2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)最新文献
Pub Date : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400121
R. Billones, M. G. Belen, Brent Joseph P. Lee, N. Salud, Roxanne C. Sergio, Gayle R. Tamayo, R. R. Vicerra, E. Dadios
This paper focuses on the study of relaxed and hypoventilation body conditions using pulse oximetry and temperature measurements. An Arduino-based portable pulse oximeter and temperature measurement device is developed to monitor these biomedical signals. Pulse oximetry is a non-invasive method for accurately estimating oxygen saturation (Sa02) level by reading the peripheral oxygen saturation (Sp02). A thermistor is used to measure body temperature. The Arduino microcontroller is used for signal extraction and processing. The measurements were acquired using two conditions: relaxed state and hypoventilation state. The relaxed state serves as the control group while the hypoventilation state is used to simulate the condition of hypoxemia which is a state of abnormally low level of oxygen.
{"title":"Determination of Relaxed and Hypoventilation Body Conditions using Pulse Oximetry and Temperature Measurements","authors":"R. Billones, M. G. Belen, Brent Joseph P. Lee, N. Salud, Roxanne C. Sergio, Gayle R. Tamayo, R. R. Vicerra, E. Dadios","doi":"10.1109/HNICEM51456.2020.9400121","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400121","url":null,"abstract":"This paper focuses on the study of relaxed and hypoventilation body conditions using pulse oximetry and temperature measurements. An Arduino-based portable pulse oximeter and temperature measurement device is developed to monitor these biomedical signals. Pulse oximetry is a non-invasive method for accurately estimating oxygen saturation (Sa02) level by reading the peripheral oxygen saturation (Sp02). A thermistor is used to measure body temperature. The Arduino microcontroller is used for signal extraction and processing. The measurements were acquired using two conditions: relaxed state and hypoventilation state. The relaxed state serves as the control group while the hypoventilation state is used to simulate the condition of hypoxemia which is a state of abnormally low level of oxygen.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115004034","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400007
A. Africa, Darlene Alyssa P. Abaluna, A. J. Abello, Joaquin Miguel B. Lalusin
In modern control theory, there are several variations to different controller designs. The same can be said for Neural Network (NN) Controllers. The goal of this paper is to implement a variant of NN controllers called the Predictive Neural Network controller for a nonlinear plant using MATLAB. The controller will not only be used to determine the performance of the plant but also model future inputs of the system by using the data it has collected. The data will undergo training to create a predictive model of the system. The predicted inputs can then be used to optimize the performance of the system. The NN controller was implemented on a nonlinear plant model and simulated using Simulink which is available in MATLAB using the Deep Learning Toolbox. Another motivation for this paper is to gain a better understanding of the applications of predictive neural networks in control systems.
{"title":"Implementation of Neural Network Control in a Nonlinear Plant Using MATLAB","authors":"A. Africa, Darlene Alyssa P. Abaluna, A. J. Abello, Joaquin Miguel B. Lalusin","doi":"10.1109/HNICEM51456.2020.9400007","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400007","url":null,"abstract":"In modern control theory, there are several variations to different controller designs. The same can be said for Neural Network (NN) Controllers. The goal of this paper is to implement a variant of NN controllers called the Predictive Neural Network controller for a nonlinear plant using MATLAB. The controller will not only be used to determine the performance of the plant but also model future inputs of the system by using the data it has collected. The data will undergo training to create a predictive model of the system. The predicted inputs can then be used to optimize the performance of the system. The NN controller was implemented on a nonlinear plant model and simulated using Simulink which is available in MATLAB using the Deep Learning Toolbox. Another motivation for this paper is to gain a better understanding of the applications of predictive neural networks in control systems.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130661551","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400011
J. G. S. San Juan, P. M. Ching, A. Mayol, A. Culaba, A. Ubando
Algal biofuels can be a potential alternative as a source of fuel while it alleviates greenhouse gas emissions that causes climate change. However, the feasibility of these is still a challenge. Hence, a biorefinery concept introduced, where the system can produce the main product such as biofuel and can cater various co-products. However, limited studies look at the environmental impact of the system. This study uses life cycle assessment (LCA) to assess the proposed algal biorefinery under the circular economy concept. The results of the LCA reveal that the transesterification and cultivation processes were the environmental hotspots of the system, while dewatering and biochar production contributed the least. Additionally, sensitivity analysis on the process inputs of the system revealed that the heat usage of transesterification most significantly influenced the global warming potential of system, indicating that improvements to the system should focus on reducing the heat requirement of transesterification to improve the global warming potential of the system the most. Lastly, the results of the scenario analysis show that incorporating biochar production, combined heat and power (CHP), and anaerobic digestion (AD) to the conventional microalgae-to-biofuel process chain will not be environmentally beneficial. Instead, system managers should only focus on integrating biochar production and either CHP to AD to the conventional system to achieve the lowest environmental impact.
