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/HNICEM51456.2020.9399998
Daniela Bonifacio, Amir Mari II E. Pascual, M. V. Caya, Janette C. Fausto
Maize disease has been one of the common problems for farmers in the Philippines as reported by the Bureau of Plant Industry. The usual process done by farmers is they would need to submit a photo of the possible disease they want to check and wait for the Bureau of Plant Industry to validate what kind of disease it is. This usually takes time and the disease would worsen before the validation would be done. The proposed study by the researcher is to determine the status of the Maize if it is healthy or is infected by a common maize disease which are Gray Leaf Spot, Leaf Rust, and Northern Leaf Blight. The study used an image processing technique which is the Gray-Level Segmentation and Edge-Detection Technique for image pre-processing which is processed by TensorFlow and Keras under a python module to train and create the model using Convolutional Neural Network. Using the open-source dataset provided by PlantVillage, a neural network model for the common maize disease stated by the Bureau of Plant Industry has been created. The study used a Raspberry Pi 3B to classify the status of the Maize in question due to the portability of the device. Using the combined image processing technique, the overall accuracy for the detection rate of the system prosed has achieved 92.50% and having the precision rate with 92.50%.
根据植物工业局的报告，玉米病害一直是菲律宾农民面临的常见问题之一。农民通常的做法是，他们需要提交一张他们想要检查的可能病害的照片，然后等待植物工业局确认是哪种病害。这通常需要时间，而且在验证之前病害会恶化。研究人员提议的研究是确定玉米的健康状况，还是受到常见玉米病害（灰叶斑病、叶锈病和北方叶枯病）的感染。该研究使用了一种图像处理技术，即灰度分割和边缘检测技术来进行图像预处理，并通过 Python 模块下的 TensorFlow 和 Keras 进行处理，使用卷积神经网络来训练和创建模型。利用 PlantVillage 提供的开源数据集，创建了一个神经网络模型，用于处理植物产业局提出的常见玉米病害。由于设备的便携性，这项研究使用 Raspberry Pi 3B 对有关玉米的状况进行分类。利用综合图像处理技术，该系统的总体检测准确率达到 92.50%，精确率为 92.50%。
{"title":"Determination of Common Maize (Zea mays) Disease Detection using Gray-Level Segmentation and Edge-Detection Technique","authors":"Daniela Bonifacio, Amir Mari II E. Pascual, M. V. Caya, Janette C. Fausto","doi":"10.1109/HNICEM51456.2020.9399998","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9399998","url":null,"abstract":"Maize disease has been one of the common problems for farmers in the Philippines as reported by the Bureau of Plant Industry. The usual process done by farmers is they would need to submit a photo of the possible disease they want to check and wait for the Bureau of Plant Industry to validate what kind of disease it is. This usually takes time and the disease would worsen before the validation would be done. The proposed study by the researcher is to determine the status of the Maize if it is healthy or is infected by a common maize disease which are Gray Leaf Spot, Leaf Rust, and Northern Leaf Blight. The study used an image processing technique which is the Gray-Level Segmentation and Edge-Detection Technique for image pre-processing which is processed by TensorFlow and Keras under a python module to train and create the model using Convolutional Neural Network. Using the open-source dataset provided by PlantVillage, a neural network model for the common maize disease stated by the Bureau of Plant Industry has been created. The study used a Raspberry Pi 3B to classify the status of the Maize in question due to the portability of the device. Using the combined image processing technique, the overall accuracy for the detection rate of the system prosed has achieved 92.50% and having the precision rate with 92.50%.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"24 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":"128598946","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/hnicem51456.2020.9400079
R. Billones, Edwin J. Calilung, E. Dadios, N. Santiago
This paper presents a technique for macroscopic classification of Aspergillus fungi species. The Aspergillus genus have several species that can be used in agricultural and medical applications. An automated process of macroscopic identification and classification of such species is described here. The scope of the study includes a 9-type Aspergillus fungi species. The learning mechanism used is a simple convolutional neural network. Using a total of 4545 macroscopic images, the model achieved a 90.06% accuracy in training, and 96.43% accuracy in validation.
