首页 > 最新文献

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)最新文献

英文 中文
Electrical Impedance Tomography with Fuzzy Logic Classification in Lung Image Reconstruction 基于模糊逻辑分类的电阻抗断层扫描在肺部图像重建中的应用
Cassandra Sze Jin Yong, Soon Yee Chong, Chelvam Dasaratha Raman, R. Chin, Sainarayanan Gopalakrishnan, K. Teo
Electrical Impedance Tomography (EIT) estimates the electrical impedance distribution within a medium and produces cross-sectional images of an admittivity distribution inside an electrically conducting object. EIT in biomedicine application was first applied in lung due to it being large organs that allow large conductivity changes and is a promising technique since it allows continuous monitoring of the ventilation distribution. This study aims to explore the potential EIT technique in medical applications, with strategies to enhance the image reconstruction of the lung images. Performance of the enhanced image reconstruction is analyzed through simulation on the thorax Finite Element Model (FEM) based on a thorax CT image generated using NETGEN Mesher. To integrate and simulate EIT image of the thorax model, data are obtained from the forward and inverse model. Graz consensus Reconstruction algorithm for EIT (GREIT) technique is then applied as the consensus linear reconstruction algorithm for lung EIT images. Subsequently, the involvement of 3D imaging opens the opportunity to explore more electrode placement strategies for enhancement in image reconstruction. Performance of the reconstructed images based on electrode numbers and placement strategies are analyzed using the five figures of merits and classified into poor, average and good using Fuzzy Logic (FL). From the analysis, planar-offset configuration with 16-electrodes outperforms all others while planar configuration with 16-electrodes followed closely.
电阻抗断层扫描(EIT)估计介质内的电阻抗分布,并产生导电物体内导纳分布的横截面图像。EIT在生物医学中的应用首先应用于肺,因为它是一个大的器官,允许大的电导率变化,是一个很有前途的技术,因为它可以连续监测通风分布。本研究旨在探讨EIT技术在医学上的潜在应用,并提出增强肺部图像重建的策略。基于NETGEN Mesher生成的胸腔CT图像,通过胸腔有限元模型(FEM)仿真分析了增强图像重建的性能。为了对胸腔模型的EIT图像进行整合和仿真,分别从正演模型和反演模型中获取数据。然后将Graz共识重建算法(GREIT)技术作为肺EIT图像的共识线性重建算法。随后,3D成像的参与为探索更多的电极放置策略以增强图像重建提供了机会。基于电极数量和放置策略对重构图像的性能进行了五位数优劣分析,并用模糊逻辑(FL)对重构图像进行了差、中、好的分类。从分析结果来看,16电极平面偏移结构的性能优于其他结构,16电极平面结构的性能紧随其后。
{"title":"Electrical Impedance Tomography with Fuzzy Logic Classification in Lung Image Reconstruction","authors":"Cassandra Sze Jin Yong, Soon Yee Chong, Chelvam Dasaratha Raman, R. Chin, Sainarayanan Gopalakrishnan, K. Teo","doi":"10.1109/IICAIET51634.2021.9573739","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573739","url":null,"abstract":"Electrical Impedance Tomography (EIT) estimates the electrical impedance distribution within a medium and produces cross-sectional images of an admittivity distribution inside an electrically conducting object. EIT in biomedicine application was first applied in lung due to it being large organs that allow large conductivity changes and is a promising technique since it allows continuous monitoring of the ventilation distribution. This study aims to explore the potential EIT technique in medical applications, with strategies to enhance the image reconstruction of the lung images. Performance of the enhanced image reconstruction is analyzed through simulation on the thorax Finite Element Model (FEM) based on a thorax CT image generated using NETGEN Mesher. To integrate and simulate EIT image of the thorax model, data are obtained from the forward and inverse model. Graz consensus Reconstruction algorithm for EIT (GREIT) technique is then applied as the consensus linear reconstruction algorithm for lung EIT images. Subsequently, the involvement of 3D imaging opens the opportunity to explore more electrode placement strategies for enhancement in image reconstruction. Performance of the reconstructed images based on electrode numbers and placement strategies are analyzed using the five figures of merits and classified into poor, average and good using Fuzzy Logic (FL). From the analysis, planar-offset configuration with 16-electrodes outperforms all others while planar configuration with 16-electrodes followed closely.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122896281","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
Optimized Energy Extraction in Tidal Current Technology using Evolutionary Algorithm 利用进化算法优化潮流技术中的能量提取
M. K. Tan, Chun Chong Loo, Kit Guan Lim, Pungut Ibrahim, H. Goh, K. Teo
