2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)最新文献_第5页

Impacts of Losses Functions on the Quality of the Ultrasound Image by Using Machine Learning Algorithms 利用机器学习算法研究损失函数对超声图像质量的影响

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495878

Soufiane Dangoury, Saad Abouzahir, A. Alali, Mohammed Sadik

{"title":"Impacts of Losses Functions on the Quality of the Ultrasound Image by Using Machine Learning Algorithms","authors":"Soufiane Dangoury, Saad Abouzahir, A. Alali, Mohammed Sadik","doi":"10.1109/I2CACIS52118.2021.9495878","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495878","url":null,"abstract":"During last decade, Artificial Intelligence (AI) has been able to reshape our life daily. Different areas were positively impacted by AI such as Healthcare, Logistic, etc. Medical imaging is one of the fields of healthcare in which AI was introduced to solve and overcome different problems. Challenges including image processing, signal processing, and data acquisition. In this paper, we deeply demonstrate the loss function as one of the main parameters that influence the quality of the ultrasound (US) image. Therefore, we introduce the main components of ultrasound systems form end-to-end perspective such as the data acquisition, the signal processing, and the image interpretation. Then, we present the losses functions as a critical performance metrics for the model validation. Metrics such as the Mean Absolute Error (MAE), Cross-Entropy loss function (CE), Dice Similarity Coefficient (DSC), and the Structural Similarity (SSIM). After that we present the adopted CNN model to generate ultrasound image. The excessive simulation results demonstrate that the selection of the loss function provides significant improvement in terms of image quality (e.g., contrast, CNR and SNR). Choosing simple loss functions such as mean square error helps to faster the convergence of the convolution neural network during the training process. However, for image quality enhancement, we propose the combination of different loss functions such structural similarity (SSIM) with Dice Similarity Coefficient (DSC).","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117107978","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

Development of a Non-Intrusive Load Monitoring (NILM) with Unknown Loads using Support Vector Machine 基于支持向量机的未知负荷非侵入式监测方法研究

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495876

Anjon S. Hernandez, A. Ballado, Aaron Paulo D. Heredia

{"title":"Development of a Non-Intrusive Load Monitoring (NILM) with Unknown Loads using Support Vector Machine","authors":"Anjon S. Hernandez, A. Ballado, Aaron Paulo D. Heredia","doi":"10.1109/I2CACIS52118.2021.9495876","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495876","url":null,"abstract":"Non-intrusive load monitoring is the process of recognizing and identifying electrical devices and its energy consumption on the entire electrical system through \"power signatures\". In this process, the aggregated load information is obtained from a single point of measurement. Compared with the traditional way of load identification by setting up multiple devices and sensors, the system uses only one energy measurement device, hence making it more efficient and economical. In this study, the focus was on designing a hardware that can obtain all power quality measurements, data analysis, and appliance identifier, which were analyzed by the microcontroller. The general information and introduction to the system, as well as the past and present literatures about the types of NILM System used by the researchers are presented. It was found that the combined unknown loads can be identified. Three different loads were analyzed at the same time from light bulb, electric fan and heater which gave 8-8.2W, 40-42W, and 238-249W respectively, all determined using a small-scale NILM system equipped with energy metering block and microcontroller that extracts and classifies loads with the use of support vector machine. This has a great significance to the industry and understanding of energy management since the demand for energy is growing rapidly.","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115598126","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: 6

Conceptual Design of Human Detection via Deep Learning for Industrial Safety Enforcement in Manufacturing Site 基于深度学习的人为检测在生产现场工业安全执法中的概念设计

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495856

M. M. Daud, Hanif Md. Saad, M. Ijab

{"title":"Conceptual Design of Human Detection via Deep Learning for Industrial Safety Enforcement in Manufacturing Site","authors":"M. M. Daud, Hanif Md. Saad, M. Ijab","doi":"10.1109/I2CACIS52118.2021.9495856","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495856","url":null,"abstract":"Industrial workers are vulnerable to hazard and accidents. There could be many factors that contribute for these to occur including human error. Standard operating procedure and safety guideline have been set up to be followed by the workers with manual supervision where total adherence is required in a wide range of operation and hence, often lead to inefficiency. Thus, this work has proposed a preliminary work on safety monitoring within the potentially danger area to make the process to be efficient and reduce the manual supervision burden via deep learning. This work has adopted YOLO network for feature extraction and human detection in several monitoring areas. Then, counting module is executed to retrieve the data of how frequent the monitoring area is being interrupted. Prior to that, a region of interest (ROI) would be set up where human is detected only in the ROI. Lastly, measure the area of intersection between human and ROI to decide whether the subject is in the monitoring area or vice versa. The number of counts indicates the risk of accidents occur in the monitoring area. The higher the counts, the higher the risk in that region. This conceptual design can be extensively used in many ways for safety monitoring as it requires less supervision and becomes a safety measure by enforcing industrial safety in manufacturing sites.","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128705481","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

