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Proceedings of the international conference on Artificial Intelligence Techniques for Electrical engineering Systems (AITEES 2022) 1

Proceedings of the International Conference on Artificial In...

引用

丛书名： Atlantis Highlights in Intelligent Systems

2023年

作者： Valentina E. Balas Ramesh C. Bansal Siva Kumar Mangipudi Subhojit Dawn

ISBN: (数字)9789462392663

ISBN: (纸本)9789462392656

The book invites submission of novel, recent area of innovation and previously unpublished research work/idea in the field of modern applications of artificial intelligence techniques to electrical engineering systems. The applications of artificial intelligence related to various fields of electrical engineering are mentioned in the conference tracks. The conference is meant to discuss the challenges and applications of latest evolutionary computing techniques, neural networks, fuzzy logic, machine learning and data analytics in the fields of power systems, power electronics, robotics, automation, instrumentation, control systems, mechatronics and photonics. It provides a platform to the students, researchers, scientists, faculty members, professionals and practitioners to interact, present and get innovative ideas in the field of electrical engineering. As a part of AITEES-2022, many keynote sessions are planned to enhance the research and innovation skills of participants. Eminent professors from academic institutions and world renowned industrial experts from India and abroad will deliver keynote sessions.

关键词： Artificial Intelligence computer Science Machine Learning Big Data

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YOLOv5s detection method for road cracks based on residual and attention mechanism 2

YOLOv5s detection method for road cracks based on residual a...

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2nd Asia conference on Advanced Robotics, automation, and Control engineering, ARACE 2023

作者： Miao, Xinfa Liu, Baolian Lanzhou Jiao Tong University School of Electronic and Information Engineering Lanzhou City China

ISBN: (纸本)9798350328363

Road surface diseases pose potential accident hazards to people's safe travel. In response to the problems of missed detection, false detection, and low detection accuracy caused by blocked cracks in existing computer vision-based road crack detection methods, a Res2Net fusion attention mechanism detection method is proposed. Firstly, in the backbone network C3 Bottleneck, the ResNet module is optimized to a Res2Net residual network. By constructing hierarchical class residual connections within a residual block, more feature information is obtained while increasing the receptive field of the model;Secondly, the introduction of quality focal loss prediction classification scores and confidence scores of regression branches makes the localization of occluded targets more accurate;Finally, add an attention module to the detection head to improve detection accuracy. The experiment shows that the improved algorithm extracts more complete information. Compared with existing methods, the detection accuracy of this method has reached 62.5%, and the detection speed has reached 56 frames/s. It can quickly and effectively detect damaged road surfaces, reducing missed and false detections. © 2023 IEEE.

关键词： Crack detection

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Advancing Robot Person Following with Multi-Object Tracking and Dynamic ID Switching 5

Advancing Robot Person Following with Multi-Object Tracking ...

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5th international conference on Artificial Intelligence and Electromechanical automation, AIEA 2024

作者： Zhu, Huaiyang Chao, Yuan Lu, Hengyu Jiangsu University of Technology School of Mechanical Engineering China

ISBN: (纸本)9798350366174

Existing Robot Person Following usually assumes that the object is selected in the initial frame, which renders it incapable of switching the tracking object in subsequent frames. In this context, traditional methods such as object selection, person re-identification, or pose tracking that rely solely on the initial frame fall short of meeting the requirements of complex human-computer interaction applications. To address these limitations, this paper proposes a comprehensive framework for mobile robot person following based on multi-object tracking (MOT-RPF). Firstly, the framework integrates the improved OC-SORT tracking algorithm with gesture detection technology, enabling dynamic selection and precise identification and location estimation of multiple objects. Secondly, by planning the Dubins path and proposing a Lateral and Longitudinal Control Model based on the omnidirectional wheel robot, precise path tracking on the omnidirectional wheel mobile robot can be achieved. The proposed improved OC-SORT tracking algorithm was evaluated on public datasets such as DanceTrack and Person Tracking, demonstrating tracking and location accuracies of 60.5HOTA and 98.1% respectively, surpassing existing multi-object tracking OC-SORT and single-object tracking ODFS. The proposed robot person following framework operates on embedded systems equipped with NVIDIA Jetson Xavier NX. It has demonstrated its effectiveness in real-time tracking and following of individuals, as evidenced by its performance on a custom Person Following dataset and its successful application in real-world scenarios. Code and models are available at https://***/djdodsjsjx/MOT-RPF/. © 2024 IEEE.

