Emerie R. Angeles, Mohammed D. Badreldin, Austine James C. Santos, C. Ostia
{"title":"Day Ahead Load Forecasting Model Using Gaussian Naïve Bayes with Ensemble Empirical Mode Decomposition","authors":"Emerie R. Angeles, Mohammed D. Badreldin, Austine James C. Santos, C. Ostia","doi":"10.1109/TENSYMP52854.2021.9550820","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550820","url":null,"abstract":"The importance of load forecasting has provided valuable information for power grid analysis since the early 2000's. It has been established that no specific load forecasting model can be generalized for all demand types. This study aims to fill the gaps among the plethora of existing mathematical forecasting methods, specifically using the Naive Bayes Theorem. Naive Bayes, by itself, has an issue when dealing with large amounts of input which is the reason it has not been used in load forecasting. The integration of Naive Bayes along with the Ensemble method and Empirical Mode Decomposition provided our Hybridized Naive Bayes Algorithm with adequate improvement in its accuracy given the large amount of input data. The results were justified using key performance indicators MAE, MAPE and MSE. We obtained an average of 34.35 for MSE, 60.72MW for MAE and 4.41% for its MAPE. Although the hybridized Naive Bayes presented in this study is not ready for industrial use, it is very promising due to its mathematical prediction model and even more improvement is highly feasible.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114857263","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}
Jonathan Paul C. Cempron, Carlo Migel Bautista, G. Cu, J. Ilao
{"title":"Frame Rate Latency Reduction for Real-time Vehicle Tracking using Network Cameras","authors":"Jonathan Paul C. Cempron, Carlo Migel Bautista, G. Cu, J. Ilao","doi":"10.1109/TENSYMP52854.2021.9550916","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550916","url":null,"abstract":"Traffic monitoring and vehicle counting systems that use surveillance cameras employ several computer vision techniques, one of which is object tracking, which approximates the trajectory of the vehicle throughout the scene. However, a major challenge in processing videos from network camera feeds is the irregular and low frame rates, affecting the performance of object tracking. In this paper, we present a concurrent implementation framework intended to increase the input network video frame rate.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114973814","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}
Junho Cho, F. Ahmed, Ethungshan Shitiri, Ho-Shin Cho
{"title":"Power Control for MACA-based Underwater MAC Protocol: A Q-Learning Approach","authors":"Junho Cho, F. Ahmed, Ethungshan Shitiri, Ho-Shin Cho","doi":"10.1109/TENSYMP52854.2021.9550973","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550973","url":null,"abstract":"Underwater acoustic sensors are battery-powered and spend a major portion of their limited energy during packet transmissions. To conserve energy, multiple access collision avoidance (MACA)-based MAC protocols are designed to lower the data packet transmission power, while using the maximum transmission power for control packets. However, lowering the data transmission power make the data packets susceptible to collisions. In this regard, a reinforcement learning-based power control scheme is proposed for MACA-based underwater MAC protocol that can reduce collisions while maintaining high energy efficiency. A key feature of the proposed scheme is that it enables the sensor nodes to prevent collisions without any prior knowledge of the interferences, eliminating the need for additional signaling. Simulation results show that the proposed scheme significantly improves the energy efficiency and the throughput of MACA-based power control schemes.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117320232","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}
{"title":"Learning Innovation through Online Lectures: A Study on Two-way Real-Time Lectures via Zoom","authors":"Yaeko Mitsumori","doi":"10.1109/TENSYMP52854.2021.9550869","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550869","url":null,"abstract":"This study analyzed the impacts of the COVID-19 pandemic on higher education in Japan by conducting a student opinion survey on Zoom lectures. After the COVID-19 pandemic hit Japan, almost all universities in Japan shifted their conventional face-to-face lectures to online lectures, including Zoom lectures. Prior to the COVID-19 pandemic, only a limited number of universities provided online lectures. As a result, neither professors nor students were familiar with online lectures at the university level. However, the COVID-19 pandemic required university professors to launch online lectures without enough time for preparation; similarly, students were also required to receive online lectures without enough preparation. This study’s results provide suggestions and recommendations for both universities and students to improve online lectures at universities during and after the COVID-19 era.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122094065","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}
P. Cabacungan, C. Oppus, Nerissa G. Cabacungan, John Paul A. Mamaradlo, Paul Ryan A. Santiago, Neil Angelo M. Mercado, E. Faustino, G. Tangonan
