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/HNICEM51456.2020.9400056
John Carlo D. Manzano, R. Montaril, A. Ballado, Martin A. Lopez, Jesus M. Martinez, Flordeliza L. Valiente
Power supplies are evolving towards the future and are now moving towards the integration of digital control systems in their circuits. This research is in line with this development and is aimed to show what a digital system can do to protect a power supply. The purpose of this research is to design an auxiliary circuit that will power all auxiliary components and implement a housekeeping for protection against voltage and current abnormalities of the power supply. The auxiliary circuit uses a quasi-resonant flyback converter topology to improve the efficiency of the device while the housekeeping system uses a DSPIC as its core processor to make use of the different signals coming from the power supply. The DSPIC will control the turn-on and turn-off sequence as well as the different fault protections to properly power up and power down the whole system. The design achieved has inputs ranging from 300 to 450 Vdc with a regulated output of 12-volts ±4% and has achieved a phase margin of 47.37 degrees and a gain margin of 12.12dB. The housekeeping system was also successful in detecting and reacting against different faults.
{"title":"Housekeeping and Auxiliary Quasi-Resonant Flyback Converter Design for a 350W Power Supply","authors":"John Carlo D. Manzano, R. Montaril, A. Ballado, Martin A. Lopez, Jesus M. Martinez, Flordeliza L. Valiente","doi":"10.1109/HNICEM51456.2020.9400056","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400056","url":null,"abstract":"Power supplies are evolving towards the future and are now moving towards the integration of digital control systems in their circuits. This research is in line with this development and is aimed to show what a digital system can do to protect a power supply. The purpose of this research is to design an auxiliary circuit that will power all auxiliary components and implement a housekeeping for protection against voltage and current abnormalities of the power supply. The auxiliary circuit uses a quasi-resonant flyback converter topology to improve the efficiency of the device while the housekeeping system uses a DSPIC as its core processor to make use of the different signals coming from the power supply. The DSPIC will control the turn-on and turn-off sequence as well as the different fault protections to properly power up and power down the whole system. The design achieved has inputs ranging from 300 to 450 Vdc with a regulated output of 12-volts ±4% and has achieved a phase margin of 47.37 degrees and a gain margin of 12.12dB. The housekeeping system was also successful in detecting and reacting against different faults.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"25 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":"131567081","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/hnicem51456.2020.9400149
Nemuel Norman F. Giron, R. Billones, Alexis M. Fillone, J. R. D. del Rosario, M. Cabatuan, A. Bandala, E. Dadios
Traffic violation apprehension is one of the traffic problems here in the Philippines. One example is the No Helmet No Ride Law that is implemented but many motorists still choose to ignore. To alleviate the problem the government has offered many solutions, one of which is the No Contact Traffic Apprehension Policy that uses CCTV Monitoring. To further enhance this solution the government has partnered with the De La Salle University to use artificial intelligence in the system. Computer Vision tasks like image classification and object detection can help automate the traffic apprehension system. Image classification and object detection are technologies which are used in computer vision in defining an image or coordinates of an object in an image. In this work, a novel approach to classifying motorcycle riders between wearing a helmet or not will be developed. It will be demonstrated using deep machine learning, specifically convolutional neural network and by utilizing different pre-trained models to a gathered dataset.
