Pub Date : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9101985
Hao Xiang, J. Gu
With the development of artificial intelligence technology, intelligent question an-swering has become a hot research direction in the field of natural language pro-cessing. This paper proposes a question answering method based on Bi-LSTM and self-attention mechanism model. This method uses Bi-LSTM to encode and align the question and answer respectively, then uses self-attention to obtain the relationship between keywords, and finally performs softmax through the fully connected layer to obtain the similarity between the question and answer. Finally, in the experiment, compared with the traditional attention model, the accuracy rate of this model was increased by 1.6%, and the recall rate was increased by 1.5%.
{"title":"Research on Question Answering System Based on Bi-LSTM and Self-attention Mechanism","authors":"Hao Xiang, J. Gu","doi":"10.1109/ICIEA49774.2020.9101985","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9101985","url":null,"abstract":"With the development of artificial intelligence technology, intelligent question an-swering has become a hot research direction in the field of natural language pro-cessing. This paper proposes a question answering method based on Bi-LSTM and self-attention mechanism model. This method uses Bi-LSTM to encode and align the question and answer respectively, then uses self-attention to obtain the relationship between keywords, and finally performs softmax through the fully connected layer to obtain the similarity between the question and answer. Finally, in the experiment, compared with the traditional attention model, the accuracy rate of this model was increased by 1.6%, and the recall rate was increased by 1.5%.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114843328","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 : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9102048
Intan Novita Dewi, R. Nurcahyo, Farizal
The volume of non-cash transaction grow rapidly all around the world. One of the global growth figures for noncash transactions is driven by the use of mobile payment. In 2018, Indonesia is proven to be a good market for mobile payment and estimated to continue to grow in 2020. This will make competition between mobile payment tougher in Indonesia. Mobile payment companies need to maintain the quality of services and applications in order to meet customer satisfaction. User reviews or complaints expressed on Twitter were used in this study. Pre-processing data is used to convert unstructured and semi-structured text into an understandable format. The Term Frequency matrix is used to calculate the number of occurrences of the token. Word cloud is used to represent the most repeated words that represent the word size. It can be used to find out what services are widely reviewed or complained by customers. The data in this study are tweets with Bahasa Indonesia therefore, the result for word cloud is also in Bahasa Indonesia. The eight frequently used words in the data can be grouped into mobile payment company, monetary rewards, mobile payment transaction and customer service.
{"title":"Word Cloud Result of Mobile Payment User Review in Indonesia","authors":"Intan Novita Dewi, R. Nurcahyo, Farizal","doi":"10.1109/ICIEA49774.2020.9102048","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9102048","url":null,"abstract":"The volume of non-cash transaction grow rapidly all around the world. One of the global growth figures for noncash transactions is driven by the use of mobile payment. In 2018, Indonesia is proven to be a good market for mobile payment and estimated to continue to grow in 2020. This will make competition between mobile payment tougher in Indonesia. Mobile payment companies need to maintain the quality of services and applications in order to meet customer satisfaction. User reviews or complaints expressed on Twitter were used in this study. Pre-processing data is used to convert unstructured and semi-structured text into an understandable format. The Term Frequency matrix is used to calculate the number of occurrences of the token. Word cloud is used to represent the most repeated words that represent the word size. It can be used to find out what services are widely reviewed or complained by customers. The data in this study are tweets with Bahasa Indonesia therefore, the result for word cloud is also in Bahasa Indonesia. The eight frequently used words in the data can be grouped into mobile payment company, monetary rewards, mobile payment transaction and customer service.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115025125","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}
In recent years, artificial intelligence technology, especially image recognition technology, has made great progress. In this paper, we will apply the image recognition technology, which based on convolutional neural networks (CNN) to the digital preservation of Intangible Cultural Heritage (ICH) craftsmanship. A novel online ICH craftsmanship inheritance community has been established, which includes a sequentially updated ICH craftsmanship database, a Search-system based on image recognition, and a coach-system with recommended guidelines. Without doubt, this ecological community will offer a professional communication platform for users with different background. It contributes to community cohesion, encouraging a sense of identity and responsibility which helps individuals to feel part of one.
