Pub Date : 2023-12-29DOI: 10.46604/aiti.2023.12576
Y. Shih, Hong-Hao Chen
This study uses the sol-gel method to modify the phase change microcapsules. The phase change material (PCM) is encapsulated by a polymer shell to reduce the leakage in the solid-liquid transition. Furthermore, the nano-graphite particle (NGP) is introduced into the shell to increase its thermal conductivity. The particle size and enthalpy value of the obtained microcapsules are approximately 3 μm and 150.3 J/g, respectively. The results show that the encapsulation efficiency of PCM in the prepared microcapsules is increased and the crystallization rate of PCM becomes faster when the NGP is added. The obtained microcapsules and wood flour are incorporated into high-density polyethylene (HDPE) to form a wood-plastic composite (WPC). The results indicate that the tensile and impact strengths of the WPC are 24.1 MPa and 48.7 J/m, respectively. Moreover, it is observed that the addition of these phase-change microcapsules can improve the heat dissipation of HDPE and accelerate the speed of thermal diffusion.
{"title":"Synthesis and Characterization of Phase Change Microcapsules Containing Nano-Graphite","authors":"Y. Shih, Hong-Hao Chen","doi":"10.46604/aiti.2023.12576","DOIUrl":"https://doi.org/10.46604/aiti.2023.12576","url":null,"abstract":"This study uses the sol-gel method to modify the phase change microcapsules. The phase change material (PCM) is encapsulated by a polymer shell to reduce the leakage in the solid-liquid transition. Furthermore, the nano-graphite particle (NGP) is introduced into the shell to increase its thermal conductivity. The particle size and enthalpy value of the obtained microcapsules are approximately 3 μm and 150.3 J/g, respectively. The results show that the encapsulation efficiency of PCM in the prepared microcapsules is increased and the crystallization rate of PCM becomes faster when the NGP is added. The obtained microcapsules and wood flour are incorporated into high-density polyethylene (HDPE) to form a wood-plastic composite (WPC). The results indicate that the tensile and impact strengths of the WPC are 24.1 MPa and 48.7 J/m, respectively. Moreover, it is observed that the addition of these phase-change microcapsules can improve the heat dissipation of HDPE and accelerate the speed of thermal diffusion.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143870","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 : 2023-12-29DOI: 10.46604/aiti.2023.12000
Chanjin Jeong, Dong-Hyun Kim, H. Yoo, Seung-Oh Lee
To reduce flood damages, the Ministry of Environment in Korea has provided a flood inundation map so that people can expediently identify flood-prone areas. However, the current flood inundation maps have been produced based on the DEM which makes it difficult to represent realistic situations due to the lack of reproduction of land surface conditions. This study aims to provide more accurate and detailed flood inundation maps for flooding events due to river overflow in small urban areas. In this study, flood inundation analysis is performed using the river analysis system, HEC-RAS 2D, with the DSM and the DEM of urban areas in the Anyang Stream Basin, Korea to examine the differences in terms of terrain data and flooded area. Finally, for urban areas with dense buildings and congested road networks, the flood inundation analysis based on DSM can represent a more realistic flood situation and create an appropriate flood inundation map.