{"title":"Image-Based Macroscopic Classification of Aspergillus Fungi Species Using Convolutional Neural Networks","authors":"R. Billones, Edwin J. Calilung, E. Dadios, N. Santiago","doi":"10.1109/hnicem51456.2020.9400079","DOIUrl":"https://doi.org/10.1109/hnicem51456.2020.9400079","url":null,"abstract":"This paper presents a technique for macroscopic classification of Aspergillus fungi species. The Aspergillus genus have several species that can be used in agricultural and medical applications. An automated process of macroscopic identification and classification of such species is described here. The scope of the study includes a 9-type Aspergillus fungi species. The learning mechanism used is a simple convolutional neural network. Using a total of 4545 macroscopic images, the model achieved a 90.06% accuracy in training, and 96.43% accuracy in validation.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"136 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":"127413481","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/HNICEM51456.2020.9400013
M. V. Caya, Jeffrey P. Ng
In the Philippines, creating ways to keep our public emergency responders safe is an everyday issue. With the help of fast-paced wireless technology, problems are now solvable such as monitoring, creating miniaturized sensors or devices, etc. This study will focus on using a barometric altimeter sensor that has a system with a Self-Adaptive Algorithm. Compared to other existing studies regarding indoor localization or identifying floor height which are time-consuming or labor-intensive, this will benefit our countries' finest specifically firefighters. Lessen their worries about accidents or death due to suffocation for losing their way inside the burning multi-floor building. Through our system, it will show the altitude measurement and estimated floor levels of the firefighter who is wearing the device. This study was conducted at Ayala Circuit Makati with 10 samples and gathered an accuracy of 93.820% with the system undergone Self-Adaptive Algorithm over 86.263% of without undergone Self-Adaptive Algorithm.
{"title":"Altitude Monitoring of Multi-Floor Building Using a Barometric Altimeter Device and Self-Adaptive Algorithm","authors":"M. V. Caya, Jeffrey P. Ng","doi":"10.1109/HNICEM51456.2020.9400013","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400013","url":null,"abstract":"In the Philippines, creating ways to keep our public emergency responders safe is an everyday issue. With the help of fast-paced wireless technology, problems are now solvable such as monitoring, creating miniaturized sensors or devices, etc. This study will focus on using a barometric altimeter sensor that has a system with a Self-Adaptive Algorithm. Compared to other existing studies regarding indoor localization or identifying floor height which are time-consuming or labor-intensive, this will benefit our countries' finest specifically firefighters. Lessen their worries about accidents or death due to suffocation for losing their way inside the burning multi-floor building. Through our system, it will show the altitude measurement and estimated floor levels of the firefighter who is wearing the device. This study was conducted at Ayala Circuit Makati with 10 samples and gathered an accuracy of 93.820% with the system undergone Self-Adaptive Algorithm over 86.263% of without undergone Self-Adaptive Algorithm.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"28 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":"127536093","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/HNICEM51456.2020.9400092
Jean Rene D. Kalaw, Wilma Tan, F. Cifra, Jesus M. Martinez, Flordeliza L. Valiente, A. Ballado
Single Phase Critical Conduction Mode is overlooked compared to Interleaved CRM and Continuous Conduction Mode as a Power Factor Correction mode when the application is medium to high power because of the design compromises that it begins to have at higher power requirement. This study explores the design and simulation of a High Efficiency and High Power Factor Single-Phase CRM Boost PFC operating at the assumed practical limit of the mode and is design with the consideration to be compliant with EMC standards. The design was able to supply an output voltage of 450V and 350W of power while maintaining a power factor greater than 0.9 and efficiency greater than 97% for a wide input range. A two stage EMI Filter was designed for the converter for the consideration of EMC, and the parameters taken into consideration for the filter design are taken from the simulation results. The design used the PFC ASIC NCP1608 which was modeled and simulated using Simplis.