Renewable energy is gaining more popularity recently. Tidal currents are driven by two different connected bodies trying to equalize their level differences, hence there will be a flow of water from the high-pressure head to the low-pressure head. It is this kind of water flow that makes tidal current suitable for power generation. The main advantage of tidal power is that it can be forecasted easily. Aside from that, sea water has higher density as compared to air, therefore for the same amount of power, the power can be generated at a lower speed. The tidal current model is composed of a permanent magnet synchronous generator, tidal velocity profile, and another two sub-systems. This model is simulated in Matlab. The resultant tidal velocity is made up of 5 different partial tides. The tidal current turbine model is tested with different inputs of pitch angle and tidal current speed. The results show that the maximum generated output power is 295kW when the pitch angle is 2.77°. Furthermore, the higher the tidal current speed, the higher the generated output power. Aside from that, as the pitch angle is gradually increased while keeping the tidal speed constant, the power coefficient will decrease. Maximum Power Point Tracking algorithm which is based on Perturb and Observe (P&O) is used to locate the maximum power coefficient of the system. It can track the maximum power coefficient successfully but there will be oscillation at the steady state. Cuckoo Search via Levy Flight is able to overcome this problem as there will be no oscillation at steady state and this can prevent power loss. The convergence of Cuckoo Search via Levy Flight is two times faster than P&O.
可再生能源最近越来越受欢迎。潮流是由两个不同的连接体驱动的,它们试图平衡它们的水位差,因此会有一股水流从高压水头流向低压水头。正是这种水流使得潮流适合于发电。潮汐能的主要优点是它可以很容易地预测。除此之外,与空气相比,海水的密度更高,因此对于相同数量的电力,可以以较低的速度产生电力。潮流模型由一个永磁同步发电机、潮汐速度剖面和另外两个子系统组成。在Matlab中对该模型进行了仿真。所得的潮汐速度由5个不同的分潮组成。在不同的俯仰角和潮流速度输入下,对潮流水轮机模型进行了试验。结果表明:当俯仰角为2.77°时,最大输出功率为295kW;此外,潮流速度越高,产生的输出功率越高。此外,在保持潮速不变的情况下,随着俯仰角的逐渐增大,功率系数也会减小。采用基于扰动与观测(P&O)的最大功率点跟踪算法定位系统的最大功率系数。它可以成功地跟踪最大功率系数,但在稳态时存在振荡。通过Levy Flight的布谷鸟搜索能够克服这个问题,因为在稳定状态下不会振荡,这可以防止功率损失。Cuckoo Search通过Levy Flight的收敛速度是P&O的两倍。
{"title":"Optimized Energy Extraction in Tidal Current Technology using Evolutionary Algorithm","authors":"M. K. Tan, Chun Chong Loo, Kit Guan Lim, Pungut Ibrahim, H. Goh, K. Teo","doi":"10.1109/IICAIET51634.2021.9573950","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573950","url":null,"abstract":"Renewable energy is gaining more popularity recently. Tidal currents are driven by two different connected bodies trying to equalize their level differences, hence there will be a flow of water from the high-pressure head to the low-pressure head. It is this kind of water flow that makes tidal current suitable for power generation. The main advantage of tidal power is that it can be forecasted easily. Aside from that, sea water has higher density as compared to air, therefore for the same amount of power, the power can be generated at a lower speed. The tidal current model is composed of a permanent magnet synchronous generator, tidal velocity profile, and another two sub-systems. This model is simulated in Matlab. The resultant tidal velocity is made up of 5 different partial tides. The tidal current turbine model is tested with different inputs of pitch angle and tidal current speed. The results show that the maximum generated output power is 295kW when the pitch angle is 2.77°. Furthermore, the higher the tidal current speed, the higher the generated output power. Aside from that, as the pitch angle is gradually increased while keeping the tidal speed constant, the power coefficient will decrease. Maximum Power Point Tracking algorithm which is based on Perturb and Observe (P&O) is used to locate the maximum power coefficient of the system. It can track the maximum power coefficient successfully but there will be oscillation at the steady state. Cuckoo Search via Levy Flight is able to overcome this problem as there will be no oscillation at steady state and this can prevent power loss. The convergence of Cuckoo Search via Levy Flight is two times faster than P&O.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125719061","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
Using Machine Learning to Forecast Residential Property Prices in Overcoming the Property Overhang Issue 利用机器学习预测住宅物业价格以克服物业过剩问题