为了减少洪水造成的损失，韩国环境部提供了洪水淹没地图，以便人们能够快速识别洪水易发区。然而，目前的洪水淹没地图是基于 DEM 制作的，由于缺乏对地表情况的再现，因此难以代表实际情况。本研究旨在为小型城市地区因河流泛滥导致的洪水事件提供更准确、更详细的洪水淹没图。在本研究中，使用河流分析系统 HEC-RAS 2D 与韩国安阳溪流域城市地区的 DSM 和 DEM 进行了洪水淹没分析，以检验地形数据和淹没面积方面的差异。最后，对于建筑物密集、道路网络拥挤的城市地区，基于 DSM 的洪水淹没分析能够更真实地反映洪水情况，并绘制出合适的洪水淹没图。
{"title":"Selection of Elevation Models for Flood Inundation Map Generation in Small Urban Stream: Case Study of Anyang Stream","authors":"Chanjin Jeong, Dong-Hyun Kim, H. Yoo, Seung-Oh Lee","doi":"10.46604/aiti.2023.12000","DOIUrl":"https://doi.org/10.46604/aiti.2023.12000","url":null,"abstract":"To reduce flood damages, the Ministry of Environment in Korea has provided a flood inundation map so that people can expediently identify flood-prone areas. However, the current flood inundation maps have been produced based on the DEM which makes it difficult to represent realistic situations due to the lack of reproduction of land surface conditions. This study aims to provide more accurate and detailed flood inundation maps for flooding events due to river overflow in small urban areas. In this study, flood inundation analysis is performed using the river analysis system, HEC-RAS 2D, with the DSM and the DEM of urban areas in the Anyang Stream Basin, Korea to examine the differences in terms of terrain data and flooded area. Finally, for urban areas with dense buildings and congested road networks, the flood inundation analysis based on DSM can represent a more realistic flood situation and create an appropriate flood inundation map.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":"136 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145697","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 : 2023-12-29DOI: 10.46604/aiti.2023.12038
Vladimír Smejkal, J. Kodl
Civil liability legislation is currently being developed, but little attention has been paid to the issue of criminal liability for the actions of robots. The study describes the generations of robots and points out the concerns about robots’ autonomy. The more autonomy robots obtain, the greater capacity they have for self-learning, yet the more difficulty in proving the failure foreseeability when designing and whether culpability or the elements of a specific crime can be considered. In this study, the tort liability depending on the category of robots is described, and the possible solutions are analyzed. It is shown that there is no need to introduce new criminal law constructions, but to focus on the process of proof. Instead of changing the legal system, it is necessary to create the most detailed audit trail telling about the robot’s actions and surroundings or to have a digital twin of the robot.
{"title":"Challenges and Solutions to Criminal Liability for the Actions of Robots and AI","authors":"Vladimír Smejkal, J. Kodl","doi":"10.46604/aiti.2023.12038","DOIUrl":"https://doi.org/10.46604/aiti.2023.12038","url":null,"abstract":"Civil liability legislation is currently being developed, but little attention has been paid to the issue of criminal liability for the actions of robots. The study describes the generations of robots and points out the concerns about robots’ autonomy. The more autonomy robots obtain, the greater capacity they have for self-learning, yet the more difficulty in proving the failure foreseeability when designing and whether culpability or the elements of a specific crime can be considered. In this study, the tort liability depending on the category of robots is described, and the possible solutions are analyzed. It is shown that there is no need to introduce new criminal law constructions, but to focus on the process of proof. Instead of changing the legal system, it is necessary to create the most detailed audit trail telling about the robot’s actions and surroundings or to have a digital twin of the robot.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144416","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 : 2023-12-29DOI: 10.46604/aiti.2023.12683
Jonni Firdaus, Usman Ahmad, W. Budiastra, I. Dewa, Made Subrata
This study investigates the feasibility of employing near-infrared (NIR) spectroscopy with multiple linear regression (MLR) to estimate macronutrients in paddy soil compared with partial least squares (PLS) and principal component regression (PCR). Seventy-nine soil samples from West Java Province, Indonesia, are subject to conventional nutrient analysis and NIR spectroscopy (1000-2500 nm). The reflectance data undergoes various pretreatment techniques, and MLR models are calibrated using the forward method to achieve correlations exceeding 0.90. The best model calibrations are selected based on high correlation coefficients, determination coefficients, RPD, and low RMSE values. Meanwhile, the comparison of performance MLR is made with the PLS and PCR models. Results indicate that simple MLR models perform less than PLS for all nutrients, better than PCR for nitrogen, and below PCR for phosphorus and potassium. However, MLR reliably estimates soil nitrogen, phosphorus, and potassium content with ratio of performance to deviation (RPD) exceeding 2.0. This study demonstrates the potential of MLR for precise macronutrient estimation in paddy soil.
