Pub Date : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786442
Dan Liu, Jonathan M. Caballero, Ronaldo Juanatas
Discrete wavelet has excellent characteristics such as multi-scale analysis, fast operation speed, noise elimination, and singular value decomposition has stable characteristics, so the blind watermarking algorithm combined with the two has high practical value. At the same time, the watermark is scrambled by Arnold transform to improve its secrecy. After using JPEG compression and shearing to attack the image after embedding the watermark, the clearer watermark can still be extracted from it. The obtained PSNR value is greater than 30. At the same time, the NC value is very close to 1. Therefore, the algorithm has good robustness and can provide effective protection for the copyright of various digital works.
{"title":"A Digital Watermarking Algorithm Based on Digital Wavelet and Singular Value Division","authors":"Dan Liu, Jonathan M. Caballero, Ronaldo Juanatas","doi":"10.1109/ICBIR54589.2022.9786442","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786442","url":null,"abstract":"Discrete wavelet has excellent characteristics such as multi-scale analysis, fast operation speed, noise elimination, and singular value decomposition has stable characteristics, so the blind watermarking algorithm combined with the two has high practical value. At the same time, the watermark is scrambled by Arnold transform to improve its secrecy. After using JPEG compression and shearing to attack the image after embedding the watermark, the clearer watermark can still be extracted from it. The obtained PSNR value is greater than 30. At the same time, the NC value is very close to 1. Therefore, the algorithm has good robustness and can provide effective protection for the copyright of various digital works.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124070826","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 : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786444
Sumeth Yuenyong, Narit Hnoohom, K. Wongpatikaseree, Sattaya Singkul
Speech emotion recognition (SER) is an important part of human-computer interaction. SER face many challenges such as acoustic environment of speech, and the amount of data available for training. For Thai in particular, there is additional challenge from the language using tones, and the size of available dataset is relatively small. In this work we propose Thai Speech Emotion Recognition With Speech Enhancement (TH-SERSE). TH-SERSE consists of speech enhancement using Conv-TasNet followed by pre-training using contrastive predictive coding. The pre-trained model was then finetuned for emotion classification. We experimented on two datasets: EMOLA and ThaiSER that has open and closed acoustic environments, respectively. The experiments show that our method outperforms recently proposed methods.
{"title":"Real-Time Thai Speech Emotion Recognition With Speech Enhancement Using Time-Domain Contrastive Predictive Coding and Conv-Tasnet","authors":"Sumeth Yuenyong, Narit Hnoohom, K. Wongpatikaseree, Sattaya Singkul","doi":"10.1109/ICBIR54589.2022.9786444","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786444","url":null,"abstract":"Speech emotion recognition (SER) is an important part of human-computer interaction. SER face many challenges such as acoustic environment of speech, and the amount of data available for training. For Thai in particular, there is additional challenge from the language using tones, and the size of available dataset is relatively small. In this work we propose Thai Speech Emotion Recognition With Speech Enhancement (TH-SERSE). TH-SERSE consists of speech enhancement using Conv-TasNet followed by pre-training using contrastive predictive coding. The pre-trained model was then finetuned for emotion classification. We experimented on two datasets: EMOLA and ThaiSER that has open and closed acoustic environments, respectively. The experiments show that our method outperforms recently proposed methods.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121368911","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 : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786383
Somsawut Nindam, T. Manmai, Hyo Jong Lee
This paper proposes a multi-label image classification of Jasmine Rice (Thai Hom Mali) seed germination using the Deep Neural Network architecture. First, we have collected the dataset of normal germination of the rice and separated them into three classes: excellent-germination, good-germination, and poor-germination. Second, we feed the dataset into the Convolutional Neural Network for multi-label classifications. The dataset consists of 970 pictures in the training set and 194 images in the validation set. Lastly, we evaluated the model based on the confusion matrix. The results show that the precision, recall, and Fl-score are 0.80, 1.00, and 0.89 for excellent germination, 0.83, 0.83, and 0.83 for good germination, and 1.00, 0.87, 0.93 for poor germination, respectively. The accuracy of the predictions is satisfactory, which is higher than 0.89.
