Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105342
Yue Sun, Shi-Qian Wang, Zhi-Qing Ou, Si-Yi Chen, S. Hsueh
Convenience stores have become important in urban communities' lives. In addition to selling convenience products in daily life, it also sells simple and diverse special meals. Other than purchasing, collecting and handling public affairs, mail, and parcels are performed on behalf of others. Convenient living services are provided for residential areas and temporary passers-by in the area. Therefore, in urban communities with large populations, establishing business bases has become an inevitable choice for convenience store chains of various brands. There are many brands and fierce competition has become an important decision-making issue for chain convenience stores. Thus, using the Delphi method and fuzzy logic theory, a multi-attribute decision-making model is established based on quantitative analysis. The result provides an effective and objective analysis for the establishment of business bases.
{"title":"Using DFuzzy to Build Multi-attribute Decision-making Model for Chain Convenience Store Marketing","authors":"Yue Sun, Shi-Qian Wang, Zhi-Qing Ou, Si-Yi Chen, S. Hsueh","doi":"10.1109/ECEI57668.2023.10105342","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105342","url":null,"abstract":"Convenience stores have become important in urban communities' lives. In addition to selling convenience products in daily life, it also sells simple and diverse special meals. Other than purchasing, collecting and handling public affairs, mail, and parcels are performed on behalf of others. Convenient living services are provided for residential areas and temporary passers-by in the area. Therefore, in urban communities with large populations, establishing business bases has become an inevitable choice for convenience store chains of various brands. There are many brands and fierce competition has become an important decision-making issue for chain convenience stores. Thus, using the Delphi method and fuzzy logic theory, a multi-attribute decision-making model is established based on quantitative analysis. The result provides an effective and objective analysis for the establishment of business bases.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114690459","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 : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105367
Z. Lee, Yun Lin, Zhang Yang, Zhong-Yuan Chen, Wei-Guo Fan, Chen-Hsin Lee
The primary cause of global climate change is carbon emissions. The world must urgently reduce carbon emissions to avoid the worst effects of climate change. Understanding the most important features of carbon emissions is the first goal in decreasing carbon emissions. One of the critical issues for carbon emissions is research on feature engineering and prediction. Therefore, we propose a novel automatic feature engineering for carbon emissions. In the proposed algorithm, automatic feature engineering is used to select important features. Furthermore, deep learning is used to reduce the prediction error for carbon emissions. The proposed algorithm, decision trees, and linear regression are compared with previous methods using the Kaggle dataset of carbon emissions. The results demonstrate that the proposed algorithm selects the four most important features from the Kaggle dataset of carbon emissions. The proposed algorithm also enhances and lessens the root mean square error (RMSE) of the prediction. The proposed algorithm outperforms the other approaches.
{"title":"Novel Automatic Feature Engineering for Carbon Emissions Prediction Base on Deep Learning","authors":"Z. Lee, Yun Lin, Zhang Yang, Zhong-Yuan Chen, Wei-Guo Fan, Chen-Hsin Lee","doi":"10.1109/ECEI57668.2023.10105367","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105367","url":null,"abstract":"The primary cause of global climate change is carbon emissions. The world must urgently reduce carbon emissions to avoid the worst effects of climate change. Understanding the most important features of carbon emissions is the first goal in decreasing carbon emissions. One of the critical issues for carbon emissions is research on feature engineering and prediction. Therefore, we propose a novel automatic feature engineering for carbon emissions. In the proposed algorithm, automatic feature engineering is used to select important features. Furthermore, deep learning is used to reduce the prediction error for carbon emissions. The proposed algorithm, decision trees, and linear regression are compared with previous methods using the Kaggle dataset of carbon emissions. The results demonstrate that the proposed algorithm selects the four most important features from the Kaggle dataset of carbon emissions. The proposed algorithm also enhances and lessens the root mean square error (RMSE) of the prediction. The proposed algorithm outperforms the other approaches.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125487276","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 : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105362
N. Srisawasdi, P. Chaipidech, Phattaraporn Pondee, Kornchawal Chaipah, P. Panjaburee, Wacharaporn Khaokhajorn, Sasivimol Premthaisong, Kulthida Tuamsuk
Teaching and learning for school science became more challenging during the COVID-19 pandemic as regular science classes were offered online in real-time remote, synchronized, asynchronized, and hybrid learning modes. In science education, students often cannot collect the real-time data necessary for inquiry in science classrooms. During the COVID-19 outbreak, web-based or e-learning platforms play a significant role in science education during and after the COVID-19 pandemic. To overcome the outbreak limitation, teachers and students were required to transform their teaching and learning to be online with and without the available online platforms, enabling both teachers and learners to easily different learning sources and make teaching and learning work efficient and effective. Therefore, this study proposed a prototype of a Web-enhanced Inquiry Learning for Literacy in Science (WILL-S) platform aiming to provide innovative and flexible teaching to enhance the science competencies of middle school students. This web platform allows teachers and students to maximize their teaching practices and learning processes in science. A preliminary evaluation of the proposed platform was carried out with eight in-service science teachers and 221 middle school students from eight different secondary schools located in northeastern Thailand to estimate their acceptance of the proposed platform from teachers' and students' perspectives. The preliminary result was the positive acceptability of the teachers and students.
