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

Local Traffic Network Formulation and Signalisation via Benchmark Webster Model 基于基准韦氏模型的地方交通网络规划与信号化

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936766

H. S. Chuo, M. K. Tan, Kit Guan Lim, L. Angeline, Tienlei Wang, K. Teo

{"title":"Local Traffic Network Formulation and Signalisation via Benchmark Webster Model","authors":"H. S. Chuo, M. K. Tan, Kit Guan Lim, L. Angeline, Tienlei Wang, K. Teo","doi":"10.1109/IICAIET55139.2022.9936766","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936766","url":null,"abstract":"Traffic models have been widely studied for general traffic understanding and transportation variable relations, but unreadily exert for real time transportation decision and planning. With the present advancement in computer technology, the used-to-be-lengthy and complicated modelling technique is now more computable and executable than ever for putting into good use. This paper discusses extensively on the formulation of local traffic network in Kota Kinabalu, Malaysia and the signal settings as referred to the Jabatan Kerja Raya (JKR) using benchmark Webster Model. This paper is crucial for bridging the current traffic network modelling towards advanced modelling techniques and transportation network optimisation strategies to be incorporated in intelligent transportation systems. The traffic responses and results from the Webster model simulation and signalization were reported in this paper as a realistic reference under different case studies including normal traffic and congested traffic scenarios, besides when traffic delays occurred.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"11 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":"115113984","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

GA-Optimized Switching Angles for 13-level Asymmetrical Multilevel Inverter 13电平非对称多电平逆变器的ga优化开关角

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936854

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

{"title":"GA-Optimized Switching Angles for 13-level Asymmetrical Multilevel Inverter","authors":"W. T. Chew, Y. W. Sea, W. Yong, J. L. Ong, J. Leong","doi":"10.1109/IICAIET55139.2022.9936854","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936854","url":null,"abstract":"In this paper, the operating principle of a 13-level asymmetrical multilevel inverter (13L-AS-MLI) with three binary-based asymmetric DC voltage sources ($V_{dc}$) is presented. The 13L-AS-MLI is constructed using 8 active power semiconductor switches and it is able to generate a 13-levels output voltage waveform. Unlike the voltage waveform generated by 13-level symmetrical multilevel inverter (13L-S-MLI) in which all the voltage step sizes are equal in magnitude, the output voltage waveform produced by the 13L-AS-MLI consists of two different voltage step sizes, which are $V_{dc}$ and $2V_{dc}$ . The switching angles utilized by the 13L-AS-MLI are derived using selective harmonic minimization pulse-width modulation (SHMPWM) concept. A nature-inspired optimization algorithm known as genetic algorithm (GA) is applied in the SHMPWM to determine the optimum switching-angle solutions. The GA-based SHMPWM switching-angle computation has been formulated to retain the fundamental voltage component of the output voltage waveform, while minimize five selected undesired low-order harmonics. A PSIM simulation model is developed to validate the operating principle of the 13L-AS-MLI. The performance of the 13L-AS-MLI is evaluated and compared to that of a 13L-S-MLI. Simulation results show that the total harmonic distortion (THD) of the output voltage generated by the 13L-AS-MLI is more or less similar to that generated by the 13L-S-MLI, whilst at certain modulation indexes the THD of the output voltage generated by the 13L-AS-MLI is lower. It is worth to note that the 13L-S-MLI requires a total number of 14 active power semiconductor switches, whilst the 13L-AS-MLI has the advantage of requiring 43 % less power semiconductor switches to produce an output voltage waveform with quality nearly similar to that generated by the 13L-S-MLI.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"247 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":"121128726","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

Discrete Wavelet Transform based EEG Feature Extraction and Classification for Mental Stress using Machine Learning Classifiers 基于离散小波变换的脑电特征提取与机器学习分类

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936800

Ng Kah Kit, H. Amin, A. Subhani

引用次数: 1

Four Wheeled Mobile Robots: A Review 四轮移动机器人:综述

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936855

Jovina Seau Ling Leong, K. Teo, H. Yoong

{"title":"Four Wheeled Mobile Robots: A Review","authors":"Jovina Seau Ling Leong, K. Teo, H. Yoong","doi":"10.1109/IICAIET55139.2022.9936855","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936855","url":null,"abstract":"Wheeled mobile robots are becoming popular in recent years due to their applications that have impacted various aspects of daily life. With the advancement of technology, the demand for wheeled mobile robots especially four wheeled mobile robots has risen. The rise in demand had urged further research and development of more advanced wheeled mobile robots. In this paper, the recent development and focused on four wheeled mobile robots such as the types of wheels, types of steering systems, and control methods are reviewed. For the four wheeled mobile robots to have better steerability and superior cornering stability, the four-wheel steering systems are employed. Depending on the application, the used of standard or fixed wheels in four-wheel steering system also has better efficiency and accuracy compared to the Omni or mecanum wheels with similar maneuverability for indoor designs application. To effectively deal with the four-wheel steering mobile robot dynamic behavior, the payload uncertainty, system uncertainties and unknown disturbances including the parametric vibrations that lead to tracking performance limitations have to be overcome. For further improvement in tracking the performance of four-wheel steering mobile robots, the main issue is to tackle the payload uncertainty. An adaptive control method is proposed to overcome the issue and this method provided future research directions for the four-wheel steering mobile robot.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"62 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":"129456861","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 hybrid Xception-Ensemble model for the detection of Computer Generated images 一种用于计算机生成图像检测的混合异常-集成模型