{"title":"Multi-Label Classification of Jasmine Rice Germination Using Deep Neural Network","authors":"Somsawut Nindam, T. Manmai, Hyo Jong Lee","doi":"10.1109/ICBIR54589.2022.9786383","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786383","url":null,"abstract":"This paper proposes a multi-label image classification of Jasmine Rice (Thai Hom Mali) seed germination using the Deep Neural Network architecture. First, we have collected the dataset of normal germination of the rice and separated them into three classes: excellent-germination, good-germination, and poor-germination. Second, we feed the dataset into the Convolutional Neural Network for multi-label classifications. The dataset consists of 970 pictures in the training set and 194 images in the validation set. Lastly, we evaluated the model based on the confusion matrix. The results show that the precision, recall, and Fl-score are 0.80, 1.00, and 0.89 for excellent germination, 0.83, 0.83, and 0.83 for good germination, and 1.00, 0.87, 0.93 for poor germination, respectively. The accuracy of the predictions is satisfactory, which is higher than 0.89.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128489865","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}
This study was to explore students’ opinions on online learning management during the COVID-19 situation and additional suggestions in order to improve students’ learning outcomes for the next semester. The population of this study was 555 second year students who enrolled in the TOEIC Preparation course in the second semester of 2021 academic year. The samples in this study were 150 students derived through Simple Random Sampling Technique. Research instrument used in this study was a questionnaire. Data analysis was percentage, mean, standard deviation, and content analysis. The research findings were: 1) students had the highest opinion at a high rank in materials, followed by accessibility and assessment; and 2) the additional suggestions were listed as quizzes and games applications should be used as the classroom activities; more listening activities and practices should be concerned; vocabulary exercises are important for students to review the vocabulary in the TOEIC test; and online applications for practice TOEIC reading should be used in the classroom learning.
{"title":"Students’ Opinions on Online Learning Management during the COVID-19 Situation","authors":"Keerati Thongsonkleeb, Suwatana Daengsubha, Nuntiporn Boonsieng","doi":"10.1109/ICBIR54589.2022.9786516","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786516","url":null,"abstract":"This study was to explore students’ opinions on online learning management during the COVID-19 situation and additional suggestions in order to improve students’ learning outcomes for the next semester. The population of this study was 555 second year students who enrolled in the TOEIC Preparation course in the second semester of 2021 academic year. The samples in this study were 150 students derived through Simple Random Sampling Technique. Research instrument used in this study was a questionnaire. Data analysis was percentage, mean, standard deviation, and content analysis. The research findings were: 1) students had the highest opinion at a high rank in materials, followed by accessibility and assessment; and 2) the additional suggestions were listed as quizzes and games applications should be used as the classroom activities; more listening activities and practices should be concerned; vocabulary exercises are important for students to review the vocabulary in the TOEIC test; and online applications for practice TOEIC reading should be used in the classroom learning.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129272379","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 : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786504
Nunnaphat Reungyu, Phuwadon Waiyanet
RPA is a business process automation software that is able to perform assigned tasks efficiently. It is easy to control and can reduce human errors, promoting RPA has greater automated roles incompatible and humane work, leading to a number of organizations have turned their attention to RPA. Therefore, this project aims to survey the RPA users and their attitudes on implementation. To examine the average of the statically behavioral impacted factors of the potential influence of RPA in organizational implementation and propose guidelines for applying RPA to organizations in the future.This project applies a questionnaire as a tool to collect data. There was a sample group of businesses that applied RPA in the organization in Thailand. 640 samples the data analysis was done by quantity method, frequency, percentage (%), mean and standard deviation. By using the ready-made statistical program SPSS to analyze the values.The results show that the sample is likely to continue to adopt RPA and it is expected that organizations will have higher application of RPA technology in the future.