{"title":"Designing and Implementation of Web-Enhanced Inquiry Learning for Literacy in Science Platform for Post COVID-19 Education","authors":"N. Srisawasdi, P. Chaipidech, Phattaraporn Pondee, Kornchawal Chaipah, P. Panjaburee, Wacharaporn Khaokhajorn, Sasivimol Premthaisong, Kulthida Tuamsuk","doi":"10.1109/ECEI57668.2023.10105362","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105362","url":null,"abstract":"Teaching and learning for school science became more challenging during the COVID-19 pandemic as regular science classes were offered online in real-time remote, synchronized, asynchronized, and hybrid learning modes. In science education, students often cannot collect the real-time data necessary for inquiry in science classrooms. During the COVID-19 outbreak, web-based or e-learning platforms play a significant role in science education during and after the COVID-19 pandemic. To overcome the outbreak limitation, teachers and students were required to transform their teaching and learning to be online with and without the available online platforms, enabling both teachers and learners to easily different learning sources and make teaching and learning work efficient and effective. Therefore, this study proposed a prototype of a Web-enhanced Inquiry Learning for Literacy in Science (WILL-S) platform aiming to provide innovative and flexible teaching to enhance the science competencies of middle school students. This web platform allows teachers and students to maximize their teaching practices and learning processes in science. A preliminary evaluation of the proposed platform was carried out with eight in-service science teachers and 221 middle school students from eight different secondary schools located in northeastern Thailand to estimate their acceptance of the proposed platform from teachers' and students' perspectives. The preliminary result was the positive acceptability of the teachers and students.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129472549","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 : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105421
Hsien-Yang Liao, Chih-Cheng Chen, An-Bang Cheng
Nowadays, the request for quality is increasing more than before, but defective products are found and cannot be prevented during the manufacturing process. Therefore, the defective products are abandoned. However, since each of the defects is not caused by the same process, which needs the manufacturing process to be optimized to reduce the occurrence of defective products. Therefore, manufacturers require an inspection process that provides immediate results. Therefore, using the Prototypical network using with few-shot learning on the bagel dataset, the effect of surface defect detection on 3D objects is proposed with its optimized accuracy in this study.
