Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612536
D. Ibrahim, D. A. Zebari, F. Y. Ahmed, D. Zeebaree
There have been quite a few studies on facial expression recognition over the years, and it is still a challenging subject due to the significant inter-class variability. Facial expression research in this field focuses on the development of techniques to identify, code, and extract facial expressions to improve prediction by computer. With great success of machine learning, the various texture descriptors are exploited to obtain a better performance. This paper proposes a method based on the aggregation between different descriptors Histogram of oriented Gradient (HOG) and Local Binary Pattern (LBP). First stage the input image has pre-processed to detect dace area which helps to extract most significant features. Then, Diagonal-HOG (D-HOG) also has extracted and aggregated all features. Finally, Support Vector Machine (SVM) has been used a classifier to classify each feature as well as aggregated features. We evaluate our method using Japanese Female Facial Expressions database (JAFFE), experimental results showed that the proposed method is accurate and efficient in recognizing facial expressions.
{"title":"Facial Expression Recognition Using Aggregated Handcrafted Descriptors based Appearance Method","authors":"D. Ibrahim, D. A. Zebari, F. Y. Ahmed, D. Zeebaree","doi":"10.1109/ICSET53708.2021.9612536","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612536","url":null,"abstract":"There have been quite a few studies on facial expression recognition over the years, and it is still a challenging subject due to the significant inter-class variability. Facial expression research in this field focuses on the development of techniques to identify, code, and extract facial expressions to improve prediction by computer. With great success of machine learning, the various texture descriptors are exploited to obtain a better performance. This paper proposes a method based on the aggregation between different descriptors Histogram of oriented Gradient (HOG) and Local Binary Pattern (LBP). First stage the input image has pre-processed to detect dace area which helps to extract most significant features. Then, Diagonal-HOG (D-HOG) also has extracted and aggregated all features. Finally, Support Vector Machine (SVM) has been used a classifier to classify each feature as well as aggregated features. We evaluate our method using Japanese Female Facial Expressions database (JAFFE), experimental results showed that the proposed method is accurate and efficient in recognizing facial expressions.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128952623","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612549
Gibson Panyau Anak Jackson Rodi, R. Sam
The purpose of this paper is to develop an automated system in facemask production process using CX-One programming which include CX-Programmer and CX-Designer. Both software demonstrates a fully automated facemask production which will results in high productivity of facemask. The process of the system will include making, labelling and packaging of the facemask. It shows why automation is needed by differentiating between human and robot which will ensure the quality of the product. Furthermore, the study pneumatic gripper is important in terms of force needed to carry the facemask. The quality can be maintained when doing the packaging process using machine compare to human. Facemask production process able to be demonstrated by using the software together with suitable actuators, sensors and process involve in production of facemask. The process ensured the automation take place which increase the productivity of the manufacturer to release more facemask and reduce the workload of human by doing automation system.
{"title":"Automation of Facemask Production Process Using CX-Programmer and CX-Designer","authors":"Gibson Panyau Anak Jackson Rodi, R. Sam","doi":"10.1109/ICSET53708.2021.9612549","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612549","url":null,"abstract":"The purpose of this paper is to develop an automated system in facemask production process using CX-One programming which include CX-Programmer and CX-Designer. Both software demonstrates a fully automated facemask production which will results in high productivity of facemask. The process of the system will include making, labelling and packaging of the facemask. It shows why automation is needed by differentiating between human and robot which will ensure the quality of the product. Furthermore, the study pneumatic gripper is important in terms of force needed to carry the facemask. The quality can be maintained when doing the packaging process using machine compare to human. Facemask production process able to be demonstrated by using the software together with suitable actuators, sensors and process involve in production of facemask. The process ensured the automation take place which increase the productivity of the manufacturer to release more facemask and reduce the workload of human by doing automation system.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"29 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116997485","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612563
Hasyira Ahmad Wafa, Raihah Aminuddin, Shafaf Ibrahim, Nur Nabilah Abu Mangshor, Normilah Wahab
Big data technologies have become an important part in our life, especially during the pandemic. These technologies can be used to collect, analyse, process, and interpret the collected data in order to produce a useful information or knowledge. In fact, we are depending on the information extracted from the large amount of data collected daily from mobile applications. One of the examples of the application that has been used in Malaysia is MySejahtera which provides useful information on the spread of the pandemic. The data can be clustered using machine learning models such as clustering algorithm. Therefore, in this project, we propose a framework that will be useful to monitor the information about COVID-19 and visualizing the information with a machine learning model. The data visualization can help with data interpretation and improving how we can manage the spread of the virus. This project was also implemented using a modified waterfall which allows the developer to return to the previous phase in order to make some modifications before the final product can be used by users. This project used a Python approach to develop a dashboard. A Density-Based Spatial Clustering with Noise algorithm was chosen for the data classification of the countries based on its number of cases and number of deaths.