{"title":"Design Optimization and Controller Modelling of an ASIC Controlled Single Phase Critical Conduction Mode Boost PFC Using Simplis","authors":"Jean Rene D. Kalaw, Wilma Tan, F. Cifra, Jesus M. Martinez, Flordeliza L. Valiente, A. Ballado","doi":"10.1109/HNICEM51456.2020.9400092","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400092","url":null,"abstract":"Single Phase Critical Conduction Mode is overlooked compared to Interleaved CRM and Continuous Conduction Mode as a Power Factor Correction mode when the application is medium to high power because of the design compromises that it begins to have at higher power requirement. This study explores the design and simulation of a High Efficiency and High Power Factor Single-Phase CRM Boost PFC operating at the assumed practical limit of the mode and is design with the consideration to be compliant with EMC standards. The design was able to supply an output voltage of 450V and 350W of power while maintaining a power factor greater than 0.9 and efficiency greater than 97% for a wide input range. A two stage EMI Filter was designed for the converter for the consideration of EMC, and the parameters taken into consideration for the filter design are taken from the simulation results. The design used the PFC ASIC NCP1608 which was modeled and simulated using Simplis.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"43 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":"129915832","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/HNICEM51456.2020.9400002
Tanya V. Olegario, R. Baldovino, N. Bugtai
Tree diseases contribute to the reduction of forest areas over the years and early detection of these diseases is essential to prevent its rapid spread and eventually provide immediate cure. In this study, the Japanese pine wilt (JPW) and the Japanese oak wilt (JOW) diseases were used. These two tree diseases were detected using high-resolution satellite imagery. JPW is a lethal disease that brought damagr and devastation to the greater number of pine trees in Japan which is primarily brought by the pinewood nematode (Bursaphelenchus xylophilus). JOW, on the other hand, is a vector-borne disease caused by a symbiotic fungus spreaded by the flying ambrosia beetle (Platypus quercivorus) that serves as a vector. A machine learning (ML) algorithm based on decision tree (DT) was implemented and programmed using the ML repository dataset obtained from the University of California, Irvine (UCI). The data will be used to classify image segments into two types: diseased or wilted trees, and others. The trained algorithm was able to classify the image segments with a high accuracy of 98.14%.
多年来，树木病害导致森林面积减少，因此必须及早发现这些病害，以防止其迅速蔓延，并最终立即治愈。本研究使用了日本松枯萎病（JPW）和日本栎枯萎病（JOW）。这两种树木病害是利用高分辨率卫星图像检测到的。日本松树枯萎病是一种致命病害，主要由松材线虫（Bursaphelenchus xylophilus）引起，给日本大量松树造成损害和破坏。另一方面，JOW 是一种病媒传染病，由作为病媒的飞伏甲（Platypus quercivorus）传播的共生真菌引起。利用从加州大学欧文分校（UCI）获得的 ML 存储库数据集，实施并编程了基于决策树（DT）的机器学习（ML）算法。这些数据将用于把图像片段分为两种类型：病树或枯萎树以及其他树。经过训练的算法能够对图像片段进行分类，准确率高达 98.14%。
{"title":"A Decision Tree-based Classification of Diseased Pine and Oak Trees Using Satellite Imagery","authors":"Tanya V. Olegario, R. Baldovino, N. Bugtai","doi":"10.1109/HNICEM51456.2020.9400002","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400002","url":null,"abstract":"Tree diseases contribute to the reduction of forest areas over the years and early detection of these diseases is essential to prevent its rapid spread and eventually provide immediate cure. In this study, the Japanese pine wilt (JPW) and the Japanese oak wilt (JOW) diseases were used. These two tree diseases were detected using high-resolution satellite imagery. JPW is a lethal disease that brought damagr and devastation to the greater number of pine trees in Japan which is primarily brought by the pinewood nematode (Bursaphelenchus xylophilus). JOW, on the other hand, is a vector-borne disease caused by a symbiotic fungus spreaded by the flying ambrosia beetle (Platypus quercivorus) that serves as a vector. A machine learning (ML) algorithm based on decision tree (DT) was implemented and programmed using the ML repository dataset obtained from the University of California, Irvine (UCI). The data will be used to classify image segments into two types: diseased or wilted trees, and others. The trained algorithm was able to classify the image segments with a high accuracy of 98.14%.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"37 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":"126956085","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/HNICEM51456.2020.9400107
Jonnel D. Alejandrino, Ronnie S. Concepcion, Sandy C. Lauguico, Ramón Flores, A. Bandala, E. Dadios