{"title":"Motorcycle Rider Helmet Detection for Riding Safety and Compliance Using Convolutional Neural Networks","authors":"Nemuel Norman F. Giron, R. Billones, Alexis M. Fillone, J. R. D. del Rosario, M. Cabatuan, A. Bandala, E. Dadios","doi":"10.1109/hnicem51456.2020.9400149","DOIUrl":"https://doi.org/10.1109/hnicem51456.2020.9400149","url":null,"abstract":"Traffic violation apprehension is one of the traffic problems here in the Philippines. One example is the No Helmet No Ride Law that is implemented but many motorists still choose to ignore. To alleviate the problem the government has offered many solutions, one of which is the No Contact Traffic Apprehension Policy that uses CCTV Monitoring. To further enhance this solution the government has partnered with the De La Salle University to use artificial intelligence in the system. Computer Vision tasks like image classification and object detection can help automate the traffic apprehension system. Image classification and object detection are technologies which are used in computer vision in defining an image or coordinates of an object in an image. In this work, a novel approach to classifying motorcycle riders between wearing a helmet or not will be developed. It will be demonstrated using deep machine learning, specifically convolutional neural network and by utilizing different pre-trained models to a gathered dataset.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"123 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":"132668528","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/HNICEM51456.2020.9400133
Elijah Alixtair L. Estolas, Agatha Faith V. Malimban, Jeremy T. Nicasio, Jyra S. Rivera, May Florence D. San Pablo, Toru Takahashi
The study is aimed to develop an Automatic Beatmap with Genre Detection, called “Efflorescence”, a mobile application which can generate a rhythm game for people who would like to improve their reflexive functions. This study also provides different music genres that will be detected during the generation process so that users are able to distinguish different types of music among the songs they have chosen and/or uploaded to play. The researchers also aim in determining known music genres and its alternatives, and to be able to generate non-fixed beat maps to give the users a little challenge than most rhythm games produced. For the researchers to create the application, the following algorithms were used: Music Information Retrieval, Onset Detection, Tempo Detection, and Machine Learning. To prove that the application is feasible, the researchers conducted a survey among 50 respondents, all composed of FEU Institute of Technology CS and IT. The respondents rated the application average of being able to produce the result they wanted towards the game. The system can be further improved by future researchers through updating the system by putting up more functions and data required for the genre detection. It is also recommended that future researchers would apply it on different other platforms that were not and to lessen the specifications of the hardware itself. Lastly, future researchers can add more interactive features to make the game more challenging yet fun at the same time.
{"title":"Automatic Beatmap Generating Rhythm Game Using Music Information Retrieval with Machine Learning for Genre Detection","authors":"Elijah Alixtair L. Estolas, Agatha Faith V. Malimban, Jeremy T. Nicasio, Jyra S. Rivera, May Florence D. San Pablo, Toru Takahashi","doi":"10.1109/HNICEM51456.2020.9400133","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400133","url":null,"abstract":"The study is aimed to develop an Automatic Beatmap with Genre Detection, called “Efflorescence”, a mobile application which can generate a rhythm game for people who would like to improve their reflexive functions. This study also provides different music genres that will be detected during the generation process so that users are able to distinguish different types of music among the songs they have chosen and/or uploaded to play. The researchers also aim in determining known music genres and its alternatives, and to be able to generate non-fixed beat maps to give the users a little challenge than most rhythm games produced. For the researchers to create the application, the following algorithms were used: Music Information Retrieval, Onset Detection, Tempo Detection, and Machine Learning. To prove that the application is feasible, the researchers conducted a survey among 50 respondents, all composed of FEU Institute of Technology CS and IT. The respondents rated the application average of being able to produce the result they wanted towards the game. The system can be further improved by future researchers through updating the system by putting up more functions and data required for the genre detection. It is also recommended that future researchers would apply it on different other platforms that were not and to lessen the specifications of the hardware itself. Lastly, future researchers can add more interactive features to make the game more challenging yet fun at the same time.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"4 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":"132703649","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/hnicem51456.2020.9400126
Marlon C. Leyesa, Noel T. Florencondia, Michael John M. Villar, Sheena Mai A. Galman
This study was conducted to primarily develop a decision support system in the form of a system software that could integrate environmental, health and safety management systems using an embedded mixed method of research. Such system was named as Decision Support System in Environmental, Health and Safety Management Systems (DSS-EHS). Using the Modified Waterfall Model, with IT experts and end-users as respondents, the study analyzed the present practice of engineers in a certain company in preparing and evaluating information be analyzed in terms of environmental, and health and safety of every project job order they acquire. It also described the solution to the problems of the existing system based on the provisions of International Organization for Standardization (ISO), specifically 14001:2015, and 45001:2018, and the integration of environmental, health, and safety management systems, and capability to print environmental, health, and safety reports. In addition, the design and development of the DSS-EHS was described using the help of the IT experts. Lastly the developed DSS-EHS was described by the end users in terms of functionality, reliability, usability, efficiency, portability, and maintainability. Based on the findings, the present environmental, health and safety management systems of the subject company needs a computer system that could aid it to facilitate easier storage, retrieval, and update of documents pertinent thereto. Also, an integrated EHS Management Systems can improve the accuracy and efficiency of the present system of the subject company The DSS-EHS can be designed with high sufficiency in terms of hardware, software and templates needed for every project job order. It can be developed based on the standards for coding and simulation and could aid to facilitate easier storage, retrieval, and update of documents pertinent in terms of functionality, reliability, usability, efficiency, portability and maintainability.