{"title":"A Data Visualization Framework during Pandemic using the Density-Based Spatial Clustering with Noise (DBSCAN) Machine Learning Model","authors":"Hasyira Ahmad Wafa, Raihah Aminuddin, Shafaf Ibrahim, Nur Nabilah Abu Mangshor, Normilah Wahab","doi":"10.1109/ICSET53708.2021.9612563","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612563","url":null,"abstract":"Big data technologies have become an important part in our life, especially during the pandemic. These technologies can be used to collect, analyse, process, and interpret the collected data in order to produce a useful information or knowledge. In fact, we are depending on the information extracted from the large amount of data collected daily from mobile applications. One of the examples of the application that has been used in Malaysia is MySejahtera which provides useful information on the spread of the pandemic. The data can be clustered using machine learning models such as clustering algorithm. Therefore, in this project, we propose a framework that will be useful to monitor the information about COVID-19 and visualizing the information with a machine learning model. The data visualization can help with data interpretation and improving how we can manage the spread of the virus. This project was also implemented using a modified waterfall which allows the developer to return to the previous phase in order to make some modifications before the final product can be used by users. This project used a Python approach to develop a dashboard. A Density-Based Spatial Clustering with Noise algorithm was chosen for the data classification of the countries based on its number of cases and number of deaths.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121400215","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612575
Raja Muhammad Hafiz Raja Khairul Annuar, S. Shahbudin, M. Kassim, Farah Yasmin Abdul Rahman
Epilepsy is a form of neurological brain disorder. It is identified by the frequent occurrence of symptoms called epileptic seizure due to abnormal activities. Using an electroencephalogram (EEG), a diagnosis of epilepsy can be done. For detection and classification purpose, there are many techniques applied in detecting epilepsy seizure such as machine learning, and nowadays deep learning algorithms are most famous to biomedical research. However, most of the deep learning methods are only analyze the epilepsy classification performance based on accuracy percentages. In term of elapsed time or learning rate analysis, it is become a rare study. Therefore, this paper proposes an epilepsy seizure detection and classification using several Residual Neural Network (ResNet) architectures and identify which ResNet architecture gives the best performance. For comparison purpose, the EEG performance analysis will be analyzed using other convolution neural network (CNN) architecture, namely GoogLeNet. Based on the results obtained, ResNet architecture give the best performance analysis for seizure detection and classification with superb performance of 100% accuracy and shortest elapsed time which only recorded 1 minute and 25 seconds
{"title":"Epilepsy Seizure Detection and Classification Analysis using Residual Neural Network","authors":"Raja Muhammad Hafiz Raja Khairul Annuar, S. Shahbudin, M. Kassim, Farah Yasmin Abdul Rahman","doi":"10.1109/ICSET53708.2021.9612575","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612575","url":null,"abstract":"Epilepsy is a form of neurological brain disorder. It is identified by the frequent occurrence of symptoms called epileptic seizure due to abnormal activities. Using an electroencephalogram (EEG), a diagnosis of epilepsy can be done. For detection and classification purpose, there are many techniques applied in detecting epilepsy seizure such as machine learning, and nowadays deep learning algorithms are most famous to biomedical research. However, most of the deep learning methods are only analyze the epilepsy classification performance based on accuracy percentages. In term of elapsed time or learning rate analysis, it is become a rare study. Therefore, this paper proposes an epilepsy seizure detection and classification using several Residual Neural Network (ResNet) architectures and identify which ResNet architecture gives the best performance. For comparison purpose, the EEG performance analysis will be analyzed using other convolution neural network (CNN) architecture, namely GoogLeNet. Based on the results obtained, ResNet architecture give the best performance analysis for seizure detection and classification with superb performance of 100% accuracy and shortest elapsed time which only recorded 1 minute and 25 seconds","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124467964","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612435
Adrian Dale M. Gomez, Yannah Nicole A. San Juan, Julius T. Sese
Physical distancing has become a part of the new normal wherein it has been difficult to implement as it needs the participation of everybody. This study used a Grid-EYE sensor to detect physical distancing violation in a controlled setup that simulates a rail transit station platform. This study also determined the effective angle and height of the Grid-EYE sensors for the best coverage area. The study also determined the accuracy of the device when it comes to physical distancing. The result of the study shows that the effective angle is 180° while the effective height is 2.1 m. The mean square value of the effective angle is 0.5849. As for the accuracy of the Grid-EYE sensors, this was determined by the outcome of the two-tailed t-test wherein the t-crit is 2.015 while the calculated t-test for both horizontal and vertical are 0.6706 and 1.2113. Thus, enough evidence shows that it can support the null hypothesis that claims that the actual distance is equal to the calculated distance. The processing time of the device is 1 second. Lastly, the Grid - EYE sensor was able to differentiate objects from humans as it did not detect thermal-emitting objects except for boiling water.
