embedded patterned flexible circuits with controllable depth and width. The conductivity of the proposed embedded circuits remains stable under cyclic tensile strains of 50% and large cyclic pressures. Compared to conventional planar circuits, the embedded circuits exhibit over a threefold increase in compressive strength under a pressure of 2 MPa. Even under cyclic pressures of 3 MPa, the conductivity of the embedded circuits remains stable while the conventional planar circuits suffer damage. The composite conductive material is compatible with the printing method, providing a new approach and technique for the mass production of large-area flexible electronic devices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Flexible electronics

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IoT-Based Edge Computing and Image Processing for Occupancy Detection 11

IoT-Based Edge Computing and Image Processing for Occupancy ...

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2023 IEEE 11th International conference on Information, Communication and Networks, ICICN 2023

作者： Guo, Chengpeng Hu, Bintao Zhang, Wenzhang Gao, Yuan Liu, Hengyan Xi'An Jiaotong-Liverpool University School of AI and Advanced Computing Suzhou China Xi'An Jiaotong-Liverpool University School of Internet of Things Suzhou China Shanghai University School of Communication and Information Engineering Shanghai China

ISBN: (纸本)9798350314014

A primary strategy for the energy-efficient operation of commercial office buildings is to offer dynamic building services, including lighting, heating, ventilating, and air conditioning (HVAC). Therefore, it is necessary to propose an effective and high-accuracy guaranteed real-time video occupancy detection optimisation algorithm according to a model predictive control (MPC) system. In this paper, we propose a YOLOv5 occupation detection algorithm to enhance the accuracy of making informed decisions and control actions on video occupancy detection systems, where the MPC system uses multiple cameras to capture and average the number of people, and a DHTII sensor will be used to measure temperature and humidity and sent to the MPC program through a Raspberry Pi-based script. Simulation results demonstrate the effectiveness of deep learning-based methods in enhancing the accuracy and real-time performance of video occupancy detection systems, paving the way for more efficient and intelligent building management solutions. © 2023 IEEE.

关键词： Model predictive control

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A Neural Network Approach to Tool Wear Detection via Infrared sensor Monitoring 11th

A Neural Network Approach to Tool Wear Detection via Infrare...

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11th International conference on Robot Intelligence Technology and applications, RiTA 2023

作者： xia, Jiahua Song, Rui Teng, Lavianna Ma, Zepei P. P. Abdul Majeed, Anwar Chen, Yi Luo, Yang School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China School of Intelligent Robotics Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9783031706837

The modern manufacturing sector encounters substantial financial setbacks due to unforeseen equipment downtime, hence mandating the implementation of more proactive maintenance strategies. The present study explores the potential of utilising AI-driven thermal imaging to monitor tool conditions in the context of predictive maintenance. In the context of the milling process, infrared camera technology was implemented to observe and assess tool wear and surface finishes. The analysis of key features derived from thermal imaging was conducted using two distinct methodologies: statistical analysis and polynomial feature extraction. Subsequently, a neural network was trained to categorise tools as either "fresh" or "worn". This study provides a comparative examination of feature extraction techniques, focusing on the significant contributions of neural network and thermal imaging in enhancing predictive maintenance in the manufacturing sector. The study demonstrates that both statistical and polynomial feature extraction methods are effective for tool condition monitoring, with statistical features showing marginally higher success rates across various regions of interest, underscoring their reliability in predictive maintenance applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Predictive maintenance

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks 3

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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3rd International conference on Electronics and Renewable systems, ICEARS 2025

作者： Endla, Purushotham Mohandas, R. Saikia, Achinta Lakshmi, C. Karthikeyan, G. Moharekar, Tejashree Tejpal Sr University School of Sciences and Humanities Department of Physics Telangana Warangal506371 India Chennai Institute of Technology Department of Ece Chennai India Mariani College Department of Statistics Mariani Assam Jorhat785634 India Saveetha Engineering College Chennai India Karpagam College of Engineering Department of Artificial Intelligence & Data Science Coimbatore India Shivaji University Yashwantrao Chavan School of Rural Development Maharashtra Kolhapur India

ISBN: (纸本)9798331509675

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model's capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model's success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability. © 2025 IEEE.

关键词： Artificial Intelligence Energy Efficiency Machine Learning Network Lifetime Reinforcement Learning Resource Allocation Wireless sensor Networks

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CMLB: a Communication-aware and Memory Load Balance Mapping Optimization for Modern NUMA systems 23

CMLB: a Communication-aware and Memory Load Balance Mapping ...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Jingbo Zhang, Yuxin Zhang, xingjun Xi'an Jiaotong University High Performance Computing Lab Xi'an China

ISBN: (纸本)9781665494571

For parallel applications, mapping parallel threads to cores according to the access behavior plays an important role to optimize the applications performance. The imbalance between thread communication and memory bandwidth will severely increase the average latency and the execution time of the application when running on modern nonuniform memory access (NUMA) architecture. Previous studies on thread mapping mostly focus on the locality of memory accesses to improve the communication efficiency. However, maximizing the locality may cause memory congestion because of the imbalance on memory bandwidth between nodes. In this paper, a communication-aware and memory load balance mapping algorithm (CMLB) for modern NUMA systems is propose which works on improving the locality of communication as well as avoiding memory congestion problem. To verify the effectiveness of the algorithm, the applications from NAS parallel benchmark and Parsec benchmark are used. Experimental results show that CMLB could greatly balance the memory bandwidth between nodes to reduce the memory latency and also improve the locality of communication, get the better performance than the state-of-the-art mapping methods. © 2021 IEEE.

关键词： Instruction sets Memory management Memory architecture Bandwidth Benchmark testing Behavioral sciences Task analysis

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Selective Attention UNet for Segmenting Liver Tumors

Selective Attention UNet for Segmenting Liver Tumors

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2023 International conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Patil, Darshan Sakarkar, Gopal Khatri, Pearl Khedkar, Atharva Gan, Hong-Seng Ramlee, Muhammad Hanif G. H. Raisoni College of Engineering Dept. of Artificial Intelligence Nagpur India Dr. Vishwanath Karad Mit World Peace University School of Computer Science Kothrud Pune India Banasthali Vidyapith Dept. of Computer Science Jaipur India Xi'an Jiaotong-Liverpool University School of Ai and Advanced Computing Suzhou China Universiti Teknologi Malaysia Dept. of Biomedical Engineering Johor Malaysia

ISBN: (纸本)9798350312492

In order to segregate liver tumours in medical imaging applications, a novel architecture called Selective Attention UNet is proposed in this article. The suggested architecture, which is based on the well-known UNet architecture, has a selective attention module that enables the network to concentrate on crucial tasks while suppressing unnecessary ones. Link skipping between the encoder and decoder routes is another element of the design that enables the network to effectively segment data using both low-level and high-level attributes. On the publicly accessible LiTS dataset, we assessed the performance of the suggested architecture and contrasted it with four fundamental models: FCN, UNet, UNet++, and SegNet. The Dice Similarity Coefficient (DSC) of 0.89 a mean IOU of 0.76 obtained in our experiments demonstrates that the suggested architecture beats all baseline models in terms of accuracy and robustness criteria. The project is accessible at: https://***/darshan8850/Liver-tumor-Segmentation © 2023 IEEE.

关键词： Computer vision

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