2023 International Conference on Robotics and Automation in Industry (ICRAI)最新文献_第5页

Detection of Grape Clusters in Images Using Convolutional Neural Network 利用卷积神经网络检测图像中的葡萄簇

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089582

M. Shahzad, A. B. Aqeel, W. S. Qureshi

{"title":"Detection of Grape Clusters in Images Using Convolutional Neural Network","authors":"M. Shahzad, A. B. Aqeel, W. S. Qureshi","doi":"10.1109/ICRAI57502.2023.10089582","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089582","url":null,"abstract":"Convolutional Neural Networks and Deep Learning have revolutionized every field since their inception. Agriculture has also been reaping the fruits of developments in mentioned fields. Technology is being revolutionized to increase yield, save water wastage, take care of diseased weeds, and also increase the profit of farmers. Grapes are among the highest profit-yielding and important fruit related to the juice industry. Pakistan being an agricultural country, can widely benefit by cultivating and improving grapes per hectare yield. The biggest challenge in harvesting grapes to date is to detect their cluster successfully; many approaches tend to answer this problem by harvest and sort technique where the foreign objects are separated later from grapes after harvesting them using an automatic harvester. Currently available systems are trained on data that is from developed or grape-producing countries, thus showing data biases when used at any new location thus it gives rise to a need of creating a dataset from scratch to verify the results of research. Grape is available in different sizes, colors, seed sizes, and shapes which makes its detection, through simple Computer vision, even more challenging. This research addresses this issue by bringing the solution to this problem by using CNN and Neural Networks using the newly created dataset from local farms as the other research and the methods used don't address issues faced locally by the farmers. YOLO has been selected to be trained on the locally collected dataset of grapes.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"11 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":"115459552","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

Variant Process Planning for a Job Shop 作业车间的变程规划

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089535

Mubashar Ali, Hassan Ali, H. Rizwan, Muhammad Umer, Hamid Abdullah, Hammas Ullah, Saad ur Rehman, Wasim Ahmad Khan

{"title":"Variant Process Planning for a Job Shop","authors":"Mubashar Ali, Hassan Ali, H. Rizwan, Muhammad Umer, Hamid Abdullah, Hammas Ullah, Saad ur Rehman, Wasim Ahmad Khan","doi":"10.1109/ICRAI57502.2023.10089535","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089535","url":null,"abstract":"Process planning in discrete manufacturing using either manual method or performed using Computer Aided Process Planning (CAPP). CAPP is further divided into Generative Process Planning (GPP) and Variant Process Planning (VPP). The VPP approach uses group technology principles to classify parts into part families based on geometric and manufacturing properties. VPP helps the human operator to select and modify the process plan from a database of the machine. It is a much easier and more economical process of planning alternatives for manufacturing similar components for each family. In this research paper, we develop a variant process plan for the compressor piston using the VPP methodology. All manufacturing procedures and tools information are stored in a database. The VPP recognizes and retrieves the existing process plane of a similar part from the database and generates a processing plane of some alteration for the new part. Memory (database) is used to retrieve the manufacturing plan for that part. If the new part differs significantly from the previous ones, there will be no similar features. The plan can then be modified by a human operator to accommodate the new part. Developed VPP is used as a module of discreet manufacturing in virtual manufacturing suite of software being developed at the GIK Institute.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"1 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":"130183621","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 and Validation of an Automation Strategy for the Strip Test Process in the Semiconductor Industry 半导体工业条带测试过程自动化策略的设计与验证

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089538

M. A. A. Rahman, Cheng-Yi Lin, Paul G. Maropoulus, Woei Sheng Teoh, Azrul Azwan Abdul Rahman, E. Mohamad

{"title":"Design and Validation of an Automation Strategy for the Strip Test Process in the Semiconductor Industry","authors":"M. A. A. Rahman, Cheng-Yi Lin, Paul G. Maropoulus, Woei Sheng Teoh, Azrul Azwan Abdul Rahman, E. Mohamad","doi":"10.1109/ICRAI57502.2023.10089538","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089538","url":null,"abstract":"The semiconductor devices may be individually tested or tested in a batch process. One type of batch process is strip testing. Strip testing is the electrical testing of a semiconductor while the device is still in the lead frame strip. Strip test offers the most cost savings for small devices and short test times. Despite their advantages, current strip testing is not a fully optimized solution. During the lot change, the operator must perform a series of system-to-physical validation and several steps of system tracking before the lot starts. Various manual activities are happening within the process, consuming many productivity issues. On top of that, human intervention during the process will increase the possibility of the quality issue. The research aims to investigate the current tasks during the strip test process. It also aimed to develop and validate a suitable automation strategy for the trip test process. The work starts with a detailed study of the current process, transaction, and hardware. Later a potential improvement using automation is proposed to replace the operator's repetitive job and simultaneously be fully integrated with the manufacturing execution system. This research is hoped to bring a significant contribution and readiness for the next level of automation in semiconductor manufacturing, especially in strip testing. Through automation functionality, this preliminary work shows an increase in productivity and quality upon implementation.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"118 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":"122467338","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