{"title":"Physical Distancing Violation Detector Using Arduino - Based Grid - EYE Sensors in Rail Transit Stations","authors":"Adrian Dale M. Gomez, Yannah Nicole A. San Juan, Julius T. Sese","doi":"10.1109/ICSET53708.2021.9612435","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612435","url":null,"abstract":"Physical distancing has become a part of the new normal wherein it has been difficult to implement as it needs the participation of everybody. This study used a Grid-EYE sensor to detect physical distancing violation in a controlled setup that simulates a rail transit station platform. This study also determined the effective angle and height of the Grid-EYE sensors for the best coverage area. The study also determined the accuracy of the device when it comes to physical distancing. The result of the study shows that the effective angle is 180° while the effective height is 2.1 m. The mean square value of the effective angle is 0.5849. As for the accuracy of the Grid-EYE sensors, this was determined by the outcome of the two-tailed t-test wherein the t-crit is 2.015 while the calculated t-test for both horizontal and vertical are 0.6706 and 1.2113. Thus, enough evidence shows that it can support the null hypothesis that claims that the actual distance is equal to the calculated distance. The processing time of the device is 1 second. Lastly, the Grid - EYE sensor was able to differentiate objects from humans as it did not detect thermal-emitting objects except for boiling water.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122733214","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612527
Raihah Aminuddin, Muhammad Akmal Bistamam, Shafaf Ibrahim, Nur Nabilah Abu Mangshor, S. Fesol, Normilah Wahab
Twitter is one of the famous social media platforms for people to share their stories and opinions on any situations, such as the COVID-19 pandemic. With the indirect influence of tweets on users and the rise in cases of COVID-19 in Malaysia, it is important to monitor information related to the pandemic in order to avoid misinformation, panic, or confusion among public. As the data from tweets are also one of the useful raw data sources that can be used for data visualization, this project aims to design and develop a web-based system for visualizing the status of pandemic in Malaysia based on the data collected from Twitter. There are four phases in the methodology of this project: (i) Planning, (ii) Analysis, (iii) Design and Development, and (iv) Testing and Documentation. In the planning and analysis phases, the data will be collected from March 2020 to March 2021 and will be filtered by using keywords and hashtags, such as #COVID19 and #Coronavirus, as well as the location tagged on the tweets. The collected data then will be pre-processed to remove any unwanted texts. The classification of the data is based on sentiment analysis using one of machine learning models that is Support Vector Machine (SVM). The performance of the classification model will be evaluated using the evaluation model: (i) accuracy, (ii) recall, (iii) precision, and (iv) F1-measure. The final output of this project is the data visualization of the sentiment analysis on COVID-19 in Malaysia based on two of its major cities: Kuala Lumpur and Klang.
{"title":"A Sentiment Analysis Framework on COVID-19 in Major Cities of Malaysia based on Tweets using Machine Learning Classification Model","authors":"Raihah Aminuddin, Muhammad Akmal Bistamam, Shafaf Ibrahim, Nur Nabilah Abu Mangshor, S. Fesol, Normilah Wahab","doi":"10.1109/ICSET53708.2021.9612527","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612527","url":null,"abstract":"Twitter is one of the famous social media platforms for people to share their stories and opinions on any situations, such as the COVID-19 pandemic. With the indirect influence of tweets on users and the rise in cases of COVID-19 in Malaysia, it is important to monitor information related to the pandemic in order to avoid misinformation, panic, or confusion among public. As the data from tweets are also one of the useful raw data sources that can be used for data visualization, this project aims to design and develop a web-based system for visualizing the status of pandemic in Malaysia based on the data collected from Twitter. There are four phases in the methodology of this project: (i) Planning, (ii) Analysis, (iii) Design and Development, and (iv) Testing and Documentation. In the planning and analysis phases, the data will be collected from March 2020 to March 2021 and will be filtered by using keywords and hashtags, such as #COVID19 and #Coronavirus, as well as the location tagged on the tweets. The collected data then will be pre-processed to remove any unwanted texts. The classification of the data is based on sentiment analysis using one of machine learning models that is Support Vector Machine (SVM). The performance of the classification model will be evaluated using the evaluation model: (i) accuracy, (ii) recall, (iii) precision, and (iv) F1-measure. The final output of this project is the data visualization of the sentiment analysis on COVID-19 in Malaysia based on two of its major cities: Kuala Lumpur and Klang.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123163756","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612561
Yip Kin Choy, Wellington Wui Lun Cheah
In today's implementation of Industry 4.0, time deterministic technology is fast becoming a critical deployment element in smart manufacturing factories with automation enabled over massively matrixed and inter-connected edge devices, providing real-time communication throughout the ecosystem. Within a closed-loop controlled environment, real-time technology enables pre-defined responses to be prioritized, primarily in demanding an immediate correction in action, be it a change in production modeling, a readjustment in manufacturing efficiency vector, and even to the extent of triggering a safety flag over potential security flaw. All these require quick, responsive, and deterministic data flow from one node to another. Virtual Channel (VC) capability is a technology introduced to warrant guaranteed data transmission in a cyber-physical communication system. This paper describes VC technology, its importance, the underlying enabling mechanism, and computational routines to achieve the best-in-class accuracy in data transmission latency.
