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Visual Positioning System of Intelligent Construction Robot based on Improved Adaptive Untraced Kalman Filter Algorithm 3

Visual Positioning System of Intelligent Construction Robot ...

引用

3rd international conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Liao, Weiwen Hou, Kang Tang, Yonglian Su, Bo Xiang, Yu Foshan Power Supply Bureau Guangdong Power Grid Co. Ltd Foshan China Institute of Semiconductor Chinese Academy of Sciences Beijing China Nanhang Jincheng College School of Mechatronical Engineering and Automation Nanjing China

ISBN: (纸本)9798350316674

The paper presents adaptive untracked Kalman Filter and multi-sensor data fusion are used for visual positioning of the robot. Adaptive untracked Kalman Filter is used to fuse the data and obtain the positioning data with higher precision. In this way, the robot can effectively avoid the inaccuracy of the position data caused by some occlusion or violent shaking, and also minimize the impact of measurement error on positioning. Simulation results show that the proposed method can effectively improve the positioning accuracy. © 2023 IEEE.

关键词： Intelligent construction robot Monocular vision Untracked Kalman Filter Visual positioning

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vision-Based Measurement Method for Internal Riveting of the Joist 3

Vision-Based Measurement Method for Internal Riveting of the...

引用

3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Ni, Zongzhe Yi, Jianjun Pan, Yihan Zhang, Xinke Wang, Shaobo Li, Fanxin East China University of Science and Technology Department of Mechanical Engineering Shanghai China

ISBN: (纸本)9798350398410

The requirement for industrial automation and quality control is the fundamental driver behind the visual examination of workpieces. Due to the vast number of workpieces, high level of complexity, and harsh production environment in traditional industrial production, manual inspection's efficiency and accuracy struggle to satisfy production standards. Automated vision inspection technology can partially address these issues. Through digital image processing and other technical methods, vision inspection technology can quickly and accurately identify and analyze features like workpiece size and shape, realizing automated quality control and production process control. In addition, workpiece vision inspection technology has grown in significance with the rise of Industry 4.0 and smart production. This paper's focus is the joist's rivet head, which must be measured for its diameter, height, circularity, and edge distance to assess whether the rivet is successfully assembled by confirming that the rivet head's dimensions are accurate. This paper suggests using Euclidean clustering to segment the point cloud surface and converting the extracted surface into an image for global threshold segmentation to extract edge information in an effort to overcome the challenges posed by the reflection and shadows on metal workpiece surfaces. This research suggests that the holes created by the blockage of light source irradiation on joist edges be filled by filling the pixel values. In this study, the riveted head's diameter is accurate to within 0.1 mm, its height is accurate to within 0.02 mm, and the edge distance is accurate to within 0.2 mm. © 2023 IEEE.

关键词： computer vision

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A Review of Adversarial Machine Learning for Cyber security in Intelligent Systems using Image Classification & Signal Classification Analysis 3

A Review of Adversarial Machine Learning for Cyber security ...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Jahan, Tasneem Lnct University Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350343274

Machine learning models with adversarial examples must be trained to differentiate between genuine and invading data. Cyber computing technology and tools face huge intrusion attacks and malicious programs. Tampered QR code for online payments, malicious face and biometric detection, incorrect facial recognition, object and sign detection are the adversarial examples in real-life scenarios. Convolutional neural networks often fail to image classification tasks due to high vulnerability. The Intelligent systems comprising computer vision, self-driving cars, radio signals, IoT Devices, industrial production and automation are prone to cyber attack with adversarial data in machine learning models. The goal of this paper is to summarize the various Convolutional Neural Network models and Recurrent Neural Network approaches used in machine learning for malicious image classification. The analysis aims to identify the area of attacks using adversarial machine learning in cyber physical systems and intrusion detection systems. © 2023 IEEE.

关键词： Cybersecurity

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Fuzzy Inference Systems for Load Balancing of Wireless Networks 3

Fuzzy Inference Systems for Load Balancing of Wireless Netwo...

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3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic control and computer Engineering, MI-STA 2023

作者： Radwan, Asma Musa Ellabib, Issmail M. Tripoli University Computer Engineering Department Tripoli Libya

ISBN: (纸本)9798350319897

Wireless networks offer many benefits such as cost-effectiveness, flexibility, and convenience. However, they have challenges that can impact their performance and security, one of which is scalability. This research focuses on the scalability challenge, specifically on the user-Access point (AP) association process and load balancing technique, which ensures that devices are associated with the best AP for their current location and network conditions. This paper presents the development of Fuzzy Inference Systems (FIS) to address the load balancing problem using Mamdani and Sugeno models to examine their impact on the candidacy connection of the AP. In addition, the use of different membership functions on Mamdani-type fuzzy inference system are investigated to evaluate the suitability for subscriber to connect to the AP (i.e., AP's candidacy of connection). © 2023 IEEE.

