Pub Date : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495851
Michael Brian L. Indab, Jamie Anne T. Ng, Livan Austin Fernando P. Reyeg, Ralph Audric G. Tuppil, Ricky D. Umali, M. Manuel, Jennifer C. Dela Cruz, Roderick C. Tud
The paper contains the study on generating electrical energy using twelve thermoelectric generators placed inside an aluminum window frame. The harvesting circuit composed of twelve thermoelectric generators, a DC-DC boost converter, and a power bank. The hot side absorbs heat through rays of the sun between the period of 1:00 pm to 3:00 pm. The cold side is being cooled by the air of the fully air-conditioned room. The researchers measured the actual voltage to calculate the actual power, actual heat input, actual power, actual thermoelectric efficiency, and the time to fully charge a 7800mah power bank. The results show that the 2:00 pm mark records the highest temperature and highest voltage output.
{"title":"Design, Fabrication, and Testing of Thermoelectric Generators Integrated on a Residential Window Frame","authors":"Michael Brian L. Indab, Jamie Anne T. Ng, Livan Austin Fernando P. Reyeg, Ralph Audric G. Tuppil, Ricky D. Umali, M. Manuel, Jennifer C. Dela Cruz, Roderick C. Tud","doi":"10.1109/I2CACIS52118.2021.9495851","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495851","url":null,"abstract":"The paper contains the study on generating electrical energy using twelve thermoelectric generators placed inside an aluminum window frame. The harvesting circuit composed of twelve thermoelectric generators, a DC-DC boost converter, and a power bank. The hot side absorbs heat through rays of the sun between the period of 1:00 pm to 3:00 pm. The cold side is being cooled by the air of the fully air-conditioned room. The researchers measured the actual voltage to calculate the actual power, actual heat input, actual power, actual thermoelectric efficiency, and the time to fully charge a 7800mah power bank. The results show that the 2:00 pm mark records the highest temperature and highest voltage output.","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":"115678298","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495918
A. Mohammed, Ayad Al-dujaili, N. Al-Shamaa
Speed control of PMDC motors finds applications in various industries. For certain structures, the controller of Proportional, Integral and Derivative (PID) is typically the first choice because of its ease of execution and fast tuning. So, all conventional techniques and optimization stochastic for PID controller tuning provide preliminary feasible parameters for, , and . This paper uses traditional PID and nonlinear PID to control the speed of the PMDC motor, which is tuned by Particle Swarm Optimization (PSO). The methodology is demonstrated by performing simulations using the MATLAB tool. The simulation results exhibit that the role of the nonlinear PID based scheme is more robust than the traditional PID controller, as well as the speed, tracked the desired reference rabidly.
{"title":"Real-time Design and Implementation of Nonlinear Speed Controller for Permanent Magnet DC Motor Based on PSO Tuner","authors":"A. Mohammed, Ayad Al-dujaili, N. Al-Shamaa","doi":"10.1109/I2CACIS52118.2021.9495918","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495918","url":null,"abstract":"Speed control of PMDC motors finds applications in various industries. For certain structures, the controller of Proportional, Integral and Derivative (PID) is typically the first choice because of its ease of execution and fast tuning. So, all conventional techniques and optimization stochastic for PID controller tuning provide preliminary feasible parameters for, , and . This paper uses traditional PID and nonlinear PID to control the speed of the PMDC motor, which is tuned by Particle Swarm Optimization (PSO). The methodology is demonstrated by performing simulations using the MATLAB tool. The simulation results exhibit that the role of the nonlinear PID based scheme is more robust than the traditional PID controller, as well as the speed, tracked the desired reference rabidly.","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":"126306775","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495899
Muhammad Akmal Hakim bin Che Mansor, Nor Ashikin Mohamad Kamal, Mohamad Hafiz bin Baharom, Muhammad Adib bin Zainol
This paper discusses the Convolutional Neural Network (CNN) applied in emergency vehicle image classification. Emergency vehicles are often found stuck in traffic congestion. It has resulted in the emergency vehicles unable to get to the scene quickly. Detecting emergency vehicles on the road can help provide a route to enable emergency vehicles to arrive more efficiently. Several methods have been used to detect the presence of these emergency vehicles on the road. Convolutional Neural Network is one of the popular classification methods nowadays. This work used VGG-16 as the pre-trained model with reduced convolutional layer and filter size. Based on the experiment, the proposed method gained an accuracy of 95%. Thus, the system has achieved the objective.