{"title":"Virtual Channel Technology to enable Real-time Internet of Things Workload Consolidation","authors":"Yip Kin Choy, Wellington Wui Lun Cheah","doi":"10.1109/ICSET53708.2021.9612561","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612561","url":null,"abstract":"In today's implementation of Industry 4.0, time deterministic technology is fast becoming a critical deployment element in smart manufacturing factories with automation enabled over massively matrixed and inter-connected edge devices, providing real-time communication throughout the ecosystem. Within a closed-loop controlled environment, real-time technology enables pre-defined responses to be prioritized, primarily in demanding an immediate correction in action, be it a change in production modeling, a readjustment in manufacturing efficiency vector, and even to the extent of triggering a safety flag over potential security flaw. All these require quick, responsive, and deterministic data flow from one node to another. Virtual Channel (VC) capability is a technology introduced to warrant guaranteed data transmission in a cyber-physical communication system. This paper describes VC technology, its importance, the underlying enabling mechanism, and computational routines to achieve the best-in-class accuracy in data transmission latency.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121545375","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612428
Teo Hong Liang, M. Ramasamy, Mohamad Khan Afthan Ahmed Khan
In DC-DC converter family, Buck converter is one of the static devices that is commonly used to step down from high input voltage to low output voltage as well as protecting the connected circuit. Harmonics phenomena at the output current are one of the major concerns as the efficiency and performance of the Buck converter could be affected by interference of the current ripples. This paper focuses on the reduction of harmonics including the current ripple in the steady state and peak current of the buck converter. The proposed method to overcome the harmonics is to increase the switching frequency using five levels Buck converters. The relationship between frequency and current ripple are considered in this study. The results obtained shows that the efficiency of high frequency five level Buck converter using MOSFET and IGBT increases with increasing duty cycle values.
{"title":"Analysis of Load Current Ripples in a Five Level Buck Converter","authors":"Teo Hong Liang, M. Ramasamy, Mohamad Khan Afthan Ahmed Khan","doi":"10.1109/ICSET53708.2021.9612428","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612428","url":null,"abstract":"In DC-DC converter family, Buck converter is one of the static devices that is commonly used to step down from high input voltage to low output voltage as well as protecting the connected circuit. Harmonics phenomena at the output current are one of the major concerns as the efficiency and performance of the Buck converter could be affected by interference of the current ripples. This paper focuses on the reduction of harmonics including the current ripple in the steady state and peak current of the buck converter. The proposed method to overcome the harmonics is to increase the switching frequency using five levels Buck converters. The relationship between frequency and current ripple are considered in this study. The results obtained shows that the efficiency of high frequency five level Buck converter using MOSFET and IGBT increases with increasing duty cycle values.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123119323","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612546
Leila G. Ablao, Zmantha Ysabel B. Tupaz, Jennifer C. Dela Cruz, Jonathan Ibera
Sleep is one of the essential parts of living. Lack of sleep may result in concerns and may also indicate underlying health conditions. Hence, the study focuses on determining the sleep phase using data extracted from the Arduino AD8232 (ECG) and Myoware (EMG) sensor to evaluate heart rate variability and EMG Power, respectively. Feature extraction using Machine Learning assisted in interpreting the data acquired from both sensors and comparing results using a commercial-grade smartwatch. The study dealt with several tests to obtain samples from people ages 14–50 years old for at least 2–3 hours to complete a whole sleep cycle. The data extracted were trained using SVM-KNN in MATLAB and Python. The proposed system model resulted in an accuracy of 64.57% for classifying sleep phases and 94 % for sleep and wake.
