2023 International Conference on Robotics and Automation in Industry (ICRAI)最新文献

Intelligent Transportation Systems in Smart City: A Systematic Survey 智慧城市中的智能交通系统研究

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089543

Muhammad Abul Hassan, Roofia Javed, Farhatullah, F. Granelli, Xin Gen, Muhammad Rizwan, Syed Haider Ali, Hazrat Junaid, Sana Ullah

引用次数: 4

Control Simulation of Proportional-Integral and Sliding Mode Control for Precision Seed Planters 精密播种机比例积分与滑模控制仿真

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089606

Y. Nawaz, M. U. Qadir, Muhammad Awais Khan, I. Haq, Kamran Shah, Tahir Khan

{"title":"Control Simulation of Proportional-Integral and Sliding Mode Control for Precision Seed Planters","authors":"Y. Nawaz, M. U. Qadir, Muhammad Awais Khan, I. Haq, Kamran Shah, Tahir Khan","doi":"10.1109/ICRAI57502.2023.10089606","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089606","url":null,"abstract":"The desired development and design of an electric control system (ECS) for a precision planter is one of the most crucial elements in evaluating the efficacy of applicators for plant production. The seeds would be sown using this approach. Years have been invested in the continuous development of a range of application methods and metering systems, each of which offers specific advantages for applying the required system with a high degree of precision. Researchers worldwide have been trying to make electric-driven seed meters (EDSM) for planters, but they haven't succeeded. This study looks into the current state of installing seed metering systems for precision planters. Proportional-integral (PI) and sliding mode control (SMC) designs of the control system have been implemented to improve the planting quality of the planters. We want to highlight the electrically driven control system (EDCS) using these designs. An overview of the noteworthy features and limitations of past research is given. We also discuss several future directions for future research, concentrating on the knowledge gaps in this area.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115990955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design, Modeling, and Analysis of Robotic Manipulator for Welding of Nuclear Spent Fuel Cask Lid 核乏燃料桶盖焊接机械臂设计、建模与分析

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089575

M. W. Faheem, Syed Muhammad Haris, Abdul Rehman Abbasi, Hasnain Tariq

引用次数: 1

Robot Autonomous Exploration and Map Building Method 机器人自主探索与地图构建方法

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089570

Xiaojing Tian, Bing Li, Yuwei Du, Minghao Wang

{"title":"Robot Autonomous Exploration and Map Building Method","authors":"Xiaojing Tian, Bing Li, Yuwei Du, Minghao Wang","doi":"10.1109/ICRAI57502.2023.10089570","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089570","url":null,"abstract":"How to enable effective autonomous exploration and construction maps in unknown indoor environments is one of the key issues in the field of mobile robots. The mobile robot autonomous exploration and map building system studied in this paper aims to further improve the autonomy of the indoor mobile robot, so that the robot can independently complete the exploration and map building task in the unknown indoor environment and realize the autonomous navigation and other functions according to the constructed environment map. An independent exploration and SLAM construction method based on the fast exploration random tree (RRT) is proposed, applying the RRT algorithm to the boundary exploration field. Through performing collision tests by randomly spreading the sampling points in the environment and adding the parameters of the adaptive step size, the boundary points are optimized and evaluated for the best explored boundary points. At the same time, using the combination of global RRT and local RRT construction method, it accelerates the exploration of the unknown environment, until throughout the whole environment. Experimental testing in a simulation environment, the experimental results show that the system works very well, and can independently go through the whole environment and complete the map drawing.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124467205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-based Feature Fusion for Action Recognition Using Skeleton Information 基于深度学习的骨骼信息特征融合动作识别

