Pub Date : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042909
Chen-Wei Huang, Jian-Jiun Ding
A two-layer analysis approach of the atrial fibrillation episode detection algorithm tested in the MIT-BIH atrial fibrillation database (MIT-BIH AFDB) is proposed in the paper. We use several methodologies, including gradient varying weighted filter, template matched filter, adaptive threshold, and sliding window to accurately extract the locations and amplitudes of P, Q, R, S, and T peaks, P wave width, and QS width in an ECG complex as basic features. On the other hand, most existing works utilize features of RR intervals, a difference of RR intervals, or amplitude of P wave for AF episode detection. In the proposed algorithm, we exploit the ratio concept to transform basic features into ratio-based features with relative relations because those features are much easier to measure the irregularity of RR intervals and P wave absence precisely in atrial fibrillation episodes. Furthermore, we apply the innovative definition of ratio variation-based features to generate robust and qualitative feature extraction sets. Finally, a rule-based ratio variation hypothesis classifier with techniques of weighted coefficient function, product-form score function, Gini index function, and Gini splitting function is adopted. The performance result of the proposed algorithm, trained and tested in the MIT-BIH AF database, achieves an average sensitivity value of 99.272% and an average specificity value of 98.495%, respectively. The accuracy is superior to that of other various AF episode detection algorithms.
{"title":"Atrial Fibrillation Detection Algorithm with Ratio Variation-Based Features","authors":"Chen-Wei Huang, Jian-Jiun Ding","doi":"10.1109/ECICE55674.2022.10042909","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042909","url":null,"abstract":"A two-layer analysis approach of the atrial fibrillation episode detection algorithm tested in the MIT-BIH atrial fibrillation database (MIT-BIH AFDB) is proposed in the paper. We use several methodologies, including gradient varying weighted filter, template matched filter, adaptive threshold, and sliding window to accurately extract the locations and amplitudes of P, Q, R, S, and T peaks, P wave width, and QS width in an ECG complex as basic features. On the other hand, most existing works utilize features of RR intervals, a difference of RR intervals, or amplitude of P wave for AF episode detection. In the proposed algorithm, we exploit the ratio concept to transform basic features into ratio-based features with relative relations because those features are much easier to measure the irregularity of RR intervals and P wave absence precisely in atrial fibrillation episodes. Furthermore, we apply the innovative definition of ratio variation-based features to generate robust and qualitative feature extraction sets. Finally, a rule-based ratio variation hypothesis classifier with techniques of weighted coefficient function, product-form score function, Gini index function, and Gini splitting function is adopted. The performance result of the proposed algorithm, trained and tested in the MIT-BIH AF database, achieves an average sensitivity value of 99.272% and an average specificity value of 98.495%, respectively. The accuracy is superior to that of other various AF episode detection algorithms.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122422413","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042838
Lu Yuan, Zhenhai Wang, Hui-Yong Chen, Hongyu Tian, Ying Ren, Xing Wang, P. Li
Image classification is the most basic and mature visual task in computer vision. Recently, image classification technology has been widely used. However, a limitation exists in single target recognition and classification tasks for multicategory images. In fruit image classification with complex content of the target image and rich fruit categories, the single use of classification network generation often cannot accurately classify a single-fruit target. To solve this problem, an interactive segmentation-based method for single-category fruit classification in multi-category fruit images is proposed. Herein, an interactive segmentation network and an attention classification network based on deep learning are combined. The interactive segmentation network based on interactive points segments the target to be classified in the image. Then, the classification network identifies and classifies the fruit separately to eliminate the interference of other categories and background information in the image. The classification network is trained on 360 datasets of fruits. The segmentation method before classification can effectively identify single-category fruits in multi-category fruit images. Also, the segmentation and background removal improve the recognition probability of the classification network for a single category of fruit images. Thus, the segmentation method before classification effectively solves single-category fruit classification tasks in multi-category fruit images.
