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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Singh, Indu Aditya, Nilesh Srivastava, Pranjal Mittal, Shivanshu Mittal, Taruvar Surin, Noel Vaibhav Delhi Technological University Department of Computer Science and Engineering India Netaji Subhas University of Technology Department of Manufacturing Processes and Automation Engineering India Netaji Subhas University of Technology Department of Information Technology India

ISBN: (纸本)9798350338607

Financial institutions are highly concerned about credit card fraud due to the growing frequency of deceitful activities linked with credit card transactions. To address this issue, a novel fraud detection system is proposed that combines advanced feature engineering and neural network architecture. The proposed approach. FFFW-RBN, employs a hybrid swarm intelligence model comprising the Fruitfly Optimization Algorithm and Fireworks Algorithm, (FFO-FWA) for feature selection, and the Neural Embeddings Generator in combination with a Shallow Radial Basis Network (RBF) for localized pattern classification. The proposed system achieves a 99.97% accuracy, 0.91 recall, and a precision of 0.94. The experimental results demonstrate that our methodology effectively and practically detects fraudulent credit card transactions. The paper presents a comprehensive evaluation of the system, including various performance metrics and a confusion matrix, highlighting its potential for real-world implementation. © 2023 IEEE.

关键词： Radial basis function networks

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Design and Implementation of an Autonomous System for Crack Detection 2

Design and Implementation of an Autonomous System for Crack ...

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2nd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2023

作者： Abdullah, Abdulkareem Al-Ashwal, Abdulwahab Debsi, Ali Kaf, Amira Abo Almadwami, Hashem School of Automation Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Mechatronic Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350371451

In recent years, visual Simultaneous Localization and Mapping (SLAM) systems, particularly the Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM2), have shown significant promise in robotic navigation in indoor environments. However, previous implementations often struggled with real-time processing and environmental dynamicity, limiting their practical deployment. This study evaluates the performance of ORB-SLAM2 using different sequences of the Tum RGBD dataset to construct 3D maps in real-time with an RGB-D camera (Kinectv2). The study findings indicate that ORB-SLAM2 facilitates effective real-time autonomous navigation using Turtlebot2, although previous related studies have demonstrated limitations in real-time processing capabilities and adaptive object recognition. To address these challenges, the deployment of a real-time object detection system using YOLO, a state-of-the-art one stage object detector, and a convolution neural network that surpasses other real-time object detectors in speed and accuracy. In this process, we first built our own dataset by labeling images, and then trained a YOLOv4 model to detect cracks. We tested the model in several ways, including real-time crack detection using an RGB-D camera (Kinectv2). The results were efficient, demonstrating the model's success in accurately identifying the object and predicting its location. © 2023 IEEE.

关键词： Crack detection

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Optimal Function Block Distribution for IEC 61499 Distributed Control Application

Optimal Function Block Distribution for IEC 61499 Distribute...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Vahid Ehteshami Bojnurdi Tuojian Lyu Udayanto Dwi Atmojo Valeriy Vyatkin Department of Electrical Engineering & Automation Aalto University Espoo Finland Department of Computer Science Computer and Space Engineering Lulea Tekniska Universitet Sweden

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

The efficient distribution of function blocks across programmable logic controllers (PLCs) is critical to improving the performance of distributed automation systems. This paper presents a novel randomized greedy optimization algorithm to minimize communication latency and maximize resource utilization by optimizing the allocation of function blocks across multiple PLCs in compliance with the IEC 61499 standard. Simulations demonstrate the effectiveness of the approach in achieving substantial performance improvements in distributed control applications. The results highlight the flexibility and scalability of the proposed optimization method, particularly in environments with varying PLC configurations.

关键词： Time-frequency analysis automation System performance Scalability Decentralized control Computational efficiency Resource management IEC Standards Optimization Standards

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Research on path planning of automated guided vehicle based on improved RRT algorithm

Research on path planning of automated guided vehicle based ...

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2022 International Conference on computer Application and Information Security, ICCAIS 2022

作者： Xu, Lunhui Xu, Runnan Kuang, Xianyan School of Computer Guangdong University of Technology Guangdong Dongguan523083 China School of Electrical Engineering and Automation Jiangxi University of Science and Technology Jiangxi Ganzhou341000 China

ISBN: (纸本)9781510663459

An improved RRT * algorithm is proposed to solve the problems of poor goal orientation, low space utilization and many inflection points in the path planning of Automated Guided Vehicle (AGV) transportation. The algorithm adopts the approach sampling and restricted sampling strategies. First, the approach sampling method improves the sampling efficiency and reduces the path search time;then, the sampling area is reduced by limiting sampling, so as to improve the probability of sampling points falling in the path neighborhood and shorten the path length. On this basis, the redundant points of the path are trimmed according to the greedy algorithm, and the curvature of the path is optimized using the cubic B-spline smoothing method to obtain the driving track that meets the requirements of AGV running stability. Finally, through MATLAB simulation and comparative analysis, in the same environment, compared with the RRT* algorithm, the improved RRT* algorithm reduces the time of path search by 61.59% and the path length by 8.6%, which verifies the effectiveness of the improved RRT* algorithm. © 2023 SPIE.