{"title":"Envinronmental Life Cycle Analysis of Algal Biorefineries for Biofuel Production Under the Circular Economy Concept","authors":"J. G. S. San Juan, P. M. Ching, A. Mayol, A. Culaba, A. Ubando","doi":"10.1109/HNICEM51456.2020.9400011","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400011","url":null,"abstract":"Algal biofuels can be a potential alternative as a source of fuel while it alleviates greenhouse gas emissions that causes climate change. However, the feasibility of these is still a challenge. Hence, a biorefinery concept introduced, where the system can produce the main product such as biofuel and can cater various co-products. However, limited studies look at the environmental impact of the system. This study uses life cycle assessment (LCA) to assess the proposed algal biorefinery under the circular economy concept. The results of the LCA reveal that the transesterification and cultivation processes were the environmental hotspots of the system, while dewatering and biochar production contributed the least. Additionally, sensitivity analysis on the process inputs of the system revealed that the heat usage of transesterification most significantly influenced the global warming potential of system, indicating that improvements to the system should focus on reducing the heat requirement of transesterification to improve the global warming potential of the system the most. Lastly, the results of the scenario analysis show that incorporating biochar production, combined heat and power (CHP), and anaerobic digestion (AD) to the conventional microalgae-to-biofuel process chain will not be environmentally beneficial. Instead, system managers should only focus on integrating biochar production and either CHP to AD to the conventional system to achieve the lowest environmental impact.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"121 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134128458","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400023
A. Yumang, G. Magwili, Sev Kyle C. Montoya, Corleone Jorel G. Zaldarriaga
In the Philippines corn is one of the top agricultural products produced in the country, specifically yellow corn. It is distributed in various cities and provinces to consumers. It is important that the corn kernels to undergo quality assurance before releasing them to the consumers. The methods for evaluating and qualifying corn kernels that are employed by most farms in the country are only done by manual human inspection and these methods are inconsistent which results to inaccurate findings. This is more prevalent when dealing with large amounts of kernels that need to be qualified. This study offers to reduce those inconsistencies by implementing a neural network-assisted method of inspection. The damages to corn kernels can be determined by its physical attributes and as such, the neural network will easily detect the type of damage within a given sample. Aside from the healthy kernels, the types of damage that was included in this study are the following: drier damage, heat damage, heat damage (drier phase), OCOL (Other Color) Type A and OCOL Type B. The neural network that will be used will be a Convolutional Neural Network wherein the images of the samples are subjected to layers of processing. This study also uses Colored Image Edge Detection. The detection method used in this study has obtained an accuracy rating of 96.66%.