An Automated System for the Classification of Bronchiolitis and Bronchiectasis Diseases using Lung Sound Analysis 用肺声分析自动分类细支气管炎和支气管扩张疾病的系统

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089608

S. Jaffery, Sumair Aziz, Muhammad Umar Khan, Syed Zohaib Hussain Naqvi, Muhammad Faraz, Adil Usman

引用次数: 1

McKibben Pneumatic Artificial Muscle Robot Actuators - A Review McKibben气动人工肌肉机器人执行器综述

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089581

Muhammad Aqib Khan, Sadaf Shaik, Mohammad Hasan Tariq, Tariq Kamal

引用次数: 0

A Time Series Regression-based Model for Predicting the Spread of Dengue Disease 基于时间序列回归的登革热传播预测模型

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089545

Muhammad Danish Waseem, Ali Nawaz, Uzair Rasheed, Abir Raza, Mubarak Omar Albarka

引用次数: 0

Role of Machine Learning in Power Analysis Based Side Channel Attacks on FPGA 基于FPGA侧信道攻击的机器学习在功耗分析中的作用

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089540

Ali Hasnain, Yame Asfia, S. G. Khawaja

{"title":"Role of Machine Learning in Power Analysis Based Side Channel Attacks on FPGA","authors":"Ali Hasnain, Yame Asfia, S. G. Khawaja","doi":"10.1109/ICRAI57502.2023.10089540","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089540","url":null,"abstract":"The cloud-based devices face many threats these days due to shared resources like power. The attacker measures the power by using some remote sensors which are present in attacker tenants. These sensors get partial or full access to a power distribution network (PDN) and work as a backdoor for the attacker. In our research, we explored potential security issues which involved power analysis-based side-channel attacks (SCAs) on Field Programmable Gate Arrays (FPGAs). We have made three major contributions to our research paper. First, we have discussed the power analysis or power profiling of FPGA, which is dependent upon voltage fluctuations' leakage while performing some encryption tasks. The voltage fluctuations of the cryptographic module are measured by some physical source like an oscilloscope or remote source like delay line sensors. Second, we have discussed potential power analysis-based SCAs that used these measurements of voltage fluctuations to extract the secret key. Third, we have designed a framework based on machine learning (ML) and deep learning (DL) models to perform secret key predictions and successful attacks. Firstly, our custom convolutional neural networks (CNN) model has revealed all 16 bytes of the secret key and performed a successful attack with only 570 attack power traces. Secondly, the multi-layer perceptron (MLP) model has successfully attacked only using 3200 traces using the same framework. Overall we have achieved a better performance in terms of the required number of power traces for a successful attack, training time, prediction time, and attack time.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"56 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":"133204726","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

A Cost-Effective Smart Labor Assistance Trolley for Industrial Applications 一个具有成本效益的智能劳动辅助小车工业应用

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089584

Muneeb Zafar, Sarmad Shafique, F. Riaz, Samia Abid, Umar Raza, W. Holderbaum

{"title":"A Cost-Effective Smart Labor Assistance Trolley for Industrial Applications","authors":"Muneeb Zafar, Sarmad Shafique, F. Riaz, Samia Abid, Umar Raza, W. Holderbaum","doi":"10.1109/ICRAI57502.2023.10089584","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089584","url":null,"abstract":"In the last couple of decades, autonomous human assistance robots have been enormously attracting the industrial sector. For this purpose, numerous researchers have contributed towards designing efficient and robust human assistance mechanisms. However, their proposed approaches do not provide a cost-effective solution due to the deployment of exorbitant sensors and sophisticated infrastructure. Besides, it was quite challenging for existing human-following robots to track their assigned human companion in different illusional states and luminous conditions while detecting obstacles and taking respective maneuvers (i.e., abrupt turns, etc.). Moreover, self-driving solutions need to take fast and real-time actions to avoid collisions in the designated environment. For this purpose, literature has shown the efficiency of YOLOv3 with respect to providing real-time results in latency-sensitive applications. Hence, to overcome this dilemma, we propose to develop an economically efficient deep learning- based smart labor assistance trolley that uses the YOLO v3 as a core detective deep learning technique with advance and efficient perception and motion planning modules. The perception module succours the autonomous trolley to precisely detect and classify the objects. While the motion planning module uses the specific intended target detection technique to follow the targeted person in crowded environment. These techniques make the autonomous trolley able to take expeditious, meticulous, and conspicuous action in real-time. The labor assistant robot detects and tracks the respective person using YOLOv3. To validate the efficiency of the proposed solution, we have performed a series of experiments considering different test cases. Our proposed work achieved a mean average precision of 0.81%.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"8 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":"123628314","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