{"title":"An Exploratory Study on the Impact of RPA (Robotic Process Automation) Implementation on Behavioral Attitudes and Intentions within Organizations","authors":"Nunnaphat Reungyu, Phuwadon Waiyanet","doi":"10.1109/ICBIR54589.2022.9786504","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786504","url":null,"abstract":"RPA is a business process automation software that is able to perform assigned tasks efficiently. It is easy to control and can reduce human errors, promoting RPA has greater automated roles incompatible and humane work, leading to a number of organizations have turned their attention to RPA. Therefore, this project aims to survey the RPA users and their attitudes on implementation. To examine the average of the statically behavioral impacted factors of the potential influence of RPA in organizational implementation and propose guidelines for applying RPA to organizations in the future.This project applies a questionnaire as a tool to collect data. There was a sample group of businesses that applied RPA in the organization in Thailand. 640 samples the data analysis was done by quantity method, frequency, percentage (%), mean and standard deviation. By using the ready-made statistical program SPSS to analyze the values.The results show that the sample is likely to continue to adopt RPA and it is expected that organizations will have higher application of RPA technology in the future.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115197860","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 : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786388
Maya Sukmawati, Andri D. Setiawan
The manufacturing industry is one of the fastest growing industries in Indonesia, the industry is a leading sector that contributes quite a lot to gross domestic product. One of the manufacturing industries is the furniture industry. The many environmental issues that occur have led to increased public awareness of the importance of environmental sustainability. This is a challenge in the manufacturing industry and requires companies to implement green supply chain management (GSCM). One of the strategies contained in GSCM is green supplier selection.Supplier selection criteria are generally only based on aspects of cost, delivery and quality.In this study, economic criteria (not only cost) and environmental criteria are used and propose a comprehensive model for selecting environmentally friendly suppliers.The method used in this research is AHP and TOPSIS. AHP is used to determine the weight of the criteria and sub-criteria, while TOPSIS is used to determine the priority of alternatives that are close to the positive ideal solution.
{"title":"A Conceptual Model of Green Supplier Selection in the Manufacturing Industry Using AHP and TOPSIS Methods","authors":"Maya Sukmawati, Andri D. Setiawan","doi":"10.1109/ICBIR54589.2022.9786388","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786388","url":null,"abstract":"The manufacturing industry is one of the fastest growing industries in Indonesia, the industry is a leading sector that contributes quite a lot to gross domestic product. One of the manufacturing industries is the furniture industry. The many environmental issues that occur have led to increased public awareness of the importance of environmental sustainability. This is a challenge in the manufacturing industry and requires companies to implement green supply chain management (GSCM). One of the strategies contained in GSCM is green supplier selection.Supplier selection criteria are generally only based on aspects of cost, delivery and quality.In this study, economic criteria (not only cost) and environmental criteria are used and propose a comprehensive model for selecting environmentally friendly suppliers.The method used in this research is AHP and TOPSIS. AHP is used to determine the weight of the criteria and sub-criteria, while TOPSIS is used to determine the priority of alternatives that are close to the positive ideal solution.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116137277","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 : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786421
Phasook Phattarasooksirot, A. Sento
Facial expression is the most common way to demonstrate individual emotional state. People intend to understand other people’s emotional state by observing their interactive partner’s facial expression. However, there are some limitations using the mentioned approach regarding the individual observative capability and the interactive partner’s privacy. Hence, the facial emotional expression recognition system based on Convolutional Neural Network (CNN) was employed. The most reliable approach is utilizing the state-of-the-art, such as Inception Net, ResNet, and VGG which had been developed to excel in their specific feature extraction approach. In addition to the mentioned models, there is also a common usage model, such as Convolutional Auto Encode (CAE) which is capable of high-efficient noise reduction. Then, some more advanced models were developed based on the concept of the initially state-of-the-art models, such as U-Net to perform high-performance image segmentation using feature fusion and transposed convolution technique. In this paper, the hybrid deep learning algorithm based on CNN and CAE is developed using the significant features from the mentioned state-of the-arts and 2 combinations of the modified CNN model to predict human emotional state. The experimental result shows the proposed model which achieves the predictive accuracy of 88%
