2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)最新文献_第5页

Arduino Rice Pest Trap using Laser Sensor 使用激光传感器的Arduino水稻害虫诱捕器

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9731920

Carlo N. Romero, Antipas T. Teologo, Arnel L. Nuesca

引用次数: 2

Web-Based Document Management System for PEP Squad Events and Marketing Services 基于web的PEP小组活动和营销服务文件管理系统

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9732033

Janelle Kyra A. Sagum

引用次数: 0

Fuzzy Logic Controlled Motor Speed in Rotating Aquaponics Based on Lactuca sativa Leaf Chlorosis and Necrosis and Environment Temperature 基于油菜叶片萎黄坏死与环境温度的旋转水培模糊控制电机转速

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9731945

Heinrick L. Aquino, Ronnie S. Concepcion, A. Bandala, Christan Hail R. Mendigoria, Oliver John Y. Alajas, E. Dadios, J. Cuello

{"title":"Fuzzy Logic Controlled Motor Speed in Rotating Aquaponics Based on Lactuca sativa Leaf Chlorosis and Necrosis and Environment Temperature","authors":"Heinrick L. Aquino, Ronnie S. Concepcion, A. Bandala, Christan Hail R. Mendigoria, Oliver John Y. Alajas, E. Dadios, J. Cuello","doi":"10.1109/HNICEM54116.2021.9731945","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731945","url":null,"abstract":"Necrosis and chlorosis are some of the leaf conditions that contribute to losses in crop production, which is the browning and yellowing of leaves caused by improper irrigation and fertigation. A rotating aquaponics, crop cultivation conceptualized initially by NASA to save space and grow their food on space stations is the inspiration of application of this study. A fuzzy logic controlled (FLC) DC motor speed controller was developed using the Mamdani system to automate the rotating mechanism that is responsible for watering turns of lettuce crops. Using CIE L*, a*, color space, and environment temperature as input linguistic values, the motor speed will adjust depending on the status of the physical pigment condition of leaves as well as the intensity of temperature. The generated fuzzy logic controller has four triangular membership functions with 16 rules on each of the inputs. This resulted in four possible outputs of rotating aquaponics motor speed measured in rpm: very slow (0.25), slow (0.5), slightly fast (1.25), and fast (2). The modeled FLC was simulated in a Simulink environment in MATLABR2021 software and had a 10-step size and manifested essentially accurate results with 100% correct outputs based on input characteristics and rules developed. This developed FLC model is a substantial contribution to mitigating losses on lettuce crops grown under rotating aquaponics by automating the water absorption frequency depending on the status of the crop and the temperature of its environment.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134536621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

PUPRBLMS: Development And Acceptability of a Proposed Library Management System 图书馆管理系统的发展和可接受性

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9731869

Dominador M. Acasamoso, Ernie C. Avila, Norman T. Ficundo

{"title":"PUPRBLMS: Development And Acceptability of a Proposed Library Management System","authors":"Dominador M. Acasamoso, Ernie C. Avila, Norman T. Ficundo","doi":"10.1109/HNICEM54116.2021.9731869","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731869","url":null,"abstract":"Managing the library in a university or school is not an easy task especially if it has huge number of books that is open for utilization and borrowing to students. The librarian faces challenges in tasks like tracking of the books on-borrow due to uncontrolled demand more particularly during examination period or research seasons. In this study, the proponents sought to measure the acceptability of the proposed Library Management System capable of handling tasks for library management. The proposed Library Management System was primarily implemented at the Polytechnic University of the Philippines, Ragay, Camarines Sur Branch to address the challenges of the university in terms of library management and was evaluated using a survey questionnaire that was participated by 64 students, 2 library staff, and 10 faculty members for the period of January to March 2020. Evaluation shows that there is an average rate of 4.18 with a verbal interpretation of highly acceptable. Given the evaluation results, it is believed that the proposed library management system can be implemented for library management.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"27 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133792150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling New Cases of Covid-19 in the Philippines using Polynomial and MLP Regression 利用多项式和MLP回归对菲律宾新冠肺炎病例进行建模

