2023 International Conference on Robotics and Automation in Industry (ICRAI)最新文献_第3页

Railway Track Joints and Fasteners Fault Detection using Principal Component Analysis 基于主成分分析的铁路轨道接头和紧固件故障检测

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089579

M. Owais, Imtiaz Hussain, Gul Shahzad, B. Khan

{"title":"Railway Track Joints and Fasteners Fault Detection using Principal Component Analysis","authors":"M. Owais, Imtiaz Hussain, Gul Shahzad, B. Khan","doi":"10.1109/ICRAI57502.2023.10089579","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089579","url":null,"abstract":"This works presents a machine learning based fault detection algorithm specifically for condition monitoring of different types of railway tracks. The algorithm relies on one of the most commonly used machine learning algorithms, Principal Component Analysis (PCA), for extracting the patterns of various defected and nondefected railway track components including rail fasteners and joints like fishplate. The algorithm ensures a very fast yet robust feature extraction workflow primarily by virtue of its inherently offered dimensionality reduction resulting in lesser computational burden. The classification task is handled by the Euclidean distance classifier that identifies the nearest neighbor of the test image in the subspace spanned by the most dominant eigenvectors extracted from the training dataset during feature extraction workflow. Two varying railway track datasets, from Bangladesh and Pakistan, have been used in this work to validate the proposed algorithm using standard training to test ratios. Multiple classification scenarios are presented and analyzed in detail for both datasets with supporting results. MATLAB_R2022a has been used for development of the proposed algorithms that offers an overall efficiency of more than ninety percent under varying scenarios.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"58 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":"121560691","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

Effect of Tine Shaped Furrow Opener on Dry Soil Using Discrete Element Modelling 用离散元模型分析开沟器对干土的影响

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089532

Abdul Mohiz, Fazal Nasir, Kamran Shah

{"title":"Effect of Tine Shaped Furrow Opener on Dry Soil Using Discrete Element Modelling","authors":"Abdul Mohiz, Fazal Nasir, Kamran Shah","doi":"10.1109/ICRAI57502.2023.10089532","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089532","url":null,"abstract":"Tillage of the soil is the most important consideration when it comes to the implementation of agricultural practices. Increases in crop yield has been achieved through a variety of agricultural practices thanks to the discoveries of various agricultural experts. The planting process is the one that has the greatest impact on the crop's overall health. The hull can be more precisely prepared for the seed-planting trench with the assistance of furrows. A variety of furrow openers are currently undergoing the implantation process. Studying dynamic systems that are discontinuous in nature can be done with the help of discrete element modelling. The simulation of a tine-shaped furrow opener using the EDEM package software was carried out for three different speeds. When the speed of an object increases, so does the force it exerts on it. Additionally, the length of an object has an effect on the force it exerts. Additionally, taken into consideration were the profiles that were generated in the soil. To carry out statistical validation of the results analysis of variation (ANOVA) was done. The results provided a basis for calculating a confidence level of 95%. For the furrow openers, it was suggested that a speed of 0.495 meters per second, which is equivalent to 1.76 kilometers per hour, be used.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"138 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":"128389332","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

Housekeeping Using Multi-Agent Service Robotics 使用多代理服务机器人的家政服务

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089576

Muhammad Faiq Malik, Haris Shahid, Muhammad Haris Saleem, Hassan Nazir, Ahmed Nouman

引用次数: 0

Clothing gripping and sorting based on a dual-arm collaborative robot 基于双臂协作机器人的衣物抓取与分拣

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089595

Yu Du, Zhong Zheng Yuan, Xiaojing Tian, Zhuang Yang

引用次数: 0

Sectional Trajectory Planning of Transmission Mechanism based on S-curve and Spline Curve 基于s曲线和样条曲线的传动机构截面轨迹规划

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089597

Cong Ming, Yao YuXuan, Liu Dong, Du Yu, Liang Bin

引用次数: 0

A Novel Deep Learning Based Framework for Cardiac Arrest Prediction 一种新的基于深度学习的心脏骤停预测框架