{"title":"Multi-Category Fruit Image Classification Based on Interactive Segmentation","authors":"Lu Yuan, Zhenhai Wang, Hui-Yong Chen, Hongyu Tian, Ying Ren, Xing Wang, P. Li","doi":"10.1109/ECICE55674.2022.10042838","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042838","url":null,"abstract":"Image classification is the most basic and mature visual task in computer vision. Recently, image classification technology has been widely used. However, a limitation exists in single target recognition and classification tasks for multicategory images. In fruit image classification with complex content of the target image and rich fruit categories, the single use of classification network generation often cannot accurately classify a single-fruit target. To solve this problem, an interactive segmentation-based method for single-category fruit classification in multi-category fruit images is proposed. Herein, an interactive segmentation network and an attention classification network based on deep learning are combined. The interactive segmentation network based on interactive points segments the target to be classified in the image. Then, the classification network identifies and classifies the fruit separately to eliminate the interference of other categories and background information in the image. The classification network is trained on 360 datasets of fruits. The segmentation method before classification can effectively identify single-category fruits in multi-category fruit images. Also, the segmentation and background removal improve the recognition probability of the classification network for a single category of fruit images. Thus, the segmentation method before classification effectively solves single-category fruit classification tasks in multi-category fruit images.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124228521","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042877
Tzu-Yuan Su, W. W. Hsu, R. Hu, Chia-Chang Tsou, Chun-Han Lin, Wei-Siang Hong
Extensive research has been conducted on the growth and the biological behaviours which both require the measurement of the fish samples. However, the existing measurement methods were time-consuming and laborintensive. In this research, we developed a faster method based on machine vision and artificial intelligence to measure the fish size and length automatically to support future ecological research.
{"title":"Estimating Fish Length Using Mask Region-Based Convolutional Neural Networks","authors":"Tzu-Yuan Su, W. W. Hsu, R. Hu, Chia-Chang Tsou, Chun-Han Lin, Wei-Siang Hong","doi":"10.1109/ECICE55674.2022.10042877","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042877","url":null,"abstract":"Extensive research has been conducted on the growth and the biological behaviours which both require the measurement of the fish samples. However, the existing measurement methods were time-consuming and laborintensive. In this research, we developed a faster method based on machine vision and artificial intelligence to measure the fish size and length automatically to support future ecological research.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115343402","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042816
Qiaoling Ye, Ting Zhou, Zheng Huang
The relationship between family risk factors and academic buoyancy has received attention through empirical studies, such as Bad family atmosphere, frequent family conflicts, disharmonious parent-child relationship, etc. Other family factors have an impact on the development of academic buoyancy, but there is less research on its combination characteristics. Based on the actual education scene of a middle school, we first score students’ academic buoyancy as the dependent variable and then collect nine types of variables representing family risk factors as independent variables. Then, a classification model between independent and dependent variables is constructed by using the decision tree method. The model provides a guideline for teaching work, and the test result of the model is ideal.
{"title":"Research on Academic Buoyancy Classification Based on Iterative Decision Tree Algorithm","authors":"Qiaoling Ye, Ting Zhou, Zheng Huang","doi":"10.1109/ECICE55674.2022.10042816","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042816","url":null,"abstract":"The relationship between family risk factors and academic buoyancy has received attention through empirical studies, such as Bad family atmosphere, frequent family conflicts, disharmonious parent-child relationship, etc. Other family factors have an impact on the development of academic buoyancy, but there is less research on its combination characteristics. Based on the actual education scene of a middle school, we first score students’ academic buoyancy as the dependent variable and then collect nine types of variables representing family risk factors as independent variables. Then, a classification model between independent and dependent variables is constructed by using the decision tree method. The model provides a guideline for teaching work, and the test result of the model is ideal.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131316899","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042863
Fangqiang Zhong, Min Tu, Zhen Wang
This study aims to quantify the risk of each major P2P lending platform. The special feature is defined based on the “user comments” text data of the platforms from the lender with the combined Word2Vec keyword extraction technology. A quantitative model of an online lending platform is proposed with the feature. The results show that the model more accurately explores the loan Internet platform with high similarity with higher accuracy in the quantitative calculation of VaR.