Data acquisition is a crucial process in smart monitoring system. Intensive monitoring of parameters used for maintenance between the process of monitoring, controls and actuation is essential. All things considered, progressive ability of data acquisition and management provided by wireless sensor networks (WSN) is dependent on several attributes of the underlying routing protocol and connectivity algorithm, predominantly the application-based cluster protocol that is utilized among network gateway, application server and sensor nodes/motes. Further along, congestions caused by numerous integrations of environmental sensors and sending of information to remote stations is greatly affected by the heterogeneity and mobility of the motes in detecting congestion in a network. A combination of application-based routing protocol and connectivity-specific algorithm has been developed to achieved better performance of multiple sensors integration in terms of network lifetime, connectivity network coverage, and other QoS metrics as an alternative to the current system being used. The Application priority-reliant transmission is utilized for the optimizing methodology, it highlights the application priority in regulating the “rate of arrival”. MATLAB R2019a was utilized in validating the proposed algorithm and then technically compared with standard setup (with fundamental parameters) and Adaptive Cuckoo Search (ACS) algorithms. Results technically showed that the proposed algorithm is preferential than the initial setup by an improvement of 62.71% in minimum coverage area, 75.03% in Congestion Level and 40.64% in networklifetime.
{"title":"Application-based Cluster and Connectivity-Specific Routing Protocol for Smart Monitoring System","authors":"Jonnel D. Alejandrino, Ronnie S. Concepcion, Sandy C. Lauguico, Ramón Flores, A. Bandala, E. Dadios","doi":"10.1109/HNICEM51456.2020.9400107","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400107","url":null,"abstract":"Data acquisition is a crucial process in smart monitoring system. Intensive monitoring of parameters used for maintenance between the process of monitoring, controls and actuation is essential. All things considered, progressive ability of data acquisition and management provided by wireless sensor networks (WSN) is dependent on several attributes of the underlying routing protocol and connectivity algorithm, predominantly the application-based cluster protocol that is utilized among network gateway, application server and sensor nodes/motes. Further along, congestions caused by numerous integrations of environmental sensors and sending of information to remote stations is greatly affected by the heterogeneity and mobility of the motes in detecting congestion in a network. A combination of application-based routing protocol and connectivity-specific algorithm has been developed to achieved better performance of multiple sensors integration in terms of network lifetime, connectivity network coverage, and other QoS metrics as an alternative to the current system being used. The Application priority-reliant transmission is utilized for the optimizing methodology, it highlights the application priority in regulating the “rate of arrival”. MATLAB R2019a was utilized in validating the proposed algorithm and then technically compared with standard setup (with fundamental parameters) and Adaptive Cuckoo Search (ACS) algorithms. Results technically showed that the proposed algorithm is preferential than the initial setup by an improvement of 62.71% in minimum coverage area, 75.03% in Congestion Level and 40.64% in networklifetime.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"1 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":"130485574","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/HNICEM51456.2020.9400065
Carlos C. Hortinela, Jessie R. Balbin, P. A. Tibayan, John Myrrh D. Cabela, G. Magwili
Honey is always among the lists for food fraud around the world. A whistleblower surfaced in a South African Honey manufacturer that claims their honey is revealed as passing off a sugar concoction. Also, in the Philippines, mid-2016 there's a manufacturer of honey named Cem's Honey that mislabels their product as real honey but in fact their product is a fake honey. The main objective of this study is to create a system that could classify a honey whether it is genuine or fake using Artificial Neural Network with Gradient Descent Backpropagation as the training algorithm, and sensors (Electrical Conductivity and pH Sensor). Through testing, the system classified the presented samples at an accuracy rate of 87.5%. In conclusion, the researchers successfully developed a system that can classify a honey whether is it genuine or fake using Artificial Neural Network.