面部表情是表现个人情绪状态最常见的方式。人们试图通过观察互动伙伴的面部表情来了解他人的情绪状态。然而，使用上述方法在个人观察能力和交互伙伴的隐私方面存在一些限制。因此，采用基于卷积神经网络(CNN)的面部情绪表情识别系统。最可靠的方法是利用最先进的技术，如Inception Net、ResNet和VGG，这些技术已经被开发出来，在其特定的特征提取方法上表现出色。除了上述模型之外，还有一种常用的模型，如卷积自动编码(Convolutional Auto Encode, CAE)，它能够高效地降噪。然后，基于最初最先进的模型的概念，开发了一些更先进的模型，如U-Net，使用特征融合和转置卷积技术进行高性能图像分割。本文基于CNN和CAE的混合深度学习算法，利用上述最先进的显著特征和改进的CNN模型的两种组合来预测人类的情绪状态。实验结果表明，该模型的预测准确率达到88%
{"title":"Facial Emotional Expression Recognition Using Hybrid Deep Learning Algorithm","authors":"Phasook Phattarasooksirot, A. Sento","doi":"10.1109/ICBIR54589.2022.9786421","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786421","url":null,"abstract":"Facial expression is the most common way to demonstrate individual emotional state. People intend to understand other people’s emotional state by observing their interactive partner’s facial expression. However, there are some limitations using the mentioned approach regarding the individual observative capability and the interactive partner’s privacy. Hence, the facial emotional expression recognition system based on Convolutional Neural Network (CNN) was employed. The most reliable approach is utilizing the state-of-the-art, such as Inception Net, ResNet, and VGG which had been developed to excel in their specific feature extraction approach. In addition to the mentioned models, there is also a common usage model, such as Convolutional Auto Encode (CAE) which is capable of high-efficient noise reduction. Then, some more advanced models were developed based on the concept of the initially state-of-the-art models, such as U-Net to perform high-performance image segmentation using feature fusion and transposed convolution technique. In this paper, the hybrid deep learning algorithm based on CNN and CAE is developed using the significant features from the mentioned state-of the-arts and 2 combinations of the modified CNN model to predict human emotional state. The experimental result shows the proposed model which achieves the predictive accuracy of 88%","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114223477","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 : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786419
Phirawat Rattanachote, T. Kaewkongka, P. Nimdum
Demolding force is still unfamiliar even though it plays a crucial role in the gripper design process for automation systems in rubber product manufacturing nowadays. This study aims to determine how demolding force can be evaluated by using contact area and rubber hardness. A full factorial design was experimentally executed to examine the relation between contact area and rubber hardness on the demolding force while the Multiple Regression Model was used to estimate the demolding force. Analysis of the experiment demonstrated that contact area has influenced demolding force more than hardness by the p-value of 6.16 x 10-14 and 0.036 respectively. Finally, the multiple regression model that researchers developed has a correlation coefficient (R2) of 74.9 %, which indicates how much variation in the contact area and rubber hardness is explained by the demolding force in the multiple regression model.
{"title":"Multiple Regression Model For Demolding Force Of Rubber Products Using A Full Factorial Design","authors":"Phirawat Rattanachote, T. Kaewkongka, P. Nimdum","doi":"10.1109/ICBIR54589.2022.9786419","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786419","url":null,"abstract":"Demolding force is still unfamiliar even though it plays a crucial role in the gripper design process for automation systems in rubber product manufacturing nowadays. This study aims to determine how demolding force can be evaluated by using contact area and rubber hardness. A full factorial design was experimentally executed to examine the relation between contact area and rubber hardness on the demolding force while the Multiple Regression Model was used to estimate the demolding force. Analysis of the experiment demonstrated that contact area has influenced demolding force more than hardness by the p-value of 6.16 x 10-14 and 0.036 respectively. Finally, the multiple regression model that researchers developed has a correlation coefficient (R2) of 74.9 %, which indicates how much variation in the contact area and rubber hardness is explained by the demolding force in the multiple regression model.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127386299","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 : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786468
Yuta Ishii, T. Nakanishi, Ryotaro Okada, A. Minematsu
In general, when a place depicted in a novel exists, users may like to visit the site that appears in the story. There is a need to efficiently visit tourist spots in or around the places depicted in the novel when visiting them. When we realize to retrieve appropriate tourist spots in areas in stories, it is possible to increase the number of new sightseeing opportunities for users. This paper presents a tourist spot recommendation method corresponding to place names appearing in novel contents. Our system recommends some actual tourist spots corresponding to a novel selected by a user. The feature of our proposed method is to recommend tourist spots within the range that users can visit in a day, based on the place names that appear in the novel. We apply this method to realize seamless linking media between the creative world like a novel and the real-world.