{"title":"Research on Risk Quantification Model of P2P Loan Internet Platform","authors":"Fangqiang Zhong, Min Tu, Zhen Wang","doi":"10.1109/ECICE55674.2022.10042863","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042863","url":null,"abstract":"This study aims to quantify the risk of each major P2P lending platform. The special feature is defined based on the “user comments” text data of the platforms from the lender with the combined Word2Vec keyword extraction technology. A quantitative model of an online lending platform is proposed with the feature. The results show that the model more accurately explores the loan Internet platform with high similarity with higher accuracy in the quantitative calculation of VaR.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115635818","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042844
Volak Nou, Wusheng Shi
Solar energy is one of the most potential renewable energy sources of sunlight. Due to increase and satisfying demand for energy in developing countries like Cambodia, solar power energy is the main and significant energy to the procedure for supply local to reduce import power energy from neighboring’s countries. In this case, the ability to an accurate solar output forecasting is critical for planning to decide based on forecast conditions, while many forecasting methods have been improved for forecasted values. However, the specific research on solar power PV output forecasting in Cambodia is still lacking to secure better accuracy during the rapidly extending inquiry of energy. This study is conducted to investigate a trial of short-term forecasting of solar power photovoltaic output in Bavet city, Cambodia, using several methods for comparisons such as Neural Network (NN), Linear Regression (LR), and Autoregressive Moving Average (ARMA). This process is based on the daily reality historical data from $mathrm{I}^{mathrm{s}mathrm{t}}$ January 2018 to 1$0^{mathrm{t}mathrm{h}}$ January 2019 which were recorded by Nation Control Center (NCC). Weather daily index data is obtained from the Renewable Energy Community of NASA Power Data Access Viewer Website Forecast of Global Energy Resources. The reliability of the forecasting of the three methods was assessed by using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Based on the simulation result of these three models, the Neural Network model showed better accuracy and results that were promising and beneficial for solar forecasting in Cambodia.
{"title":"Solar Power Photovoltaic Output Forecasting Using Multiple Methods Approach, Case Study: Cambodia","authors":"Volak Nou, Wusheng Shi","doi":"10.1109/ECICE55674.2022.10042844","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042844","url":null,"abstract":"Solar energy is one of the most potential renewable energy sources of sunlight. Due to increase and satisfying demand for energy in developing countries like Cambodia, solar power energy is the main and significant energy to the procedure for supply local to reduce import power energy from neighboring’s countries. In this case, the ability to an accurate solar output forecasting is critical for planning to decide based on forecast conditions, while many forecasting methods have been improved for forecasted values. However, the specific research on solar power PV output forecasting in Cambodia is still lacking to secure better accuracy during the rapidly extending inquiry of energy. This study is conducted to investigate a trial of short-term forecasting of solar power photovoltaic output in Bavet city, Cambodia, using several methods for comparisons such as Neural Network (NN), Linear Regression (LR), and Autoregressive Moving Average (ARMA). This process is based on the daily reality historical data from $mathrm{I}^{mathrm{s}mathrm{t}}$ January 2018 to 1$0^{mathrm{t}mathrm{h}}$ January 2019 which were recorded by Nation Control Center (NCC). Weather daily index data is obtained from the Renewable Energy Community of NASA Power Data Access Viewer Website Forecast of Global Energy Resources. The reliability of the forecasting of the three methods was assessed by using Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE). Based on the simulation result of these three models, the Neural Network model showed better accuracy and results that were promising and beneficial for solar forecasting in Cambodia.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134183867","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042894
Jose Monteiro, Óscar Oliveira, Davide Carneiro
Machine Learning problems are becoming increasingly complex, mostly due to the size of datasets. Data are also generated at increasing speed, which requires models to be updated regularly, at a significant computational cost. The project Continuously Evolving Distributed Ensembles proposes the creation of a distributed Machine Learning environment, in which datasets are divided into fixed-size blocks, and stored in a fault-tolerant distributed file system with replication. The base-models of the Ensembles, with a 1:1 relationship with data blocks, are then trained in a distributed manner, according to the principle of data locality. Specifically, the system is able to select which data blocks to use and in which nodes of the cluster, in order to minimize training time. A similar process takes place when making predictions: the best base-models are selected in real-time, according to their predictive performance and to the state of the nodes where they reside. This paper addresses the problem of assigning base model training tasks to cluster nodes, adhering to the principle of data locality. We present an instance generator and three datasets that will provide a means for comparison while studying other solution methods. For testing the system architecture, we solved the datasets with an exact method and the computational results validate, to comply to the project requirements, the need for a more stable and less demanding (in computational resource terms) solution method.