{"title":"Determination of Shelled Corn Damages using Colored Image Edge Detection with Convolutional Neural Network","authors":"A. Yumang, G. Magwili, Sev Kyle C. Montoya, Corleone Jorel G. Zaldarriaga","doi":"10.1109/HNICEM51456.2020.9400023","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400023","url":null,"abstract":"In the Philippines corn is one of the top agricultural products produced in the country, specifically yellow corn. It is distributed in various cities and provinces to consumers. It is important that the corn kernels to undergo quality assurance before releasing them to the consumers. The methods for evaluating and qualifying corn kernels that are employed by most farms in the country are only done by manual human inspection and these methods are inconsistent which results to inaccurate findings. This is more prevalent when dealing with large amounts of kernels that need to be qualified. This study offers to reduce those inconsistencies by implementing a neural network-assisted method of inspection. The damages to corn kernels can be determined by its physical attributes and as such, the neural network will easily detect the type of damage within a given sample. Aside from the healthy kernels, the types of damage that was included in this study are the following: drier damage, heat damage, heat damage (drier phase), OCOL (Other Color) Type A and OCOL Type B. The neural network that will be used will be a Convolutional Neural Network wherein the images of the samples are subjected to layers of processing. This study also uses Colored Image Edge Detection. The detection method used in this study has obtained an accuracy rating of 96.66%.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132574611","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400048
Meygen D. Cruz, J. Keh, Ramiel G. Deticio, Carl Vincent T. Tan, John Anthony C. Jose, E. Dadios
This paper focuses on the feasibility of implementing a vision-based people counting system using footage from an existing surveillance camera in a restaurant establishment. The main challenge is to do so given the unique fixed viewpoint of the camera, which is optimized for security instead of data analytics. A three-step approach, namely people detection, tracking, and then people counting, is employed in creating the system. Neural networks such as YOLOv3 and Deep SORT are used. The proponents then partnered with a retail establishment in a high-traffic business district, to test the system. The results show that it is possible to achieve an accuracy of 82.76% for days when the restaurant waiting area is not crowded. The system also achieved an overall accuracy of 66.17% over five days of extensive testing, which includes extreme conditions wherein people in the video are densely packed and occluded. However, the system performance and accuracy can still be improved through downsizing the frames, retraining the models, and exploring other models.
{"title":"A People Counting System for Use in CCTV Cameras in Retail","authors":"Meygen D. Cruz, J. Keh, Ramiel G. Deticio, Carl Vincent T. Tan, John Anthony C. Jose, E. Dadios","doi":"10.1109/HNICEM51456.2020.9400048","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400048","url":null,"abstract":"This paper focuses on the feasibility of implementing a vision-based people counting system using footage from an existing surveillance camera in a restaurant establishment. The main challenge is to do so given the unique fixed viewpoint of the camera, which is optimized for security instead of data analytics. A three-step approach, namely people detection, tracking, and then people counting, is employed in creating the system. Neural networks such as YOLOv3 and Deep SORT are used. The proponents then partnered with a retail establishment in a high-traffic business district, to test the system. The results show that it is possible to achieve an accuracy of 82.76% for days when the restaurant waiting area is not crowded. The system also achieved an overall accuracy of 66.17% over five days of extensive testing, which includes extreme conditions wherein people in the video are densely packed and occluded. However, the system performance and accuracy can still be improved through downsizing the frames, retraining the models, and exploring other models.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128328026","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400067
Meygen D. Cruz, J. Keh, Maverick Rivera, N. Velasco, John Anthony C. Jose, E. Sybingco, E. Dadios, Wira Madria, Angelimarie Miguel
Large high-quality annotated datasets are essential in training deep learning models, but are expensive and time-consuming to create. A large chunk of time in the annotation process goes into adjusting bounding boxes to fit the desired object. In this paper, we propose the facilitation of human machine collaboration through the creation of an Auto-Fit feature which automatically tightens an initial bounding box around an object being annotated. The challenge lies in making this feature class agnostic in order to allow its usage regardless of the type of object being annotated. This is achieved through the use of various computer vision algorithms to extract the desired object as a foreground mask, determine the coordinates of its extremities, and redraw the bounding box based on these new coordinates. The best results were achieved with the Grabcut algorithm, which attained an accuracy of 84.69% on small boxes. The Pytorch implementation of ResNet-101 pre-trained on the COCO train2017 dataset is also used as a foreground extractor in one iteration of the implementation, in order to provide a baseline comparison between the performance of a computer vision-based solution versus one based on a standalone object detection model. This garnered an accuracy of 83.04% on small boxes, showing that the computer vision-based solution is able to surpass the accuracy of a standalone object detection model.