{"title":"Emergency Vehicle Type Classification using Convolutional Neural Network","authors":"Muhammad Akmal Hakim bin Che Mansor, Nor Ashikin Mohamad Kamal, Mohamad Hafiz bin Baharom, Muhammad Adib bin Zainol","doi":"10.1109/I2CACIS52118.2021.9495899","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495899","url":null,"abstract":"This paper discusses the Convolutional Neural Network (CNN) applied in emergency vehicle image classification. Emergency vehicles are often found stuck in traffic congestion. It has resulted in the emergency vehicles unable to get to the scene quickly. Detecting emergency vehicles on the road can help provide a route to enable emergency vehicles to arrive more efficiently. Several methods have been used to detect the presence of these emergency vehicles on the road. Convolutional Neural Network is one of the popular classification methods nowadays. This work used VGG-16 as the pre-trained model with reduced convolutional layer and filter size. Based on the experiment, the proposed method gained an accuracy of 95%. Thus, the system has achieved the objective.","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":"131054921","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495880
Jessie R. Balbin, Jp Ysrael P. Damilig, Marcos G. Pelayo
This paper covers information about a system that detects the proper golf posture by showing the results in a graphical user interface after the testing which is a joint comparison method using a Kinect V2 as the main sensor. The researchers created a system that uses skeletal imaging to compare two frames in which one frame is the input and the other one is the video showing the depth skeletal image. After comparing the results, the computed correlation coefficient is 0.88 which means the statistical accuracy of the evaluation is approximately 88 percent.
{"title":"Golf Driving Posture Analysis Using Joint Comparison via Kinect V2 Sensor","authors":"Jessie R. Balbin, Jp Ysrael P. Damilig, Marcos G. Pelayo","doi":"10.1109/I2CACIS52118.2021.9495880","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495880","url":null,"abstract":"This paper covers information about a system that detects the proper golf posture by showing the results in a graphical user interface after the testing which is a joint comparison method using a Kinect V2 as the main sensor. The researchers created a system that uses skeletal imaging to compare two frames in which one frame is the input and the other one is the video showing the depth skeletal image. After comparing the results, the computed correlation coefficient is 0.88 which means the statistical accuracy of the evaluation is approximately 88 percent.","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":"122304454","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495919
Muhammad Yunus bin Yakob, M. Z. Baharuddin, A. R. M. Khairudin, M. H. A. Karim
Bionic prosthetic hands are widely utilised to support people who have upper limb amputations and incapability by making it movable and controllable. However, the price of bionic prosthetic arms in the current market is quite expensive, hence limiting people from owning it. Nevertheless, Myo Armband appears to provide fine hand movement and sensitive to input, making it a suitable alternative to gesture-based wireless control. Hence, in this project, it will be used to read and control signals for the 3d printed robotic prosthetic arm. The control of prosthetic robot arm is achieved by sensing the gestures of amputees’ upper limb. Bluetooth is used for the wireless communication between Myo armband and the computer, while communication between computer and the prosthetic robot arm used serial communication. It is observed that fist gestures from Myo armband controlling the prosthetic robot hand have been successfully achieved. The proposed configuration can be used in human-robot interaction and telecontrol studies.
