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

{"title":"Post Disaster Indoor Position Tracking Device with Pulse Detection in Wireless Sensor Networks","authors":"G. Magwili, N. Linsangan, J. Marasigan, Carl Joseph V. Villanueva","doi":"10.1109/HNICEM54116.2021.9731892","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731892","url":null,"abstract":"Wireless sensor networks have a lot of applications and can apply in post-disaster management. The study’s main objective is to develop post-disaster indoor position tracking in wireless sensor networks. It also aims to determine the estimated indoor position of the wrist strap wearer using the distance measuring technique. NodeMCU ESP8266 was used to pass along the RSSI values and send them to the sink node located outside the establishment, and viewing the GUI from the sink node. The researchers tested the calculated distance at various distances by getting RSSI values from the fixed node to determine the distance measurement formula. The researchers did the same testing in different scenarios: no obstructions, large obstructions. At one meter, the average calculated distance at fixed node 1 was 1. 2598m for no obstructions and 2. 1061m for large obstructions. This meant that the RSSI generated with large obstructions was becoming smaller. The sample standard deviation also suggested that the distance also affected how spread the RSSI values at longer distances","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"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":"116356065","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":"Mga Kwento ni Lola Basyang: An Augmented Reality On Selected Philippine Folklore","authors":"Melba Besa","doi":"10.1109/HNICEM54116.2021.9732029","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9732029","url":null,"abstract":"Children storybooks have gone far, from flat books to embossed to Audio-book to Pop-up book and now Augmented Reality books. Augmented Reality or AR is one of the innovative technologies that will be universally used given its potential and fascination.The goal of this study is to create a new way of learning with children storybooks with new technology. The innovation underpinning this research is the embedded Augmented Reality 2-Dimensional of children’s book on a mobile application. The research provides an insight into what was done using AR on children’s story books enabling the reader to place this example of AR in perspective and understand it more clearly. This paper specifically highlights an innovative development of the interfaces for providing an AR children storybook that enhances story reading and learning experience for preschool and young schoolers children via mobile AR application.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"10 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":"126374962","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":"Rainfall And Meteorological Drought Forecasting in Albay, Philippines Using Artificial Neural Network","authors":"Sophia Chloe Caress, Angela Abigail Belen, Ivan John Esguerra, Harian Dea Wacan, Florante D. Poso, Melvin B. Solomon","doi":"10.1109/HNICEM54116.2021.9731900","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731900","url":null,"abstract":"Agriculture relies heavily on weather forecasts, and a reliable weather forecasting system can help mitigate the calamities which can affect this industry. Rainfall and meteorological drought duration forecasting are some of the most important yet challenging tasks. This paper presents the creation of feedforward backpropagation artificial neural networks for daily rainfall forecasting and monthly meteorological drought forecasting. Artificial Neural Networks can capture the variability of these phenomena. Rainfall data from nine stations all over Albay, the Philippines, spanning from 1967 to 2000, were used to create the models. The input parameters used for developing the models for daily rainfall forecasting were 14-day antecedent rainfall, current-day rainfall, relative humidity, mean temperature, and sunshine duration. The monthly meteorological drought forecasting parameters were 1-month SPI, current-month rainfall, relative humidity, mean temperature, and sunshine duration. Having the results presented in this paper, the performance of the ANN Models of the stations were compared based on R and RMSE. The rainfall forecasting models and meteorological drought forecasting models have provided satisfactory performance. A satisfactory performance for forecasting has an R-value ranging from 0.2 to 0.5. Sensitivity analysis indicated that the most significant parameter for rainfall forecast is the relative humidity and mean temperature for drought forecast.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"32 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":"128112679","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":"Chemical Reaction Optimization (CRO) of Deep Neural Network (DNN) Model for Characterization of Algae Drying Kinetics","authors":"Amir A. Bracino, D. G. Evangelista, A. Mayol, Ronnie S. Concepcion, A. Culaba, E. Dadios, C. Madrazo, A. Ubando, R. R. Vicerra","doi":"10.1109/HNICEM54116.2021.9731859","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731859","url":null,"abstract":"Drying is an essential step needed to improve the extraction of lipids and other valuable compounds in the algae for biodiesel production. However, there is a limited amount of information available regarding its drying kinetics. Previous studies have used computational intelligence e.g., artificial neural networks (ANN) and deep neural networks (DNN) to characterize the drying kinetics of algae. Chemical Reaction optimization (CRO), a recently introduced metaheuristic optimization approach, is employed in this study to identify the