{"title":"An Online Community Applying CNN Technology for ICH Craftsmanship Inheritance and Preservation","authors":"Enmao Liu, Qiming Jin, Lijuan Liu, Junwu Wang, Cheng Yao, Fangtian Ying","doi":"10.1109/ICIEA49774.2020.9101983","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9101983","url":null,"abstract":"In recent years, artificial intelligence technology, especially image recognition technology, has made great progress. In this paper, we will apply the image recognition technology, which based on convolutional neural networks (CNN) to the digital preservation of Intangible Cultural Heritage (ICH) craftsmanship. A novel online ICH craftsmanship inheritance community has been established, which includes a sequentially updated ICH craftsmanship database, a Search-system based on image recognition, and a coach-system with recommended guidelines. Without doubt, this ecological community will offer a professional communication platform for users with different background. It contributes to community cohesion, encouraging a sense of identity and responsibility which helps individuals to feel part of one.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116458753","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 : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9101920
A. Cilardo
Industrial Computed Tomography (ICT) has a potential for improving processes in such areas as manufacturing, electrical and electronic devices, inhomogeneous materials, and the food industry. To be effective and scalable in industrial settings, however, its implementation must meet crucial constraints, particularly including fast response matching the short cycle times and throughput levels required, for example, by manufacturing applications. One possible bottleneck for ICT is the inherent high-performance computing demand posed by image reconstruction, an important step of scanner data processing. This paper presents the development of an FPGA-based Maximum Likelihood Expectation Maximization (MLEM) kernel, an iterative algorithm used for image reconstruction. We rely on an OpenCL-based design flow and explore a set of optimizations applied through high-level code. The results show that a carefully designed OpenCL-based accelerator can achieve performance gains as high as 8X against an unoptimized design.
{"title":"Evaluating Reconfigurable Hardware for Accelerating Industrial CT","authors":"A. Cilardo","doi":"10.1109/ICIEA49774.2020.9101920","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9101920","url":null,"abstract":"Industrial Computed Tomography (ICT) has a potential for improving processes in such areas as manufacturing, electrical and electronic devices, inhomogeneous materials, and the food industry. To be effective and scalable in industrial settings, however, its implementation must meet crucial constraints, particularly including fast response matching the short cycle times and throughput levels required, for example, by manufacturing applications. One possible bottleneck for ICT is the inherent high-performance computing demand posed by image reconstruction, an important step of scanner data processing. This paper presents the development of an FPGA-based Maximum Likelihood Expectation Maximization (MLEM) kernel, an iterative algorithm used for image reconstruction. We rely on an OpenCL-based design flow and explore a set of optimizations applied through high-level code. The results show that a carefully designed OpenCL-based accelerator can achieve performance gains as high as 8X against an unoptimized design.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117126376","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 : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9102076
Kushagra Bhatia, R. Mittal, Nisha, M. M. Tripathi
Long-term projection of electricity demand is necessary for strategizing production, transmission, distribution and grid expansion in power systems. In this work, we propose a model for forecasting hourly profile of load data which must be taken into consideration by power system planners to produce cost optimal and realizable solutions. The developed ensemble model is formulated in two phases, with the initial phase primarily centered on stacking of gradient and adaptive boosting regressors. In the subsequent phase, the variance is diminished by bagging Lasso LARS regressor on the stacked dataset. For implementation of the proposed model, we collect real-world data of the Germany electricity market for thirteen years spanning from 2006 to 2018. Electricity demand forecasts have been evaluated for the duration of five-years from 2014 to 2018 and are found to be extremely accurate as well as consistent. The presented model on comparison with five benchmark load forecasting models is observed to surpass all of them with a mean absolute percentage error of 1.59 on the test set. Furthermore, unlike neural network models, the proposed ensemble is computationally inexpensive with a training time of 110s.