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9731891

Isaiah Tupal, R. Gustilo, M. Cabatuan

{"title":"Modelling New Cases of Covid-19 in the Philippines using Polynomial and MLP Regression","authors":"Isaiah Tupal, R. Gustilo, M. Cabatuan","doi":"10.1109/HNICEM54116.2021.9731891","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731891","url":null,"abstract":"Covid-19 has been a serious issue in the Philippines for the past two years. Its spread has taken a toll on the country’s economy and society. Furthermore, the populous has been suffering throughout the pandemic as new cases and deaths are increasing. These massive problems warrant research on modelling and predicting this pandemic. Although there are numerous research with regards to using statistical modelling, Machine learning, deep learning, and artificial intelligence to model and understand the pandemic throughout the world, few pieces of researches focus on the Philippines alone. In addition to that, simple models are seen to fit the Covid-19 data more than complex ones. With these in mind, the authors fit and modelled Philippine new cases of Covid-19 using Sklearn Polynomial and MLP regressors. It was found out that Polynomial models fit the entire dataset from January 2020 to September 2021, but MLP model fits the recent September 2021 data better. Further research using different countries as case studies or different models is recommended.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":" 33","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114051703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Wind Power Potential using the Developed Windmill with Data Logger 基于数据记录仪的风力发电潜力分析

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9731980

Vicente Z. Delante, V. J. Ylaya, R. R. Vicerra, Robert R. Bacarro

{"title":"Analysis of Wind Power Potential using the Developed Windmill with Data Logger","authors":"Vicente Z. Delante, V. J. Ylaya, R. R. Vicerra, Robert R. Bacarro","doi":"10.1109/HNICEM54116.2021.9731980","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731980","url":null,"abstract":"Wind power generating is costly and necessitates a full feasibility assessment before deciding to build and invest. Wind Power Table is a guide that categorizes the feasibility of building wind power potential based on wind data provided by satellites, which are forecasted values. This study uses actual wind data values, which are then analyzed using the Weibull and Rayleigh Distributions, and the computed values are compared to the Wind Power Classification Table as the basis for the target area’s wind power potential. The recorder utilized to automate the process of gathering data for wind speed evaluation is a constructed windmill with a data logger powered by a Linux-based Raspberry Pi-4 and a sensor to measure wind speed, which will be saved on the system. It also offers a graphical user interface that is user-friendly and displays simple analysis such as graphs and tables on an LCD. The device records the wind speed for one week on the island of Masapelid, Barangay Pateñio, and then exports the data to a CSV file. The results show that the average computed Weibull is 312.81 W/m2, which is classified as excellent for rural and good for utility. Furthermore, the computed average Rayleigh value is 263.63, which is classified as good for utility and moderate for rural. Based on computed data, the wind power potential of the island of Masapelid Barangay Pateñio is categorized as good for rural use.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115669444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of Healthy and Unhealthy Abaca leaf using a Convolutional Neural Network (CNN) 使用卷积神经网络(CNN)对健康和不健康的Abaca叶进行分类

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9732050

Lyndon T. Buenconsejo, N. Linsangan

{"title":"Classification of Healthy and Unhealthy Abaca leaf using a Convolutional Neural Network (CNN)","authors":"Lyndon T. Buenconsejo, N. Linsangan","doi":"10.1109/HNICEM54116.2021.9732050","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9732050","url":null,"abstract":"Early suppression or identification of Abaca plant diseases is one of the difficulties for the farmers in Abaca fields, relaying only to the manual process of identifying Abaca plant diseased which were lack of time efficiency and feasibility solution that can cause widespread outbreaks of the diseased Abaca plants. But through the help of the system using the Raspberry Pi 4 and the Raspberry Pi HQ camera, the developed prototype can identify the healthy and unhealthy leaves through the deep learning algorithm of the CNN upon the architecture method ResNet50. The system trained over 200 images sample through the gathered data by the researcher with two classification sets of images consisting of 100 healthy leaves and 100 unhealthy leaves samples under the approval and labeled by the PhilFIDA Catanduanes. The researcher manually took the data sets on the Abaca leaves from the Abaca plantation area in Barangay San Miguel Baras Catanduanes. The thorough division of the Abaca leaf training model by the CNN – ResNet50 and the accuracy training and validation rate reached 100%. The precision rate of the two-output data classification reached 100%.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114104914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Strawberry Quality Classification Utilizing Convolutional Neural Network 基于卷积神经网络的草莓品质分类

