Pub Date : 2023-06-01DOI: 10.1109/IS3C57901.2023.00026
Hung-Hsin Li, Sheng-Chih Yang, Jyun-Jie Wang, Chi-Yuan Lin, Zong-Shang Hong
This system is based on the aeroponic planting method, provides the nutrient source and root humidity maintenance required for planting crops through water mist. The liquid fertilizer cooling control system designed by the refrigeration chip; the liquid fertilizer cooling control system is mainly the part that controls the water temperature. This system will be based on controlling the water temperature of the plants, and can control the appropriate water temperature according to the growth temperature required by each different plant. Improve the survival rate of crops, conduct big data analysis through the collected water temperature information, making information interpretation easier. Make plants grow smoothly in the suitable water temperature range.
{"title":"IoT Liquid Fertilizer Cooling Control System Designed for Agricultural Applications","authors":"Hung-Hsin Li, Sheng-Chih Yang, Jyun-Jie Wang, Chi-Yuan Lin, Zong-Shang Hong","doi":"10.1109/IS3C57901.2023.00026","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00026","url":null,"abstract":"This system is based on the aeroponic planting method, provides the nutrient source and root humidity maintenance required for planting crops through water mist. The liquid fertilizer cooling control system designed by the refrigeration chip; the liquid fertilizer cooling control system is mainly the part that controls the water temperature. This system will be based on controlling the water temperature of the plants, and can control the appropriate water temperature according to the growth temperature required by each different plant. Improve the survival rate of crops, conduct big data analysis through the collected water temperature information, making information interpretation easier. Make plants grow smoothly in the suitable water temperature range.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"26 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121003493","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00020
Yuepeng Shen, Jenhui Chen
Existing violent behavior datasets are not perfect in quantity and quality due to the difficulty of collecting. Although the state-of-the-art Transformer models had shown their capability in behavior recognition, it is unsuitable for the task of short-term behavior understanding (e.g., violent behavior recognition) due to the need for a large amount of data to achieve their best performance. Recently, a simple deep learning architecture, an all multilayer perceptron (MLP) architecture called MLP-Mixer, was proposed against Transformer in the task of a few-sample dataset to obtain competitive results. Motivated by spatio-temporal features on neurons, we invent a dual-form dataset for MLP-Mixer-based model training called aggregated spatio-temporal MLP-Mixer (ASM) to handle video understanding tasks. We show that ASM outperforms the state-of-the-art Transformer models as well as some of the best-performed convolutional neural network (CNN) approaches on three public datasets, smart-city CCTV violence detection dataset (SCVD), real-life violence situations (RLVS) dataset, and Hockey fight. Experimental results further validate our idea on short-term behavior scene understanding improvement.
{"title":"Aggregated Spatio-temporal MLP-Mixer for Violence Recognition in Video Clips","authors":"Yuepeng Shen, Jenhui Chen","doi":"10.1109/IS3C57901.2023.00020","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00020","url":null,"abstract":"Existing violent behavior datasets are not perfect in quantity and quality due to the difficulty of collecting. Although the state-of-the-art Transformer models had shown their capability in behavior recognition, it is unsuitable for the task of short-term behavior understanding (e.g., violent behavior recognition) due to the need for a large amount of data to achieve their best performance. Recently, a simple deep learning architecture, an all multilayer perceptron (MLP) architecture called MLP-Mixer, was proposed against Transformer in the task of a few-sample dataset to obtain competitive results. Motivated by spatio-temporal features on neurons, we invent a dual-form dataset for MLP-Mixer-based model training called aggregated spatio-temporal MLP-Mixer (ASM) to handle video understanding tasks. We show that ASM outperforms the state-of-the-art Transformer models as well as some of the best-performed convolutional neural network (CNN) approaches on three public datasets, smart-city CCTV violence detection dataset (SCVD), real-life violence situations (RLVS) dataset, and Hockey fight. Experimental results further validate our idea on short-term behavior scene understanding improvement.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121143279","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00024
Hung-Ming Chen, Shih-Ying Chen, Sheng-Hsien Hsueh, Sheng-Kai Wang
As the fields related to machine learning (ML)/deep learning (DL) continue to mature, the MLOps machine learning automation process is also gradually emerging. Then, many open-source MLOps frameworks based on Kubernetes have begun to be proposed. Currently, most Kubernetes-based MLOps frameworks aim to establish a common and easy-to-use ML pipeline environment for users to use based on ML containerized tasks. However, Kubernetes’ default container scheduler only considers the resource conditions of individual containerized tasks, rather than considering the scheduling of the entire containerized ML task composition. Such a situation may lead to the system resources not being utilized properly. Therefore, this study designs an improved ML task mechanism based on the Kubernetes-based platform to replace the Kubernetes default scheduler. The scheduling strategy in Kubernetes can be modified to better suit the needs of the machine learning development environment.