{"title":"Decision Support System in Environmental, Health and Safety (DSS-EHS) Management Systems","authors":"Marlon C. Leyesa, Noel T. Florencondia, Michael John M. Villar, Sheena Mai A. Galman","doi":"10.1109/hnicem51456.2020.9400126","DOIUrl":"https://doi.org/10.1109/hnicem51456.2020.9400126","url":null,"abstract":"This study was conducted to primarily develop a decision support system in the form of a system software that could integrate environmental, health and safety management systems using an embedded mixed method of research. Such system was named as Decision Support System in Environmental, Health and Safety Management Systems (DSS-EHS). Using the Modified Waterfall Model, with IT experts and end-users as respondents, the study analyzed the present practice of engineers in a certain company in preparing and evaluating information be analyzed in terms of environmental, and health and safety of every project job order they acquire. It also described the solution to the problems of the existing system based on the provisions of International Organization for Standardization (ISO), specifically 14001:2015, and 45001:2018, and the integration of environmental, health, and safety management systems, and capability to print environmental, health, and safety reports. In addition, the design and development of the DSS-EHS was described using the help of the IT experts. Lastly the developed DSS-EHS was described by the end users in terms of functionality, reliability, usability, efficiency, portability, and maintainability. Based on the findings, the present environmental, health and safety management systems of the subject company needs a computer system that could aid it to facilitate easier storage, retrieval, and update of documents pertinent thereto. Also, an integrated EHS Management Systems can improve the accuracy and efficiency of the present system of the subject company The DSS-EHS can be designed with high sufficiency in terms of hardware, software and templates needed for every project job order. It can be developed based on the standards for coding and simulation and could aid to facilitate easier storage, retrieval, and update of documents pertinent in terms of functionality, reliability, usability, efficiency, portability and maintainability.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"31 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":"133318365","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/HNICEM51456.2020.9400050
Ronnie S. Concepcion, Maria Gemel B. Palconit, E. Dadios, Joy N. Carpio, R. Bedruz, A. Bandala
Isolation of individual crop in a multiple cropping agricultural system exhibits a challenging issue of mispredictions for each crop specially when plant plots are too near with each other. Likewise, manual phenotyping of numerous crops is time and labor intensive. In this study, phenotype signatures of 24 Arabidopsis thaliana weeds with rosette leaves horticultured in pot-based configuration were nondestructively tracked and extracted from germination to head development stage (27 days) to quantify its growth. It is employed using two major feature engineering processes, namely generation of centroid-based Arabidopsis localization using Raspberry Pi-captured top-view image and growth signature analysis of localized Arabidopsis. To filter annotated images, mask size was modeled using cubic regression. ImageJ platform was configured to generate ground truth images and measurements. Arabidopsis localized raw spectro-morphological signatures namely RGB reflectances, canopy area, convex-hull area, canopy diameter, and perimeter were extracted using blob analysis. Stockiness, relative growth rate, and compactness relatively increases by 28.2×10−3, 0.46×10−3 and 220.1×10−3 per day. Stockiness was observed to be a strong indicator that a weed is growing on its basal vegetative stage. This developed model with sensitivity of 98% is a recommendable approach using computer vision for both field and indoor individual crop analysis such as in lettuce and mustard farms.