ideal number of neurons to use in a Deep Neural Network (DNN) model that will produce the lowest root mean squared error (RMSE). CRO can reduce the computational time since the population does not need to be coordinated in each computing units. The molecular structure in the CRO contains the set of neurons, while the potential energy (II) corresponds to the RMSE of the DNN model. At a minimum RMSE value, the accuracy of the moisture removal rate prediction increases given maximum temperature, sample temperature, time of drying, heat rate, and percent weight of the remaining algae. The DNN model created obtained an RMSE value of 4.9430 x$10^{-4}$ which corresponds to R -value of 0.9996 and 0.99958 in the training and validation phases.","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":"134018169","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":"Development of Earthquake Liquefaction Maps of Laguna, Philippines","authors":"N. Concha, S. Clemente, Ron David Lance S. Añonuevo, Allyssa Rose T. Carpio, Aneeza Venus B. Sales, Mel Christine E. Sto. Domingo","doi":"10.1109/HNICEM54116.2021.9731993","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731993","url":null,"abstract":"Structures built on high seismic areas are likely to experience earthquake liquefaction. This in turn will compromise the integrity of the structures and thus, assessment of the susceptibility to liquefaction is essential. To evaluate the likelihood and severity of earthquake induced liquefaction particularly in the 2nd district of Laguna, 74 geotechnical reports from various locations were collected. Using deterministic approach, safety factors and liquefaction severity index were calculated at different locations to generate liquefaction probability and severity maps. Results showed that there is a wide range of liquefaction severity levels from very low severity of 3.8% of the areas to high severity of 5.06% of the areas. The probability map further showed that an average of 90.49% of the areas are susceptible to liquefaction when an 8.0 earthquake magnitude occurs. The developed maps can be used by site planners and engineers to identify the severity of liquefaction at specific locations and appropriately apply remedial measures in the design of structures.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"60 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":"131722219","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":"Development of Predictive Machine Learning Model using Neural Network for Threshold Value Determination of Buildings","authors":"F. Cruz, Earl Quinn Christian Marcos","doi":"10.1109/HNICEM54116.2021.9732008","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9732008","url":null,"abstract":"Machine learning (ML), a subset off artificial intelligence (AI), is now part of people’s everyday lives. It is now applied in many fields and industries like the automotive industry, medical field, e-commerce and many more. Some examples of this can be found in the self-driving cars, medical diagnosis, recommendation engines, patient sickness prediction and many more. In the past years, engineering had been showing growing interest over the application of AI in the field. In fact, several studies had been conducted to see what advantages it can bring to the engineering discipline. It is evident that ML is now being applied in lots of field of engineering. However, ML as applied to structural health monitoring (SHM), specifically to the determination of threshold for buildings has not yet been established. The threshold plays a very important role in SHM as it will be the basis for evaluating the integrity of a structure after it ages as time goes by or even after earthquake events. This study focuses on developing a predictive machine learning model that will be incorporated in an earthquake recording instrument that will give the threshold value specifically for a building given specific input parameters. To do the predictive model, structural data of thirty (30) buildings were collected. It consisted of acceleration data, maximum displacement on non-linear and linear state, lower and upper limit of moderate damage state, and its threshold. The proponent was able to gather 3750 rows of data to be used for the training of network. Creating of the neural network model was done using the MATLAB neural network tool, and trained using the Levenberg-Marquadt algorithm which yielded the best performance among the training algorithms in MATLAB neural network tool. After training, a MATLAB function was generated and run compatibly with python to allow integration with the earthquake recording instrument. Furthermore, an accuracy test was done wherein it produced a 91.77% accuracy. Through the predictive ML model, structural engineers are expected to experience a great amount of savings in terms of time and effort on determining the threshold value for a specific model","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"4 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":"127574701","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":"Classification of Fire Related Tweets on Twitter Using Bidirectional Encoder Representations from Transformers (BERT)","authors":"Jairus Mingua, Dionis A. Padilla, Evan Joy Celino","doi":"10.1109/HNICEM54116.2021.9731956","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731956","url":null,"abstract":"Bidirectional Encoder Representation from Transformers (BERT) is a transfer learning model approach in natural language processing (NLP). BERT has different types of pre-trained models that can pre-train a language representation to create a model that can be finetuned on specific tasks using a dataset like text classification to produce state of the art predictions. Recent studies providing the use of BERT in natural language processing