Lim Wan Yee, N. A. A. Bakar, N. H. Hassan, N. M. M. Zainuddin, R. Yusoff, N. A. Rahim
Overhang property issue has sustained over the past ten years in Malaysia. Major overhang property issue was contributed from the unsold residential property. Though the government announced to build a data system and provide the housing data to prevent a mismatch of supply-demand in the property market, there are still not many relevant studies or research on predicting residential property prices. Hence, it is essential to understand the factors that influence the price of residential properties. The study aims to predict the price of a residential property by using a machine learning algorithm. Three algorithms were selected, namely Decision Tree, Linear Regression, and Random Forest, tested against the training and testing datasets obtained from the Malaysian Valuation and Property Services Department. Results show that the Random Forest model produced high accuracy with lower r_squared (R2), RMSE, and MAE values. Significantly, the study has contributed a new insight into essential property features that primarily influence the property price, which will be useful for property developers and buyers who wish to invest in the property market.
过去十年来,马来西亚的房地产问题一直存在。主要的悬置物业问题来自未售出的住宅物业。虽然政府宣布要建立数据系统并提供住房数据,以防止房地产市场的供需不匹配,但有关住宅房地产价格预测的研究仍然不多。因此,有必要了解影响住宅物业价格的因素。该研究旨在通过使用机器学习算法来预测住宅物业的价格。选择了三种算法,即决策树,线性回归和随机森林,针对从马来西亚估值和物业服务部获得的训练和测试数据集进行测试。结果表明,随机森林模型具有较低的r_squared (R2)、RMSE和MAE值,具有较高的预测精度。值得注意的是,这项研究对主要影响房地产价格的基本房地产特征提供了新的见解,这对房地产开发商和希望投资房地产市场的买家很有帮助。
{"title":"Using Machine Learning to Forecast Residential Property Prices in Overcoming the Property Overhang Issue","authors":"Lim Wan Yee, N. A. A. Bakar, N. H. Hassan, N. M. M. Zainuddin, R. Yusoff, N. A. Rahim","doi":"10.1109/IICAIET51634.2021.9573830","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573830","url":null,"abstract":"Overhang property issue has sustained over the past ten years in Malaysia. Major overhang property issue was contributed from the unsold residential property. Though the government announced to build a data system and provide the housing data to prevent a mismatch of supply-demand in the property market, there are still not many relevant studies or research on predicting residential property prices. Hence, it is essential to understand the factors that influence the price of residential properties. The study aims to predict the price of a residential property by using a machine learning algorithm. Three algorithms were selected, namely Decision Tree, Linear Regression, and Random Forest, tested against the training and testing datasets obtained from the Malaysian Valuation and Property Services Department. Results show that the Random Forest model produced high accuracy with lower r_squared (R2), RMSE, and MAE values. Significantly, the study has contributed a new insight into essential property features that primarily influence the property price, which will be useful for property developers and buyers who wish to invest in the property market.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127447859","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
Engine Fault Diagnosis using Probabilistic Neural Network 基于概率神经网络的发动机故障诊断
Sheng Zhu, M. K. Tan, R. Chin, B. Chua, Xiaoxi Hao, K. Teo
Engine failure is one of the major factors caused vehicle breakdown. In the current practice, the engine faults are diagnosed manually by mechanics and the accuracy is highly relied on their experience. Therefore, this study would like to explore the feasibility of implementing auto fault diagnosis using Probabilistic Neural Network (PNN). A benchmarked engine fault model is developed and simulated in Maltab. The proposed algorithm is designed to detect 9 common engine faults based on the information extracted from exhaust gas, such as hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), carbon dioxide (CO2) and dioxygen (O2). The proposed PNN is trained using the collected engine fault data from experiment and the probability density of PNN is determined based on the Parzen window estimation method. Bayes decision rule is implemented for classifying the types of the engine faults. The simulated results show that the proposed algorithm has faster diagnosis speed, higher accuracy and consistent. The algorithm takes 0.038 s in diagnosing the fault and the average accuracy is 98.3 %.