Precise Speed Control of DC Motor by Implementing Cascade PI Controller 采用级联PI控制器实现直流电动机的精确调速

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495889

Alfiyah Shaldzabila Yustin, H. Nugroho, W. Wibowo

引用次数: 0

Analysis of Kaffir Lime Oil Chemical Compounds by Gas Chromatography-Mass Spectrometry (GC-MS) and Z-Score Technique 气相色谱-质谱联用- Z-Score技术分析卡菲尔石灰油中的化学成分

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495909

Nor Syahira Jak Jailani, Z. Muhammad, Mohd Hezri Fazalul Rahiman, M. Nasir Taib

{"title":"Analysis of Kaffir Lime Oil Chemical Compounds by Gas Chromatography-Mass Spectrometry (GC-MS) and Z-Score Technique","authors":"Nor Syahira Jak Jailani, Z. Muhammad, Mohd Hezri Fazalul Rahiman, M. Nasir Taib","doi":"10.1109/I2CACIS52118.2021.9495909","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495909","url":null,"abstract":"Currently, the quality and grading of essential oil are done manually which is through sensory evaluation. It was performed based on the essential oil physical properties for example human experience and perception of the oil colour, odour, and long-lasting aroma. The sensory evaluation method is very subjective and may vary from one person to another such as a human sensory organ easily get fatigued to deal with repeatability experiment, the result obtained by trained grader usually is not consistent since it may vary to each other and the process itself is lengthy and high time-consuming. To face this problem, many researchers found that the chemical profile of the oil can be used to grad kaffir lime oil more accurately and save time. This study proposed analyses the chemical compound by gas chromatography-mass spectrometry (GC-MS) and Z-score technique from the 11 samples of kaffir lime oils with different brands in Malaysia. The chemical compounds in these samples were extracted by using GC-MS and being analyzed. The significant compound was identified by the Z-score technique. It was found that six chemical compounds such as Citronellal, Limonene, β-pinene, terpinene-4-ol, E-caryophyllene, and terpinolene were highlighted as significant for kaffir lime oil and can be used as a major compound in classifying the kaffir lime oil.","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114811292","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

Assessment of Technical Impacts of EV Charging to Malaysian Distribution Grid 电动汽车充电对马来西亚配电网的技术影响评估

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495925

Mohd Syahmi Hashim, Jia Ying Yong, V. Ramachandaramurthy, M. Mansor, K. Tan

{"title":"Assessment of Technical Impacts of EV Charging to Malaysian Distribution Grid","authors":"Mohd Syahmi Hashim, Jia Ying Yong, V. Ramachandaramurthy, M. Mansor, K. Tan","doi":"10.1109/I2CACIS52118.2021.9495925","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495925","url":null,"abstract":"Electrifying the transportation sector helps reducing carbon emissions. However, the need of electric vehicle to receive charging from the power grid introduces various technical impacts to the grid operation. This paper presents a comprehensive investigation of technical impacts of electric vehicle charging to Malaysian distribution grid. The charging assessment considered different power grid loadings, electric vehicle charger types, electric vehicle charging locations, and electric vehicle charging times. These factors were taken into considerations to ensure the practicality of the study. The technical impact study was implemented in a typical Malaysian distribution grid under four different scenarios with respect to the interconnection schemes outlined in the Technical Guidelines for Interconnection of Electric Vehicle to Distribution System. The studies were performed using Matlab/Simulink software. The results indicated that the charging of electric vehicles in Connection Schemes I, II, and III caused overloading of cables and substation transformer, whereas DC fast charging in Connection Scheme IV caused severe grid voltage violation.","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121684925","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

Social Spider Optimization for Solving Inverse Kinematics for Both Humanoid Robotic Arms 两类人机械臂运动学逆解的社会蜘蛛优化