{"title":"Telecontrol of Prosthetic Robot Hand Using Myo Armband","authors":"Muhammad Yunus bin Yakob, M. Z. Baharuddin, A. R. M. Khairudin, M. H. A. Karim","doi":"10.1109/I2CACIS52118.2021.9495919","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495919","url":null,"abstract":"Bionic prosthetic hands are widely utilised to support people who have upper limb amputations and incapability by making it movable and controllable. However, the price of bionic prosthetic arms in the current market is quite expensive, hence limiting people from owning it. Nevertheless, Myo Armband appears to provide fine hand movement and sensitive to input, making it a suitable alternative to gesture-based wireless control. Hence, in this project, it will be used to read and control signals for the 3d printed robotic prosthetic arm. The control of prosthetic robot arm is achieved by sensing the gestures of amputees’ upper limb. Bluetooth is used for the wireless communication between Myo armband and the computer, while communication between computer and the prosthetic robot arm used serial communication. It is observed that fist gestures from Myo armband controlling the prosthetic robot hand have been successfully achieved. The proposed configuration can be used in human-robot interaction and telecontrol studies.","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":"129104343","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495907
Jessie R. Balbin, J. D. De Guzman, Joaquin Gerard N. Trinidad, Francis Dominic S. Yaya
One of the most overlooked parts of the body is the feet. Foot health can generally affect the overall health of a person if not treated well. This study utilized Support Vector Machines and an artificial neural network to determine the foot deformity of a person by using a foot plantar pressure sensor matrix called Velostat. The researchers used raspberry pi to run the GUI programmed using Python. The developed device will determine if the feet are normal, high arched, or low arched. The testing was done on 40 respondents and resulted in 95% accuracy in determining foot deformity.
{"title":"Foot Deformity Determination and Health Risk Prediction Through Foot Plantar Analysis Using Pressure Sensor Matrix","authors":"Jessie R. Balbin, J. D. De Guzman, Joaquin Gerard N. Trinidad, Francis Dominic S. Yaya","doi":"10.1109/I2CACIS52118.2021.9495907","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495907","url":null,"abstract":"One of the most overlooked parts of the body is the feet. Foot health can generally affect the overall health of a person if not treated well. This study utilized Support Vector Machines and an artificial neural network to determine the foot deformity of a person by using a foot plantar pressure sensor matrix called Velostat. The researchers used raspberry pi to run the GUI programmed using Python. The developed device will determine if the feet are normal, high arched, or low arched. The testing was done on 40 respondents and resulted in 95% accuracy in determining foot deformity.","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":"125704824","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495915
Guhdar Youcif Izadeen, A. Abdulazeez, D. Zeebaree, D. A. Hasan, F. Y. Ahmed
Blood is one of the most vital and essential elements in human existence. When the population increases, so do the request for blood. People who need blood in an emergency are unable to provide it promptly. This paper suggests an effective method of contacting donors, that can be useful in an emergency. When a person requires blood, they request it through a website or mobile device; the request is then routed to the person who meets the matching blood type. the application is then sent an SMS to the donor to approve it, after accepting the request, the application will inform the requestor about it and he/she will get the donor phone number by using an MCU ESP8266 and a SIM800L. The privacy of the person must be protected in the current environment. The donor will be deleted from the reception of notification for the next three months after the blood donation is done. User name, password, and phone number are used to check registered accounts.
{"title":"Data Integration Using Data Mining and SMS Reminder for Automation of Blood Donation","authors":"Guhdar Youcif Izadeen, A. Abdulazeez, D. Zeebaree, D. A. Hasan, F. Y. Ahmed","doi":"10.1109/I2CACIS52118.2021.9495915","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495915","url":null,"abstract":"Blood is one of the most vital and essential elements in human existence. When the population increases, so do the request for blood. People who need blood in an emergency are unable to provide it promptly. This paper suggests an effective method of contacting donors, that can be useful in an emergency. When a person requires blood, they request it through a website or mobile device; the request is then routed to the person who meets the matching blood type. the application is then sent an SMS to the donor to approve it, after accepting the request, the application will inform the requestor about it and he/she will get the donor phone number by using an MCU ESP8266 and a SIM800L. The privacy of the person must be protected in the current environment. The donor will be deleted from the reception of notification for the next three months after the blood donation is done. User name, password, and phone number are used to check registered accounts.","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":"128998083","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495866
M. A. Subari, K. Hudha, Z. A. Kadir, S. M. F. Syed Mohd Dardin, N. H. Amer
In propelling the tracked vehicle, each track is fitted with an electric motor which will regulate torque to the tracks. In this study, the development of electric motor model and motor speed tracking control will be described. To do this, a non-parametric model was chosen by generalizing the experiment data from system input-output properties. However, characterization of tracked vehicle electric motor need to be done before creating the non-parametric model. The final model developed using non-parametric model are then used to study the speed tracking control of tracked vehicle electric motor through simulation and experiment. The tracked vehicle electric motor was tested in two different inputs, which are step input and sine input. The resulted show that the maximum percentage of overshoot recorded for both cases are less than 15%.