关键词： Motion planning

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Adaptive rapid defect identification in ECPT based on K-means and automatic segmentation algorithm

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 1-18页

作者： Huang, Xuegang Yin, Chun Dadras, Sara Cheng, Yuhua Bai, Libing Hypervelocity Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Electrical and Computer Engineering Department Utah State University LoganUT84321 United States

To enhance the detection efficiency in eddy current pulsed thermography, an adaptive feature extraction algorithm for defect identification is developed in this paper. The proposed algorithm involves four stages, namely, the thermal image segmentation, the variable interval search, the distance correlation clustering analysis and the between-class distance decision making. The transient thermal responses (TTRs) with similar characteristics are collected into one data block. The thermal image segmentation and variable interval search can help reduce the repetitive calculation in defect identification by choosing local optimums in each data block. The global optimum that has the largest sum of the between-class distance, is derived by first classifying the local optimums and then calculating the correlation distance of the thermal responses with the center points of each class. Finally, the TTRs with the largest between-class distance are regarded as the typical ones which can be used to identify the discriminative defect features of infrared image sequence. Finally, the comparison experiments are carried out to demonstrate the effectiveness and advantages of the proposed approach. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： K-means clustering

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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Analysis of Errors in Roll and Pitch Measurements for Moving Objects 34

Analysis of Errors in Roll and Pitch Measurements for Moving...

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34th International Scientific Symposium Metrology and Metrology Assurance, MMA 2024

作者： Dichev, Dimitar Zhelezarov, Iliya Georgiev, Borislav Pulov, Dimcho Kovachev, Hristofor Malakov, Ivo Kupriyanov, Oleksandr Dicheva, Ralitza Technical University of Gabrovo Department of Machine and Precision Engineering Gabrovo Bulgaria Technical University of Sofia Department of Automation of Discrete Production Engineering Sofia Bulgaria Ukrainian Engineering Pedagogics Academy Department of Information Computer Technologies and Mathematics Kharkiv Ukraine Technical University of Sofia Department of Precision Engineering and Measurement Instruments Sofia Bulgaria

ISBN: (纸本)9798350352832

This paper examines the challenges associated with the accuracy of angular orientation measurements of moving objects under dynamic conditions. These measurements are critical to the safety and operational efficiency of mobile objects such as ships, aircraft, drones and autonomous vehicles. Sources of error range from the motions of the objects themselves and vibrations to internal noises and changes in the environment. The paper proposes an error minimization method through the integration of mechanical pendulums, electronic sensors, and adaptive data processing algorithms, including the Kalman filter. The presented method is based on a detailed analysis of accuracy in a dynamic environment and offers innovative approaches to increase the reliability of measurements. These improvements have the potential for widespread application in a variety of industries, increasing the safety and efficiency of moving objects. © 2024 IEEE.

关键词： Kalman filters

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Designing Web Application Client Program Modules for Asynchronous Event Handling

Designing Web Application Client Program Modules for Asynchr...

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Informatics, Electronics, and Radio engineering (PIERE), International Conference Problems of

作者： Vadim O. Mokrushin Sergey P. Ilinykh Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The article considers possible approaches to designing program modules in web application clients. A design approach is proposed, the use of which provides an increase in the ease of debugging programs in the development process, localization of errors and reduction of program code duplication. The proposed design approach is based on the division of responsibility between parts of the program code for the implementation of synchronous and asynchronous operations in asynchronous events. Responsibility for processing network interaction with the server and receiving data from the user through controls in the user interface should be assigned to asynchronous operations and their responsibility should be limited only to receiving data from external sources. In turn, updating the application state based on data from external sources should be limited to the responsibility of synchronous operations. The paper shows how the proposed design approach provides the above improvements and provides examples of program code.

关键词： Location awareness Codes Process control Debugging User interfaces Servers Informatics Testing

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Wound Tissue Classification: A Comparative Analysis of Deep Neural Network Models

Wound Tissue Classification: A Comparative Analysis of Deep ...

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International Conference on Smart Systems and Technologies (SST)

作者： Antonio Antunović Emmanuel Karlo Nyarko Damir Filko Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Computer Engineering and Automation Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (数字)9798350386394

ISBN: (纸本)9798350386400

Wound tissue classification is an important task in medical imaging, with applications ranging from wound assessment to treatment planning. In this study, we investigated different neural network architectures and loss functions to improve the accuracy and efficiency of wound tissue classification. The study included eight different neural network architectures, including classic U-Net, MobileNet U-Net, Attention U-Net, Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net), Attention Recurrent Residual U-Net (R2AU-Net), Residual Network (ResNet-50), EfficientNet and SegFormer. Each architecture was trained separately with four different loss functions: categorical cross-entropy, weighted categorical cross-entropy, focal loss and soft dice loss. The comparative analysis revealed that the SegFormer architecture in conjunction with soft dice loss function achieved the most promising results on all classification metrics. The results of the study highlight the potential for further research in this area and emphasise the need for more comprehensive datasets to improve model performance.

关键词： Training Semantic segmentation Transformers Smart systems Robustness Distance measurement Data models Planning Decoding Residual neural networks

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Optimal view point and kinematic control for grape stem detection and cutting with an in-hand camera robot

Optimal view point and kinematic control for grape stem dete...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sotiris Stavridis Zoe Doulgeri Automation & Robotics Lab School of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this work, a methodology to find the best view of a grape stem and approach angle in order to crop it is proposed. The control scheme is based only on a classified point cloud obtained by the in-hand camera attached to the robot’s end effector without continuous stem tracking. It is shown that the proposed controller finds and reaches the optimal view point and subsequently the stem fast and efficiently, accelerating the overall harvesting procedure. The proposed control scheme is evaluated through experiments in the lab with a UR5e robot with an in-hand RealSense camera on a mock-up vine.

关键词： Point cloud compression Robot vision systems Pipelines Crops Kinematics Cameras End effectors Reliability Intelligent robots

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