{"title":"Tourist Spots Recommendation Method Corresponding to Place Names Appearing in Novel Contents","authors":"Yuta Ishii, T. Nakanishi, Ryotaro Okada, A. Minematsu","doi":"10.1109/ICBIR54589.2022.9786468","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786468","url":null,"abstract":"In general, when a place depicted in a novel exists, users may like to visit the site that appears in the story. There is a need to efficiently visit tourist spots in or around the places depicted in the novel when visiting them. When we realize to retrieve appropriate tourist spots in areas in stories, it is possible to increase the number of new sightseeing opportunities for users. This paper presents a tourist spot recommendation method corresponding to place names appearing in novel contents. Our system recommends some actual tourist spots corresponding to a novel selected by a user. The feature of our proposed method is to recommend tourist spots within the range that users can visit in a day, based on the place names that appear in the novel. We apply this method to realize seamless linking media between the creative world like a novel and the real-world.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125979794","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 : 2022-05-19DOI: 10.1109/ICBIR54589.2022.9786426
Pornchai Chityuenyong, W. Wongsuwan, P. Chaiwiwatworakul, Narong Anankawanich
The application of the Radiant Cooling technology for air-conditioning purpose in the hot and humid climatic condition such like Thailand, was investigated by simulation and experimentation. The radiant cooling panel was integrated to the dedicated outdoor air system called RCP-DOAS, which the ceiling type was implemented. The existing system was controlled by the Proportional-Integral-Derivative (PID) controller. The research focused on the employment of the Model Predictive Control (MPC) to enhance the PLC control of the chilled water supply condition of the RCP-DOAS. The thermal circuit equivalent was used for modeling of the heat transfer characteristics of the ceiling type RCP dynamically. The modeling and simulation was conducted in MATLAB Simulink to predict the time required to approach the set point air-conditioned room temperature about 24 °C. In comparison between the PID controller and MPC controller, it could be expected well sooner approaching the set point. The simulated results showed reduction of the time to reach stability by 50.9%. Thereafter, the MPC controller will be implemented to control the RCP-DOAS system during the actual experiment expecting better set point approaching and energy saving.
{"title":"Improvement of Radiant Cooling Panel (RCP) Air Conditioning (AC) using Model Predictive Control (MPC)","authors":"Pornchai Chityuenyong, W. Wongsuwan, P. Chaiwiwatworakul, Narong Anankawanich","doi":"10.1109/ICBIR54589.2022.9786426","DOIUrl":"https://doi.org/10.1109/ICBIR54589.2022.9786426","url":null,"abstract":"The application of the Radiant Cooling technology for air-conditioning purpose in the hot and humid climatic condition such like Thailand, was investigated by simulation and experimentation. The radiant cooling panel was integrated to the dedicated outdoor air system called RCP-DOAS, which the ceiling type was implemented. The existing system was controlled by the Proportional-Integral-Derivative (PID) controller. The research focused on the employment of the Model Predictive Control (MPC) to enhance the PLC control of the chilled water supply condition of the RCP-DOAS. The thermal circuit equivalent was used for modeling of the heat transfer characteristics of the ceiling type RCP dynamically. The modeling and simulation was conducted in MATLAB Simulink to predict the time required to approach the set point air-conditioned room temperature about 24 °C. In comparison between the PID controller and MPC controller, it could be expected well sooner approaching the set point. The simulated results showed reduction of the time to reach stability by 50.9%. Thereafter, the MPC controller will be implemented to control the RCP-DOAS system during the actual experiment expecting better set point approaching and energy saving.","PeriodicalId":216904,"journal":{"name":"2022 7th International Conference on Business and Industrial Research (ICBIR)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126999709","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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