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9731924

L. Acero, Jonathan Daniel S. Ong, Christalline Jhine L. Shi, E. Dadios, R. Billones

{"title":"Strawberry Quality Classification Utilizing Convolutional Neural Network","authors":"L. Acero, Jonathan Daniel S. Ong, Christalline Jhine L. Shi, E. Dadios, R. Billones","doi":"10.1109/HNICEM54116.2021.9731924","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731924","url":null,"abstract":"Strawberry quality has been a crucial factor when it comes to consumer satisfaction. Having quality and cost-efficient strawberries would increase consumer satisfaction while increasing sales from the merchants’ perspective. As such, being able to classify strawberries into the desirable and undesirable categories would aid small businesses and consumers in determining whether the strawberries sold and bought are desirable based on key indicators such as shape and color. To address that, this study was conducted with the use of convolutional neural networks. The strawberry datasets used are a combination of a pre-classified dataset from another study and a dataset of images photographed solely for the purpose of this study. The images are classified as desirable and undesirable wherein 350 images of each set are used for training, 200 images for validation, and 100 images for testing. The generated CNN model is simulated using epochs=15 and batch size=8. The resulting CNN model has a training accuracy=98.41%, a validation accuracy=92.75%, and a testing accuracy=100% which makes the model efficient in classifying strawberries into the desirable and undesirable categories.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114407990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Impact of COVID-19 on Air Quality in Hanoi and Ho Chi Minh City, Vietnam 2019冠状病毒病对越南河内和胡志明市空气质量的影响

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9731995

Botond Hámori, R. Naguib, Quynh T. Nguyen, Nghia Ton

引用次数: 0

Water Level Monitoring and Flood Warning System using Light Detection and Ranging (LiDAR) Sensor with Hybrid Renewable Solar-Wind Power 利用混合可再生太阳能-风能的光探测和测距(激光雷达)传感器的水位监测和洪水预警系统

2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) Pub Date : 2021-11-28 DOI: 10.1109/HNICEM54116.2021.9731998

Angelica F. De Leon, F. Cruz

{"title":"Water Level Monitoring and Flood Warning System using Light Detection and Ranging (LiDAR) Sensor with Hybrid Renewable Solar-Wind Power","authors":"Angelica F. De Leon, F. Cruz","doi":"10.1109/HNICEM54116.2021.9731998","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731998","url":null,"abstract":"This paper describes the design and implementation of a water level monitoring and flood warning system using light detection and ranging (LiDAR) as a water level sensor. The integration of hybrid solar-wind renewable power supply is also included in this paper. The developed system was installed at Bucayao Bridge in Calapan City, Oriental Mindoro. The system used a floater object, enclosed in a heavy-duty plastic mat, as a reflector of light pulses from the LiDAR sensor. During a 5-day observation period, in which red rainfall warning was raised at the test site in Calapan City, the water level monitoring system measured a maximum height of water at 4.52 m. Whereas, during good weather conditions, the system water level measurements were only around 0.28 m to 0.44 m. The algorithms were embedded in microcontrollers and made it possible for the system to provide early flood warnings via short message service (SMS) and siren. The maximum output power of the wind turbine was detected at around 6:00 PM with 370 W at a wind speed of 5.56 m/s. While the high output power of the solar panel was recorded from noontime until around 3:00 PM with 290 W to 320 W. The hybrid solar-wind power produced a maximum of 454.97 W when the weather was mostly clear and windy. The combined output power of solar panel and wind turbine met the daily power requirement of the water level monitoring and flood warning system.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"422 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123365014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1