{"title":"A Multi-Phase Ensemble Model for Long Term Hourly Load Forecasting","authors":"Kushagra Bhatia, R. Mittal, Nisha, M. M. Tripathi","doi":"10.1109/ICIEA49774.2020.9102076","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9102076","url":null,"abstract":"Long-term projection of electricity demand is necessary for strategizing production, transmission, distribution and grid expansion in power systems. In this work, we propose a model for forecasting hourly profile of load data which must be taken into consideration by power system planners to produce cost optimal and realizable solutions. The developed ensemble model is formulated in two phases, with the initial phase primarily centered on stacking of gradient and adaptive boosting regressors. In the subsequent phase, the variance is diminished by bagging Lasso LARS regressor on the stacked dataset. For implementation of the proposed model, we collect real-world data of the Germany electricity market for thirteen years spanning from 2006 to 2018. Electricity demand forecasts have been evaluated for the duration of five-years from 2014 to 2018 and are found to be extremely accurate as well as consistent. The presented model on comparison with five benchmark load forecasting models is observed to surpass all of them with a mean absolute percentage error of 1.59 on the test set. Furthermore, unlike neural network models, the proposed ensemble is computationally inexpensive with a training time of 110s.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129387000","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 : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9101921
Daranee Senarak, D. Kritchanchai
Supply, Processing and Distribution management, or SPD, is a supply chain management model that can improve hospital supply chain management effectiveness. It is successfully developed and applied by Japanese and Chinese Hospitals as the internal-hospital department function and still has potential to adjust for more corresponding to actual hospital situations [1]. In Thailand, the SPD model is adopted in a large private hospital network. From the case study of hospital supply chain in Thailand, the SPD model was studied in order to design the most effective supply chain structures. Of nine models constructed from theoretical basis and all validated as potential structures, participated experts considered four models outperform the others. To discover the most suitable one for the studied case, AHP was used to evaluate relative importance-weight of hospital logistics performance indicators. Then fuzzy TOPSIS was used to select the model. Finally, the SPD model with 3PL-managed centralized warehouse, Group Purchasing Organization and regional hubs was rated as the most appropriate model for the private hospital network case study.
供应、加工和分销管理(Supply, Processing and Distribution management，简称SPD)是一种能够提高医院供应链管理效率的供应链管理模式。这是日本和中国医院成功开发和应用的医院内部科室功能，仍有可能根据医院实际情况进行更多调整[1]。在泰国，大型私立医院网络采用SPD模式。以泰国医院供应链为例，研究SPD模型，以设计最有效的供应链结构。根据理论基础构建的9个模型均被验证为潜在结构，参与专家认为其中4个模型的表现优于其他模型。采用层次分析法对医院物流绩效指标的相对重要度-权重进行评价，找出最适合本研究案例的指标。然后采用模糊TOPSIS法对模型进行选择。最后，由第三方物流管理的集中仓库、集团采购组织和区域中心组成的SPD模型被评为最适合民营医院网络案例研究的模型。
{"title":"3PL Managed SPD-Based Logistics Management Model for Hospital Supply Chain: A Case Study of Hospital Supply Chain in Thailand","authors":"Daranee Senarak, D. Kritchanchai","doi":"10.1109/ICIEA49774.2020.9101921","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9101921","url":null,"abstract":"Supply, Processing and Distribution management, or SPD, is a supply chain management model that can improve hospital supply chain management effectiveness. It is successfully developed and applied by Japanese and Chinese Hospitals as the internal-hospital department function and still has potential to adjust for more corresponding to actual hospital situations [1]. In Thailand, the SPD model is adopted in a large private hospital network. From the case study of hospital supply chain in Thailand, the SPD model was studied in order to design the most effective supply chain structures. Of nine models constructed from theoretical basis and all validated as potential structures, participated experts considered four models outperform the others. To discover the most suitable one for the studied case, AHP was used to evaluate relative importance-weight of hospital logistics performance indicators. Then fuzzy TOPSIS was used to select the model. Finally, the SPD model with 3PL-managed centralized warehouse, Group Purchasing Organization and regional hubs was rated as the most appropriate model for the private hospital network case study.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129873704","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 : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9102077
Noppachai Saivaew, S. Butdee
This paper proposes Fuzzy Logic for Assembly parts for making a decision and selecting effective fitting for spindle shaft and bearing for each stage of assembly. Multi-criteria decision making is concerned with interference fits, material hardness, surface roughness and spindle force. Assembly needs to concern with the first stage of machined part design which is created by CAD. Fuzzy Logic is applied for effective making decision combined with experts and rules of prediction vibration spindle design modeling. Example cases are illustrated.