{"title":"Designing an Improved ML Task Scheduling Mechanism on Kubernetes","authors":"Hung-Ming Chen, Shih-Ying Chen, Sheng-Hsien Hsueh, Sheng-Kai Wang","doi":"10.1109/IS3C57901.2023.00024","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00024","url":null,"abstract":"As the fields related to machine learning (ML)/deep learning (DL) continue to mature, the MLOps machine learning automation process is also gradually emerging. Then, many open-source MLOps frameworks based on Kubernetes have begun to be proposed. Currently, most Kubernetes-based MLOps frameworks aim to establish a common and easy-to-use ML pipeline environment for users to use based on ML containerized tasks. However, Kubernetes’ default container scheduler only considers the resource conditions of individual containerized tasks, rather than considering the scheduling of the entire containerized ML task composition. Such a situation may lead to the system resources not being utilized properly. Therefore, this study designs an improved ML task mechanism based on the Kubernetes-based platform to replace the Kubernetes default scheduler. The scheduling strategy in Kubernetes can be modified to better suit the needs of the machine learning development environment.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115976663","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00039
Mu-Wei Li, Po-Lung Wu, S. Lo, Y. Chan, Shyr-Shen Yu
Measurement of plant root system architecture (RSA) traits is an important task for botany. Usually, the botanists put the plants in a transparent gel container for easy observation. Under this configuration, an easy-to-use way to measure RSA traits is to take images for observation. However, in single-view-angle 2D image often has problems such as occlusion and lack of depth information, so it is not convenient for measurement. Therefore, the reconstruction of the 3D root model from multi-view-angle images is a better solution. Under the above-mentioned planting configuration, the refractive distortion problem usually arises. This will lead to serious distortion of model reconstruction, so in this paper, a method based on ray tracing for correcting refraction distortion of objects in cylindrical containers is proposed.
{"title":"A Refractive Distortion Correction Method for 3D Root Reconstruction","authors":"Mu-Wei Li, Po-Lung Wu, S. Lo, Y. Chan, Shyr-Shen Yu","doi":"10.1109/IS3C57901.2023.00039","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00039","url":null,"abstract":"Measurement of plant root system architecture (RSA) traits is an important task for botany. Usually, the botanists put the plants in a transparent gel container for easy observation. Under this configuration, an easy-to-use way to measure RSA traits is to take images for observation. However, in single-view-angle 2D image often has problems such as occlusion and lack of depth information, so it is not convenient for measurement. Therefore, the reconstruction of the 3D root model from multi-view-angle images is a better solution. Under the above-mentioned planting configuration, the refractive distortion problem usually arises. This will lead to serious distortion of model reconstruction, so in this paper, a method based on ray tracing for correcting refraction distortion of objects in cylindrical containers is proposed.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132025594","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00059
J. Fang, Chen Ou, Ting-Chen Yeh, Yu-Yang Wang
H.266/VVC modifies the quadtree structure of HEVC and adopts the Quadtree with nested multi-type tree (QT-MTT) encoding structure to search for the best encoding unit. Although the QT-MTT encoding structure has better encoding efficiency, it also increases the computational complexity and encoding time. This paper mainly focuses on the QT-MTT structure of H.266/VVC intra-frame coding and proposes the use of convolutional neural networks (CNNs) based on deep learning to prematurely terminate the decision of the horizontal binary tree, horizontal ternary tree, vertical binary tree, or vertical ternary tree of $32times 32$ coding units, and skip the rate distortion optimization (RDO) step to save encoding time of H.266/VVC. Experiments show that this paper only approximately increases BDBR by 0.45 dB, but can reduce% of encoding time.