关键词： Mobile robots

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Design and Analysis of Four-Wheeler Chassis for Improved Performance 4

Design and Analysis of Four-Wheeler Chassis for Improved Per...

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4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Dhabliya, Dharmesh Alkkhayat, A.H. Sivakumar, Janaki Bhokde, Ramesh Maheshwari, B. Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India College of Technical Engineering The Islamic University Najaf Iraq Global College of Engineering and Technology Department of Csct Oman Ies Institute of Technology and Management Ies University Bhopal India Ies College of Technology Ece Department of Mechanical Engineering Bhopal India

ISBN: (纸本)9798350393248

In the automobile business, designing and assessing a four-wheeler chassis for enhanced performance is a crucial responsibility. The goal of this study is to meet the increasing demand for automobiles with improved handling, stability, and safety features and lower total weight. To accomplish these goals, a thorough investigation is carried out using computer-aided design (CAD), modern materials, and structural analysis methods. The preliminary portion of this research evaluates the shortcomings of the existing chassis designs and emphasises the need of innovative solutions. High-strength metals and composites are examples of strong, lightweight materials that are highlighted in the following phases that deal with material selection. Finite element analysis (FEA) is used to further evaluate the structural soundness of the suggested chassis, guaranteeing its ability to handle a variety of loads and provide the greatest degree of crash safety. The chassis design incorporates aerodynamic considerations and suspension geometry to improve handling and stability. A comprehensive approach to performance optimisation is made possible by this tactic. The results of this research ultimately aid in the creation of a lightweight, safe, and highly effective four-wheeler chassis, demonstrating the possibility of better driving circumstances, reduced pollutants, and increased fuel economy. This study emphasises the significance of chassis design in the pursuit of more efficient and environmentally friendly vehicle solutions, laying the foundation for future developments in the automotive industry. © 2023 IEEE.

关键词： Structural analysis

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A CNN Reconstruction Scheme with Low and High Frequency Branches for Image Compressive Sensing 3

A CNN Reconstruction Scheme with Low and High Frequency Bran...

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3rd international conference on Artificial Intelligence, automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Liangjun Yang, Chenhao School of Computer Science and Telecommunication Engineering Jiangsu University Zhenjiang212013 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Recently, Convolutional Neural Network-based Compressive Sensing(CNN-CS) have drawn attentions of extensive researchers since it can achieve faster reconstruction speed than traditional ones. Instead of traditional iterative reconstruction algorithms, a CNN reconstruction network is used with the goal of extracting key features of the images. For this purpose, the network should be trained on large amount of image data. However, most images usually poses both abundant low-frequency components and sparse high-frequency components, thus it is unreasonable to extract image features through a single network as in most CNN-CS algorithms due to that they may degrade the feature extraction of each other. In order to get a better result, a two-branch CNN-CS is proposed in the paper. In the network, the image signals are decomposed into two parts according to frequency firstly, and one branch is dedicated to extract the features of low frequency components, while the other is used to learn the high frequency ones. Considering the sparsity of high frequency, Mean Absolute Error (MAE) is chosen as the training penalty loss function. As such, both low and high frequency components can be well reconstructed respectively. Experimental results show that our method is prior to all traditional CS methods and single network CNN-CS method(CSNet+). The gain of PSNR over CSNet+ can reach 0.45dB. © 2023 SPIE.