{"title":"Few-shot Learning for Bagel Defect Detection","authors":"Hsien-Yang Liao, Chih-Cheng Chen, An-Bang Cheng","doi":"10.1109/ECEI57668.2023.10105421","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105421","url":null,"abstract":"Nowadays, the request for quality is increasing more than before, but defective products are found and cannot be prevented during the manufacturing process. Therefore, the defective products are abandoned. However, since each of the defects is not caused by the same process, which needs the manufacturing process to be optimized to reduce the occurrence of defective products. Therefore, manufacturers require an inspection process that provides immediate results. Therefore, using the Prototypical network using with few-shot learning on the bagel dataset, the effect of surface defect detection on 3D objects is proposed with its optimized accuracy in this study.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134044760","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 : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105266
Z. Lee, Yun Lin, Zhong-Yuan Chen, Zhang Yang, Wei-Guo Fang, Chen-Hsin Lee
Buildings use energy and produce carbon dioxide considerably. Despite progress in economics, this trend of rising emissions continues. Research on building energy consumption allows for determining building energy efficiency and developing energy-saving strategies. Additionally, it helps to forecast trends in future building energy consumption. The research on building energy consumption has emerged as one of the critical issues for achieving carbon neutrality. Therefore, we propose an ensemble deep learning applied to predict building energy consumption. The proposed algorithm employs ensemble architecture to improve deep learning's effectiveness in predicting error of the reduction in building energy consumption. Furthermore, with negative correlation learning (NCL), learning across all samples is improved. The dataset from the American Society of Heating and Air-Conditioning Engineers (ASHERE) is used to compare several approaches, including the proposed algorithm, deep learning, decision trees, and linear regression. The results demonstrate that the proposed algorithm enhances and lessens the prediction of the root mean squared error (RMSE). Among the approaches compared, the proposed algorithm has the lowest RMSE. The proposed ensemble deep learning algorithm outperforms the other approaches compared.
{"title":"Ensemble Deep Learning Applied to Predict Building Energy Consumption","authors":"Z. Lee, Yun Lin, Zhong-Yuan Chen, Zhang Yang, Wei-Guo Fang, Chen-Hsin Lee","doi":"10.1109/ECEI57668.2023.10105266","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105266","url":null,"abstract":"Buildings use energy and produce carbon dioxide considerably. Despite progress in economics, this trend of rising emissions continues. Research on building energy consumption allows for determining building energy efficiency and developing energy-saving strategies. Additionally, it helps to forecast trends in future building energy consumption. The research on building energy consumption has emerged as one of the critical issues for achieving carbon neutrality. Therefore, we propose an ensemble deep learning applied to predict building energy consumption. The proposed algorithm employs ensemble architecture to improve deep learning's effectiveness in predicting error of the reduction in building energy consumption. Furthermore, with negative correlation learning (NCL), learning across all samples is improved. The dataset from the American Society of Heating and Air-Conditioning Engineers (ASHERE) is used to compare several approaches, including the proposed algorithm, deep learning, decision trees, and linear regression. The results demonstrate that the proposed algorithm enhances and lessens the prediction of the root mean squared error (RMSE). Among the approaches compared, the proposed algorithm has the lowest RMSE. The proposed ensemble deep learning algorithm outperforms the other approaches compared.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130039193","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 : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105394
Xizhi Zhang, Tao Han, K. Wen
Digital high-precision models and surface materials are used in drawing large and complex architectural sites, topographic maps, architectural, engineering, and ceramic design, production and quality control, geological investigation, film special effects, and post-production. It is an important part of digital imaging and calculation. At present, the artificial and 3D model scanning technology does not meet the rapid development requirements of ceramic digital model development, aided design, and manufacturing. Thus, a new photogrammetric method based on digital high-precision ceramic modeling and the original 3D model imaging technology is developed and improved. The original 3D model imaging is tested and analyzed in the actual production. The new technology can shorten the product development cycle and improve production efficiency. The quality of digital imaging is enhanced to make it more scientific and accurate, and so is the artistic expression. Photogrammetry is in three development stages: analog, analytical, and digital.
{"title":"Research on Influence of Photogrammetry in Digital High Precision Modeling Technology on Production Efficiency of Ceramic Enterprises","authors":"Xizhi Zhang, Tao Han, K. Wen","doi":"10.1109/ECEI57668.2023.10105394","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105394","url":null,"abstract":"Digital high-precision models and surface materials are used in drawing large and complex architectural sites, topographic maps, architectural, engineering, and ceramic design, production and quality control, geological investigation, film special effects, and post-production. It is an important part of digital imaging and calculation. At present, the artificial and 3D model scanning technology does not meet the rapid development requirements of ceramic digital model development, aided design, and manufacturing. Thus, a new photogrammetric method based on digital high-precision ceramic modeling and the original 3D model imaging technology is developed and improved. The original 3D model imaging is tested and analyzed in the actual production. The new technology can shorten the product development cycle and improve production efficiency. The quality of digital imaging is enhanced to make it more scientific and accurate, and so is the artistic expression. Photogrammetry is in three development stages: analog, analytical, and digital.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126644944","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 : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105355
Liwei Liu
In the development of tourism resources of karst wetland type, the role of tourism benefits is often uncertain due to the joint action of various environmental factors. When traditional methods are used to evaluate the effect of the development of karst wetland tourism resources on tourism benefits, the accuracy of the evaluation is reduced due to the large influence of objective factors in the selection of relevant technical parameters. Therefore, an evaluation method of the effect of the development of tourism resources of karst wetland type on tourism benefits is proposed based on the mechanism of environmental conditions. After normalizing the actual value of the effect of the karst wetland ecological factors on China's tourism, a neural network model of the effect of the ecological tourism development process based on the environmental constraint mechanism is established, which makes up for the corresponding errors in the model. At the same time, the effect of different types of karst wetland ecological factors on the benefits of China's tourism was trained, and the training results were input into the data to finally obtain more accurate evaluation data. The experimental results show that the improved method can effectively improve the accuracy of the evaluation, thus greatly improving the evaluation efficiency.
