2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)最新文献_第4页

Development of an LSTM-based Model for Energy Consumption Prediction with Data Pre-analysis 基于lstm的数据预分析能耗预测模型的建立

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530898

M. Asri, N. Zaini, M. Latip

{"title":"Development of an LSTM-based Model for Energy Consumption Prediction with Data Pre-analysis","authors":"M. Asri, N. Zaini, M. Latip","doi":"10.1109/ICCSCE52189.2021.9530898","DOIUrl":"https://doi.org/10.1109/ICCSCE52189.2021.9530898","url":null,"abstract":"Electricity demand is increasing rapidly due to growth in development. Based on this trend, it is important to plan energy usage efficiently to eliminate energy waste and thus reduce carbon emissions. Towards more accurate energy consumption predictions, this study focuses on the time series data analysis and Long Short-Term Memory model in predicting energy consumption. The initial data analysis techniques adopted could be used to detect energy usage patterns and to gain a better understanding of the data. Such data analysis is important since it is crucial to understand the data before selecting an appropriate model to make predictions. The data analysis technique used was the augmented Dicky-Fuller test and the ETS Decomposition. Based on the nature and pattern of the data that have been analyzed, the LSTM method was adopted in generating energy consumption predictions. To determine the quality of prediction results, the accuracy-test methods used on the generated predictions were the Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and R-square methods. The accuracy test results of this study showed that for all the datasets used, the highest MAPE value was 7.68%, while the MSE value was 10.23%, and thus proved that the LSTM model is highly accurate in making predictions.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121558214","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

LifeDoc: Availability and Monitoring System of Online Medical Consultation LifeDoc:在线医疗咨询的可用性和监测系统

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530857

Jovan Carlo S. Caballero, Carl Benedick D. Ching, S. Co, Hazel O. Noble, Arne B. Barcelo

{"title":"LifeDoc: Availability and Monitoring System of Online Medical Consultation","authors":"Jovan Carlo S. Caballero, Carl Benedick D. Ching, S. Co, Hazel O. Noble, Arne B. Barcelo","doi":"10.1109/ICCSCE52189.2021.9530857","DOIUrl":"https://doi.org/10.1109/ICCSCE52189.2021.9530857","url":null,"abstract":"With or without a crisis such as a pandemic, the continuous service of quality healthcare is a necessity for a community to thrive. Given the current problem, the researchers determined that the integration of Availability Management and Events and Monitoring Management Information Technology Infrastructure Library (ITIL) frameworks in a healthcare mobile application to ensure the reliability and effectiveness of the services provided. Hence, the researchers created the LifeDoc: Availability and Monitoring System of Online Medical Consultation that caters to both patients and doctors and is available for Android users only. Technologies such as React Native and WebRTC were used for the development of the mobile application wherein the processing of information occurring throughout the system is stored in a secure cloud-hosted database called MongoDB. The researchers adopted the Agile Methodology approach throughout the execution of the project as this approach was determined by the researchers to be the effective and suitable approach given the time constraints and user requirements. Tests were performed in the system based on the Institute of Electronics and Electrical Engineering (IEEE) standard for Software Test Documentation to certify that the functionalities of the project were working according to specification. The importance of implementing appropriate service management framework coupled with mobile technology makes quality healthcare accessible and a right for all.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125989228","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

Classification of Handwriting Impairment Using CNN for Potential Dyslexia Symptom 使用CNN对潜在阅读障碍症状的书写障碍分类

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530989

Noor Syaheena Long Seman, I. Isa, S. A. Ramlan, Wang Li-Chih, M. Maruzuki

{"title":"Classification of Handwriting Impairment Using CNN for Potential Dyslexia Symptom","authors":"Noor Syaheena Long Seman, I. Isa, S. A. Ramlan, Wang Li-Chih, M. Maruzuki","doi":"10.1109/ICCSCE52189.2021.9530989","DOIUrl":"https://doi.org/10.1109/ICCSCE52189.2021.9530989","url":null,"abstract":"Early detection of symptoms is very important to help dyslexic children because they do not imply low intelligence. If dyslexic children are not assisted at an early stage, they will be left behind in education by their peers. Therefore, this project is helpful for diagnosing dyslexia symptoms by detecting handwriting impairment at early detection using machine learning. Dyslexia can occur in all languages but usually dyslexia in other than non-letter such as Chinese characters is lack focusing due to different handwriting characters. This study is focusing on processing Chinese character handwriting images to classify the potential dyslexia symptoms. The classification of potential dyslexia symptom is classified into 4 classes which Normal, Radical Error, Radical-Structure Error and Structure Error. The image augmentation is used to improve the performance of CNN based on in terms of its accuracy and precision. Thus, the accuracy of the training performance classification is 95.66%, while the accuracy of the validation is 96.20%.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125664690","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