发动机故障是造成车辆故障的主要原因之一。在目前的实践中,发动机故障的诊断主要依靠机械师的人工诊断,其诊断的准确性很大程度上依赖于机械师的经验。因此,本研究旨在探讨概率神经网络(PNN)实现汽车故障诊断的可行性。在Maltab中建立了发动机基准故障模型并进行了仿真。该算法基于从尾气中提取的碳氢化合物(HC)、一氧化碳(CO)、氮氧化物(NOx)、二氧化碳(CO2)和双氧(O2)等信息,对发动机常见的9种故障进行检测。利用实验采集到的发动机故障数据对PNN进行训练,并基于Parzen窗估计方法确定PNN的概率密度。采用贝叶斯决策规则对发动机故障类型进行分类。仿真结果表明,该算法具有较快的诊断速度、较高的准确率和一致性。该算法的故障诊断时间为0.038 s,平均准确率为98.3%。
{"title":"Engine Fault Diagnosis using Probabilistic Neural Network","authors":"Sheng Zhu, M. K. Tan, R. Chin, B. Chua, Xiaoxi Hao, K. Teo","doi":"10.1109/IICAIET51634.2021.9573654","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573654","url":null,"abstract":"Engine failure is one of the major factors caused vehicle breakdown. In the current practice, the engine faults are diagnosed manually by mechanics and the accuracy is highly relied on their experience. Therefore, this study would like to explore the feasibility of implementing auto fault diagnosis using Probabilistic Neural Network (PNN). A benchmarked engine fault model is developed and simulated in Maltab. The proposed algorithm is designed to detect 9 common engine faults based on the information extracted from exhaust gas, such as hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), carbon dioxide (CO2) and dioxygen (O2). The proposed PNN is trained using the collected engine fault data from experiment and the probability density of PNN is determined based on the Parzen window estimation method. Bayes decision rule is implemented for classifying the types of the engine faults. The simulated results show that the proposed algorithm has faster diagnosis speed, higher accuracy and consistent. The algorithm takes 0.038 s in diagnosing the fault and the average accuracy is 98.3 %.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132188440","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
Eye-Tank: Monitoring and Predicting Water and pH Level in Smart Farming 眼槽:智能农业中水和pH值的监测和预测
Chee-Hong Ting, Y. Leau, Po-Hung Lai, S. Tan, Asni Tahir
Water is the most critical resource in agriculture. However, concerns are raised about low-purity water, which contributes adverse effects to the soil and plant. It causes significant losses to farmers. Hence, this study proposed a project using sensors to identify and predict water and pH levels. Once triggered (water or pH level exceeds or dropped below standard requirement), the sensor can activate the alarm system and notify the target user via email and SMS. In addition, this project includes predicting pH levels by using the data collected from the pH sensor. Raspberry Pi 3 serves as the central processing unit - implementing and powers up the system and enabling sensors to read and display data. This project utilized rapid prototyping, which comprised several phases, which consist of building, testing, and revising until an acceptable prototype is created. Besides, the system is accessed via remot3.it platform, which connects the device to the system. The system interface is displayed through Virtual Network Computing (VNC) viewer. Overall, this study presents the details in developing a gadget capable of displaying water readings and communicating with the target user. Also, the monthly report will be generated and notify the user via email and SMS.