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936738

C. S. Sychandran, R. Shreelekshmi

引用次数: 1

Diagnosis of Acute Respiratory Syndromes from X-Rays using Customised CNN Architecture 使用定制CNN架构从x射线诊断急性呼吸综合征

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936750

Palaniappan S, S. V. Sai Sripriya, Amalladinna Rama Lalitha Pranathi, M. Muthulakshmi

引用次数: 1

Simulation of Smart Traffic Light By Using Image Processing and Reinforcement Learning 基于图像处理和强化学习的智能交通灯仿真

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936772

Chin Chun Keat, Sharifah Sakinah Syed Ahmad

{"title":"Simulation of Smart Traffic Light By Using Image Processing and Reinforcement Learning","authors":"Chin Chun Keat, Sharifah Sakinah Syed Ahmad","doi":"10.1109/IICAIET55139.2022.9936772","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936772","url":null,"abstract":"Vehicle travel is on the rise across the world, particularly in metropolitan cities. As a result, simulating and optimizing traffic control algorithms is required to better handle this growing demand. In this research, we investigate the simulation and optimization of traffic light controllers in a city and provide a reinforcement learning-based approach. The algorithm that is used is deep Q-learning. There are four processes in this study. The first is data collection. Next, a simulation is built. Then, a reinforcement learning model is trained and tested in the model. Last, the results are compared between the traditional traffic lights and traffic lights that were applied with the reinforcement learning model. The results obtained in this study are queue length of vehicles in front of traffic light and delay time of vehicles after given green signal in two scenarios, which are an environment that uses traditional traffic light and an environment that uses traffic light that applied with reinforcement learning model. From the result, the environment that applied with reinforcement learning agent has shorter time delay and queue length of vehicles. Queue length of vehicles is reduced from 58 to 18 and time delay is reduced from 3900 seconds to 380 seconds.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"4 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":"128123601","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

Identification of COVID-19 from Chest CT Scan Using CNN as Feature Extractor and Voting Classifier 基于CNN特征提取和投票分类器的胸部CT扫描COVID-19识别

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936837

Ferdib-Al-Islam, P. C. Shill

{"title":"Identification of COVID-19 from Chest CT Scan Using CNN as Feature Extractor and Voting Classifier","authors":"Ferdib-Al-Islam, P. C. Shill","doi":"10.1109/IICAIET55139.2022.9936837","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936837","url":null,"abstract":"COVID-19 was first identified in Wuhan (China) and swiftly spread over the world, resulting in a global pandemic emergency. It has had a profound effect on everyday living, general well-being, and international finance. Rapid diagnosis of susceptible people is critical. There is no precise testing for COVID-19 except for RT-PCR, which is expensive and time-consuming. Recent studies conducted using radiological imaging techniques suggest that such pictures include characteristics of the COVID-19 infection. The implication of machine learning algorithms in conjunction with chest imaging may aid in the accurate detection of this illness and help to overcome the shortage of specialized physicians. This work aims to construct a model for the automated recognition of COVID-19 infection using chest CT scans. To extract features from patient's chest CT scans, a convolutional neural network was used, and Principle Component Analysis was used to decrease computing cost. The proposed model (an ensemble of machine learning classifiers) was created to offer accurate diagnostics by incorporating the five categories (Normal, Mycoplasma pneumonia, Bacterial pneumonia, Viral pneumonia, and COVID-19). The proposed model reached an accuracy of 99.3%, positive predictive value (ppv) of 99.3%, and sensitivity of 99.2 %.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"6 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":"121619766","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: 2

EZM-AI: A Yolov5 Machine Vision Inference Approach of the Philippine Corn Leaf Diseases Detection System EZM-AI:菲律宾玉米叶病检测系统的Yolov5机器视觉推理方法

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936848

Yolanda C. Austria, Maria Concepcion A. Mirabueno, D. J. Lopez, Dexter James L. Cuaresma, Jonel R. Macalisang, Cherry D. Casuat

引用次数: 2

Fall and Normal Activity Classification via Multiple Wearable Sensors 基于多个可穿戴传感器的跌倒和正常活动分类

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) Pub Date : 2022-09-13 DOI: 10.1109/IICAIET55139.2022.9936745

Rabia Khalid, Sharjeel Anjum, Chansik Park

{"title":"Fall and Normal Activity Classification via Multiple Wearable Sensors","authors":"Rabia Khalid, Sharjeel Anjum, Chansik Park","doi":"10.1109/IICAIET55139.2022.9936745","DOIUrl":"https://doi.org/10.1109/IICAIET55139.2022.9936745","url":null,"abstract":"A fall detection and classification system is crucial for reducing the severe consequences of falls, which account for the leading cause of accidents on construction sites. Wearable sensors are one of the technologies used to detect falls. Although much academic work has been dedicated to the study of this class of systems, little attention has been paid to the evaluation of simpler algorithms prior to training on complex ones. This study utilizes the open-source UP Fall Detection Dataset and proposes that effective data processing and simpler baseline models give better results for fall-direction classification. Several data-processing techniques like windowing and filtering are used prior to using simpler baseline models like Neural Network (NN), K-Nearest Neighbor (kNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Discriminant Analysis (DA) Classifiers. It is also investigated how to minimize multisensor cost while achieving acceptable detection accuracy. Based on this robustness analysis, fine kNN and wide NN yield 99.5% accuracy for all five wearable sensors. In comparison, using the best of these sensors (belt and pocket) results in 99% accuracy, with accuracy of all 11 individual activities exceeding 93%. The findings of this study bode well for the development of real-world fall-prediction systems as they enable accurate fall direction identification.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"89 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":"115945668","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