{"title":"Auto-Fit: A Human-Machine Collaboration Feature for Fitting Bounding Box Annotations","authors":"Meygen D. Cruz, J. Keh, Maverick Rivera, N. Velasco, John Anthony C. Jose, E. Sybingco, E. Dadios, Wira Madria, Angelimarie Miguel","doi":"10.1109/HNICEM51456.2020.9400067","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400067","url":null,"abstract":"Large high-quality annotated datasets are essential in training deep learning models, but are expensive and time-consuming to create. A large chunk of time in the annotation process goes into adjusting bounding boxes to fit the desired object. In this paper, we propose the facilitation of human machine collaboration through the creation of an Auto-Fit feature which automatically tightens an initial bounding box around an object being annotated. The challenge lies in making this feature class agnostic in order to allow its usage regardless of the type of object being annotated. This is achieved through the use of various computer vision algorithms to extract the desired object as a foreground mask, determine the coordinates of its extremities, and redraw the bounding box based on these new coordinates. The best results were achieved with the Grabcut algorithm, which attained an accuracy of 84.69% on small boxes. The Pytorch implementation of ResNet-101 pre-trained on the COCO train2017 dataset is also used as a foreground extractor in one iteration of the implementation, in order to provide a baseline comparison between the performance of a computer vision-based solution versus one based on a standalone object detection model. This garnered an accuracy of 83.04% on small boxes, showing that the computer vision-based solution is able to surpass the accuracy of a standalone object detection model.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128510268","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400035
Maria Clarice R. Madrid, Ernesto G. Malaki, P. Ong, M. V. Solomo, Rizelle Anne L. Suntay, Heintjie N. Vicente
Digitalization of different industries led to new systems that provide accurate information that results in efficient and effective services. This information is vital for decision-making and on the larger scale, policymaking especially in the health sector. In the Philippines, some healthcare establishments have not adjusted to this digital change. This study aims to develop an enhanced model of healthcare management system that can perform digitization of data, predictive health analytics and sales trend analysis. The researchers identified these three features as the focus of the system because it improves data quality, accessibility, reliability, and autonomy. The system is based on prescriptive analytics - a type of analytics that uses machine learning to process historical and predictive data. The artificially intelligent management system caters to the needs of the healthcare sector in this digital age to improve its services to the people.
{"title":"Healthcare Management System with Sales Analytics using Autoregressive Integrated Moving Average and Google Vision","authors":"Maria Clarice R. Madrid, Ernesto G. Malaki, P. Ong, M. V. Solomo, Rizelle Anne L. Suntay, Heintjie N. Vicente","doi":"10.1109/HNICEM51456.2020.9400035","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400035","url":null,"abstract":"Digitalization of different industries led to new systems that provide accurate information that results in efficient and effective services. This information is vital for decision-making and on the larger scale, policymaking especially in the health sector. In the Philippines, some healthcare establishments have not adjusted to this digital change. This study aims to develop an enhanced model of healthcare management system that can perform digitization of data, predictive health analytics and sales trend analysis. The researchers identified these three features as the focus of the system because it improves data quality, accessibility, reliability, and autonomy. The system is based on prescriptive analytics - a type of analytics that uses machine learning to process historical and predictive data. The artificially intelligent management system caters to the needs of the healthcare sector in this digital age to improve its services to the people.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"5 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116764417","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9399999
Alvin Brian D. Aballe, K. Cruz, Vincent John H. Dela Rosa, G. Magwili, C. Ostia
In this paper, the group wanted to maximize the amount of wind the prototype can gather using a linear generator with spring mechanism for vortex bladeless wind turbine using the wind energy conversion (WEC) system. A small – scale prototype was developed to harvest wind energy through the use of two linear generators that are designed to accept any angular movement from the horizontal plane. It utilizes the phenomenon called Vortex Shedding effect wherein once the wind passes through a bluff body, it produces shedding effect that yields vibration. The linear generator uses the mechanism of a spring wherein excess motion due to wind energy is captured by the spring and stored as elastic potential energy which will later be converted as oscillating kinetic energy when the speed drops. The prototype was subjected to a controlled and uncontrolled environment measuring the voltage and current according to the obtained wind speed. The increased rate of the linear generator with spring mechanism for controlled and uncontrolled environment are 35.54% and 20.05% respectively. From the wind profiling, the average wind speed obtained is 7.694 kph. It was evident that the linear generator with spring mechanism yielded a higher voltage than the one without spring with resulting values of 2.347 V and 1.873 V respectively.