{"title":"Arabidopsis Tracker: A Centroid-Based Vegetation Localization Model for Automatic Leaf Canopy Phenotyping in Multiple-Pot Cultivation System","authors":"Ronnie S. Concepcion, Maria Gemel B. Palconit, E. Dadios, Joy N. Carpio, R. Bedruz, A. Bandala","doi":"10.1109/HNICEM51456.2020.9400050","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400050","url":null,"abstract":"Isolation of individual crop in a multiple cropping agricultural system exhibits a challenging issue of mispredictions for each crop specially when plant plots are too near with each other. Likewise, manual phenotyping of numerous crops is time and labor intensive. In this study, phenotype signatures of 24 Arabidopsis thaliana weeds with rosette leaves horticultured in pot-based configuration were nondestructively tracked and extracted from germination to head development stage (27 days) to quantify its growth. It is employed using two major feature engineering processes, namely generation of centroid-based Arabidopsis localization using Raspberry Pi-captured top-view image and growth signature analysis of localized Arabidopsis. To filter annotated images, mask size was modeled using cubic regression. ImageJ platform was configured to generate ground truth images and measurements. Arabidopsis localized raw spectro-morphological signatures namely RGB reflectances, canopy area, convex-hull area, canopy diameter, and perimeter were extracted using blob analysis. Stockiness, relative growth rate, and compactness relatively increases by 28.2×10−3, 0.46×10−3 and 220.1×10−3 per day. Stockiness was observed to be a strong indicator that a weed is growing on its basal vegetative stage. This developed model with sensitivity of 98% is a recommendable approach using computer vision for both field and indoor individual crop analysis such as in lettuce and mustard farms.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"80 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":"133503874","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/HNICEM51456.2020.9400102
Carlos C. Hortinela, Jessie R. Balbin, Janette C. Fausto, A.E.D. Catli, Karl J.R. Cui, Joy A.F. Tan, Earlvic O.S. Zuñega
Rice is a staple food in many countries. The price of rice depends on the qualities that are often quantified based on color, size, and presence of some regional color information. In the Philippines, the National Food Authority released the National Grain Standards for milled rice grains to facilitate the uniform classification of rice. The standards specify the grades: Premium and Grade 1–5 to grade milled rice grain samples based on the number of immature, red, fermented, chalky grains, and others, present in the sample. This study aimed to design and develop a standalone system capable of grading rice samples using grain validation, color and area analysis, and support vector machines with adaptive boosting. The image acquisition platform was created to provide a constant lighting setting and an enclosed staging platform capable of extracting an average of fifty grain images per sample. Seven support vector machine classifiers boosted with adaptive boosting, one chalky classifier, one grain size classifier, were created, trained, and tested. Feature vectors for the SVMs were histogram of gradients features and the color histogram properties: mean, skew, and dominant. The evaluation of the device resulted with an overall micro-average precision of 0.8667 and a micro-average recall of 0.8667 with an Fl-Score of 0.8667.