水是农业中最重要的资源。然而,人们担心低纯度的水会对土壤和植物产生不利影响。它给农民造成了重大损失。因此,本研究提出了一个使用传感器识别和预测水和pH值的项目。一旦触发(水或pH值超过或低于标准要求),传感器可以激活报警系统,并通过电子邮件和短信通知目标用户。此外,该项目还包括利用从pH传感器收集的数据预测pH值。树莓派3作为中央处理单元-实现和启动系统,并使传感器能够读取和显示数据。这个项目利用了快速原型,它包括几个阶段,包括构建、测试和修改,直到创建一个可接受的原型。此外,系统可以通过远程访问。It平台,将设备连接到系统。通过VNC查看器显示系统界面。总的来说,这项研究展示了开发一个能够显示水读数并与目标用户通信的小工具的细节。此外,将生成月度报告,并通过电子邮件和短信通知用户。
{"title":"Eye-Tank: Monitoring and Predicting Water and pH Level in Smart Farming","authors":"Chee-Hong Ting, Y. Leau, Po-Hung Lai, S. Tan, Asni Tahir","doi":"10.1109/IICAIET51634.2021.9573955","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573955","url":null,"abstract":"Water is the most critical resource in agriculture. However, concerns are raised about low-purity water, which contributes adverse effects to the soil and plant. It causes significant losses to farmers. Hence, this study proposed a project using sensors to identify and predict water and pH levels. Once triggered (water or pH level exceeds or dropped below standard requirement), the sensor can activate the alarm system and notify the target user via email and SMS. In addition, this project includes predicting pH levels by using the data collected from the pH sensor. Raspberry Pi 3 serves as the central processing unit - implementing and powers up the system and enabling sensors to read and display data. This project utilized rapid prototyping, which comprised several phases, which consist of building, testing, and revising until an acceptable prototype is created. Besides, the system is accessed via remot3.it platform, which connects the device to the system. The system interface is displayed through Virtual Network Computing (VNC) viewer. Overall, this study presents the details in developing a gadget capable of displaying water readings and communicating with the target user. Also, the monthly report will be generated and notify the user via email and SMS.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114409087","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
Sensitivity Analysis of Tracking Point for A Visual Tracking System on Lower Limb Joint Assessment 下肢关节评估视觉跟踪系统跟踪点灵敏度分析
L. C. Chin, Marwan Affandi, M. N. Shah, S. Basah, T. Jian, Muhammad Yazid Din
No matter accurate that putting a sensor in its place there is always a possibility that the position of the sensor is not correct. An inaccurate position may produce an error, which eventually affects the result of the measurement. Sensitivity analysis is intended to determine the amount of error that may occur in measurement by varying important parameters slightly in that measurement and calculating the change of the result. In this paper, sensitivity analysis was simulated in the visual tracking system for lower limb joint measurements. In doing the measurements, markers were put on the limbs of the patients at determined positions. Sensitivity analysis was then simulated by moving the points slightly. There was a total of 729 possible positions coming from three marker positions. The effects of the changes for the distances to be measured were analyzed. It is found that the errors depend on the size of the marker; for a 10-mm marker, the maximum error is only 7.85%, which is relatively small for practical application. When the marker diameter is 13 mm, the maximum error is slightly over 10%, which is still acceptable for practical purposes. There are exactly 27 positions that do not produce errors. Knowing these positions will help the user to reduce the error that may occur during the measurement.
无论将传感器放置在其位置是否准确,总有可能传感器的位置不正确。不准确的位置可能产生误差,最终影响测量结果。灵敏度分析旨在通过稍微改变测量中的重要参数并计算结果的变化来确定测量中可能发生的误差量。本文对下肢关节测量视觉跟踪系统的灵敏度分析进行了仿真。在进行测量时,在确定的位置将标记放在患者的四肢上。然后通过稍微移动点来模拟灵敏度分析。总共有729个可能的位置来自三个标记位置。分析了这些变化对被测距离的影响。研究发现,误差与标记的大小有关;对于10mm的标记,最大误差仅为7.85%,对于实际应用来说是比较小的。当标记直径为13mm时,最大误差略大于10%,在实际使用中仍然可以接受。总共有27个位置不会产生误差。了解这些位置将有助于用户减少测量过程中可能发生的误差。
{"title":"Sensitivity Analysis of Tracking Point for A Visual Tracking System on Lower Limb Joint Assessment","authors":"L. C. Chin, Marwan Affandi, M. N. Shah, S. Basah, T. Jian, Muhammad Yazid Din","doi":"10.1109/IICAIET51634.2021.9573938","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573938","url":null,"abstract":"No matter accurate that putting a sensor in its place there is always a possibility that the position of the sensor is not correct. An inaccurate position may produce an error, which eventually affects the result of the measurement. Sensitivity analysis is intended to determine the amount of error that may occur in measurement by varying important parameters slightly in that measurement and calculating the change of the result. In this paper, sensitivity analysis was simulated in the visual tracking system for lower limb joint measurements. In doing the measurements, markers were put on the limbs of the patients at determined positions. Sensitivity analysis was then simulated by moving the points slightly. There was a total of 729 possible positions coming from three marker positions. The effects of the changes for the distances to be measured were analyzed. It is found that the errors depend on the size of the marker; for a 10-mm marker, the maximum error is only 7.85%, which is relatively small for practical application. When the marker diameter is 13 mm, the maximum error is slightly over 10%, which is still acceptable for practical purposes. There are exactly 27 positions that do not produce errors. Knowing these positions will help the user to reduce the error that may occur during the measurement.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122216180","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
Preliminary Design of Portable Electromyography (EMG) System for Clinical Signal Acquisition 用于临床信号采集的便携式肌电系统的初步设计
B. Chan, I. Saad, N. Bolong, Kang Eng Siew
The surface electromyogram was found very useful in muscle activity scanning and diagnosis purposes. With the high demand from the physiotherapist and neurophysiologist, electromyography (EMG) has been developing rapidly to meet the needs. The quantitative analysis of the EMG signal is required to provide particular characteristics of the EMG signal. In this paper, the EMG signals system's design is presented, and the proposed portable EMG system design concept is discussed to improve the current difficulties of EMG signal collection. The sampling frequency of the EMG signal is between 20–500Hz. The EMG signal is received successfully using the wired devices during the contraction of the muscle. The portable non-invasive EMG system was successfully reduce the interference of the signal whereby the movement of the muscle can be easily detected during the data collection.