在本文中，该小组希望使用一个带弹簧机构的线性发电机，将原型机可以收集的风量最大化，该发电机用于使用风能转换(WEC)系统的旋涡无叶片风力涡轮机。一个小规模的原型被开发出来，通过使用两个线性发电机来收集风能，这两个发电机被设计成可以接受来自水平面的任何角度运动。它利用了一种叫做涡流脱落效应的现象，即一旦风穿过钝体，它就会产生脱落效应，从而产生振动。线性发电机使用弹簧的机制，其中由于风能产生的多余运动被弹簧捕获并存储为弹性势能，当速度下降时，弹性势能将被转换为振荡动能。将原型置于受控和非受控环境中，根据获得的风速测量电压和电流。在受控和非受控环境下，弹簧机构直线发电机的增益率分别为35.54%和20.05%。从风廓线得到的平均风速为7.694 km / h。有弹簧机构的线性发电机产生的电压明显高于无弹簧机构，分别为2.347 V和1.873 V。
{"title":"Development of a Linear Generator With Spring Mechanism for Vortex Bladeless Wind Turbine","authors":"Alvin Brian D. Aballe, K. Cruz, Vincent John H. Dela Rosa, G. Magwili, C. Ostia","doi":"10.1109/HNICEM51456.2020.9399999","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9399999","url":null,"abstract":"In this paper, the group wanted to maximize the amount of wind the prototype can gather using a linear generator with spring mechanism for vortex bladeless wind turbine using the wind energy conversion (WEC) system. A small – scale prototype was developed to harvest wind energy through the use of two linear generators that are designed to accept any angular movement from the horizontal plane. It utilizes the phenomenon called Vortex Shedding effect wherein once the wind passes through a bluff body, it produces shedding effect that yields vibration. The linear generator uses the mechanism of a spring wherein excess motion due to wind energy is captured by the spring and stored as elastic potential energy which will later be converted as oscillating kinetic energy when the speed drops. The prototype was subjected to a controlled and uncontrolled environment measuring the voltage and current according to the obtained wind speed. The increased rate of the linear generator with spring mechanism for controlled and uncontrolled environment are 35.54% and 20.05% respectively. From the wind profiling, the average wind speed obtained is 7.694 kph. It was evident that the linear generator with spring mechanism yielded a higher voltage than the one without spring with resulting values of 2.347 V and 1.873 V respectively.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122066734","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400158
Jason Cris A. Pelayo, Stephen Jireh V. Tan, Paulyn Sophia D. Yu, G. Magwili, F. Cruz
This paper presents a simulated Battery Management System or BMS design with fuzzy temperature control, active cell balancing, and state of charge estimation using the coulomb counting method to increase system runtime and safely optimize battery usage of a lithium-ion battery pack as these types of batteries are highly used for modern electric vehicles which are increasing in current market demand. The simulation software used is MATLAB Simulink and the system is taking charging inputs from a 220VAC in the charging process while in the discharging process, the solar panel will act as the input for charging. The simulation results show that the features mentioned earlier positively impact the system in doing its intended outcome and benefits by comparing the systems with and with the aforementioned features. Furthermore, Mean Absolute Percentage Error or MAPE was used to determine the reliability of the SoC estimation as compared to the True SoC.