{"title":"Development of Non-Parametric Model and Speed Tracking Control of Tracked Vehicle Electric Motor","authors":"M. A. Subari, K. Hudha, Z. A. Kadir, S. M. F. Syed Mohd Dardin, N. H. Amer","doi":"10.1109/I2CACIS52118.2021.9495866","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495866","url":null,"abstract":"In propelling the tracked vehicle, each track is fitted with an electric motor which will regulate torque to the tracks. In this study, the development of electric motor model and motor speed tracking control will be described. To do this, a non-parametric model was chosen by generalizing the experiment data from system input-output properties. However, characterization of tracked vehicle electric motor need to be done before creating the non-parametric model. The final model developed using non-parametric model are then used to study the speed tracking control of tracked vehicle electric motor through simulation and experiment. The tracked vehicle electric motor was tested in two different inputs, which are step input and sine input. The resulted show that the maximum percentage of overshoot recorded for both cases are less than 15%.","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":"130411975","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495913
Jessie R. Balbin, Aldwin Ian T. Yap, Benedict D. Calicdan, Lester Allan M. Bernabe
Heart rhythm problems, more commonly known as heart arrhythmias, is the phenomenon in which heartbeats don’t work properly due to electrical impulses. This causes the heart to irregularly, sometimes too slow, or too fast, depending on the condition. Fluttering and racing heart are the most common arrhythmia symptoms, which is most of the time harmless. Sometimes heart arrhythmias can even be life-threatening and may manifest several alarming signs and symptoms. This paper is about the acquisition and analysis of heart activity. Using the AD8232 module, the heart’s electrical activity is captured using the principles of Electrocardiography (ECG). For the acoustic activity of the heart, a stethoscope and an electret microphone are used to convert the acoustic energy to electrical energy. The signal from each practice is passed through an ADC to translate the signal to a digital signal to allow further operation. Upon acquiring the data, it is then analyzed whether the subject has arrhythmia, murmur, or is normal using a Deep Learning algorithm. The said algorithm is provided using a Convolutional Neural Network (ConvNet/CNN). Remote communities where medical assistance is scarce will benefit from this research as it can be operated with no medical experience. The study was able to successfully acquire ECGs and PCGs and analyze the heart condition of the data source. The researchers successfully integrated preprocessing techniques to better analyze the gathered data from human subjects. Lastly, the tuned CNN model correctly classified human subjects based on 4 classes which are normal, abnormal, others, and noisy. The classification accuracy shows that 80% of the 20 subjects were correctly classified based on their current medical condition. The performance sensitivity of the study is 100%, while performance specificity is 77.78%. The detection of error rate is at 20%. All values for conformance testing were deemed acceptable considering the testing restrictions due to the ongoing COVID-19 pandemic.