Development of CNN Transfer Learning for Dyslexia Handwriting Recognition CNN迁移学习在失读症手写识别中的发展

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530971

Mohamed Rosli, I. Isa, S. A. Ramlan, S. N. Sulaiman, M. Maruzuki

{"title":"Development of CNN Transfer Learning for Dyslexia Handwriting Recognition","authors":"Mohamed Rosli, I. Isa, S. A. Ramlan, S. N. Sulaiman, M. Maruzuki","doi":"10.1109/ICCSCE52189.2021.9530971","DOIUrl":"https://doi.org/10.1109/ICCSCE52189.2021.9530971","url":null,"abstract":"Dyslexia is categorized as learning disorder that influence the ability of reading, writing and spelling. In Malaysia, “Instrumen Senarai Semak Disleksia (ISD)” that is provided by Ministry of Education is used to detect dyslexic student at early stage. However, such evaluations are time consuming, non-standardize and can lead to a biasing result since the evaluation is based on the teacher’s experiences with the student. Hence, this research focus on the development of dyslexic handwriting recognition. The purpose of this research is to develop a transfer learning of Dyslexia handwriting recognition by using Convolutional Neural Network (CNN) based on famous architecture of handwriting recognition using of LeNet-5. Data augmentation and pre-processing was employed to a total of 138,500 handwriting image dataset before feeding it into network. The hyper-parameter of the model was tuned and analyzed to classify the 3 classes of dyslexic handwriting. The developed CNN model has successfully achieved a remarkable accuracy of 95.34% in classifying 3 classes of dyslexic handwriting. From the result, the objective in developing the CNN model for dyslexia handwriting recognition was successfully achieved.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131538718","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: 9

Deep Learning Approach for Transmission Line Fault Classification 输电线路故障分类的深度学习方法

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530747

Mohamad Arif Mohamad Nasrin, A. M. Omar, S. S. Ramli, A. Ahmad, N. F. Jamaludin, M. K. Osman

{"title":"Deep Learning Approach for Transmission Line Fault Classification","authors":"Mohamad Arif Mohamad Nasrin, A. M. Omar, S. S. Ramli, A. Ahmad, N. F. Jamaludin, M. K. Osman","doi":"10.1109/ICCSCE52189.2021.9530747","DOIUrl":"https://doi.org/10.1109/ICCSCE52189.2021.9530747","url":null,"abstract":"As technology advanced, electrical interruption or disturbance still becomes a significant problem in power systems. A fault is one example of electrical disturbance or power failure in a power system. In order to recover the system, the fault must be detected, classify and locate to eliminate as fast as possible. Four types of fault occur in the transmission line. Those four types are Line-to-Ground Fault (L-G), Line-to-Line Fault (L-L), Double Line-to-Ground Fault (L-L-G), and Three Line Fault (L-L-L). These Days, fault has been one of the significant problems in the transmission line system. Fault can lead to power losses in transmission lines as well as power failure. Electrical service in the transmission line system needs to be recovered immediately after fault appears to avoid more energy losses. Thus, it is crucial to create a system that will detect and eliminate fault faster, more accurately, and effectively. Typically, transmission line fault classification required complex signal processing, required expert knowledge, and complex mathematical modeling to process the output signal. This paper proposed a deep learning technique to classify ten types of fault through simulation. The objective of this study is to propose automated signal processing and features extraction. This technique can model a system that generates the automated signal processing and extract features learning with a deep learning framework and classify all the ten fault types in transmission lines accurately and effectively.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116526035","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

Early Flow Table Eviction Impact on Delay and Throughput in Software-Defined Networks 软件定义网络中早期流表移除对延迟和吞吐量的影响