{"title":"Simulated Solar Assisted Battery Management System with Fuzzy Temperature Control, Flyback Converter Active Cell Balancing Circuit and Coulomb Counting SoC Estimation Method using MATLAB Simulink","authors":"Jason Cris A. Pelayo, Stephen Jireh V. Tan, Paulyn Sophia D. Yu, G. Magwili, F. Cruz","doi":"10.1109/HNICEM51456.2020.9400158","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400158","url":null,"abstract":"This paper presents a simulated Battery Management System or BMS design with fuzzy temperature control, active cell balancing, and state of charge estimation using the coulomb counting method to increase system runtime and safely optimize battery usage of a lithium-ion battery pack as these types of batteries are highly used for modern electric vehicles which are increasing in current market demand. The simulation software used is MATLAB Simulink and the system is taking charging inputs from a 220VAC in the charging process while in the discharging process, the solar panel will act as the input for charging. The simulation results show that the features mentioned earlier positively impact the system in doing its intended outcome and benefits by comparing the systems with and with the aforementioned features. Furthermore, Mean Absolute Percentage Error or MAPE was used to determine the reliability of the SoC estimation as compared to the True SoC.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125979399","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 : 2020-12-03DOI: 10.1109/HNICEM51456.2020.9400080
J.C.D.C. Gallano, V.J.D. Malvas, J.L.F. Quirona, R.C.S. Soriano, M. Pacis, F. Cruz
The phase angle or phase difference is an important parameter in Electrical Engineering because it affects the flow of power in a system. The purpose of this study is to develop a phasor measurement unit and integrate it with IoT Technology. The researchers simulated an electronic circuit using the Proteus software simulation tool, implemented a prototype, and integrated the prototype with IoT. The design works by acquiring the phase difference between the two sinusoidal waveforms. For the experimentation, typical household appliances were used to test if the prototype is working. The measured value of the prototype is verified by simulations and observation of the phase shift shown in the oscilloscope. The graph for Voltage and Current was shown using Excel. Study Cases were also conducted to check if the prototype is producing good results. Simulations and computations were also compared to check the accuracy and correctness of the prototype. Based on the statistical treatment, at the 10% significance level, $0.097> 0.1$, reject $H_{0}$. Therefore, the value of the simulated phase angle is almost the same value of the computed phase angle.
{"title":"Design and Implementation of Phasor Measurement Unit with IoT Technology","authors":"J.C.D.C. Gallano, V.J.D. Malvas, J.L.F. Quirona, R.C.S. Soriano, M. Pacis, F. Cruz","doi":"10.1109/HNICEM51456.2020.9400080","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400080","url":null,"abstract":"The phase angle or phase difference is an important parameter in Electrical Engineering because it affects the flow of power in a system. The purpose of this study is to develop a phasor measurement unit and integrate it with IoT Technology. The researchers simulated an electronic circuit using the Proteus software simulation tool, implemented a prototype, and integrated the prototype with IoT. The design works by acquiring the phase difference between the two sinusoidal waveforms. For the experimentation, typical household appliances were used to test if the prototype is working. The measured value of the prototype is verified by simulations and observation of the phase shift shown in the oscilloscope. The graph for Voltage and Current was shown using Excel. Study Cases were also conducted to check if the prototype is producing good results. Simulations and computations were also compared to check the accuracy and correctness of the prototype. Based on the statistical treatment, at the 10% significance level, $0.097> 0.1$, reject $H_{0}$. Therefore, the value of the simulated phase angle is almost the same value of the computed phase angle.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130276564","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}
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)
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