{"title":"Classification of Honey as Genuine or Fake via Artificial Neural Network using Gradient Descent Backpropagation Algorithm","authors":"Carlos C. Hortinela, Jessie R. Balbin, P. A. Tibayan, John Myrrh D. Cabela, G. Magwili","doi":"10.1109/HNICEM51456.2020.9400065","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400065","url":null,"abstract":"Honey is always among the lists for food fraud around the world. A whistleblower surfaced in a South African Honey manufacturer that claims their honey is revealed as passing off a sugar concoction. Also, in the Philippines, mid-2016 there's a manufacturer of honey named Cem's Honey that mislabels their product as real honey but in fact their product is a fake honey. The main objective of this study is to create a system that could classify a honey whether it is genuine or fake using Artificial Neural Network with Gradient Descent Backpropagation as the training algorithm, and sensors (Electrical Conductivity and pH Sensor). Through testing, the system classified the presented samples at an accuracy rate of 87.5%. In conclusion, the researchers successfully developed a system that can classify a honey whether is it genuine or fake using Artificial Neural Network.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"42 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":"127848935","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/HNICEM51456.2020.9400087
R. R. Vicerra, Edwin J. Calilung, Jason L. Española, E. Dadios, A. Culaba, E. Sybingco, A. Bandala, Alma Bella Madrazo, L. G. Lim, R. Billones, Siegfred Lopez, Dino Dominic F. Ligutan, Julius Palingcod, Carl John Patrick Castillo
Automation is considered as the driving force of Fourth Industrial Revolution (Industry 4.0) to develop smart and automated devices for existing manufacturing processes. However, the global medical outbreak perpetrated by the Coronavirus Disease 2019 (COVID-19) challenged researchers to explore new concepts and innovate existing technologies whilst resolving the ongoing health crisis. Thus, the demand for utilizing the automation concept in biomedical devices is reasonably high. For this study, the researchers have successfully implemented an industrial-grade programmable logic controller that will control the mechanical ventilation process of a bag-valve-mask-based emergency ventilator. Various mechanisms were observed, and the results have been documented.
{"title":"Implementation of a Programmable Logic Controller (PLC)-Based Control System for a Bag-Valve-Mask-Based Emergency Ventilator","authors":"R. R. Vicerra, Edwin J. Calilung, Jason L. Española, E. Dadios, A. Culaba, E. Sybingco, A. Bandala, Alma Bella Madrazo, L. G. Lim, R. Billones, Siegfred Lopez, Dino Dominic F. Ligutan, Julius Palingcod, Carl John Patrick Castillo","doi":"10.1109/HNICEM51456.2020.9400087","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400087","url":null,"abstract":"Automation is considered as the driving force of Fourth Industrial Revolution (Industry 4.0) to develop smart and automated devices for existing manufacturing processes. However, the global medical outbreak perpetrated by the Coronavirus Disease 2019 (COVID-19) challenged researchers to explore new concepts and innovate existing technologies whilst resolving the ongoing health crisis. Thus, the demand for utilizing the automation concept in biomedical devices is reasonably high. For this study, the researchers have successfully implemented an industrial-grade programmable logic controller that will control the mechanical ventilation process of a bag-valve-mask-based emergency ventilator. Various mechanisms were observed, and the results have been documented.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"153 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":"127943661","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/HNICEM51456.2020.9399989
Ricky D. Umali, M. Manuel, Leo Paolo A. Andaya, Enric John D. Bajo, Lloyd Christopher B. Gasis, Erlvin Fernando A. Santos, J. D. dela Cruz, Roderick C. Tud, Marvin S. Verdadero
A renewable energy source is a promising technology; however, the cost, stability, and feasibility are difficult to attain compared to non-renewable energy. In this study, the researchers constructed a testing rig that assessed the different designs of Well's turbine rotors. The output power was measured by varying the turbine parameter such as the tip clearance, hub-to-tip ratio, and turbine configuration: monoplane, biplane, and contra-rotating. The Full Fractional design was used to facilitate the design of the experiment. A statistical software tool, Minitab 17 was used to perform the design of experiment (DOE) that helped the researchers to investigate the effects of the input data to the response at the same time. The application of full factorial design saved time, testing, and resources. The turbine rotors were fabricated using a 3D printer with Acrylonitrile Butadiene Styrene (ABS) filament. The results of the experiment showed the best combination, which is the biplane configuration, with 1% tip clearance and 0.6 hub-to-tip ratio, an average output power of 15.6204 watts with 94.3548% confidence. Also, it was observed that the three chosen parameters have no interaction within the range of values of the parameters. The DOE revealed that turbine configuration and tip clearance are the only significant parameters and the hub- to-tip ratio is not significant having greater than 0.05 p-value.