{"title":"Deep Learning Technology to Improve the Coding Efficiency of H.266/VVC","authors":"J. Fang, Chen Ou, Ting-Chen Yeh, Yu-Yang Wang","doi":"10.1109/IS3C57901.2023.00059","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00059","url":null,"abstract":"H.266/VVC modifies the quadtree structure of HEVC and adopts the Quadtree with nested multi-type tree (QT-MTT) encoding structure to search for the best encoding unit. Although the QT-MTT encoding structure has better encoding efficiency, it also increases the computational complexity and encoding time. This paper mainly focuses on the QT-MTT structure of H.266/VVC intra-frame coding and proposes the use of convolutional neural networks (CNNs) based on deep learning to prematurely terminate the decision of the horizontal binary tree, horizontal ternary tree, vertical binary tree, or vertical ternary tree of $32times 32$ coding units, and skip the rate distortion optimization (RDO) step to save encoding time of H.266/VVC. Experiments show that this paper only approximately increases BDBR by 0.45 dB, but can reduce% of encoding time.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127202282","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 : 2023-06-01DOI: 10.1109/is3c57901.2023.00047
Yuqing Gao, Ruey-Maw Chen
The capacitated vehicle routing problems (CVRPs) are well-known as NP-Hard, which aims to find the optimal route planning with the least cost without violating the constraints. A modified coronavirus herd immunity optimization with an associative customers savings algorithm, named MCASA, is designed to solve CVRPs. First, the individual solution update is modified to make the exploration more flexible. Second, a new saving algorithm, named ACSAAD, is suggested to adjust the customer visit order. Finally, a population state update mechanism is designed to prevent the CHIO from entering the exploitation stage quickly. Three different scale instances on the CVRPs dataset of CVPLIB were tested. The simulation results show that the MCASA can find the optimal solution for the tested instances, with ARPD no more than 0.2, indicating that the MCASA can effectively and efficiently solve CVRPs.
{"title":"Modified Coronavirus Herd Immunity optimization with an ACSAAD Algorithm for Capacitated Vehicle Routing Problems Vehicle Routing Problems","authors":"Yuqing Gao, Ruey-Maw Chen","doi":"10.1109/is3c57901.2023.00047","DOIUrl":"https://doi.org/10.1109/is3c57901.2023.00047","url":null,"abstract":"The capacitated vehicle routing problems (CVRPs) are well-known as NP-Hard, which aims to find the optimal route planning with the least cost without violating the constraints. A modified coronavirus herd immunity optimization with an associative customers savings algorithm, named MCASA, is designed to solve CVRPs. First, the individual solution update is modified to make the exploration more flexible. Second, a new saving algorithm, named ACSAAD, is suggested to adjust the customer visit order. Finally, a population state update mechanism is designed to prevent the CHIO from entering the exploitation stage quickly. Three different scale instances on the CVRPs dataset of CVPLIB were tested. The simulation results show that the MCASA can find the optimal solution for the tested instances, with ARPD no more than 0.2, indicating that the MCASA can effectively and efficiently solve CVRPs.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115934868","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00041
Bo-Yan Lin, Wei-Che Huang, Ming Wu, Iching Lin, S. Shih, Ya-Ling Kao, Yu-Da Lin
Ama is a challenging and risky profession. The income of this profession is highly dependent on the weather and the tides. Although technology can conveniently assist Ama in checking weather and tide information, no platform currently integrates both and performs analysis for Ama’s reference. Therefore, this study developed a system that uses data from the Central Weather Bureau and the Open Data Platform for Meteorological Data to build a dedicated database for integrated analysis. After obtaining the coastal hazard prediction results through algorithms and Recurrent Neural Networks, the information is displayed in an APP for the user’s reference. The APP also records the user’s current location, and if any danger occurs, it can report to the rescue unit through the APP. The rescue unit can know the location through the web page and proceed with the rescue.