表面肌电图在肌肉活动扫描和诊断方面非常有用。随着物理治疗师和神经生理学家的需求越来越高,肌电图(EMG)得到了迅速的发展以满足需求。需要对肌电信号进行定量分析,以提供肌电信号的特定特征。本文介绍了肌电信号系统的设计,并讨论了提出的便携式肌电信号系统的设计概念,以改善目前肌电信号采集的困难。肌电信号的采样频率在20-500Hz之间。肌电图信号在肌肉收缩过程中通过有线装置成功接收。该便携式非侵入性肌电图系统成功地减少了信号的干扰,从而在数据收集过程中可以很容易地检测到肌肉的运动。
{"title":"Preliminary Design of Portable Electromyography (EMG) System for Clinical Signal Acquisition","authors":"B. Chan, I. Saad, N. Bolong, Kang Eng Siew","doi":"10.1109/IICAIET51634.2021.9573901","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573901","url":null,"abstract":"The surface electromyogram was found very useful in muscle activity scanning and diagnosis purposes. With the high demand from the physiotherapist and neurophysiologist, electromyography (EMG) has been developing rapidly to meet the needs. The quantitative analysis of the EMG signal is required to provide particular characteristics of the EMG signal. In this paper, the EMG signals system's design is presented, and the proposed portable EMG system design concept is discussed to improve the current difficulties of EMG signal collection. The sampling frequency of the EMG signal is between 20–500Hz. The EMG signal is received successfully using the wired devices during the contraction of the muscle. The portable non-invasive EMG system was successfully reduce the interference of the signal whereby the movement of the muscle can be easily detected during the data collection.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120968405","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
Performance of Machine Learning Classifiers in Distress Keywords Recognition for Audio Surveillance Applications 机器学习分类器在音频监控遇险关键字识别中的性能
Nadhirah Johari, Mazlina Mamat, A. Chekima
The ability to recognize distress speech is the essence of an intelligent audio surveillance system. With this ability, the surveillance system can be configured to detect specific distress keywords and launch appropriate actions to prevent unwanted incidents from progressing. This paper aims to find potential distress keywords that the audio surveillance system could recognize. The idea is to use a machine learning classifier as the recognition engine. Five distress keywords: ‘Help’, ‘No’, ‘Oi’, ‘Please’, and ‘Tolong’ were selected to be analyzed. A total of 515 audio signals comprising these five distress keywords were collected and used in the training and testing of 27 classifier models, derived from the Decision Tree, Naïve Bias, Support Vector Machine, K-Nearest Neighbour, Ensemble, and Artificial Neural Network. The features extracted from each audio signal are the Mel-frequency Cepstral Coefficients, while the Principal Component Analysis was applied for feature reduction. The results show that the keyword ‘Please’ is the most recognized, followed by ‘Help’, ‘Oi’, ‘No’ and ‘Tolong’, respectively. This observation was achieved using the Ensemble Bagged Trees classifier, which can recognize ‘Please’ with 99% accuracy in training and 100% accuracy in testing.