{"title":"Performance Analysis on Monoplane, Biplane, and Contra-Rotating Well's Turbine","authors":"Ricky D. Umali, M. Manuel, Leo Paolo A. Andaya, Enric John D. Bajo, Lloyd Christopher B. Gasis, Erlvin Fernando A. Santos, J. D. dela Cruz, Roderick C. Tud, Marvin S. Verdadero","doi":"10.1109/HNICEM51456.2020.9399989","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9399989","url":null,"abstract":"A renewable energy source is a promising technology; however, the cost, stability, and feasibility are difficult to attain compared to non-renewable energy. In this study, the researchers constructed a testing rig that assessed the different designs of Well's turbine rotors. The output power was measured by varying the turbine parameter such as the tip clearance, hub-to-tip ratio, and turbine configuration: monoplane, biplane, and contra-rotating. The Full Fractional design was used to facilitate the design of the experiment. A statistical software tool, Minitab 17 was used to perform the design of experiment (DOE) that helped the researchers to investigate the effects of the input data to the response at the same time. The application of full factorial design saved time, testing, and resources. The turbine rotors were fabricated using a 3D printer with Acrylonitrile Butadiene Styrene (ABS) filament. The results of the experiment showed the best combination, which is the biplane configuration, with 1% tip clearance and 0.6 hub-to-tip ratio, an average output power of 15.6204 watts with 94.3548% confidence. Also, it was observed that the three chosen parameters have no interaction within the range of values of the parameters. The DOE revealed that turbine configuration and tip clearance are the only significant parameters and the hub- to-tip ratio is not significant having greater than 0.05 p-value.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"133 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":"122976731","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/HNICEM51456.2020.9400034
Teodoro F. Revano, Manuel B. Garcia
Design Thinking is commonly used by businesses as a mindset and approach for problem-solving, learning, and collaboration. Such methodology is a beneficial addition to the pedagogy selections used in the education landscape especially to fields that build products (e.g., computer systems) requiring significant considerations to its functional designs. In this study, the use of Design Thinking Curriculum was explored in Higher Education Institutions particularly on Information Technology and Computer Science programs to determine its impact to the skills and abilities of future computing professionals. To do this, a self-assessment scale that comprises of 31 measurement items divided into seven dimensions was given to computing students. Findings establish that computing students enrolled in a Design Thinking Curriculum have significantly improved in all scales compared to those who are not. Therefore, this study validates the application of Design Thinking Curriculum in education as an approach to encourage innovation in the computing field.
{"title":"Manufacturing Design Thinkers in Higher Education Institutions: The Use of Design Thinking Curriculum in the Education Landscape","authors":"Teodoro F. Revano, Manuel B. Garcia","doi":"10.1109/HNICEM51456.2020.9400034","DOIUrl":"https://doi.org/10.1109/HNICEM51456.2020.9400034","url":null,"abstract":"Design Thinking is commonly used by businesses as a mindset and approach for problem-solving, learning, and collaboration. Such methodology is a beneficial addition to the pedagogy selections used in the education landscape especially to fields that build products (e.g., computer systems) requiring significant considerations to its functional designs. In this study, the use of Design Thinking Curriculum was explored in Higher Education Institutions particularly on Information Technology and Computer Science programs to determine its impact to the skills and abilities of future computing professionals. To do this, a self-assessment scale that comprises of 31 measurement items divided into seven dimensions was given to computing students. Findings establish that computing students enrolled in a Design Thinking Curriculum have significantly improved in all scales compared to those who are not. Therefore, this study validates the application of Design Thinking Curriculum in education as an approach to encourage innovation in the computing field.","PeriodicalId":230810,"journal":{"name":"2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"1 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":"131168319","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)
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：