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089577

Fahad Ul Hassan Asif Mattoo, U. S. Khan, Tahir Nawaz, N. Rashid

引用次数: 0

Smart Railway Level Crossing System for Avoiding Accidents 避免事故的智能铁路平交道口系统

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089536

G. F. Mirza, Yahya Sameen Junejo, F. Shaikh, B. S. Chowdhry, A. Shah

{"title":"Smart Railway Level Crossing System for Avoiding Accidents","authors":"G. F. Mirza, Yahya Sameen Junejo, F. Shaikh, B. S. Chowdhry, A. Shah","doi":"10.1109/ICRAI57502.2023.10089536","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089536","url":null,"abstract":"In railways, one of the foremost issue is the safety of railway level crossing. Crashes related to railway level crossing lead to a great loss with respect to goods and lives. However, the existing methods used for the accidents' avoidance are manual, sensors and cameras based which possess certain drawbacks. Manual control of crossing gates is not only time-consuming but requires human labor and generate errors. Besides, camera based control systems are costly, involve experts to process the images, require human intervention, and do not provide clear images in fog/rain. On the other hand, sensors based systems have short range issues and are dependent on environmental conditions. This paper reviews the existing control systems of railway infrastructure and their classification in different countries. It then presents an automatic, novel, high range and cost-effective approach to prevent critical accidents intelligently. The novel concept avoids level crossing accidents using Internet of Things (IoT) technology. The train GPS based on-board and track controller nodes communicate to control level crossing gates and alert road users. The communication between modules (i.e., on-board train and track devices) and remote database establishes using WiFi. The review of effective communication network for critical and non-critical communication is presented with respect to mobility, capacity, coverage, availability and reliability. This research provides concept which can greatly mitigate the accidents' frequency associated with the trains, pedestrians and road vehicles on crossing using smart technologies.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determination of the Strain Energy and Stress Intensity Factors with the Crack's Inclination under biaxial loading 双轴加载下随裂纹倾角的应变能和应力强度因子的确定

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089534

Salman Khan, Massab Junaid, F. Khan, Tauheed Shehbaz

{"title":"Determination of the Strain Energy and Stress Intensity Factors with the Crack's Inclination under biaxial loading","authors":"Salman Khan, Massab Junaid, F. Khan, Tauheed Shehbaz","doi":"10.1109/ICRAI57502.2023.10089534","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089534","url":null,"abstract":"All components have certain flaws either interior or exterior on the surface which causes stress intensity under loading. The stress intensity exceeds the fracture toughness of the materials and causes failure of the component. A finite element analysis (FEA) was performed on Inconel 718 plate with a central crack under biaxial loading to compute the stress intensity factor (SIF) and the strain energy released rate (SERR) with the crack inclination ranging (ß) 0 °-150° and loading factor (B). The applied load is 10 MPa in the Y-direction (αy) while a range of -30 - +30 MPa in the X -direction (σx) with B of -3 to +3 was studied using COMSOL Multiphysics. The ratio of height to width of the plate is 1.25. The results show that the opening displacement was maximum at negative B (compression) while the minimum for positive B (tension) was at 90°. For mode I, the SIFs increase in compression and maximum at 90° while decreasing in tension and minimum at 90°. Beyond 90°, the SIFs increase under tensile while decreasing in compression load. For mode II, the SIFs were maximum at 45° for negative B while minimum at positive B.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123930533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Adversarial Network based Chest Disease Detection and Binary Mask Generation 基于生成对抗网络的胸部疾病检测与二值掩码生成