{"title":"Design and Development of A-vent: A Low-Cost Ventilator with Cost-Effective Mobile Cloud Caching and Embedded Machine Learning","authors":"P. Cabacungan, C. Oppus, Nerissa G. Cabacungan, John Paul A. Mamaradlo, Paul Ryan A. Santiago, Neil Angelo M. Mercado, E. Faustino, G. Tangonan","doi":"10.1109/TENSYMP52854.2021.9550920","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550920","url":null,"abstract":"We designed and developed a low-cost mechanical ventilator prototype that meets the government's minimum viable standards. We substituted alternative off-the-shelf food-grade for the medical-grade parts and improvised some components for our prototype. We cleaned the air from the oxygen tanks and compressors before going to the lung test bag. We designed a solar-powered battery system that can run electronic components for a fail-safe operation. We demonstrated how the AIC Near Cloud system can store air flow rate and air pressure data which were generated during the prototype's operation. We used Embedded Machine Learning in sensors and data processing by using flow and pressure sensors to provide accumulated data that can be utilized in training the machine learning software. The patient-ventilator asynchrony detection model was tested using data generated from the emulated ventilator waveform events that mimic the patient-ventilator asynchrony. A different compression pattern was applied to the test lung and results showed the training, validation, and model testing that yielded 98.7%, 99.1%, and 97.18 percent accuracy, respectively. Having demonstrated that the Tiny ML can be trained to detect anomalies from several data points, we realized the feasibility of detecting ventilator patient vibration anomaly, and unusual acoustic signatures, among others, for future works.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129143878","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}
John Edward A. Abutin, Henrich T. Jovena, Robin Paul L. Trinidad, M. Pacis, G. Magwili
{"title":"Voltage Profile Enhancement by Optimal Placement of Hybrid Distribution Generators using Genetic Algorithm","authors":"John Edward A. Abutin, Henrich T. Jovena, Robin Paul L. Trinidad, M. Pacis, G. Magwili","doi":"10.1109/TENSYMP52854.2021.9550767","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550767","url":null,"abstract":"Placing Distributed Generator (DG) plays an important role in electric power systems as it reduces power losses and increases the voltage profiles. It is also important to take note that aside from placing DGs in the system, it is also important to locate its optimum position because the wrong placement of DGs can result to further power losses and voltage profile violation. This paper presents three cases for the study of the IEEE – 33 Bus system. A genetic algorithm was used as the optimization technique. In this test case, we used the 33-bus as the base case, while the other cases are putting a 0.29MW solar, 0.10MW wind, and lastly combining the 0.29MW solar and 0.10MW wind at the end of the simulation the case that has the lowest percent loss is the Hybrid system with Solar and Wind, which has 95.2092 percentage loss.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130580726","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}
{"title":"Analysis of Cognition Completion Time in RFID Networks Supported by Static Naive MAC Scheme","authors":"Heewon Seo, Jun Ha, J. Park, C. Choi","doi":"10.1109/TENSYMP52854.2021.9550980","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550980","url":null,"abstract":"We consider an RFID network that consists of a single reader and multiple tags sojourning in the vicinity of the reader. In such an RFID network, collisions occur among the packets which are simultaneously sent by two or more tags. In order to support the reader to cognize tags while resolving the collision problem, we consider a static naive MAC scheme that employs a collision arbitration method rooted in framed slotted ALOHA. A basic mission of a reader in an RFID network is to cognize every neighboring tag in a prescribed time. Thus, it is of necessity to analyze the cognition completion time, e.g., the time elapsed until the reader cognized all the tags. In this paper, we first mathematically describe the cognition completion time as a hitting time of a homogeneous non-decreasing Markov chain on a finite state space. Next, we construct a fast converging Markov chain on the same state space by using a sequence of random variables that have stochastic dominance. Finally, we propose a numerically efficient method in which the cognition completion time is approximated by a hitting time of the fast converging Markov chain. Numerical examples confirm that the approximate expected value of the cognition completion time provides a lower bound on the exact expected value.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130801637","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}
Y. Jusman, Muhammad Ahdan Fawwaz Nurkholid, Dhimas Arief Darmawan, Feriandri Utomo