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495922

S. F. Abulhail, M. Z. Al-Faiz

{"title":"Social Spider Optimization for Solving Inverse Kinematics for Both Humanoid Robotic Arms","authors":"S. F. Abulhail, M. Z. Al-Faiz","doi":"10.1109/I2CACIS52118.2021.9495922","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495922","url":null,"abstract":"The non-linearity of Inverse kinematics (IK) equations are complex. A Social Spider Optimization (SSO) and Particle Swarm Optimization (PSO) algorithms are proposed in this paper to solve the IK of Humanoid Robotic Arms (HRA). These optimization algorithms are applied on both right and left arms to find the required angles and desired positions with minimum error. Mathematical model of HRA is simulated depending on Denavit-Hartenberg (D-H) method for each arm in which each arm has five Degree Of Freedom (DOF). Performance of HRA model is tested by many positions to be reach by both arms to obtain which optimization algorithm is better. Comparisons are listed between optimal solution using PSO and SSO algorithms. These optimization algorithms are assessed by calculating the Root Mean Squared Error (RMSE) for the absolute error vector of the positions. Simulations and calculation results showed that RMSE value using SSO is less than RMSE value using PSO. We got the largest RMSE of 0.0864 using PSO algorithm. while the lowest possible error, which is 0.00004 was acquired by SSO algorithm. The Graphical User Interface (GUI) is designed and built for motional characteristics of the HRA model in the Forward Kinematics (FK) and IK.","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115972130","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 Nonlinear Static Simulation of a Small–Scale Vortex Bladeless Wind Power Generator 小型涡旋无叶片风力发电机的设计与非线性静态仿真

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495882

Angela Ciara R. Buela, Rodolfo Rey M. Torres, Fermin II G. Unisa, P. R. Meris, M. Manuel, Jennifer C. Dela Cruz, Roderick C. Tud

{"title":"Design and Nonlinear Static Simulation of a Small–Scale Vortex Bladeless Wind Power Generator","authors":"Angela Ciara R. Buela, Rodolfo Rey M. Torres, Fermin II G. Unisa, P. R. Meris, M. Manuel, Jennifer C. Dela Cruz, Roderick C. Tud","doi":"10.1109/I2CACIS52118.2021.9495882","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495882","url":null,"abstract":"Wind turbines can be a replacement for coal as an energy source. However, the conventional wind turbines are expensive and are very complicated due to many mechanical components translating to high manufacturing and maintenance costs. This study aims to help improve the efficiency and design of bladeless wind power generators through generation of power using vortex induced vibration. A bladeless wind power generator was designed using Autodesk Fusion 360 wherein the prototype was modeled to be approximately 1.35 m x 0.5m x 0.5m (overall height, width, and length). A Nonlinear Static Simulation, using ANSYS®Academic Student MechanicalTM, was performed to determine the effect of two different wind velocities, 4.5 m/s and 6.5 m/s, acting on the mast. The design of the bladeless wind turbine was focused on simplifying its manufacturability by using a helical spring to connect the mast to the base while also attaining maximum vortex shedding at a low velocity. The researchers of this study used a static simulation to simplify the study. The predetermined wind velocities were converted into a pressure value, allowing the researchers to obtain the total deformation, directional deformation, and maximum principal and shear stresses (in the spring). It has been determined that the maximum deformation experienced by the bladeless wind power generator was 131.800 mm and 253.270 mm for wind velocities of 4.5 m/s and 6.5 m/s resulting to a theoretical power output of 9.765W and 29.428W, respectively.","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126671592","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

SMS based Curfew Monitoring System for Detecting Minors from a Facial Database to Aid the Local Government Unit Using Image Processing 基于短信的宵禁监控系统，从人脸数据库中检测未成年人，辅助地方政府单位使用图像处理

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495921

Jessie R. Balbin, John Maverick Ramos, Joseph Nathaniel Reyes, C. Santiago

引用次数: 0

Supervised and Unsupervised Machine Learning for Cancer Classification: Recent Development 癌症分类的监督和非监督机器学习:最新进展

2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) Pub Date : 2021-06-26 DOI: 10.1109/I2CACIS52118.2021.9495888

Aina Umairah Mazlan, N. A. Sahabudin, Muhammad Akmal bin Remli, N. N. Ismail, M. S. Mohamad, Nor Bakiah Abd Warif

{"title":"Supervised and Unsupervised Machine Learning for Cancer Classification: Recent Development","authors":"Aina Umairah Mazlan, N. A. Sahabudin, Muhammad Akmal bin Remli, N. N. Ismail, M. S. Mohamad, Nor Bakiah Abd Warif","doi":"10.1109/I2CACIS52118.2021.9495888","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495888","url":null,"abstract":"This is models with the ability to detect and classify cancer is important in the industrial of healthcare. The most difficult aspect for such model is the classification of cancer, which can be addressed using machine learning methods. The methods are used to improve classification accuracy between system output and test data. The classification process becomes more difficult due to vast data information. This paper presents an overview on current development of cancer classification techniques using machine learning methods, which have received increasing attention within the area of healthcare. This review will mainly focus on the development of machine learning methods for classification of cancer diseases. Recently, there are various researchers proposed different kinds of methods for cancer classification. The results show that the successful of cancer classification is dependent on the machine learning models. Besides, various types of healthcare data used in the experiments would also be discussed in this paper. The development of many optimization methods for cancer classification has brought a lot of improvement in the healthcare field. There is demand for further improvements in optimization methods to develop better machine learning models for cancer classification.","PeriodicalId":210770,"journal":{"name":"2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115300630","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