{"title":"Milled Rice Grain Grading using Raspberry Pi with Image Processing and Support Vector Machines with Adaptive Boosting","authors":"Carlos C. Hortinela, Jessie R. Balbin, Janette C. Fausto, A.E.D. Catli, Karl J.R. Cui, Joy A.F. Tan, Earlvic O.S. Zuñega","doi":"10.1109/HNICEM51456.2020.9400102","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400102","url":null,"abstract":"Rice is a staple food in many countries. The price of rice depends on the qualities that are often quantified based on color, size, and presence of some regional color information. In the Philippines, the National Food Authority released the National Grain Standards for milled rice grains to facilitate the uniform classification of rice. The standards specify the grades: Premium and Grade 1–5 to grade milled rice grain samples based on the number of immature, red, fermented, chalky grains, and others, present in the sample. This study aimed to design and develop a standalone system capable of grading rice samples using grain validation, color and area analysis, and support vector machines with adaptive boosting. The image acquisition platform was created to provide a constant lighting setting and an enclosed staging platform capable of extracting an average of fifty grain images per sample. Seven support vector machine classifiers boosted with adaptive boosting, one chalky classifier, one grain size classifier, were created, trained, and tested. Feature vectors for the SVMs were histogram of gradients features and the color histogram properties: mean, skew, and dominant. The evaluation of the device resulted with an overall micro-average precision of 0.8667 and a micro-average recall of 0.8667 with an Fl-Score of 0.8667.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"51 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":"132214775","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/HNICEM51456.2020.9400114
Kevin Rowel A. Batin, G. Magwili, Flordeliza L. Valiente
Nonlinearity test is composed of differential nonlinearity (DNL) and integral nonlinearity (INL). Nonlinearity error test for digital potentiometer can be very challenging, high resolution digital potentiometer requires larger sample size that will lead to longer test time and higher test cost. Thus, a test method for testing nonlinearity error of a digital potentiometer using automated test equipment (ATE) was developed to reduce the test time for the said parameters. A digital potentiometer of Analog Devices Inc, AD5144, was used on the evaluation. All measurements were within the datasheet specification, which shows that the measurements were accurate. With both DNL and INL measured accurately, a 7.476% test time reduction for nonlinearity error testing of digital potentiometer was achieved on the developed test method.
{"title":"Test Time Reduction for Nonlinearity Error Testing of Digital Potentiometer","authors":"Kevin Rowel A. Batin, G. Magwili, Flordeliza L. Valiente","doi":"10.1109/HNICEM51456.2020.9400114","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400114","url":null,"abstract":"Nonlinearity test is composed of differential nonlinearity (DNL) and integral nonlinearity (INL). Nonlinearity error test for digital potentiometer can be very challenging, high resolution digital potentiometer requires larger sample size that will lead to longer test time and higher test cost. Thus, a test method for testing nonlinearity error of a digital potentiometer using automated test equipment (ATE) was developed to reduce the test time for the said parameters. A digital potentiometer of Analog Devices Inc, AD5144, was used on the evaluation. All measurements were within the datasheet specification, which shows that the measurements were accurate. With both DNL and INL measured accurately, a 7.476% test time reduction for nonlinearity error testing of digital potentiometer was achieved on the developed test method.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"50 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":"132031761","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/HNICEM51456.2020.9400108
R. Billones, Joshua M. Lim, Ricardo Cardenas, Michael V. Manguerra, R. R. Vicerra, N. Bugtai, E. Dadios
This study presents a design of a prosthetic arm which is intended for transradial and wrist disarticulated amputees. The prosthetic wrist aims to perform ulnar and radial deviation. It incorporates a Myo Ware muscle sensor and integrates it with an Arduino Uno to actuate the servo motors which can drag the load of a mannequin hand. Surface electromyogram (EMG) signals that is gathered from the forearm are used to control the angle of servo motors.
{"title":"Prototyping a Prosthetic Arm for Ulnar and Radial Deviation","authors":"R. Billones, Joshua M. Lim, Ricardo Cardenas, Michael V. Manguerra, R. R. Vicerra, N. Bugtai, E. Dadios","doi":"10.1109/HNICEM51456.2020.9400108","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400108","url":null,"abstract":"This study presents a design of a prosthetic arm which is intended for transradial and wrist disarticulated amputees. The prosthetic wrist aims to perform ulnar and radial deviation. It incorporates a Myo Ware muscle sensor and integrates it with an Arduino Uno to actuate the servo motors which can drag the load of a mannequin hand. Surface electromyogram (EMG) signals that is gathered from the forearm are used to control the angle of servo motors.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"11 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":"117199090","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/hnicem51456.2020.9400140
Zoren P. Mabunga, J. D. dela Cruz, G. Magwili, Angelica Samortin
This study describes the development of five machine learning models for the detection of groundwater contamination due to leachate leakage in a sanitary landfill. A prototype was constructed using Arduino Uno, Wi-Fi module, pH, electrical conductivity and temperature sensors. This prototype was used to gather data from the groundwater and leachate samples in the sanitary landfill. The sensors that were used in the study was calibrated prior to the actual data gathering in the sanitary landfill. Five machine learning model based on logistic regression, quadratic discriminant analysis, k-nearest neighbour, decision tree and support vector machine algorithm was trained and evaluated. Matlab software was used in this study for the development of each model. The accuracy of each model was then compared which results to a 97.8% accuracy for KNN, 97.7% for SVM and Decision Tree, 93.7% for quadratic discriminant and 92.6% for logistic regression model. Based on the results, KNN, SVM and decision tree based models provide the highest accuracy for the detection of leachate leakage on the groundwater located in a sanitary landfill.