{"title":"Estimating Macronutrient Content of Paddy Soil Based on Near-Infrared Spectroscopy Technology Using Multiple Linear Regression","authors":"Jonni Firdaus, Usman Ahmad, W. Budiastra, I. Dewa, Made Subrata","doi":"10.46604/aiti.2023.12683","DOIUrl":"https://doi.org/10.46604/aiti.2023.12683","url":null,"abstract":"This study investigates the feasibility of employing near-infrared (NIR) spectroscopy with multiple linear regression (MLR) to estimate macronutrients in paddy soil compared with partial least squares (PLS) and principal component regression (PCR). Seventy-nine soil samples from West Java Province, Indonesia, are subject to conventional nutrient analysis and NIR spectroscopy (1000-2500 nm). The reflectance data undergoes various pretreatment techniques, and MLR models are calibrated using the forward method to achieve correlations exceeding 0.90. The best model calibrations are selected based on high correlation coefficients, determination coefficients, RPD, and low RMSE values. Meanwhile, the comparison of performance MLR is made with the PLS and PCR models. Results indicate that simple MLR models perform less than PLS for all nutrients, better than PCR for nitrogen, and below PCR for phosphorus and potassium. However, MLR reliably estimates soil nitrogen, phosphorus, and potassium content with ratio of performance to deviation (RPD) exceeding 2.0. This study demonstrates the potential of MLR for precise macronutrient estimation in paddy soil.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":"109 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146972","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 : 2023-12-29DOI: 10.46604/aiti.2023.12682
Ying-Tung Hsiao, J. Sheu, Hsu Ma
This study aims to develop an innovative image recognition and information display approach based on you only look once version 4 (YOLOv4)-tiny framework. The lightweight YOLOv4-tiny model is modified by replacing convolutional modules with Fire modules to further reduce its parameters. Performance reductions are offset by including spatial pyramid pooling, and they also improve the model’s detection ability for objects of various sizes. The pattern analysis, statistical modeling, and computational learning visual object classes (PASCAL VOC) 2012 dataset are used, the proposed modified YOLOv4-tiny architecture achieves a higher mean average precision (mAP) that is 1.59% higher than its unmodified counterpart. This study addresses the need for efficient object detection and recognition on resource-constrained devices by leveraging YOLOv4-tiny, Fire modules, and SPP to achieve accurate image recognition at a low computational cost.
{"title":"Efficient Object Detection and Intelligent Information Display Using YOLOv4-Tiny","authors":"Ying-Tung Hsiao, J. Sheu, Hsu Ma","doi":"10.46604/aiti.2023.12682","DOIUrl":"https://doi.org/10.46604/aiti.2023.12682","url":null,"abstract":"This study aims to develop an innovative image recognition and information display approach based on you only look once version 4 (YOLOv4)-tiny framework. The lightweight YOLOv4-tiny model is modified by replacing convolutional modules with Fire modules to further reduce its parameters. Performance reductions are offset by including spatial pyramid pooling, and they also improve the model’s detection ability for objects of various sizes. The pattern analysis, statistical modeling, and computational learning visual object classes (PASCAL VOC) 2012 dataset are used, the proposed modified YOLOv4-tiny architecture achieves a higher mean average precision (mAP) that is 1.59% higher than its unmodified counterpart. This study addresses the need for efficient object detection and recognition on resource-constrained devices by leveraging YOLOv4-tiny, Fire modules, and SPP to achieve accurate image recognition at a low computational cost.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":"131 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145730","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 : 2023-12-29DOI: 10.46604/aiti.2023.12687
Kittisak Lathong, K. Wisaeng
This study aims to predict the possibility of low-rise building construction costs by applying machine learning models, and the performance of each model is evaluated and compared with ensemble methods. The artificial neural network (ANN) emerges as the top-performing individual model, attaining an accuracy of 0.891, while multiple linear regression and decision trees follow closely with accuracies of 0.884 and 0.864 respectively. Ensemble methods like maximum voting ensemble (MVE) improve the accuracy beyond individual models with an impressive accuracy rate of 0.924. Meanwhile, the stacking ensemble and averaging ensemble also demonstrate competitive performance with accuracies of 0.883 and 0.871, respectively. These findings can result in more informed decision-making, which is valuable for the real estate industry.