关键词： Compressed sensing

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Standardized assessment of rock climbing movements based on posture recognition 4

Standardized assessment of rock climbing movements based on ...

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4th international conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Yi, Tong Tan, Guangxing Tang, Tiannan Jin, Yangying Zhang, Dawei School of Automation Guangxi University of Science and Technology Guangxi Zhuang Autonomous Region Liuzhou China

ISBN: (纸本)9781510682528

Aiming at the improper rock climbing movements may cause sports injuries, and the traditional sports motion capture analysis method is insufficient in real time, the accuracy of movement evaluation is low and other problems, this paper applies the pose recognition technology to rock climbing, and researches on the recognition and standard degree evaluation of rock climbing movements. Firstly, a rock climbing action detection algorithm based on improved YOLOv8-Pose is proposed, MobileNetV3 is used as the backbone feature extraction network of the model to realize the lightweight of the model, and the attention mechanism CBAM module is introduced to pay attention to the important channel information in the network, so that more effective feature information can be extracted from the channel and spatial dimensions, and the model recognition accuracy can be improved;then, for the the characteristics of rock climbing action, eight joint angle indicators are selected, and the degree of standard rock climbing action is evaluated using action similarity. Experiments show that: the size of the improved detection model in this paper is reduced by44.07%compared with the original model, and the number of parameters is also reduced by51.54%, and the results have a better differentiation when comparing the standard action and the action to be tested by using the action similarity, thus verifying the effectiveness of the algorithm. Thus, the validity of the algorithm is verified. © 2024 SPIE.

关键词： Rock mechanics

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Design and Implementation of Neuro-Fuzzy Control Approach for Robot's Trajectory Tracking 7

Design and Implementation of Neuro-Fuzzy Control Approach fo...

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7th international conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kiruba Sandou, D. Sunad Kumara, A.N. Choudhary, Binod Kumar Gurpur, Shashikala Sarishma Natrayan, L. Sivaramkumar, M. Manakula Vinayagar Institute of Technology Department of Robotics and Automation Pondicherry605107 India BGS Institute of Technology Department of Computer Science and Engineering Adichunchanagiri University Karnataka Mandya571448 India ARKA Jain University Department of Electrical and Electronics Engineering Jharkhand Jamshedpur832108 India Maharashtra Pune411014 India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun India Saveetha School of Engineering SIMATS Department of Mechanical Engineering Tamilnadu Chennai602105 India Karpagam Academy of Higher Education Department of EEE Tamiinadu Coimbatore India

ISBN: (纸本)9798350340600

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications. © 2023 IEEE.

关键词： Controllers

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Research on Infant Behavior Feature Classification Based on GWO-ELM Algorithm 4

Research on Infant Behavior Feature Classification Based on ...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Xu, Mingshen Shi, Xinyu Guan, Po Zhu, Zhixue Department of Mathematics and Physics North China Electric Power University Hebei Baodiing071000 China Department of Automation North China Electric Power University Hebei Baodiing071000 China Department of Mechanical Engineering North China Electric Power University Hebei Baodiing071000 China Department of Electrical Engineering North China Electric Power University Hebei Baodiing071000 China

ISBN: (纸本)9781510672505

Regarding the association between maternal mental and physical health and infant behavior, this paper proposes a method that integrates Grey Wolf Optimization (GWO) with Extreme Learning Machine (ELM) for the correlation and classification of infant behavioral features and maternal health indicators. Firstly, a dataset of maternal mental and physical health and infant behavioral features is established. Using the Random Forest method, physical and psychological indicators of the mother and their eight primary influencing factors are extracted from the dataset. Subsequently, the classification accuracy of infant behavior is used as the fitness function for the Grey Wolf algorithm. The GWO algorithm optimizes the kernel and regularization parameters of the ELM, which are then fed into the ELM to train the four key feature parameters of infant behavior. Finally, it is applied to the classification task of infant behavior. The results indicate that the classification method combining GWO and ELM has an accuracy of 86% and a precision of 91%. This outperforms traditional classification models such as XGBoost and GBDT in detection and classification. It also surpasses the performance of ELM optimized by traditional optimization algorithms like SSA and PSO, validating the feasibility and effectiveness of the proposed method. © 2023 SPIE.