{"title":"Task Scheduling with Makespan Minimization for Distributed Machine Learning Ensembles","authors":"Jose Monteiro, Óscar Oliveira, Davide Carneiro","doi":"10.1109/ECICE55674.2022.10042894","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042894","url":null,"abstract":"Machine Learning problems are becoming increasingly complex, mostly due to the size of datasets. Data are also generated at increasing speed, which requires models to be updated regularly, at a significant computational cost. The project Continuously Evolving Distributed Ensembles proposes the creation of a distributed Machine Learning environment, in which datasets are divided into fixed-size blocks, and stored in a fault-tolerant distributed file system with replication. The base-models of the Ensembles, with a 1:1 relationship with data blocks, are then trained in a distributed manner, according to the principle of data locality. Specifically, the system is able to select which data blocks to use and in which nodes of the cluster, in order to minimize training time. A similar process takes place when making predictions: the best base-models are selected in real-time, according to their predictive performance and to the state of the nodes where they reside. This paper addresses the problem of assigning base model training tasks to cluster nodes, adhering to the principle of data locality. We present an instance generator and three datasets that will provide a means for comparison while studying other solution methods. For testing the system architecture, we solved the datasets with an exact method and the computational results validate, to comply to the project requirements, the need for a more stable and less demanding (in computational resource terms) solution method.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114772989","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042872
An-Yuan Chang, Po-Yen Lai
With increasingly sophisticated global manufacturing technology and fierce competition in the international market, the automobile manufacturing industry has entered the stage of internationalization. Therefore, it is necessary to innovate and change, improve production technology continuously, strengthen physical fitness, and maintain competitiveness to survive in the rapidly changing market. Lean Management (LM) and automation equipment are important methods to achieve the above goals. Among them, the use of Automated Guided Vehicles (AGV) has become a crucial equipment in the industry that many manufacturers have widely used. AGVs are unmanned vehicles that can travel according to a program or a predetermined path. AGVs can replace the original logistics, shortens the handling time greatly, and enhances the competitiveness of enterprises.This research focuses on the introduction of LM in an automobile oil seal manufacturing industry in Taiwan and focuses on improving logistics efficiency. It uses Value Stream Mapping (VSM) to discuss the logistics problems existing on the production line and proposes specific improvement plans. This study aims to improve logistics efficiency using AGVs and logistics improvement based on its cooperation with Jidoka. This study proposes a new route transportation method, which uses multiple AGVs to perform transportation tasks at the same time, combined with Karakuri’s base-running path transportation mode of multi-functional racks.The results include (1) proposing a new route transportation method, AGVs combined with Karakuri’s running route transportation mode; (2) saving two full-time logistics manpower; and (3) increasing the Cycle Time (CT) to within 5 minutes.
{"title":"Promoting the Application of Lean Automation - Take the Automobile Oil Seal Manufacturing Industry as an Example","authors":"An-Yuan Chang, Po-Yen Lai","doi":"10.1109/ECICE55674.2022.10042872","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042872","url":null,"abstract":"With increasingly sophisticated global manufacturing technology and fierce competition in the international market, the automobile manufacturing industry has entered the stage of internationalization. Therefore, it is necessary to innovate and change, improve production technology continuously, strengthen physical fitness, and maintain competitiveness to survive in the rapidly changing market. Lean Management (LM) and automation equipment are important methods to achieve the above goals. Among them, the use of Automated Guided Vehicles (AGV) has become a crucial equipment in the industry that many manufacturers have widely used. AGVs are unmanned vehicles that can travel according to a program or a predetermined path. AGVs can replace the original logistics, shortens the handling time greatly, and enhances the competitiveness of enterprises.This research focuses on the introduction of LM in an automobile oil seal manufacturing industry in Taiwan and focuses on improving logistics efficiency. It uses Value Stream Mapping (VSM) to discuss the logistics problems existing on the production line and proposes specific improvement plans. This study aims to improve logistics efficiency using AGVs and logistics improvement based on its cooperation with Jidoka. This study proposes a new route transportation method, which uses multiple AGVs to perform transportation tasks at the same time, combined with Karakuri’s base-running path transportation mode of multi-functional racks.The results include (1) proposing a new route transportation method, AGVs combined with Karakuri’s running route transportation mode; (2) saving two full-time logistics manpower; and (3) increasing the Cycle Time (CT) to within 5 minutes.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598249","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 : 2022-10-28DOI: 10.1109/ECICE55674.2022.10042862
Jehn-Ruey Jiang, Chun-Wei Chu
Quadratic unconstrained binary optimization (QUBO) formulas of quantum annealing (QA) algorithms are classified into four categories. QA algorithms using different QUBO formulas solve specific NP-hard problems as examples of the classification. The NP-hard problems solved are the subset sum, the vertex cover, the graph coloring, and the traveling salesperson problems. The QA algorithms are compared with their classical counterparts in terms of the quality of the solution and the time to the solution. Based on the comparison results, observations and suggestions are given for each QUBO formula category, so that proper actions can be adopted to improve the performance of QA algorithms. Compared with classical algorithms, QA algorithms are competitive in the current noisy intermediate-scale quantum (NISQ) era and beyond.