识别遇险语音的能力是智能音频监控系统的本质。有了这种能力,监控系统可以配置为检测特定的遇险关键字,并启动适当的行动,以防止意外事件的发展。本文旨在寻找音频监控系统能够识别的潜在遇险关键词。这个想法是使用机器学习分类器作为识别引擎。选取“Help”、“No”、“Oi”、“Please”、“Tolong”五个遇险关键词进行分析。共收集了515个包含这5个遇险关键词的音频信号,并将其用于27个分类器模型的训练和测试,这些分类器模型分别来自决策树、Naïve Bias、支持向量机、k近邻、Ensemble和人工神经网络。从每个音频信号中提取的特征是mel频倒谱系数,而主成分分析用于特征约简。结果表明,“请”是最容易被识别的关键词,其次是“帮助”、“我”、“不”和“Tolong”。这一观察结果是使用Ensemble Bagged Trees分类器实现的,该分类器在训练中识别“请”的准确率为99%,在测试中准确率为100%。
{"title":"Performance of Machine Learning Classifiers in Distress Keywords Recognition for Audio Surveillance Applications","authors":"Nadhirah Johari, Mazlina Mamat, A. Chekima","doi":"10.1109/IICAIET51634.2021.9573852","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573852","url":null,"abstract":"The ability to recognize distress speech is the essence of an intelligent audio surveillance system. With this ability, the surveillance system can be configured to detect specific distress keywords and launch appropriate actions to prevent unwanted incidents from progressing. This paper aims to find potential distress keywords that the audio surveillance system could recognize. The idea is to use a machine learning classifier as the recognition engine. Five distress keywords: ‘Help’, ‘No’, ‘Oi’, ‘Please’, and ‘Tolong’ were selected to be analyzed. A total of 515 audio signals comprising these five distress keywords were collected and used in the training and testing of 27 classifier models, derived from the Decision Tree, Naïve Bias, Support Vector Machine, K-Nearest Neighbour, Ensemble, and Artificial Neural Network. The features extracted from each audio signal are the Mel-frequency Cepstral Coefficients, while the Principal Component Analysis was applied for feature reduction. The results show that the keyword ‘Please’ is the most recognized, followed by ‘Help’, ‘Oi’, ‘No’ and ‘Tolong’, respectively. This observation was achieved using the Ensemble Bagged Trees classifier, which can recognize ‘Please’ with 99% accuracy in training and 100% accuracy in testing.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133189254","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
Text Analytics on Course Reviews from Coursera Platform Coursera平台课程评论的文本分析
Huan Yang Chan, Ramindhran Rajamohan, Gan Keng Hoon, Nur-Hana Samsudin
Ratings and reviews are always the major consideration factor by online course seekers before they join the course. However, it can be time-consuming to read all the information especially the course reviews. In this research work, our objective is to propose a text analytics pipeline that includes text cleaning, text lemmatization, sentiment analysis, text mining, and visualization that can help course seekers to gain a quick insight into the courses as well as enables them to make a quick comparison between multiple courses. The proposed text analytic pipeline was created in Python Jupyter Notebook. Three different Python-related courses were chosen for the study. The proposed text analytics pipeline solution was proved able to achieve our research objective. It can help course seekers to gain a quick insight including the positive and negative reviews into the courses as well as enables them to make a quick comparison between multiple courses. The n-gram analysis and word cloud generated were sufficient to provide an accurate and informative glance into the course. However, it fell short on sentiment analysis especially in detecting the negative reviews.