{"title":"Arrhythmia Detection using Electrocardiogram and Phonocardiogram Pattern using Integrated Signal Processing Algorithms with the Aid of Convolutional Neural Networks","authors":"Jessie R. Balbin, Aldwin Ian T. Yap, Benedict D. Calicdan, Lester Allan M. Bernabe","doi":"10.1109/I2CACIS52118.2021.9495913","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495913","url":null,"abstract":"Heart rhythm problems, more commonly known as heart arrhythmias, is the phenomenon in which heartbeats don’t work properly due to electrical impulses. This causes the heart to irregularly, sometimes too slow, or too fast, depending on the condition. Fluttering and racing heart are the most common arrhythmia symptoms, which is most of the time harmless. Sometimes heart arrhythmias can even be life-threatening and may manifest several alarming signs and symptoms. This paper is about the acquisition and analysis of heart activity. Using the AD8232 module, the heart’s electrical activity is captured using the principles of Electrocardiography (ECG). For the acoustic activity of the heart, a stethoscope and an electret microphone are used to convert the acoustic energy to electrical energy. The signal from each practice is passed through an ADC to translate the signal to a digital signal to allow further operation. Upon acquiring the data, it is then analyzed whether the subject has arrhythmia, murmur, or is normal using a Deep Learning algorithm. The said algorithm is provided using a Convolutional Neural Network (ConvNet/CNN). Remote communities where medical assistance is scarce will benefit from this research as it can be operated with no medical experience. The study was able to successfully acquire ECGs and PCGs and analyze the heart condition of the data source. The researchers successfully integrated preprocessing techniques to better analyze the gathered data from human subjects. Lastly, the tuned CNN model correctly classified human subjects based on 4 classes which are normal, abnormal, others, and noisy. The classification accuracy shows that 80% of the 20 subjects were correctly classified based on their current medical condition. The performance sensitivity of the study is 100%, while performance specificity is 77.78%. The detection of error rate is at 20%. All values for conformance testing were deemed acceptable considering the testing restrictions due to the ongoing COVID-19 pandemic.","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":"114259521","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 : 2021-06-26DOI: 10.1109/I2CACIS52118.2021.9495870
Marl D. Barroquillo, Patrick Stephen L. Duque, Eugene S. Bellosillo, Jazer Mesha V. Espanola, Sherwin S. Magon, M. Manuel, Jennifer C. Dela Cruz, Marvin S. Verdadero
Mapúa University's woodworking shop, metalworking shop, and the Universal Testing Machine (UTM) laboratory are facilities that help engineering students, particularly the School of Mechanical and Manufacturing Engineering (SMME), in learning the parts, operations, and safety precautions of different machines and tools. With the use of a 360° camera and a virtual tour building platform, an interactive virtual walkthrough of the facilities was developed as an informational guide for students enrolled in ME136P, ME123L, and ME137L. The researchers conducted an assessment to see the effectiveness of the virtual walkthrough and a survey to get insights from students who are finished with the courses. The analyzed data from the assessment showed that the virtual walkthrough is an effective informational guide since students who used it had a significantly higher scores compared to those that did not. Most of the students who took the survey thought that the virtual walkthrough really assisted in providing necessary information about the machines and tools used in the shop and laboratory courses.
{"title":"Developing an Interactive 360 Walkthrough of MME Shop and Laboratory for ME136P, ME123L, and ME137L","authors":"Marl D. Barroquillo, Patrick Stephen L. Duque, Eugene S. Bellosillo, Jazer Mesha V. Espanola, Sherwin S. Magon, M. Manuel, Jennifer C. Dela Cruz, Marvin S. Verdadero","doi":"10.1109/I2CACIS52118.2021.9495870","DOIUrl":"https://doi.org/10.1109/I2CACIS52118.2021.9495870","url":null,"abstract":"Mapúa University's woodworking shop, metalworking shop, and the Universal Testing Machine (UTM) laboratory are facilities that help engineering students, particularly the School of Mechanical and Manufacturing Engineering (SMME), in learning the parts, operations, and safety precautions of different machines and tools. With the use of a 360° camera and a virtual tour building platform, an interactive virtual walkthrough of the facilities was developed as an informational guide for students enrolled in ME136P, ME123L, and ME137L. The researchers conducted an assessment to see the effectiveness of the virtual walkthrough and a survey to get insights from students who are finished with the courses. The analyzed data from the assessment showed that the virtual walkthrough is an effective informational guide since students who used it had a significantly higher scores compared to those that did not. Most of the students who took the survey thought that the virtual walkthrough really assisted in providing necessary information about the machines and tools used in the shop and laboratory courses.","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":"127848871","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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