{"title":"Comparison Performance of Prostate Cell Images Classification using Pretrained Convolutional Neural Network Models","authors":"Y. Jusman, Muhammad Ahdan Fawwaz Nurkholid, Dhimas Arief Darmawan, Feriandri Utomo","doi":"10.1109/TENSYMP52854.2021.9550865","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550865","url":null,"abstract":"Prostate cancer is the most common cancer in men in 2019. In that year, in the United States 174,650 men (20%) had prostate cancer and the remaining 696.32 men (80%) had other cancers (lung, bronchus) etc). In cancer diagnosis, there are several problems such as errors in reporting the diagnosis and the need for a long time. Artificial intelligence has long been known to facilitate the detection process, but a comparison analysis of the model is needed to get more optimal results. This study aims to compare the performance of two pretrained models (i.e. AlexNet and GoogLeNet). The data used is the image of prostate cells taken from a light microscope at the Universitas Indonesia (UI) Hospital. This study uses k-fold cross-validation to validate the accuracy of a model used. Performance evaluation of pretrained models is based on performance metrics: accuracy, precision, recall (sensitivity), specificity and f-score and running time in the testing process. The best accuracy is obtained by GoogLeNet with 99.63% and 97.74% and the lowest accuracy is obtained by AlexNet with 99.13% and 94.11%. During the training, AlexNet had a shorter time with 47 seconds than GoogLeNet with 112 seconds. In testing times, AlexNet was also faster with 0.307 seconds than GoogLeNet with 0.372. This research is expected to assist researchers (pathologists, physician assistants, etc.) in choosing the right architecture for the classification of prostate cancer images in terms of time and accuracy.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121633574","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}
{"title":"Viewpoint-Switching Delay of Multi-viewpoint 360 VR in a Bandwidth-limited Environment","authors":"Bong-Seok Seo, Su-min Hwang, Y. Lee, Dong Ho Kim","doi":"10.1109/TENSYMP52854.2021.9550894","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550894","url":null,"abstract":"The transmission of multi-view or free-view 360VR contents, which selects the viewer's viewpoint and experiences 360VR contents, consumes a very large bandwidth. In a cellular environment in which multiple users exist, only a limited bandwidth can be allocated to a specific user, and thus transmission quality is inevitably deteriorated or transmission delay occurs. In the case of multi-viewpoint 360VR content, if the viewer changes the position of the viewpoint, the contents of the corresponding position are transmitted. In a bandwidth-limited communication environment, a large transmission delay occurs when the viewpoint is moved, resulting in a very poor user experience. In this paper, in order to reduce the transmission delay when attempting to change the viewpoint in a multi-view 360 video, a transmission method of appropriately pre-loading the video of the surrounding location is considered. In addition, the delay performance according to the viewpoint change was analyzed in a practical 5G network environment. In order to lower the viewpoint switching delay below a certain level, an appropriate prediction technique for the user movement pattern is required, and the decrease in the delay in accordance with the accuracy is presented through an experiment.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122724474","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}
Dewinda Julianensi Rumala, Eko Mulyanto Yuniarno, R. F. Rachmadi, Supeno Mardi Susiki Nugroho, Yudhi Adrianto, A. Sensusiati, I. Ketut Eddy Purnama
{"title":"Bilinear MobileNets for Multi-class Brain Disease Classification Based on Magnetic Resonance Images","authors":"Dewinda Julianensi Rumala, Eko Mulyanto Yuniarno, R. F. Rachmadi, Supeno Mardi Susiki Nugroho, Yudhi Adrianto, A. Sensusiati, I. Ketut Eddy Purnama","doi":"10.1109/TENSYMP52854.2021.9550987","DOIUrl":"https://doi.org/10.1109/TENSYMP52854.2021.9550987","url":null,"abstract":"Early detection of brain diseases is necessary to deliver further and suitable treatment to save the patient life. Automated brain disease diagnosis is possible to be carried out with the availability of imaging techniques and the Deep Learning method. In recent years, many researchers have been interested in medical image classification problems, including brain disease detection based on Magnetic Resonance Images (MRI) using the Convolutional Neural Network (CNN) algorithm of Deep Learning. CNN has a unique advantage compared with traditional Machine Learning to do automated image feature extraction. However, CNN will perform better if numerous datasets are provided. Unfortunately, the lack of data due to privacy is still a problem in the medical image analysis topic. In order to solve that problem, many researchers have implemented a transfer learning technique to train the CNN models with small data. This study has proposed bilinear models based on CNN to distinguish brain MR images into five classes. In this study, MobileNetV1 and MobileNetV2 are employed as backbone networks to extract features via transfer learning, and the bilinear method is implemented to integrate the features from both networks. The proposed method improved the classification performance of the CNN model with a testing accuracy of 98.03%.","PeriodicalId":137485,"journal":{"name":"2021 IEEE Region 10 Symposium (TENSYMP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122744105","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}