关键词： Particle swarm optimization (PSO)

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Machine History Based Data Learning in CNC Machining 1

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33rd international conference on Flexible automation and Intelligent Manufacturing

作者： Kim, S. G. Im, C. S. Lee, Y. S. Song, Y. H. Kim, D. W. EDIM Inc 67 Yusang Ro Jeonju 54852 South Korea Korea Elect Technol Inst 71 Changi Daero Chang Won 51394 South Korea Jeonbuk Natl Univ Dept Ind & Informat Syst Engn Jeonju 54896 South Korea

ISBN: (数字)9783031744853

ISBN: (纸本)9783031744846;9783031744853

With the recent development of the machine tool industry, there is an increasing trend of research to collect real-time cutting physics data in CNC cutting and control the CNC. Cutting physics data is the information collected by various sensors such as current, acceleration, voltage, and torque sensors, which indirectly measure the cutting load. This cutting physics data is used for tool condition diagnosis, cutting condition control, and as a key evaluation indicator for predictive maintenance of the spindle. However, in cutting processes, cutting physics data is less useful due to various factors such as machine, workpiece, tool, cutting conditions, and coolant. For more precise machining condition diagnosis or control, a data learning process using the collected cutting physics data is required. In this study, we present amethod for storing machining history data that synchronises CNC data and cutting physics data, and data learning using the data. Data learning methods based on machining history data include (1) optimising NC data to improve productivity by controlling the feed rate, (2) learning data separated by tool unit to manage the life of individual tools or diagnose the degree of wear, and (3) generating reference control curves for real-time feed rate control during machining. Such data learning is particularly effective in the case of mass-produced products and is essential for the development of a digital twin system for CNC machining.

关键词： computer numerical control (CNC) machining machining history data data learning advanced adaptive feed control

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An IoT-Enabled Smart Helmet for Health Monitoring and Location Tracking of Miners 7

An IoT-Enabled Smart Helmet for Health Monitoring and Locati...

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2023 7th international conference On Computing, Communication, Control And automation, ICCUBEA 2023

作者： Sagare, Sakshi Patil, Sakshi Choudhari, Srushti Sontakke, Prakash Bhattacharjee, Taniya Pimpri Chinchwad College of Engineering Department of Electronics and Telecommunication Pune India Pimpri Chinchwad College of Engineering Department of Electronics and Telecommunication Engineering Pune India Cmr Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350304268

In the mining industry, different complex operations create varying risk factors that allow a multitude of risk factors to appear. The Smart Helmet has been developed to provide safety to miners who work in an environment with many risk factors. In the mining area, Miners come into contact with hazardous gases which cause various respiratory diseases, lungs infection, and suffocation, sometimes resulting in the death of miners. Existing helmets help to detect and alert miners about abnormal conditions using buzzers. The proposed system helps to monitor the health of mining workers. Also, it detects hazardous gases using gas sensors. When hazardous gases cross the threshold value, oxygen is supplied to miners. The base station can track the location of miners and all the collected data from sensors is sent to the cloud using IoT. Real-time data is monitored at the base station (Mobile application/Workstation). According to the parameters, needful help will be provided to mining workers. Also, A message alert and emergency call will be sent to the registered contact information during an emergency. Thus, the proposed system helps the mining industry to reduce the number of accidents Whenever Hazardous gases exceed their threshold values, a buzzer gets activated to warn nearby workers. There is also a provision for a manual panic switch. In case of an unavoidable situation, workers will use it so that others can get alerted. Thus, the proposed system helps the mining industry to reduce the number of accidents. © 2023 IEEE.

关键词： Hazardous Gas detection Mining Industry Mobile Applications Node MCU 8266 Wireless communication

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