{"title":"Solving NP-hard Problems with Quantum Annealing","authors":"Jehn-Ruey Jiang, Chun-Wei Chu","doi":"10.1109/ECICE55674.2022.10042862","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042862","url":null,"abstract":"Quadratic unconstrained binary optimization (QUBO) formulas of quantum annealing (QA) algorithms are classified into four categories. QA algorithms using different QUBO formulas solve specific NP-hard problems as examples of the classification. The NP-hard problems solved are the subset sum, the vertex cover, the graph coloring, and the traveling salesperson problems. The QA algorithms are compared with their classical counterparts in terms of the quality of the solution and the time to the solution. Based on the comparison results, observations and suggestions are given for each QUBO formula category, so that proper actions can be adopted to improve the performance of QA algorithms. Compared with classical algorithms, QA algorithms are competitive in the current noisy intermediate-scale quantum (NISQ) era and beyond.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115680654","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}
Injection molding is one of the mainstream modern plastic processes. Proper molding methods are used based on the functional requirements of the product. As smart machinery and digital manufacturing evolve constantly, visualization by data acquisition and integration is becoming an inevitable topic, By installing sensors inside the mold, or even extending into the machine bed, variations of cavity pressure as well as relationships between the exertion of toggle mechanism force and product quality are further explored for achieving data integration and parameter adjustments accordingly. Taking a toggle clamping mechanism of an injection molding machine as the sensing subject, we performed analytical research and verified the weight changes of the product. Comparisons were made with sensor data acquired from molds and the tie bar of the molding system at the same time. Also, different parameters were used for researching the injection/packing process and the resulting stress-strain variations on the toggle mechanism (relevant processes include clamping force, injection speed, and packing switch-over position). The results show that(1) changes of sustained force are successfully sensed and monitored via a real-time sensor module installed on the toggle mechanism, (2) alterations of packing switch-over position, packing pressure as well as injection speed correlate to the toggle mechanism force variation and the melt filling pressure inside the cavity, and (3) toggle mechanism force variations observed positively correlate to product appearance and weight.
{"title":"Real-time Toggle Clamping-mechanism Force Measurement and Melt Pressure Variation, and Its Effect on Injection Molded Part Weight","authors":"You-Wen Chung, H. Peng, Fang-Ru Lin, Po-Wei Huang, Diancheng Wu, Yen-Ju Chen","doi":"10.1109/ECICE55674.2022.10042867","DOIUrl":"https://doi.org/10.1109/ECICE55674.2022.10042867","url":null,"abstract":"Injection molding is one of the mainstream modern plastic processes. Proper molding methods are used based on the functional requirements of the product. As smart machinery and digital manufacturing evolve constantly, visualization by data acquisition and integration is becoming an inevitable topic, By installing sensors inside the mold, or even extending into the machine bed, variations of cavity pressure as well as relationships between the exertion of toggle mechanism force and product quality are further explored for achieving data integration and parameter adjustments accordingly. Taking a toggle clamping mechanism of an injection molding machine as the sensing subject, we performed analytical research and verified the weight changes of the product. Comparisons were made with sensor data acquired from molds and the tie bar of the molding system at the same time. Also, different parameters were used for researching the injection/packing process and the resulting stress-strain variations on the toggle mechanism (relevant processes include clamping force, injection speed, and packing switch-over position). The results show that(1) changes of sustained force are successfully sensed and monitored via a real-time sensor module installed on the toggle mechanism, (2) alterations of packing switch-over position, packing pressure as well as injection speed correlate to the toggle mechanism force variation and the melt filling pressure inside the cavity, and (3) toggle mechanism force variations observed positively correlate to product appearance and weight.","PeriodicalId":282635,"journal":{"name":"2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122705077","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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