Overall Survial Prediction from Brain MRI in Glioblastoma 脑MRI预测胶质母细胞瘤患者的总体生存期

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089587

Sobia Yousaf, Nimra Ibrar, Muhammad Majid, S. Anwar

{"title":"Overall Survial Prediction from Brain MRI in Glioblastoma","authors":"Sobia Yousaf, Nimra Ibrar, Muhammad Majid, S. Anwar","doi":"10.1109/ICRAI57502.2023.10089587","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089587","url":null,"abstract":"Tumor segmentation using radiological images, particularly in the brain region, is a challenging task due to the heterogeneous nature of the tissue representing the brain lesions. In computerized diagnostic systems, this method is an essential step in isolating tumor regions for visualization and examination. Recently, deep learning (DL) technology has resulted in major breakthroughs in computer vision and artificial intelligence. This has impacted clinical tasks such as brain tumor segmentation, where deep learning allows learning hierarchical and distinctive characteristics from radiographs. A major paradigm shift has evolved, when compared with traditional machine learning approaches, where pathological and healthy tissue can be differentiated without relying on extracting features that required a significant expertise. Herein, with our proposed method, we aim to help radiologists in assisting them to detect tumor regions and further predict the overall patient survival rate using magnetic resonance images effectively. Towards this, we use U-Net based architecture to perform the segmentation. We achieve acceptable segmentation accuracy, 82% and 75% on training and validation datasets, respectively. We further used our segmentation results for survival predication task. We computed and selected 16 most significant 3D and 2D radiomic features from the segmented regions. By combining age with radiomics features, we trained Convolutional Neural Network (CNN) model and five different machine learning (ML) models and achieved 65.57% and 63% accuracy in survival rate prediction when using CNN and support vector machine (SVM) classification model.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"29 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":"126969127","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

Configuration Tool for Generating Multi-Type and Multi-Robot Work Cell Layout 生成多类型多机器人工作单元布局的配置工具

2023 International Conference on Robotics and Automation in Industry (ICRAI) Pub Date : 2023-03-03 DOI: 10.1109/ICRAI57502.2023.10089607

M. A. A. Rahman, Kaafi N. Shikder, P. Maropoulos, E. Mohamad, Azrul Azwan Abdul Rahman, M. Salleh

{"title":"Configuration Tool for Generating Multi-Type and Multi-Robot Work Cell Layout","authors":"M. A. A. Rahman, Kaafi N. Shikder, P. Maropoulos, E. Mohamad, Azrul Azwan Abdul Rahman, M. Salleh","doi":"10.1109/ICRAI57502.2023.10089607","DOIUrl":"https://doi.org/10.1109/ICRAI57502.2023.10089607","url":null,"abstract":"With the continuous increment in the installation and industrial robots in manufacturing industries, it is getting increasingly complicated for engineers to design efficient layouts for these robot work cells. This study has attempted to solve this problem by developing a configuration tool that would automatically propose and simulate a robot work cell layout upon receiving input data from the user with the help of a Graphical User Interface (GUI). This GUI and the underlying solution algorithms embedded in the configuration tool have been developed employing Microsoft Visual Basic, which is integrated with SOLIDWORKS API to demonstrate the layout design solutions in SOLIDWORKS directly. Five types of robot models have been considered in this study: with each of these robots being 6-DOF and primarily used for welding tasks. The developed configuration tool can produce design solutions for three types of layout shapes: Linear Horizontal/Vertical, Linear Parallel and L-Shaped, for a maximum of ten robots per layout. Specifically, this tool can generate layout design solutions for approximately 12 million combinations of these five types of robots per layout or about 36 million types of solutions. This configuration tool can potentially assist the design engineers in simplifying this complicated robot work cell layout and, therefore, can act as a guiding tool for effective cell layout designs. At this stage, the developed tool is preliminary work involving only simulation works.","PeriodicalId":447565,"journal":{"name":"2023 International Conference on Robotics and Automation in Industry (ICRAI)","volume":"12 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":"130215047","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