{"title":"An intelligent Ama safety protection system based on smart IoT data and deep learning","authors":"Bo-Yan Lin, Wei-Che Huang, Ming Wu, Iching Lin, S. Shih, Ya-Ling Kao, Yu-Da Lin","doi":"10.1109/IS3C57901.2023.00041","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00041","url":null,"abstract":"Ama is a challenging and risky profession. The income of this profession is highly dependent on the weather and the tides. Although technology can conveniently assist Ama in checking weather and tide information, no platform currently integrates both and performs analysis for Ama’s reference. Therefore, this study developed a system that uses data from the Central Weather Bureau and the Open Data Platform for Meteorological Data to build a dedicated database for integrated analysis. After obtaining the coastal hazard prediction results through algorithms and Recurrent Neural Networks, the information is displayed in an APP for the user’s reference. The APP also records the user’s current location, and if any danger occurs, it can report to the rescue unit through the APP. The rescue unit can know the location through the web page and proceed with the rescue.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116662013","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00025
Hsing-Hung Lin
Because of climate change and global warming, the demand for renewable energy grows continually. Among the renewable energy sources, solar power is the most common type due to its low construction cost and easy parallel connection with existing power grids. The power company can not only dispatch power but obtain better electricity price contracts by forecasting the power generation of photovoltaic panels. In the past, many studies have focused on the research of solar power generation, from statistical regression to mathematical planning models to heuristic meta methods and evolutionary algorithms. Recently, there are more and more literatures using machine learning to establish power generation forecasting models and even the deep learning model of artificial intelligence. However, research on hyperparameter optimization to make ensemble learning algorithms perform better is still scarce. This paper attempts to optimize the hyperparameters in the modeling process of ensemble learning with evolutionary algorithms and construct more accurate solar power prediction models. Gradient boosting regressor is employed as ensemble learning models where the hyperparameters are optimized by differential evolution, Jaya algorithm, particle swarm optimization and genetic algorithm for comparison. The data is based on practical data and weather forecasting data of solar power plants in central Taiwan. The computational results reveal that differential evolution outperforms to explore the optimal hyperparameter combination of the prediction model for solar power generation.
{"title":"Applying Evolutionary Algorithms to Optimize Hyperparameters for Prediction Model of Solar Power Generation","authors":"Hsing-Hung Lin","doi":"10.1109/IS3C57901.2023.00025","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00025","url":null,"abstract":"Because of climate change and global warming, the demand for renewable energy grows continually. Among the renewable energy sources, solar power is the most common type due to its low construction cost and easy parallel connection with existing power grids. The power company can not only dispatch power but obtain better electricity price contracts by forecasting the power generation of photovoltaic panels. In the past, many studies have focused on the research of solar power generation, from statistical regression to mathematical planning models to heuristic meta methods and evolutionary algorithms. Recently, there are more and more literatures using machine learning to establish power generation forecasting models and even the deep learning model of artificial intelligence. However, research on hyperparameter optimization to make ensemble learning algorithms perform better is still scarce. This paper attempts to optimize the hyperparameters in the modeling process of ensemble learning with evolutionary algorithms and construct more accurate solar power prediction models. Gradient boosting regressor is employed as ensemble learning models where the hyperparameters are optimized by differential evolution, Jaya algorithm, particle swarm optimization and genetic algorithm for comparison. The data is based on practical data and weather forecasting data of solar power plants in central Taiwan. The computational results reveal that differential evolution outperforms to explore the optimal hyperparameter combination of the prediction model for solar power generation.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117011985","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 : 2023-06-01DOI: 10.1109/is3c57901.2023.00058
T. Tseng, Jian-Jiun Ding
With the development of radar systems, hand gesture recognition of radar sensors has become easier to operate, and the resolution has increased. Nowadays, radar gesture recognition frequency employs (multiple-input and multiple-output) MIMO radar as a sensor since it has a better spatial resolution. This article proposes a hand gesture recognition based on a MIMO radar sensor, which can differentiate five gestures: swipe left, swipe right, pat, push, and pull. After receiving the data from the radar sensor, we first apply a two-dimensional Fast-Fourier Transform (2D-FFT) for a time series of range-Doppler maps. Next, we detect the moving target range, velocity, and angle values through time. Finally, the classification and regression tree (CART) algorithm is applied to a collected dataset, with targets’ time-variant characteristics as the parameters. The overall recognition rate of 94% is obtained from the proposed system using a decision-tree-based classifier. The experiment results show that this gesture-recognition system is promising in classifying multiple gestures.