{"title":"Research on Evaluation of Karst Wetland Ecotourism Benefits Based on Improved BP Algorithm","authors":"Liwei Liu","doi":"10.1109/ECEI57668.2023.10105355","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105355","url":null,"abstract":"In the development of tourism resources of karst wetland type, the role of tourism benefits is often uncertain due to the joint action of various environmental factors. When traditional methods are used to evaluate the effect of the development of karst wetland tourism resources on tourism benefits, the accuracy of the evaluation is reduced due to the large influence of objective factors in the selection of relevant technical parameters. Therefore, an evaluation method of the effect of the development of tourism resources of karst wetland type on tourism benefits is proposed based on the mechanism of environmental conditions. After normalizing the actual value of the effect of the karst wetland ecological factors on China's tourism, a neural network model of the effect of the ecological tourism development process based on the environmental constraint mechanism is established, which makes up for the corresponding errors in the model. At the same time, the effect of different types of karst wetland ecological factors on the benefits of China's tourism was trained, and the training results were input into the data to finally obtain more accurate evaluation data. The experimental results show that the improved method can effectively improve the accuracy of the evaluation, thus greatly improving the evaluation efficiency.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114183743","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 : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105313
Zhang Liu, Fang Mei, Canhua Li, Quan Yang
In the research of pork price forecasting, due to the strong nonlinear relationship between the fluctuation of pork price and complex influencing factors, the traditional forecasting model cannot measure the nonlinear relationship and make an accurate prediction of pork price. To solve these problems, we propose a PCA-BP Neural Network prediction model to predict the price of pork. Firstly, the main factors affecting the fluctuation of pork prices are analyzed. 162 groups of data are used, including the national average weekly price of pork, white striped chicken, beef, mutton, corn, and soybean from the first week of January 2018 to the first week of February 2021. Three principal components with a 96% contribution rate are used as the input layer data of the BP neural network, and pork price is selected as the output layer data of the BP neural network. By comparing the predicted value with the actual value, the predicted value of the PCA-BP Neural network model is close to the actual value, and it has a better fitting effect and accuracy than the traditional BP neural network. The results show that the PCA-BP Neural Network pork prediction model provides new ideas for pork price prediction, which is of great significance to stabilizing the daily life of urban and rural residents and protecting the income of farmers.
{"title":"Prediction of Pork Price Based on PCA-BP Neural Network","authors":"Zhang Liu, Fang Mei, Canhua Li, Quan Yang","doi":"10.1109/ECEI57668.2023.10105313","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105313","url":null,"abstract":"In the research of pork price forecasting, due to the strong nonlinear relationship between the fluctuation of pork price and complex influencing factors, the traditional forecasting model cannot measure the nonlinear relationship and make an accurate prediction of pork price. To solve these problems, we propose a PCA-BP Neural Network prediction model to predict the price of pork. Firstly, the main factors affecting the fluctuation of pork prices are analyzed. 162 groups of data are used, including the national average weekly price of pork, white striped chicken, beef, mutton, corn, and soybean from the first week of January 2018 to the first week of February 2021. Three principal components with a 96% contribution rate are used as the input layer data of the BP neural network, and pork price is selected as the output layer data of the BP neural network. By comparing the predicted value with the actual value, the predicted value of the PCA-BP Neural network model is close to the actual value, and it has a better fitting effect and accuracy than the traditional BP neural network. The results show that the PCA-BP Neural Network pork prediction model provides new ideas for pork price prediction, which is of great significance to stabilizing the daily life of urban and rural residents and protecting the income of farmers.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123275676","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}
Automated mechanical equipment is adjusted as the production line changes with the manipulator. Through real-time equipment monitoring and management, it is ensured that the equipment runs and adjust at any time. Modern equipment monitoring and control are used to accurately judge equipment usage status with the help of sensors, big data, the Internet, and artificial intelligence. The computer-aided design (CAD) is used to establish a six-axis manipulator including the dimensions of each axis, the speed, and acceleration of the motor, and kinematics. All the data is collected in one archive to create a 3D model and simulate and analyze its performance with the computer-aided engineering (CAE) method.
