2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献_第4页

An Investigation of Automating Fungus Inspection Process of Aircraft Fuel Tank via Image Processing 基于图像处理的飞机油箱真菌检测自动化过程研究

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936744

Sin Y. Beh, V. L. Jauw, C. S. Lim, Leong L. Chee

{"title":"An Investigation of Automating Fungus Inspection Process of Aircraft Fuel Tank via Image Processing","authors":"Sin Y. Beh, V. L. Jauw, C. S. Lim, Leong L. Chee","doi":"10.1109/IICAIET55139.2022.9936744","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936744","url":null,"abstract":"Fungus growth in the fuel tank can be harmful to the safety of an aircraft due to its corrosive nature and the sludge it produced. Thus, maintenance is often conducted within a definite period of time by draining the fuel tank to inspect the presence of fungus manually via limited access points. This hinders the full view of the tank resulting in the undiscovered fungus growth, which is contrary to the aim of the inspection, at the expense of resources. This study aims at automating the inspection process to detect the presence of fungus colonies in the aircraft's fuel tank based on the camera's image. It was observed that fungus colonies are often formed irregularly surrounding the bolts of the tank's inner structure. This makes it challenging to differentiate as the color of bolt's edge and fungus colonies is similar. The proposed algorithm aims at addressing the challenge through background and edge removal by Gaussian filtering, adaptive thresholding, convolution for eliminating rogue pixels and boundary calculation. It was tested against the images taken from both experimental rig and aircraft's fuel tank, where the algorithm detected the fungus colonies from the experimental rig with 100% accuracy. In contrary, there were several false detections observed in detecting the fungus grown in the aircraft's fuel tank but it is still satisfactory.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130340201","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

Third Eye Hand Glove Object Detection for Visually Impaired using You Only Look Once (YOLO)v4-Tiny Algorithm 使用You Only Look Once (YOLO)v4-Tiny算法的视障人士第三眼手手套目标检测

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936740

Jeloux P. Docto, Angelika Ice Labininay, J. Villaverde

引用次数: 1

Rice Leaf Disease Detection with Transfer Learning Approach 基于迁移学习方法的水稻叶病检测

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936780

A. Hosain, Md Humaion Kabir Mehedi, Tamanna Jerin, Md. Manik Hossain, Sanowar Hossain Raja, Humayra Ferdoushi, Shadab Iqbal, Annajiat Alim Rasel

{"title":"Rice Leaf Disease Detection with Transfer Learning Approach","authors":"A. Hosain, Md Humaion Kabir Mehedi, Tamanna Jerin, Md. Manik Hossain, Sanowar Hossain Raja, Humayra Ferdoushi, Shadab Iqbal, Annajiat Alim Rasel","doi":"10.1109/IICAIET55139.2022.9936780","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936780","url":null,"abstract":"Rice (Oryza sativa) is among the most widely cul-tivated crops all over the world. The seed of the grass species Oryza sativa is commonly identified as rice. Rice is consumed all over the world as a main source of carbohydrate, specially in Asian countries. As a South Asian country, our homeland Bangladesh has identified rice as its staple food. Throughout the world, rice leaf diseases cause a huge loss in rice production each year. Traditionally, rice leaf diseases are detected in laboratory tests, which is time consuming. If machine learning and computer vision based approaches-which are faster and more accurate comparing to manual detection of rice leaf diseases- can be implemented to detect rice diseases, a substantial amount of production loss pertaining to these diseases can be mitigated. Deep learning frameworks, such as, convolutional neural networks (CNN) shows higher efficacy in image classification and object detection from images. They can be utilized to classify various rice diseases and, as a result, can play an important role in early detection of rice diseases and, consequently, improving the production. In this paper, we have utilized transfer learning approach by using three pretrained CNN models: InceptionV3, DenseNet201, and EfficientNet V2S to detect five prominent diseases of rice (Oryza Sativa) leaves along with healthy leaves seen in our country and have demonstrated extensive comparison between these models. Among the models, DenseNet201 showcased the highest accuracy which was 92.05%.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124475149","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: 3

Vehicle Type Classification and Counting Using YOLOv4 Algorithm 基于YOLOv4算法的车型分类与计数

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936874

Samuel II C. Imperial, Ana Lowela L. Lucas, M. V. Caya

引用次数: 2

A PI Controller-based Water Supplying and Priority Based SCADA System for Industrial Automation using PLC-HMI Scheme 基于PI控制器的供水和基于优先级的PLC-HMI工业自动化SCADA系统