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089542

Muhammad Asad Junaid, S. Anwar, Gulbadan Sikander, Muhammad Tahir Khan

{"title":"Generative Adversarial Network based Chest Disease Detection and Binary Mask Generation","authors":"Muhammad Asad Junaid, S. Anwar, Gulbadan Sikander, Muhammad Tahir Khan","doi":"10.1109/ICRAI57502.2023.10089542","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089542","url":null,"abstract":"The infectious disease tuberculosis (TB) continues to pose a serious risk to global health specially in developing world. There are over 10 million new cases of tuberculosis each year. Machine and deep learning models are trained to recognize specific pixels inside a medical image for the purposes of classification and disease progression tracking, however the decision-making mechanism of these models is hidden from the user. In this context, Explainable artificial intelligence (XAI) refers to strategies that allow humans to understand the results of AI algorithms. Recently, a variety of XAI methods for classification and generative have been proposed; however, these methods only use a subset of most discriminative power characteristics, resulting in false positives. This article proposes CycleGAN-based multi-functional generative adversarial networks to efficiently solve these challenges. Proposed model is trained in weakly supervised context to identify and visualize the disease effects and finally generate binary mask in data augmentation context. The model takes a Chest radiography (CXR) as input, creates a change map showing the disease's effect at a specific spot, and then uses this map to create a binary mask of original image. Results on publicly available TB dataset, TBX11K, confirm that the proposed model produces highly accurate result.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129606136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning Based Phonocardiogram Signals Analysis for Cardiovascular Abnormalities Detection 基于深度学习的心音图信号分析用于心血管异常检测

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089537

Sayed Shahid Hussain, M. Ashfaq, Muhammad Salman Khan, S. Anwar

{"title":"Deep Learning Based Phonocardiogram Signals Analysis for Cardiovascular Abnormalities Detection","authors":"Sayed Shahid Hussain, M. Ashfaq, Muhammad Salman Khan, S. Anwar","doi":"10.1109/ICRAI57502.2023.10089537","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089537","url":null,"abstract":"Cardiovascular diseases are among the vital causes of mortality worldwide which need early detection with the use of auscultation examination. Heart diseases could be diagnosed in a convenient way of heartbeat sound analysis. Manual auscultation is time-consuming and problematic to differentiate heart sounds related to different kinds of heart abnormalities. Also, as it requires an expert in the field, it becomes costly and quite prone to human error. Due to all these issues, there is a high demand for an automatic diagnostic system, an alternative way to human examination. This research focuses on developing Artificial Intelligence (AI) based system of the latest computational algorithms for detecting heart abnormalities from heart sounds. Heart sounds are classified as to be normal or abnormal from Phonocardiogram (PCG) signals. One of the recent techniques introduced in deep learning algorithms for audio classification is 1-Dimensional Convolutional Neural Network (1D-CNN). This research work includes a 1D-CNN as a classification algorithm. A widely used publicly available dataset of heart sounds from PhysioNet/CinC (2016) challenge is utilized. The method acquires accuracy, sensitivity, specificity, F1 score, and precision of 95.45%, 97.44%, 93.6%, 95.45%, and 95.54% respectively. The proposed approach uses a less-complicated customized 1D-CNN algorithm, outshining most of the previous competitive methods by securing high performance that makes it appropriate for diagnosing heart diseases from PCG data.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125163904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploration of Unknown Environment using Deep Reinforcement Learning 利用深度强化学习探索未知环境

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089589

Asad Ali, Sarah Gul, Tallat Mahmood, A. Ullah

{"title":"Exploration of Unknown Environment using Deep Reinforcement Learning","authors":"Asad Ali, Sarah Gul, Tallat Mahmood, A. Ullah","doi":"10.1109/ICRAI57502.2023.10089589","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089589","url":null,"abstract":"Exploring the unknown environment is a very crucial task where human life is at risks like search and rescue operations, abandoned nuclear plants, covert operations and more. Autonomous robots could serve this task efficiently. The existing methods use uncertainty models for localization and map building to explore the unknown areas requiring high onboard computation and time. We propose to use Deep Reinforcement Learning (DRL) for the autonomous exploration of unknown environments. In DRL, the agent interacts with the environment and learns based on experiences (feedback/reward). We propose extrinsic and curiosity-driven reward functions to explore the environment. The curiosity-based reward function motivates the agent to explore unseen areas by predicting future states, while the extrinsic reward function avoids collisions. We train the differential drive robot in one environment and evaluate its performance in another unknown environment. We observe curiosity-driven reward function outperformed the extrinsic reward by exploring more areas in the unknown environment. The test results show the generalization capability to explore unknown environments with the proposed methods.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125178750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1