{"title":"The Prediction of Low-Rise Building Construction Cost Estimation Using Extreme Learning Machine","authors":"Kittisak Lathong, K. Wisaeng","doi":"10.46604/aiti.2023.12687","DOIUrl":"https://doi.org/10.46604/aiti.2023.12687","url":null,"abstract":"This study aims to predict the possibility of low-rise building construction costs by applying machine learning models, and the performance of each model is evaluated and compared with ensemble methods. The artificial neural network (ANN) emerges as the top-performing individual model, attaining an accuracy of 0.891, while multiple linear regression and decision trees follow closely with accuracies of 0.884 and 0.864 respectively. Ensemble methods like maximum voting ensemble (MVE) improve the accuracy beyond individual models with an impressive accuracy rate of 0.924. Meanwhile, the stacking ensemble and averaging ensemble also demonstrate competitive performance with accuracies of 0.883 and 0.871, respectively. These findings can result in more informed decision-making, which is valuable for the real estate industry.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":"89 s378","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139146404","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 : 2023-09-28DOI: 10.46604/aiti.2023.11957
None Jin-Siang Shaw, None Yi-Hua Huang
This study aims to improve the traditional control methods of industrial robotic arms for path planning in line with efforts to conserve energy and reduce carbon emissions. The digital twin of a six-axis industrial robotic arm with an energy consumption model is innovatively designed. By directly dragging the end effector of a digital twin model, the robotic arm can be controlled for path planning, allowing path tuning to be easily made. In addition, the dynamic equation of the industrial robotic arm is derived, and the energy consumption of the corresponding path can be estimated. Four cases are designed to test the validity of the digital twin. Experimental results show that the physical robotic arm follows its digital twin model with the corresponding energy consumption computed. The estimated energy consumptions agree quite well with each designed case scenario.
{"title":"Virtual Modeling of an Industrial Robotic Arm for Energy Consumption Estimation","authors":"None Jin-Siang Shaw, None Yi-Hua Huang","doi":"10.46604/aiti.2023.11957","DOIUrl":"https://doi.org/10.46604/aiti.2023.11957","url":null,"abstract":"This study aims to improve the traditional control methods of industrial robotic arms for path planning in line with efforts to conserve energy and reduce carbon emissions. The digital twin of a six-axis industrial robotic arm with an energy consumption model is innovatively designed. By directly dragging the end effector of a digital twin model, the robotic arm can be controlled for path planning, allowing path tuning to be easily made. In addition, the dynamic equation of the industrial robotic arm is derived, and the energy consumption of the corresponding path can be estimated. Four cases are designed to test the validity of the digital twin. Experimental results show that the physical robotic arm follows its digital twin model with the corresponding energy consumption computed. The estimated energy consumptions agree quite well with each designed case scenario.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135427252","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 : 2023-09-28DOI: 10.46604/aiti.2023.11744
None Agim Tetuko, None Subiyanto, None Muhammad Addin Malik
This article presents an optimal energy control system that considers economic dispatch (ED) for a campus microgrid to reduce its operating cost. A newly developed crow search algorithm (CSA) is used to enforce the ED in this work. To achieve this purpose, an optimal size of distributed energy resources (DERs) in the campus microgrid is assumed. CSA is used to optimize the energy control system and find the minimum operating cost of the campus microgrid. To indicate the effectiveness of CSA, several scenarios under various load demand conditions in grid-connected and stand-alone microgrid modes are investigated in this work. According to the findings, the suggested model is capable of sufficient power supply in all scenarios and reduces the operating costs more effectively than the reference delineated in the same case. The outcomes confirm that the suggested model’s performance is optimal for the energy control system of a campus microgrid.
{"title":"An Optimal Energy Control System for Campus Microgrid Using Crow Search Algorithm Considering Economic Dispatch","authors":"None Agim Tetuko, None Subiyanto, None Muhammad Addin Malik","doi":"10.46604/aiti.2023.11744","DOIUrl":"https://doi.org/10.46604/aiti.2023.11744","url":null,"abstract":"This article presents an optimal energy control system that considers economic dispatch (ED) for a campus microgrid to reduce its operating cost. A newly developed crow search algorithm (CSA) is used to enforce the ED in this work. To achieve this purpose, an optimal size of distributed energy resources (DERs) in the campus microgrid is assumed. CSA is used to optimize the energy control system and find the minimum operating cost of the campus microgrid. To indicate the effectiveness of CSA, several scenarios under various load demand conditions in grid-connected and stand-alone microgrid modes are investigated in this work. According to the findings, the suggested model is capable of sufficient power supply in all scenarios and reduces the operating costs more effectively than the reference delineated in the same case. The outcomes confirm that the suggested model’s performance is optimal for the energy control system of a campus microgrid.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135427249","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 : 2023-09-28DOI: 10.46604/aiti.2023.11743
None Amit Pimpalkar, None Jeberson Retna Raj
The rapid escalation in global COVID-19 cases has engendered profound emotions of fear, agitation, and despondency within society. It is evident from COVID-19-related tweets that spark panic and elevate stress among individuals. Analyzing the sentiment expressed in online comments aids various stakeholders in monitoring the situation. This research aims to improve the performance of pre-trained bidirectional encoder representations from transformers (BERT) by employing transfer learning (TL) and fine hyper-parameter tuning (FT). The model is applied to three distinct COVID-19-related datasets, and each of the datasets belongs to a different class. The evaluation of the model’s performance involves six different machine learning (ML) classification models. This model is trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. Heat maps are generated for each model to visualize the results. The performance of the model demonstrates accuracies of 83%, 97%, and 98% for Class-5, Class-3, and binary classifications, respectively.