{"title":"Machine Learning Sleep Phase Monitoring using ECG and EMG","authors":"Leila G. Ablao, Zmantha Ysabel B. Tupaz, Jennifer C. Dela Cruz, Jonathan Ibera","doi":"10.1109/ICSET53708.2021.9612546","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612546","url":null,"abstract":"Sleep is one of the essential parts of living. Lack of sleep may result in concerns and may also indicate underlying health conditions. Hence, the study focuses on determining the sleep phase using data extracted from the Arduino AD8232 (ECG) and Myoware (EMG) sensor to evaluate heart rate variability and EMG Power, respectively. Feature extraction using Machine Learning assisted in interpreting the data acquired from both sensors and comparing results using a commercial-grade smartwatch. The study dealt with several tests to obtain samples from people ages 14–50 years old for at least 2–3 hours to complete a whole sleep cycle. The data extracted were trained using SVM-KNN in MATLAB and Python. The proposed system model resulted in an accuracy of 64.57% for classifying sleep phases and 94 % for sleep and wake.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125863599","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}
Pub Date : 2021-11-06DOI: 10.1109/ICSET53708.2021.9612571
M. Zakir, M. A. Anuar, W. Mohamad
The vibrational effect is one of the primary considerations during construction due to the ambient excitation from the surrounding environment. Thus, it is necessary to find the most dependable Finite Element (FE) model for the actual structure to improve its quality and life span on site. In this context, the study included a 3-story building model that was analyzed using finite element analysis to determine its dynamic properties such as natural frequency and mode shape to avoid the resonance effect. It is due to the importance of obtaining a reliable and accurate model by verifying with the results of the Operational Modal Analysis (OMA). In general, the goal of this study is to update the FE model so that it is close to the actual scaled model based on the previous OMA study. As a result, an initial linear FE model of the 3-story building scaled model was created using CAD software based on actual geometry to describe the structure's physical properties. The FE model was then imported into CAE software, where the boundary condition and estimated material properties were assigned to determine the effect of random vibration. Through the pairing of Finite Element Analysis results and previous studies, 9 natural frequencies and 9 mode shapes were extracted. The Modal Assurance Criterion (MAC) was used to compare the mode shape of FE results against the OMA to determine the degree of consistency between paired mode shapes. A model updating process was carried out to reduce the discrepancy between the methods. The uncertainties arising from the initial conditions have been discussed in terms of the stiffness of the material used. The updated model allows for an evaluation of the structure's current actions as well as the development of models for a wide range of potential future research scenarios.
{"title":"Dynamic Characteristics Study on a 3-Storey Building Model through Finite Element Analysis","authors":"M. Zakir, M. A. Anuar, W. Mohamad","doi":"10.1109/ICSET53708.2021.9612571","DOIUrl":"https://doi.org/10.1109/ICSET53708.2021.9612571","url":null,"abstract":"The vibrational effect is one of the primary considerations during construction due to the ambient excitation from the surrounding environment. Thus, it is necessary to find the most dependable Finite Element (FE) model for the actual structure to improve its quality and life span on site. In this context, the study included a 3-story building model that was analyzed using finite element analysis to determine its dynamic properties such as natural frequency and mode shape to avoid the resonance effect. It is due to the importance of obtaining a reliable and accurate model by verifying with the results of the Operational Modal Analysis (OMA). In general, the goal of this study is to update the FE model so that it is close to the actual scaled model based on the previous OMA study. As a result, an initial linear FE model of the 3-story building scaled model was created using CAD software based on actual geometry to describe the structure's physical properties. The FE model was then imported into CAE software, where the boundary condition and estimated material properties were assigned to determine the effect of random vibration. Through the pairing of Finite Element Analysis results and previous studies, 9 natural frequencies and 9 mode shapes were extracted. The Modal Assurance Criterion (MAC) was used to compare the mode shape of FE results against the OMA to determine the degree of consistency between paired mode shapes. A model updating process was carried out to reduce the discrepancy between the methods. The uncertainties arising from the initial conditions have been discussed in terms of the stiffness of the material used. The updated model allows for an evaluation of the structure's current actions as well as the development of models for a wide range of potential future research scenarios.","PeriodicalId":433197,"journal":{"name":"2021 IEEE 11th International Conference on System Engineering and Technology (ICSET)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131972630","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}
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