关键词： Wireless networks

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Potato Plant Leaf Disease Detection and Recognition Using R-CNN Model 3

Potato Plant Leaf Disease Detection and Recognition Using R-...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Balakrishnan, Minu Raja, M. Kumar, P.Vinoth Subramaniam, Suvitha Latha, R. Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Chennai Institute of Technology Depatment of Mechatronics Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350322842

A smart farming system that makes use of the appropriate infrastructure is an example of a new technology that aids in the improvement of the quality and quantity of the country's agricultural goods, such as potatoes. Diseases cannot be prevented since developing potato plants requires consideration of factors such as the environment, soil, and quantity of sunlight. Deep learning has resulted in recent advances in computer vision. These advancements have enabled Potato to utilize a camera to diagnose disease. This study developed an effective new method for detecting infections in potato plants. Yukon Gold, a kind of potato, served as the trial subject. Specifically, Alternaria, Blackleg, and Target Spot were targeted for detection using this method. Using 5,615 images of diseased and healthy Potato plant leaves taken in controlled environments, deep CNN is trained to identify between the presence and absence of three illnesses. To identify the Potato illnesses affecting the tracked plants, the system used a CNN. Anomaly detection using the F-RCNN-trained model achieved a confidence score of 80%, while illness identification using the Transfer Learning model achieved an accuracy of 97.85%. The effectiveness of the automated image-capturing system for diagnosing diseases in Potato plant leaves was determined to be 93.33%. © 2023 IEEE.

关键词： Convolutional neural networks

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Personalised AI Based Yoga-Pose Recommendation System Using Game Theory 3

Personalised AI Based Yoga-Pose Recommendation System Using ...

引用

3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Parab, Nidhi Pravin Rampurawala, Alefiya Abbas Khade, Siddhi Sunil Mahato, Manimala Shah and Anchor Kutchhi Engineering College Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Yoga is an ancient practice that seeks to unite the mind with divine awareness. However, to derive maximum benefits from yoga, it is important to select the appropriate asana according to an individual's needs. Not everybody has access to good yoga instructors who can make tailored yoga routines for them. An artificial intelligence (AI)-based model which uses Game Theory on yoga asanas with similar benefits, could help recommend a personalized yoga routine to support users on their journey towards a healthier lifestyle. In certain cases, the person is unable to determine the part of their body that is stiff or causing unease, for example, the stiffness may be very mild or only occur during certain movements or activities. In some cases, the person may simply not be paying close attention to their body or may have become accustomed to the sensation of stiffness in that particular area. A model which uses computer vision can help overcome this issue. The traditional approach to yoga instruction relies on a one-size-fits-all approach, but Game Theory offers a more nuanced approach that considers individual preferences, like medical conditions, areas of discomfort, level of experience and lifestyle goals. The model is tested using data analysis of a test dataset. The paper concludes with a discussion of the implications of the findings for the field of yoga instruction and the results suggest that Game Theory is an effective tool for developing personalized yoga recommendations. © 2023 IEEE.

关键词： Python

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THERP-CREAM Prediction Method for Human Failure Probability for Air Traffic control 3

THERP-CREAM Prediction Method for Human Failure Probability ...

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3rd IEEE international conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Tan, Ruisi Gan, Xusheng Yang, Tianyu Li, Shenghou Air Force Engineering University Air Traffic Control and Navigation College Shaanxi Xi'an710051 China

ISBN: (纸本)9781665472784

To Improve the human reliability of Air Traffic control (ATC) for reducing the human error of air traffic controllers, a combined human failure probability prediction method based on THERP and CREAM is proposed. Based on binary tree model of THERP method, the basic context of incident development is constructed, and the trigger probability of each behavior is corrected on the basis of CREAM method, so as to get a more accurate probability prediction conclusion. Through the comparative analysis of similar incidents, the key incident impact factors can be identified to formulate targeted control measures, thus providing an effective analysis means for studying and predicting the failure probability of air traffic controllers. © 2023 IEEE.

关键词： Air traffic control

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Activity Recognition in Video Frames for Enhancing Human-Robot Collaboration: A Machine Learning Perspective 3

Activity Recognition in Video Frames for Enhancing Human-Rob...