{"title":"A Novel Paradigm for Sentiment Analysis on COVID-19 Tweets with Transfer Learning Based Fine-Tuned BERT","authors":"None Amit Pimpalkar, None Jeberson Retna Raj","doi":"10.46604/aiti.2023.11743","DOIUrl":"https://doi.org/10.46604/aiti.2023.11743","url":null,"abstract":"The rapid escalation in global COVID-19 cases has engendered profound emotions of fear, agitation, and despondency within society. It is evident from COVID-19-related tweets that spark panic and elevate stress among individuals. Analyzing the sentiment expressed in online comments aids various stakeholders in monitoring the situation. This research aims to improve the performance of pre-trained bidirectional encoder representations from transformers (BERT) by employing transfer learning (TL) and fine hyper-parameter tuning (FT). The model is applied to three distinct COVID-19-related datasets, and each of the datasets belongs to a different class. The evaluation of the model’s performance involves six different machine learning (ML) classification models. This model is trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. Heat maps are generated for each model to visualize the results. The performance of the model demonstrates accuracies of 83%, 97%, and 98% for Class-5, Class-3, and binary classifications, respectively.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135427251","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 : 2023-09-28DOI: 10.46604/aiti.2023.11678
None Karishma Agrawal, None Supachai Vorapojpisut
This study proposes a statistical model to characterize the temporal characteristics of an entire production process. The model utilizes received signal strength indicator (RSSI) data obtained from a Bluetooth low energy (BLE) network. A hidden semi-Markov model (HSMM) is formulated based on the characteristics of the production process, and the forward-backward algorithm is employed to re-estimate the probability distribution of state durations. The proposed method is validated through numerical, simulation, and real-world experiments, yielding promising results. The results show that the Kullback-Leibler divergence (KLD) score of 0.1843, while the simulation achieves an average vector distance score of 0.9740. The real-time experiment also shows a reasonable accuracy, with an average HSMM estimated throughput time of 30.48 epochs, compared to the average real throughput time of 33.99 epochs. Overall, the model serves as a valuable tool for predicting the cycle time and throughput time of a production line.
{"title":"A Hidden Semi-Markov Model for Predicting Production Cycle Time Using Bluetooth Low Energy Data","authors":"None Karishma Agrawal, None Supachai Vorapojpisut","doi":"10.46604/aiti.2023.11678","DOIUrl":"https://doi.org/10.46604/aiti.2023.11678","url":null,"abstract":"This study proposes a statistical model to characterize the temporal characteristics of an entire production process. The model utilizes received signal strength indicator (RSSI) data obtained from a Bluetooth low energy (BLE) network. A hidden semi-Markov model (HSMM) is formulated based on the characteristics of the production process, and the forward-backward algorithm is employed to re-estimate the probability distribution of state durations. The proposed method is validated through numerical, simulation, and real-world experiments, yielding promising results. The results show that the Kullback-Leibler divergence (KLD) score of 0.1843, while the simulation achieves an average vector distance score of 0.9740. The real-time experiment also shows a reasonable accuracy, with an average HSMM estimated throughput time of 30.48 epochs, compared to the average real throughput time of 33.99 epochs. Overall, the model serves as a valuable tool for predicting the cycle time and throughput time of a production line.","PeriodicalId":52314,"journal":{"name":"Advances in Technology Innovation","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135428549","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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