{"title":"Development of CAE and CAD Sy1stem for Six-Axis Manipulator Control System","authors":"Hao-Jyun Jhang, Chih-Cheng Chen, Wu Zheng, Chiu-Hung Chen, Cheng-Fu Yang, Wen-Yuan Yu","doi":"10.1109/ECEI57668.2023.10105338","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105338","url":null,"abstract":"Automated mechanical equipment is adjusted as the production line changes with the manipulator. Through real-time equipment monitoring and management, it is ensured that the equipment runs and adjust at any time. Modern equipment monitoring and control are used to accurately judge equipment usage status with the help of sensors, big data, the Internet, and artificial intelligence. The computer-aided design (CAD) is used to establish a six-axis manipulator including the dimensions of each axis, the speed, and acceleration of the motor, and kinematics. All the data is collected in one archive to create a 3D model and simulate and analyze its performance with the computer-aided engineering (CAE) method.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125117308","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 : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105353
Po-Chiang Lin
Using the robotic system, students' learning outcomes are improved in the Capstone Course as graduation projects. The Capstone Course provides opportunities for undergraduate students to integrate and reflect learning experiences at the university. In the guidance, there are two major challenges: how to teach the system integration and how to attract students' interest in the graduation project. To overcome the first challenge, we invited industry experts to join the teaching and learning in this research. Students learned the system integration method adopted in the industry to bridge the gap between industry and academia. We invite four industry experts in robotics to be in charge of short courses and hands-on workshops so that students could learn professional skills and valuable experience from these experts. To overcome the second challenge, we chose robotic system integration as the goal of the graduation project. We used interesting and explicit goals to attract students. The gamification technique was used to attract students' interest in the graduation project. The questionnaire survey and the Octalysis Framework were used for gamification and to analyze the factors that motivated the students to accomplish their graduation projects. The analytical results showed that gamification integrated into the robotic system was the goal of the graduation project in the Octalysis to motivate students. The result showed that unpredictability, accomplishment, and empowerment were the three most important factors.
{"title":"Robotic System Integration for Improving Learning Outcomes in Graduation Project Capstone Course","authors":"Po-Chiang Lin","doi":"10.1109/ECEI57668.2023.10105353","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105353","url":null,"abstract":"Using the robotic system, students' learning outcomes are improved in the Capstone Course as graduation projects. The Capstone Course provides opportunities for undergraduate students to integrate and reflect learning experiences at the university. In the guidance, there are two major challenges: how to teach the system integration and how to attract students' interest in the graduation project. To overcome the first challenge, we invited industry experts to join the teaching and learning in this research. Students learned the system integration method adopted in the industry to bridge the gap between industry and academia. We invite four industry experts in robotics to be in charge of short courses and hands-on workshops so that students could learn professional skills and valuable experience from these experts. To overcome the second challenge, we chose robotic system integration as the goal of the graduation project. We used interesting and explicit goals to attract students. The gamification technique was used to attract students' interest in the graduation project. The questionnaire survey and the Octalysis Framework were used for gamification and to analyze the factors that motivated the students to accomplish their graduation projects. The analytical results showed that gamification integrated into the robotic system was the goal of the graduation project in the Octalysis to motivate students. The result showed that unpredictability, accomplishment, and empowerment were the three most important factors.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125238149","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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