{"title":"Vibration Prediction of a Spindle Shaft and Bearing Fitting Assembly Design using Fuzzy Logic","authors":"Noppachai Saivaew, S. Butdee","doi":"10.1109/ICIEA49774.2020.9102077","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9102077","url":null,"abstract":"This paper proposes Fuzzy Logic for Assembly parts for making a decision and selecting effective fitting for spindle shaft and bearing for each stage of assembly. Multi-criteria decision making is concerned with interference fits, material hardness, surface roughness and spindle force. Assembly needs to concern with the first stage of machined part design which is created by CAD. Fuzzy Logic is applied for effective making decision combined with experts and rules of prediction vibration spindle design modeling. Example cases are illustrated.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131732589","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 : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9101940
Mary Christy O. Mendoza, Ronell Ray C. Santos, Jeremiah Eli H. Magdaraog
Application of technology in financial services paved the way for banking institutions to shift from the traditional way of banking to a more efficient and less costly operation by means of electronic banking. According to a survey conducted by the Bangko Sentral ng Pilipinas (2017), though there is a significant internet usage and awareness of online payment methods amongst Filipinos, almost half of those with bank accounts and using the internet remains indecisive about electronic transactions due to various behavioral factors. Therefore, this paper was made to study and assess the significant factors, or dimensions, of service quality which cause online banking to impact on customer satisfaction - including efficiency, fulfilment, system availability and privacy. A conceptual framework was developed to create a structure for the hypothesis testing. Following the construct of e-service quality measurement model developed by Parasuraman et.al. (2005), E-S-QUAL survey for e-services was adapted to assess the overall online banking experience of respondents (from a given sample size) via convenience sampling. Analysis was conducted to validate statistical normality using normal probability plot, confirm test validity and reliability by means of measuring Cronbach's Alpha and establish interrelationship by means of Pearson correlation and multiple regression analysis for the core dimensions vs. perceived value and loyalty intentions. Amongst the quality dimensions examined, it was found out that efficiency and fulfilment have the greatest impact on perceived value and loyalty retention of customers.
科技在金融服务方面的应用，为银行机构从传统的银行业务方式，转向更有效率和成本更低的电子银行业务，铺平了道路。根据Bangko central ng Pilipinas(2017年)进行的一项调查，尽管菲律宾人对互联网的使用和在线支付方式的认识很高，但由于各种行为因素，几乎有一半拥有银行账户并使用互联网的人对电子交易仍然犹豫不决。因此，本文研究和评估导致网上银行影响客户满意度的服务质量的重要因素或维度-包括效率，履行，系统可用性和隐私。建立了一个概念框架来创建假设检验的结构。基于Parasuraman等人构建的电子服务质量测量模型。(2005)，电子服务E-S-QUAL调查被改编为通过方便抽样来评估受访者(来自给定样本量)的整体网上银行体验。采用正态概率图验证统计正态性，采用Cronbach’s Alpha检验检验效度和信度，采用Pearson相关和多元回归分析对核心维度与感知价值和忠诚意向的关系进行相互关系分析。研究发现，在质量维度中，效率和履行对顾客感知价值和忠诚保留的影响最大。
{"title":"Assessment of E-Service Quality Dimensions and Its Influence on Customer Satisfaction: A Study on the Online Banking Services in the Philippines","authors":"Mary Christy O. Mendoza, Ronell Ray C. Santos, Jeremiah Eli H. Magdaraog","doi":"10.1109/ICIEA49774.2020.9101940","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9101940","url":null,"abstract":"Application of technology in financial services paved the way for banking institutions to shift from the traditional way of banking to a more efficient and less costly operation by means of electronic banking. According to a survey conducted by the Bangko Sentral ng Pilipinas (2017), though there is a significant internet usage and awareness of online payment methods amongst Filipinos, almost half of those with bank accounts and using the internet remains indecisive about electronic transactions due to various behavioral factors. Therefore, this paper was made to study and assess the significant factors, or dimensions, of service quality which cause online banking to impact on customer satisfaction - including efficiency, fulfilment, system availability and privacy. A conceptual framework was developed to create a structure for the hypothesis testing. Following the construct of e-service quality measurement model developed by Parasuraman et.al. (2005), E-S-QUAL survey for e-services was adapted to assess the overall online banking experience of respondents (from a given sample size) via convenience sampling. Analysis was conducted to validate statistical normality using normal probability plot, confirm test validity and reliability by means of measuring Cronbach's Alpha and establish interrelationship by means of Pearson correlation and multiple regression analysis for the core dimensions vs. perceived value and loyalty intentions. Amongst the quality dimensions examined, it was found out that efficiency and fulfilment have the greatest impact on perceived value and loyalty retention of customers.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182364","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 : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9101950
Y. Prasetyo
Human Factors and Ergonomics (HFE) is one of the core subjects in industrial engineering. Despite the development of industrial engineering over the past 100 years, there is limited information regarding the standard HFE education for the undergraduate programs. The purpose of this study was to propose a standard HFE education for the undergraduate programs in industrial engineering. Several institutions in Indonesia, Philippines, and Taiwan were listed and the HFE courses were evaluated as case studies. The results indicated that there were discrepancies between several institutions regarding HFE education and it is required to standardized the topics under the compulsory courses. This study is one of the first studies that proposed a standard HFE education in the industrial engineering field. The proposed standard could be extended to other institutions worldwide that offer industrial engineering programs. In addition, the proposed approach could also be applied for standardizing master and doctoral programs with a specialization in HFE. Finally, the proposed approach would also be very beneficial for academicians, HFE engineers, and even policymakers.