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530933

Usman Humayun, Mosab Hamdan, M. N. Marsono

{"title":"Early Flow Table Eviction Impact on Delay and Throughput in Software-Defined Networks","authors":"Usman Humayun, Mosab Hamdan, M. N. Marsono","doi":"10.1109/ICCSCE52189.2021.9530933","DOIUrl":"https://doi.org/10.1109/ICCSCE52189.2021.9530933","url":null,"abstract":"In a software-defined network (SDN), the forwarding rules are installed at switch’s flow tables that are built using Ternary Content Addressable Memory (TCAM). TCAM has a limited storage capacity which causes flow tables to overflow which degrades the performance of SDN. Early eviction can reduce the flow table overflow problem to maximize their usage. This paper analyzes the impact of early eviction of flow entries from the flow tables before overflow happens. The analysis is based on three schemes, namely First-In First-Out (FIFO), Random, and Least Recently Used (LRU). These schemes are used for early eviction at certain threshold values. The values decide the flow table capacity at which the flow entries start to evict before it reaches overflow. We have used the flow tables of limited size and Distributed Internet Traffic Generator (D-ITG) to inject the traffic. Our results show the decrease in delay and increase in throughput in the case of early eviction of flows from flow tables as compared to the normal eviction. In all schemes, the LRU shows the best results to minimize the delay that occurs due to the extensive communication between switch and controller.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"309 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120840286","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 Evaluation of Newton Euler & Quaternion Mathematics-based Dynamic Models for an Underactuated Quadrotor UAV 欠驱动四旋翼无人机的牛顿欧拉四元数动力学模型性能评价

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530907

G. M. Abro, S. Zulkifli, V. Asirvadam

引用次数: 3

Enhancing Precision on Pneumatic Actuator Positioning using Cascaded Finite-time Prescribed Performance Control 利用级联有限时间规定性能控制提高气动执行机构定位精度

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530956

M. Azahar, A. Irawan

引用次数: 1

Flexible Biphasic Functional Electrical Stimulator for Children with Cerebral Palsy 脑性麻痹儿童柔性双相功能电刺激器

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530858

Z. Hussain, Harith Firdaus Mustapha, E. Noorsal, K. A. Ahmad, K. Sooksood

{"title":"Flexible Biphasic Functional Electrical Stimulator for Children with Cerebral Palsy","authors":"Z. Hussain, Harith Firdaus Mustapha, E. Noorsal, K. A. Ahmad, K. Sooksood","doi":"10.1109/ICCSCE52189.2021.9530858","DOIUrl":"https://doi.org/10.1109/ICCSCE52189.2021.9530858","url":null,"abstract":"The functional electrical stimulator, FES has been extensively used for rehabilitation, however, in children with cerebral palsy, the need for a suitable FES device is vital. The current available FES device on the market is mostly not a robust and multifunction device. The need of flexible FES where the parameter that can be adjusted is very important to develop In this paper, a biphasic functional electrical stimulator (FES) has been designed and implemented.. The parameter that need to be controlled in a rehabilitation activity for children with cerebral palsy such as voltage, current, frequency and pulse width. The main parts in the design of the simple biphasic functional electrical stimulator are controller, a digital-to-analog converter, and a constant current source. The constant current source consists of a biphasic amplifier, summing amplifier and Howland pump charge circuit. The result show that the Functional Electrical Stimulator developed capable to meet with the performance of the stimulated design in Proteus and capable to achieved the desired output needed for rehabilitation for children with cerebral palsy.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116615875","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

Contextual Anomaly Detection Based Video Surveillance System 基于上下文异常检测的视频监控系统

2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2021-08-27 DOI: 10.1109/ICCSCE52189.2021.9530859

S. A. Mahmood, Azal Monshed Abid, Wedad Abdul Khuder Naser

{"title":"Contextual Anomaly Detection Based Video Surveillance System","authors":"S. A. Mahmood, Azal Monshed Abid, Wedad Abdul Khuder Naser","doi":"10.1109/ICCSCE52189.2021.9530859","DOIUrl":"https://doi.org/10.1109/ICCSCE52189.2021.9530859","url":null,"abstract":"In this paper, a contextual anomaly event detection method is presented using a new clips boundaries detection approach and Bayesian classifier. Fall event is considered as anomaly event in our experiments and reported as case study. The anomaly score at frame levels is obtained. The proposed method involves three main phases; preprocessing for video content preparing, clips boundaries detection for anomaly behavior classification and fall event detection. The anomaly behavior - based fall event detection is classified into three main types; sudden change, gradual change and normal change within video sequence. To this end, a Bayesian classifier is trained to predict the anomaly score of video clips using similarity score prediction and acceleration raw data of sensors. We state quantitative results for clips boundaries detection, anomaly score prediction, and fall event detection rate. Further, the performance of the proposed anomaly event detection is evaluated based on results of common performance metrics (precision, sensitivity, specificity and accuracy) on public fall event datasets. The performance evaluation demonstrates a superiority of fall detection rate compared with recent researches in term of frame-level.","PeriodicalId":285507,"journal":{"name":"2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116054516","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