{"title":"Development of Sanitary Landfill's Groundwater Contamination Detection Model Based on Machine Learning Algorithms","authors":"Zoren P. Mabunga, J. D. dela Cruz, G. Magwili, Angelica Samortin","doi":"10.1109/hnicem51456.2020.9400140","DOIUrl":"https://doi.org/10.1109/hnicem51456.2020.9400140","url":null,"abstract":"This study describes the development of five machine learning models for the detection of groundwater contamination due to leachate leakage in a sanitary landfill. A prototype was constructed using Arduino Uno, Wi-Fi module, pH, electrical conductivity and temperature sensors. This prototype was used to gather data from the groundwater and leachate samples in the sanitary landfill. The sensors that were used in the study was calibrated prior to the actual data gathering in the sanitary landfill. Five machine learning model based on logistic regression, quadratic discriminant analysis, k-nearest neighbour, decision tree and support vector machine algorithm was trained and evaluated. Matlab software was used in this study for the development of each model. The accuracy of each model was then compared which results to a 97.8% accuracy for KNN, 97.7% for SVM and Decision Tree, 93.7% for quadratic discriminant and 92.6% for logistic regression model. Based on the results, KNN, SVM and decision tree based models provide the highest accuracy for the detection of leachate leakage on the groundwater located in a sanitary landfill.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"2 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":"115196903","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/HNICEM51456.2020.9400001
V. J. Ylaya, O. J. Gerasta, Jesrey Martin S. Macasero, Daryl P. Pongcol, Najie M. Pandian, R. R. Vicerra
The study is to develop a Linear Frequency Modulated Continuous Wave LFM-CW short-range radar for detecting subsurface water content with deep learning. Implementation of signal transmission/reception, signal processing, and graphical user interface in LabView. Fabrication of antenna from the design program and water table are enclosed with a Styrofoam box and buried 1m, 3m, and 5m respectively for the experiments. The experiments also involve metal and plastic buried 1m, 3m, and 5m, respectively, for data comparison. The researcher dug a 9×3×5m hole and divided it into three sections with buried objects of different deepness. The first section has a size of 3×3×5m with 2×1 metal plate, 2×1 plastic plate, and 2×1×0.5 water table box. The object is separated by 1m in a triangular manner at 5m depth from the ground. The second section has 3×3×5m with buried object 2×1 metal plate, 2×1 plastic plate, and 2×1×0.5 water table box separated by 1m in a triangular manner with 3m depth from the ground. The last section has 3×3×5m with buried object 2×1 metal plate, 2×1 plastic plate, and 2×1×0.5 water table box separated by 1m in a triangular manner with 1m depth from the ground. The results show a trend with regards to the A-scan measurement window characterizes the different dielectric properties of the water table, metal, and plastic and able to detect objects greater than 1m using the optimized systems. The deep learning method able to prove the interpreted result from the observed A-scan. The study recommends a higher bandwidth and transmitting power hardware to increased range resolution, which will be able to detect shallower objects. Consideration of ultrawideband antenna with higher directivity and gain can also improve the system subsurface detection.
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2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)
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