{"title":"Standardizing Human Factors and Ergonomics Education for the Undergraduate Programs in Industrial Engineering: A Comparative Analysis between Indonesia, Philippines, and Taiwan","authors":"Y. Prasetyo","doi":"10.1109/ICIEA49774.2020.9101950","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9101950","url":null,"abstract":"Human Factors and Ergonomics (HFE) is one of the core subjects in industrial engineering. Despite the development of industrial engineering over the past 100 years, there is limited information regarding the standard HFE education for the undergraduate programs. The purpose of this study was to propose a standard HFE education for the undergraduate programs in industrial engineering. Several institutions in Indonesia, Philippines, and Taiwan were listed and the HFE courses were evaluated as case studies. The results indicated that there were discrepancies between several institutions regarding HFE education and it is required to standardized the topics under the compulsory courses. This study is one of the first studies that proposed a standard HFE education in the industrial engineering field. The proposed standard could be extended to other institutions worldwide that offer industrial engineering programs. In addition, the proposed approach could also be applied for standardizing master and doctoral programs with a specialization in HFE. Finally, the proposed approach would also be very beneficial for academicians, HFE engineers, and even policymakers.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121104441","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 : 2020-04-01DOI: 10.1109/ICIEA49774.2020.9101936
Wuttichai Saheaw, S. Jaiyen, Anantaporn Hanskunatai
Human speech possesses characteristics in each of the word that can be recognized and learned by computers. In this research, It is being proposed the use of the Deep Learning Model to predict speech turn-on and turn-off various electrical appliances, by using the sound conversion method that has been through the process to get the value of sound waves and applied toward training process in different ways. As the sound has more than 1 syllable and having characteristics of similar words that might difficult to predict. This research is based on Convolutional Neural Network (CNN) for comparison with the use of Long Short-Term Memory (LSTM), which is part of the Recurrent Neural Network (RNN) and Thai language Speech Dataset turn-on and turn-off by the 7 types of electrical appliances, the process of reducing noise and silence of the front and back of the audio files by 14 classes in total. The experimental results signify that the proposed Long Short-Term Memory can achieve the best accuracy.
{"title":"Thai Voice Recognition for Controlling Electrical appliances Using Long Short-Term Memory","authors":"Wuttichai Saheaw, S. Jaiyen, Anantaporn Hanskunatai","doi":"10.1109/ICIEA49774.2020.9101936","DOIUrl":"https://doi.org/10.1109/ICIEA49774.2020.9101936","url":null,"abstract":"Human speech possesses characteristics in each of the word that can be recognized and learned by computers. In this research, It is being proposed the use of the Deep Learning Model to predict speech turn-on and turn-off various electrical appliances, by using the sound conversion method that has been through the process to get the value of sound waves and applied toward training process in different ways. As the sound has more than 1 syllable and having characteristics of similar words that might difficult to predict. This research is based on Convolutional Neural Network (CNN) for comparison with the use of Long Short-Term Memory (LSTM), which is part of the Recurrent Neural Network (RNN) and Thai language Speech Dataset turn-on and turn-off by the 7 types of electrical appliances, the process of reducing noise and silence of the front and back of the audio files by 14 classes in total. The experimental results signify that the proposed Long Short-Term Memory can achieve the best accuracy.","PeriodicalId":306461,"journal":{"name":"2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116696476","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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