引用

3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Chaubey, Ved Prakash Sharma, Shamneesh Kumar, Aman Malik, Arun Malik, Praveen Batra, Isha UpGrad Education Private Limited Delivery and Student Success UpGrad Campus Karnataka Bangalore India School of Computer Science & Engineering Lovely Professional University Punjab India School of Electronics and Communication Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350330724

Human activity recognition is a prominent area of investigation within computer vision research. Its potential applications span diverse fields such as security monitoring, healthcare, and human-computer interaction. The identification and classification of human activity through the utilization of machine learning techniques has yielded a plethora of proposed models within this dynamic field of scientific inquiry. By employing diverse kinetic models that are linked to the learning of spatial or temporal features, the characteristics of a collection of image or video data are extracted. The primary aim of this model is to identify and classify ongoing activities. This objective has been successfully achieved by various deep-layer trained models that have been implemented in this field. To fulfill the specified criteria, the proposed approach utilizes the Long Recurrent Convolutional Network (LRCN) and Long Short-Term Memory (LSTM) convolutional neural network architectures. The implementation of vision-Based Human Activity Recognition utilizes the 3D Convolutional Neural Network (CNN) model. The model undergoes training utilizing the UCF-50 dataset. The classification of the photos by the model is contingent upon the action being performed. The model will receive videos as input, which will undergo appropriate pre-processing before being classified based on the activity. The vision-based human activity identification system offers several advantages, including but not limited to accuracy, cost-effectiveness, and user-friendliness. © 2023 IEEE.

关键词： Neural network models

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AI-Powered Financial Fraud Detection System 3

AI-Powered Financial Fraud Detection System

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Prabha, M. Sunder, Sowmiya Kumarabaabu, Rajasreenithaa Davis, Rachel Saraswathi, P. Velammal College of Engineering and Technology Department of Information Technology Madurai India

ISBN: (纸本)9798331515911

Credit card blackmail may be a major issue, and numerous innocents are mishandled with their private pieces of data. Since credit cards are both available and commendable, they have been a well-known outline installment elective. Their wide utilization comes with the natural issue of securing individual and installment information against blackmail or unauthorized get-to. This makes a distinction in keeping the belief and certainty of the clients while premonition security hones are crucial. To address this fundamental issue, we have utilized the display day concepts of credit card extortion disclosure and we'll propose coordinates metaphysics for a computer-aided handle in which property imprisonments are characterized by interior reification components by utilizing progressed twins as well as blockchain. That exploration besides prescribes that understanding and combatting credit card blackmail are basic for making strides in the welfare of buyers as well as budgetary teach. This consider gives a comprehensive see of credit card blackmail examination counting classification of unmistakable sorts of it to show the chance scene. It presents a new computerized twin strategy, which contrasts with prevalent extortion discovery. In the slightest complex terms, a computerized twin is the virtual adjustment of something honest to goodness. The foremost critical issues for financial specialists and clients are hacking, scamming, etc. People do not have appropriate mindfulness of this issue. In this issue, the most challenging task is recognizing. To overcome this issue afterward considers multi-source content information to overhaul the blackmail area. This investigates a more significant consideration of interior organization and outside media which contrasts recognizing the precision for location of cash-related blackmail revelation systems. This method of multi-source substance data procedure is utilized in the Chinese promotion system between 2017 and 2021. © 2025 IEEE

关键词： Budget control

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Semi-autonomous Patient Transfer System Assisted by computer vision 11th

Semi-autonomous Patient Transfer System Assisted by Computer...

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11th international conference on E-Health and Bioengineering (EHB)

作者： Dimitrova, Alexandru Ianosi-Andreeva Mandru, Dan Silviu Tech Univ Cluj Napoca Mechatron & Machine Dynam Cluj Napoca Romania

ISBN: (纸本)9783031625220;9783031625237

The paper presents a system designed to improve the mobility of patients for transfer operations with a high degree of independence from a caregiver;as such the proposed system consists of a mobility device and a 3D camera that scans the environment from possible hazards and, if needed, take over the control to actively avoid them. The paper is focused on the integration of a machine vision system with a mobility device, with a special care for the design of the dual input subassembly, that has to retain the ability of being human-controllable and also to accept the machine-computed corrections. For this purpose, a custommade compliant linkage was designed, in which the degree of compliance might be adjusted in concordance with the level of control the patient is able to accomplish. The paper also briefly discusses the possibility of integration of other means of human input systems, more useful for people with an even higher degree of disability than the hypothesized use case scenario, presumed challenges and possible solutions.

关键词： Rehabilitation Mobility aid computer vision

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