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936768

Ahsan Kabir Nuhel, Mir Mohibullah Sazid, Kaushik Ahmed, Md. Nafim Mahmud Bhuiyan, Md. Yeasib Bin Hassan

引用次数: 2

Environment-Based Oil Palm Yield Prediction Using K-Nearest Neighbour Regression 基于环境的k -最近邻回归油棕产量预测

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936752

Nuzhat Khan, M. A. Kamaruddin, U. U. Sheikh, Y. Yusup, Muhammad Paend Bakht

{"title":"Environment-Based Oil Palm Yield Prediction Using K-Nearest Neighbour Regression","authors":"Nuzhat Khan, M. A. Kamaruddin, U. U. Sheikh, Y. Yusup, Muhammad Paend Bakht","doi":"10.1109/IICAIET55139.2022.9936752","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936752","url":null,"abstract":"Oil palm is a profitable tree crop, producing two types of oil from fresh fruit bunch (FFB). Oil palm yield prediction is required for import/ export, global food security, and field management. However, complex variations in oil palm yield on account of weather and soil conditions complicate the predictability. Supervised machine learning models can learn nonlinear patterns from complex agrometeorological data. However, environment-based predictive analysis of oil palm yield with machine learning methods is not widely explored. Therefore, this work presents the application of a non-parametric regression algorithm k-nearest neighbor (KNN) for oil palm yield prediction using weather and soil data. This work utilized 35 years of yield, soil, and weather records from Pahang state Malaysia. Data visualization during preprocessing assessment led to an in-depth understanding of environmental impacts on yield patterns. After model selection and training, the statistical evaluation using six different metrics along with an examination of the model's learning process was performed. Results suggested that a substantial amount of data from multiple sources allows reliable forecasts with machine learning models. It is concluded that machine learning is a great potential tool for oil palm yield prediction as an essential part of precision agriculture.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121628475","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 Low-Cost Prototyping Approach for Autonomous Unmanned Ground Vehicle for Real-Time Surveillance 一种用于实时监视的自主无人地面车辆的低成本原型设计方法

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936808

S. Vishnu, M. A. Kumar, M. G. Manjesha, Zaheer Pasha, S. Madhu

{"title":"A Low-Cost Prototyping Approach for Autonomous Unmanned Ground Vehicle for Real-Time Surveillance","authors":"S. Vishnu, M. A. Kumar, M. G. Manjesha, Zaheer Pasha, S. Madhu","doi":"10.1109/IICAIET55139.2022.9936808","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936808","url":null,"abstract":"The Unmanned Ground Vehicle (UGV) system architecture is a versatile platform for surveillance. The use of such technology improves the reach of security personnel in remote patrolling areas. The main objective of this paper is to design and develop a low-cost autonomous Unmanned Ground Vehicle prototype using open-source platforms for surveillance. The system consists of Wireless Sensor Network (WSN) for real-time data acquisition, an Ardupilot system for the UGV control, and LoRa (Long Range) transceiver system for internet-free sensor data transmission for security. The GPS and the inertial sensor are interfaced with the Ardupilot system to achieve autonomous operation with preset waypoints mapping. The real-time video obtained from the UGV is processed with OpenCV to detect the human face and eyes. Different electrical test data of the UGV under various operating conditions are presented. Integration of such systems results in an effective autonomous UGV to provide better patrolling in remote and harsh environmental conditions.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121655584","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: 3

Harmonic Minimization in Multilevel Inverter Using PSO-based Soft-computing Technique 基于pso软计算技术的多电平逆变器谐波最小化

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936829

Y. W. Sea, W. T. Chew, J. L. Ong, W. Yong, J. Leong

{"title":"Harmonic Minimization in Multilevel Inverter Using PSO-based Soft-computing Technique","authors":"Y. W. Sea, W. T. Chew, J. L. Ong, W. Yong, J. Leong","doi":"10.1109/IICAIET55139.2022.9936829","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936829","url":null,"abstract":"A conventional asymmetrical cascaded H-bridge multilevel inverter (CHBMLI) requires a total number of twelve power switches to generate an output voltage waveform with 15 voltage levels. In this paper, the operating principle and performance of a 15-level asymmetrical multilevel inverter (MLI) with a reduced number of power switches are presented. The 15-level asymmetrical MLI is constructed with only ten power switches, which is reduced by 16.67 % compared to the 15-level asymmetrical CHBMLI. The switching-angle calculation applied to the MLI is another important design aspect in MLI research and the switching angles must be computed properly to obtain an output voltage waveform with low total harmonic distortion (THD). In this work, a particle swarm optimization (PSO) based selective harmonic minimization pulse-width modulation (SHMPWM) technique is used to obtain the optimal switching angles applied to the 15-level asymmetrical MLI. A PSIM simulation model is developed to validate the effectiveness of the optimal switching angles applied in the 15-level asymmetrical MLI. Simulation results suggest that the 15-level asymmetrical MLI is able to produce a sinusoidal-like staircase output voltage waveform using PSO-based SHMPWM optimized switching angles at modulation index of 0.70. At the same modulation index, the performance of the 15-level asymmetrical MLI connected with different inductive loads is also validated.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122617723","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