{"title":"Hand Gesture Recognition via MIMO Radar Sensors and Space-Frequency Domain Information","authors":"T. Tseng, Jian-Jiun Ding","doi":"10.1109/is3c57901.2023.00058","DOIUrl":"https://doi.org/10.1109/is3c57901.2023.00058","url":null,"abstract":"With the development of radar systems, hand gesture recognition of radar sensors has become easier to operate, and the resolution has increased. Nowadays, radar gesture recognition frequency employs (multiple-input and multiple-output) MIMO radar as a sensor since it has a better spatial resolution. This article proposes a hand gesture recognition based on a MIMO radar sensor, which can differentiate five gestures: swipe left, swipe right, pat, push, and pull. After receiving the data from the radar sensor, we first apply a two-dimensional Fast-Fourier Transform (2D-FFT) for a time series of range-Doppler maps. Next, we detect the moving target range, velocity, and angle values through time. Finally, the classification and regression tree (CART) algorithm is applied to a collected dataset, with targets’ time-variant characteristics as the parameters. The overall recognition rate of 94% is obtained from the proposed system using a decision-tree-based classifier. The experiment results show that this gesture-recognition system is promising in classifying multiple gestures.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128649657","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 : 2023-06-01DOI: 10.1109/IS3C57901.2023.00045
Wen-Chung Tsai, Huan-Hsiuan Lin, Tsung-Sheng Hsu
The research implemented a set of health-promoting devices. One of the Internet-of-Things (IoT) devices is a wearable bracelet that can measure the user’s blood oxygen value of foots in real time. When abnormal values are detected, the bracelet can immediately notify the user through LINE messages, and simultaneously automatically power on another device of a foot-spa machine to preheat the water in it. Consequently, the hypoxic user can use the foot-spa machine to relieve blood hypoxia condition. Furthermore, when it is detected that the user not using the foot-spa machine, other warning messages will issue to the family members in the same LINE group, and then automatically turn-off the foot-spa machine to save power. Especially, in order to ensure personal physiological information is protected transmitting through the network, the implementation perform special encryption processing to save the computational burden of the microprocessor or to reduce the transmission latency over the network. Performance benefits for the encryption adaptions of the implemented platform are provided and discussed in the section of experimental results.
{"title":"Implementatons of Health-Promotion IoT Devices for Secure Physiological Information Protection","authors":"Wen-Chung Tsai, Huan-Hsiuan Lin, Tsung-Sheng Hsu","doi":"10.1109/IS3C57901.2023.00045","DOIUrl":"https://doi.org/10.1109/IS3C57901.2023.00045","url":null,"abstract":"The research implemented a set of health-promoting devices. One of the Internet-of-Things (IoT) devices is a wearable bracelet that can measure the user’s blood oxygen value of foots in real time. When abnormal values are detected, the bracelet can immediately notify the user through LINE messages, and simultaneously automatically power on another device of a foot-spa machine to preheat the water in it. Consequently, the hypoxic user can use the foot-spa machine to relieve blood hypoxia condition. Furthermore, when it is detected that the user not using the foot-spa machine, other warning messages will issue to the family members in the same LINE group, and then automatically turn-off the foot-spa machine to save power. Especially, in order to ensure personal physiological information is protected transmitting through the network, the implementation perform special encryption processing to save the computational burden of the microprocessor or to reduce the transmission latency over the network. Performance benefits for the encryption adaptions of the implemented platform are provided and discussed in the section of experimental results.","PeriodicalId":142483,"journal":{"name":"2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116218934","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}
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