评分和评论一直是在线课程寻求者在参加课程之前的主要考虑因素。然而,阅读所有的信息,尤其是课程评论,可能是很耗时的。在这项研究工作中,我们的目标是提出一个文本分析管道,包括文本清洗、文本排版、情感分析、文本挖掘和可视化,可以帮助课程寻求者快速了解课程,并使他们能够快速比较多个课程。建议的文本分析管道是在Python Jupyter Notebook中创建的。研究中选择了三门不同的python相关课程。提出的文本分析管道解决方案被证明能够实现我们的研究目标。它可以帮助求职者快速了解课程的正面和负面评价,并使他们能够在多个课程之间进行快速比较。n-gram分析和生成的词云足以提供准确和信息丰富的课程概览。然而,在情感分析方面,特别是在发现负面评论方面,它表现得很差。
{"title":"Text Analytics on Course Reviews from Coursera Platform","authors":"Huan Yang Chan, Ramindhran Rajamohan, Gan Keng Hoon, Nur-Hana Samsudin","doi":"10.1109/IICAIET51634.2021.9573868","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573868","url":null,"abstract":"Ratings and reviews are always the major consideration factor by online course seekers before they join the course. However, it can be time-consuming to read all the information especially the course reviews. In this research work, our objective is to propose a text analytics pipeline that includes text cleaning, text lemmatization, sentiment analysis, text mining, and visualization that can help course seekers to gain a quick insight into the courses as well as enables them to make a quick comparison between multiple courses. The proposed text analytic pipeline was created in Python Jupyter Notebook. Three different Python-related courses were chosen for the study. The proposed text analytics pipeline solution was proved able to achieve our research objective. It can help course seekers to gain a quick insight including the positive and negative reviews into the courses as well as enables them to make a quick comparison between multiple courses. The n-gram analysis and word cloud generated were sufficient to provide an accurate and informative glance into the course. However, it fell short on sentiment analysis especially in detecting the negative reviews.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131131624","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
Stacked Bidirectional Long Short-Term Memory for Stock Market Analysis 股票市场分析的堆叠双向长短期记忆
Jing Yee Lim, K. Lim, C. Lee
Stock market prediction is a difficult task as it is extremely complex and volatile. Researchers are exploring methods to obtain good performance in stock market prediction. In this paper, we propose a Stacked Bidirectional Long Short-Term Memory (SBLSTM) network for stock market prediction. The proposed SBLSTM stacks three bidirectional LSTM networks to form a deep neural network model that can gain better prediction performance in the stock price forecasting. Unlike LSTM-based methods, the proposed SBLSTM uses bidirectional LSTM layers to obtain the temporal information in both forward and backward directions. In this way, the long-term dependencies from the past and future stock market values are encapsulated. The performance of the proposed SBLSTM is evaluated on six datasets collected from Yahoo Finance. Additionally, the proposed SBLSTM is compared with the state-of-the-art methods using root mean square error. The empirical studies on six datasets demonstrates that the proposed SBLSTM outperforms the state-of-the-art methods.
股市预测是一项艰巨的任务,因为它极其复杂和不稳定。研究人员正在探索如何在股票市场预测中获得良好的效果。本文提出了一种用于股票市场预测的堆叠双向长短期记忆(SBLSTM)网络。本文提出的SBLSTM将三个双向LSTM网络叠加,形成一个深度神经网络模型,在股票价格预测中可以获得更好的预测性能。与基于LSTM的方法不同,本文提出的SBLSTM使用双向LSTM层来获取正反向的时间信息。通过这种方式,对过去和未来股票市场价值的长期依赖被封装起来。本文在雅虎财经收集的六个数据集上对所提出的SBLSTM的性能进行了评估。此外,利用均方根误差将所提出的SBLSTM与最先进的方法进行了比较。在6个数据集上的实证研究表明,本文提出的SBLSTM优于目前最先进的方法。
{"title":"Stacked Bidirectional Long Short-Term Memory for Stock Market Analysis","authors":"Jing Yee Lim, K. Lim, C. Lee","doi":"10.1109/IICAIET51634.2021.9573812","DOIUrl":"https://doi.org/10.1109/IICAIET51634.2021.9573812","url":null,"abstract":"Stock market prediction is a difficult task as it is extremely complex and volatile. Researchers are exploring methods to obtain good performance in stock market prediction. In this paper, we propose a Stacked Bidirectional Long Short-Term Memory (SBLSTM) network for stock market prediction. The proposed SBLSTM stacks three bidirectional LSTM networks to form a deep neural network model that can gain better prediction performance in the stock price forecasting. Unlike LSTM-based methods, the proposed SBLSTM uses bidirectional LSTM layers to obtain the temporal information in both forward and backward directions. In this way, the long-term dependencies from the past and future stock market values are encapsulated. The performance of the proposed SBLSTM is evaluated on six datasets collected from Yahoo Finance. Additionally, the proposed SBLSTM is compared with the state-of-the-art methods using root mean square error. The empirical studies on six datasets demonstrates that the proposed SBLSTM outperforms the state-of-the-art methods.","PeriodicalId":234229,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133328635","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
期刊
2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1