Optimizing High-Density Aquaculture Rotifer Detection Using Deep Learning Algorithm 利用深度学习算法优化高密度水产养殖轮虫检测

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936794

Alixson Polumpung, Kit Guan Lim, M. K. Tan, S. R. M. Shaleh, R. Chin, K. T. T. Kin

{"title":"Optimizing High-Density Aquaculture Rotifer Detection Using Deep Learning Algorithm","authors":"Alixson Polumpung, Kit Guan Lim, M. K. Tan, S. R. M. Shaleh, R. Chin, K. T. T. Kin","doi":"10.1109/IICAIET55139.2022.9936794","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936794","url":null,"abstract":"The dynamics of marine aquaculture depend heavily on zooplankton, which is the basis of the marine food chain. Zooplankton like Rotifer brachionus plicatillis, which are rich in nutrients, small size and rapid reproductive rate are necessary for fish in the larval stage. Rotifer must therefore be supplied to larvae culture in the correct quantity, which can be determined by counting it. In addition, it is necessary to estimate the rotifer population to ensure that, aside from care, it can support the demands of all larvae batches. Currently, the traditional method of counting small-sized rotifers still involves counting it manually. One easy potential way to count rotifer is by using binary image segmentation provided that the sample is clear from debris. In this paper, we present the method and performance to detect rotifer Brachionus plicatilis in 1ml sample automatically using deep learning algorithm YOLOv3. Detected rotifer will be counted for estimating the amount of rotifer for feeding or the density population in a rotifer culture. The method of this project consists of following steps. First, dataset acquisition from digital microscope and manual labelling annotation divided by 60, 20 and 20 percent for training, validation and testing consecutively. Second, is to develop the deep learning algorithm based on YOLOv3. Third step is to training and evaluate the model using loss function. Finally, the model is tested with average precision of 85.1 percent with average of 1.4645s inference detection speed.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128946560","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 Comparative Study of Chatbot Catered Toward Mental Health 面向心理健康的聊天机器人的比较研究

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936778

Pranto Dev, Sameeha Haque, Asmita Noor, Abir Alam Srabon, Mashruk Mohammed Wasik, Sumaiya Mim, Shadman Bin Sharife, Fariha Rahman, Syeda Rifa Syara, Shadab Iqbal, Md Humaion Kabir Mehedi, Annajiat Alim Rasel

{"title":"A Comparative Study of Chatbot Catered Toward Mental Health","authors":"Pranto Dev, Sameeha Haque, Asmita Noor, Abir Alam Srabon, Mashruk Mohammed Wasik, Sumaiya Mim, Shadman Bin Sharife, Fariha Rahman, Syeda Rifa Syara, Shadab Iqbal, Md Humaion Kabir Mehedi, Annajiat Alim Rasel","doi":"10.1109/IICAIET55139.2022.9936778","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936778","url":null,"abstract":"The number of people suffering from severe de-pression has risen in recent years. The majority of patients are apprehensive about seeking counseling and are unwilling to open up. A chat bot might be a viable tool for involving customers in artificial intelligence-powered behavioral health therapies. Chat bots are artificial intelligence entities that answer to users in normal language, exactly like a person would. Social chat bots, in particular, are those that form a deep emotional bond with the user. We shall explore and compare such chat bots in this paper, as they play an important role in assisting patients with mental illness. The study will compare and contrast chat bots such as CARO, XiaoIce, DEPRA, PRERONA, and Eviebot, as well as their role in resolving the depression problem. The article will show how the different chat bots compare in terms of methodology, underlying algorithms, accuracy, population demographics, and limitations. Finally, the paper will provide a quick overview of chat bots' future advancements in this field. The therapeutic component, which determines a person's level of depression,l is also a priority.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132453765","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