have highlighted that there are no publicly available Filipino tweet datasets regarding fire reports on social media that lead to a lack of classification models. This paper aims to design and implement a system to classify Filipino tweets using different pre-trained BERT models. Upon creating a model exclusive for organizing Filipino tweets using 2,081 tweets as a dataset that contains fire-related tweets, the researchers were able to compare the accuracy of the different finetuned pre-trained BERT models. The data shows a significant difference in the accuracy of each pre-trained BERT model. The highest of which is the BERT Base Uncased WWM model with a test accuracy of 87.50% and a train loss of 0.06 during training of the dataset. The least accurate among the pre-trained BERT models is the BERT Base Cased WWM model, with a test accuracy of 76.34% and a train loss of 0.2. The result shows that BERT Base Uncased WWM model can be a reliable model in classifying fire-related tweets. The accuracy obtained by the models may vary depending on how large the dataset is.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"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":"131051698","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":"Controlled Environment for Spinach Cultured Plant with Health Analysis using Machine Learning","authors":"Mdas Angelo M. Araneta, Daniel V. Asenjo, Carl Jeremiah L. Lamprea, Argilyn Mae L. Reyes, Oliver A. Medina, Anna-liza F. Sigue, Marielle M. Cabal, Aldrin J. Soriano, M. G. Beaño","doi":"10.1109/HNICEM54116.2021.9732020","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9732020","url":null,"abstract":"The combination of mobile application and machine learning with environmental sensing and image processing device in monitoring the health and growth of the plant in real-time. The goal of the study is to develop a controlled environment for spinach by monitoring its health condition using image processing and supervised machine learning. The device collects real-time data for humidity, temperature, and soil conditions using different sensors. Diagnosis of spinach health status is done by capturing the images of spinach using image processing techniques. Two classifiers were used in detecting spinach health conditions, Green for healthy spinach or no damages on leaves and Green, Yellow, Brown for unhealthy or with holes, damages; this classifier is also called datasets. Spinach health status is monitored using a mobile application called Spinach Monitoring Application (SPIMON) and all collected data are stored in the cloud. The result spinach health status showed that its best to culture spinach in a controlled environment.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"107 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":"131118029","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":"Development and Analysis of Footstep Power Harvester – A Case Study for the Viability of the Device in Surigao City","authors":"Robert R. Bacarro, V. J. Ylaya, R. R. Vicerra, Vicente Z. Delante","doi":"10.1109/HNICEM54116.2021.9732040","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9732040","url":null,"abstract":"This study develops a footstep generator and its viability to harvest energy in a two-shopping center in Surigao City. The footstep power harvester module was enclosed in a wood-tile type 3x2ft size where parallel piezoelectric were embedded inside to increase the output current and placed strategically in the main entrance where people generally pass through. In this research, a microcontroller was used to regulate the dc from the piezoelectric to the 3.7-volt battery. The voltage sensor, like the current sensor, was used to Figure out how much voltage was contained in two AA batteries. Data collection of harvested energy was done using two establishments, 12hours from 6 am to 12 pm and 12 to 6 pm. The total average amount of harvested power on one 3x2ft size was equal to 668.5 mW. Tripling the footstep power harvester module would increase the power generated to 2W, enough to charge a mobile phone.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"32 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":"132179021","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":"Vision-Based Hand Tracking System Development for Non-Face-to-Face Interaction","authors":"Isaiah Tupal, M. Cabatuan","doi":"10.1109/HNICEM54116.2021.9731873","DOIUrl":"https://doi.org/10.1109/HNICEM54116.2021.9731873","url":null,"abstract":"Human-computer interaction (HCI) focuses on the interaction between humans and computers and it exists ubiquitously in our daily lives, especially in post COVID era where non-face-to-face interaction is common. Since HCI usually uses a physical controller such as a mouse or a keyboard, it hinders National User Interface, giving a middle ground between the user and the computer. This paper presents a vision-based hand tracking system development for non-face-to-face interaction, which aims to improve HCI by being able to track the hand which will act as the pen and functioning as a reusable writing surface for creating texts, drawings, and such as well as removing or erasing using the user’s hand as the pen, and utilizing Open Computer Vision Library (OpenCV) and Mediapipe. Using the computer’s camera the hand will be tracked as the pen for creating basic drawings and handwriting. The vision-based board where the user can draw on and the pen or marker will be the user’s hand. The results indicate that this system is accurate enough to be a feasible application for handwriting ad basic drawings.","PeriodicalId":129868,"journal":{"name":"2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)","volume":"44 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":"131673162","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}