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089604

N. Fatima, Aun Irtaza, Rehan Ali

{"title":"A Novel Deep Learning Based Framework for Cardiac Arrest Prediction","authors":"N. Fatima, Aun Irtaza, Rehan Ali","doi":"10.1109/ICRAI57502.2023.10089604","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089604","url":null,"abstract":"Cardiovascular diseases are a major health issue that calls for prompt medical attention. In order to determine the most advantageous methods in this field, numerous techniques and studies have been carried out over the past few years. It should be mentioned that the majority of these cardiovascular illnesses are treatable with earlier detection and prediction. This paper suggests using an automated methodology to predict and classify the likelihood of cardiac arrests in a patient. Due to their collection from various sources, the Electrocardiogram (ECG) signals are initially preprocessed by normalizing them to the [0 1] range. The significant features from these preprocessed signals are then extracted using Mel-Frequency Cepstrum (MFCC), Melspectrogram, MFCC Delta 1 & 2, MFCC Merge File, etc. We also propose a novel feature vector using ensemble of MFCC features. The features are then classified using Artificial Neural Network (ANN), Support Vector Machine (SVM), TPOT and K-Nearest Neighbor (KNN). The proposed vector outperformed all other feature vectors and obtained highest accuracy of 95.8% via ANN. The study is conducted using publicly accessible dataset composed of approximately 52 thousand ECG signals. The suggested strategy is compared with existing techniques and the results indicate the robustness and effectiveness of our approach. Therefore, the proposed methodology can be effectively deployed in a clinical setting to classify ECG data and identify the risks likelihood of cardiac arrests in a patient.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"20 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":"132927770","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

Evaluation and Comparison of TRMS Performance for Classical and Optimal Control Strategies 经典控制策略与最优控制策略的TRMS性能评价与比较

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089583

Usman Ahmad, Waleed Ahmed

引用次数: 0

Design and Control of a Self-Driving Vehicle using RP Lidar 基于RP激光雷达的自动驾驶汽车设计与控制

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089600

Areesha Ikhtiar, Dureshahwar Qurban, S. Samo

引用次数: 0

Design and Implementation of Model Predictive Control (MPC) Based Pressure Regulation System for a Precision Agricultural Sprayer 基于模型预测控制(MPC)的精准农用喷雾器压力调节系统设计与实现

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089578

Adrees Khan, Fazal Nasir, Muhammad Tufail, Muhammad Haris, Muhammad Tahir Khan, Zhang Dong

{"title":"Design and Implementation of Model Predictive Control (MPC) Based Pressure Regulation System for a Precision Agricultural Sprayer","authors":"Adrees Khan, Fazal Nasir, Muhammad Tufail, Muhammad Haris, Muhammad Tahir Khan, Zhang Dong","doi":"10.1109/ICRAI57502.2023.10089578","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089578","url":null,"abstract":"Model Predictive Control (MPC) is a control scheme that involves predicting the future behavior of a system and optimizing control actions to accomplish the desired objective. In this study, we develop an intelligent control algorithm, based on MPC to regulate the target pressure in variable-rate agriculture sprayer robots. Modeling and simulation steps of the spraying system are developed using MATLAB/Simulink environment, before passing to the description of the MPC algorithm. Real-time implementation of the MPC algorithm was conducted on an Arduino Mega 2560 controller board by using Simulink Support Package for Arduino Hardware in MATLAB/Simulink to experimentally validate the preliminary results of simulations. The work evaluates MPC to regulate the pressure in the system and compares the results with a traditional PID control system. Moreover, MPC is a novel method for nonlinear system control that achieves zero steady-state error, low transient response, and reduces peak overshoot compared to the results obtained with a PID controller, thereby reducing the waste of chemicals, and minimizing the toxicology and environmental risk.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"136 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":"121617766","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

BactPNet: A Novel Automated Detection Approach for Bacterial Pneumonia Patients BactPNet:一种新的细菌性肺炎患者自动检测方法

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089605

Syed M Iqtidar Shah, Mubashir Ayuub Minhas, Farman Hassan

{"title":"BactPNet: A Novel Automated Detection Approach for Bacterial Pneumonia Patients","authors":"Syed M Iqtidar Shah, Mubashir Ayuub Minhas, Farman Hassan","doi":"10.1109/ICRAI57502.2023.10089605","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089605","url":null,"abstract":"Every year, a large number of people around the globe, particularly, children die due to pneumonia disease. Approximately, 1.2 million cases of pneumonia have been reported in children of age ranges from 1 to 5. Out of 1.2 million, 880,000 died in 2016. Therefore, pneumonia is considered a major cause of mortality among children, particularly, in South Asia as well as African countries. It is among the top ten causes of mortality in developed countries, namely, the UK, the USA, and other European countries. However, an early diagnosis and treatment can significantly minimize the death rates among children in those countries that have a high prevalence. The research community has worked to diagnose the patients using traditional and deep learning (DL)-based methods; however, the existing approaches have various limitations in terms of accurate detection of the patients. Therefore, to address the above problem, we have presented a novel DL-based framework, BactPNet, for the detection of bacterial pneumonia patients. Our approach has achieved an accuracy of 91.98%, precision is 90%, recall is 84%, and F1-score is 86%. The above results of our approach confirm that it can be utilized to enhance the diagnosis of pneumonia from the chest x-ray images. By adopting the BactPNet, the quality of treatment and correct prediction can further be improved. More specifically, experimental findings and comparative assessment with other techniques show that BactPNet can better detect pneumonia patients and can be adopted by medical experts in hospitals.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"47 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":"123698286","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