Pub Date : 2021-10-13DOI: 10.1109/QIR54354.2021.9716192
Takaaki Kawai, Naoki Fukuta
When a person cannot predict how his or her speech will be interpreted by others, communication problems will happen in person-to-person communications. In the case of communication at workplaces, junior staff may receive his or her supervisor’s words as verbal violence even if the supervisor spoke no offense words. This research aims to achieve the method that shows the candidates of other person’s interpretations in advance. If the interpretations were shown in advance, we can avoid speaking the words eliciting misunderstanding. As a concrete application, this research focuses on the conversation on text chat software. The text chat software shows the candidates of text interpretation which the other person will feel. An opinion mining research has reported that building a semantic tree is effective for text meaning recognition. The research of misinformation detection also has reported the effectiveness of graph data use. In this study, we construct a semantic tree to recognize Japanese text conversations. We also implement the function that transforms the text based on the grammar to show malicious meaning the receiver may perceive. The evaluation showed that the proposed method can transform texts into other texts that clearly express malicious meanings. A translation process was done in practical time, which was 0.32 seconds on average.
{"title":"“Do you mean I was wrong?” A Preliminary Approach on a Graph-based Framework for Suggesting Alternate Interpretations on Japanese Conversations","authors":"Takaaki Kawai, Naoki Fukuta","doi":"10.1109/QIR54354.2021.9716192","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716192","url":null,"abstract":"When a person cannot predict how his or her speech will be interpreted by others, communication problems will happen in person-to-person communications. In the case of communication at workplaces, junior staff may receive his or her supervisor’s words as verbal violence even if the supervisor spoke no offense words. This research aims to achieve the method that shows the candidates of other person’s interpretations in advance. If the interpretations were shown in advance, we can avoid speaking the words eliciting misunderstanding. As a concrete application, this research focuses on the conversation on text chat software. The text chat software shows the candidates of text interpretation which the other person will feel. An opinion mining research has reported that building a semantic tree is effective for text meaning recognition. The research of misinformation detection also has reported the effectiveness of graph data use. In this study, we construct a semantic tree to recognize Japanese text conversations. We also implement the function that transforms the text based on the grammar to show malicious meaning the receiver may perceive. The evaluation showed that the proposed method can transform texts into other texts that clearly express malicious meanings. A translation process was done in practical time, which was 0.32 seconds on average.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133496502","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716171
D. Sudiana, M. Rizkinia, Fahri Alamsyah
The digital world, especially image processing, has been evolving due to the needs of society and the importance of digital-based system security. One of the rapidly progressing technologies is the face recognition system using artificial intelligence. It recognizes a person’s face registered in the database for verification purposes. In this study, we evaluate the face recognition systems based on machine learning classifier algorithms and Principal Component Analysis (PCA) for feature extraction. Seven machine learning algorithms were considered, i.e., Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbour (KNN), Logistic Regression, Naïve Bayes, Multi-Layer Perceptron (MLP), and Convolutional Neural network (CNN). In the CNN scenario, PCA was not used since it has its feature extraction capability. The first six classifiers were set to the most optimal settings. At the same time, CNN used the LeNet-5 architecture trained with a dropout rate of 0.25, 60 epochs, batch size of 20, Adam optimizer, and cross-categorical entropy for the loss function. The input image size was 64×64×1 obtained from the Olivetti faces database. CNN, SVM, and LR achieved the three highest accuracies, i.e., 98.75%, 98.75%, and 97.50%, respectively.
{"title":"Performance Evaluation of Machine Learning Classifiers for Face Recognition","authors":"D. Sudiana, M. Rizkinia, Fahri Alamsyah","doi":"10.1109/QIR54354.2021.9716171","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716171","url":null,"abstract":"The digital world, especially image processing, has been evolving due to the needs of society and the importance of digital-based system security. One of the rapidly progressing technologies is the face recognition system using artificial intelligence. It recognizes a person’s face registered in the database for verification purposes. In this study, we evaluate the face recognition systems based on machine learning classifier algorithms and Principal Component Analysis (PCA) for feature extraction. Seven machine learning algorithms were considered, i.e., Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbour (KNN), Logistic Regression, Naïve Bayes, Multi-Layer Perceptron (MLP), and Convolutional Neural network (CNN). In the CNN scenario, PCA was not used since it has its feature extraction capability. The first six classifiers were set to the most optimal settings. At the same time, CNN used the LeNet-5 architecture trained with a dropout rate of 0.25, 60 epochs, batch size of 20, Adam optimizer, and cross-categorical entropy for the loss function. The input image size was 64×64×1 obtained from the Olivetti faces database. CNN, SVM, and LR achieved the three highest accuracies, i.e., 98.75%, 98.75%, and 97.50%, respectively.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122122940","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 : 2021-10-13DOI: 10.1109/QIR54354.2021.9716174
F. Husnayain, T. Noguchi, Kiyohiro Iwama, F. Yusivar
A major problem in IPM motor drive during magnetic saturation is parameter mismatch between controller and motor model. This study aims to reduce mismatch by using 2-D inductance map. The 2-D map was determined by dq-axis current feedback, then applied to PI current controller and cross-coupling. Three scenarios were used to evaluate the effectiveness of the proposed method. The first two scenarios were neglect and consider saturation impact, respectively, under $I_{d}^{*}=0A$. The last scenario was by MTPA current controller with magnetic saturation being considered. This induction map was applied in PI current control and cross-coupling calculation between dq-axis. The key finding, the undershoot magnitude decreased by 66% during the transient under $I_{d}^{*}=0 A$. The JMAG simulation results confirmed the ineffectiveness of the PI current control occurs as parameter mismatch happened. The utilization of Ld and Ld map successfully reduces the mismatch and increases the robustness of PI current control.
{"title":"Mismatch Reduction using 2-D Inductance Map for Robust Vector Control of IPM Motor","authors":"F. Husnayain, T. Noguchi, Kiyohiro Iwama, F. Yusivar","doi":"10.1109/QIR54354.2021.9716174","DOIUrl":"https://doi.org/10.1109/QIR54354.2021.9716174","url":null,"abstract":"A major problem in IPM motor drive during magnetic saturation is parameter mismatch between controller and motor model. This study aims to reduce mismatch by using 2-D inductance map. The 2-D map was determined by dq-axis current feedback, then applied to PI current controller and cross-coupling. Three scenarios were used to evaluate the effectiveness of the proposed method. The first two scenarios were neglect and consider saturation impact, respectively, under $I_{d}^{*}=0A$. The last scenario was by MTPA current controller with magnetic saturation being considered. This induction map was applied in PI current control and cross-coupling calculation between dq-axis. The key finding, the undershoot magnitude decreased by 66% during the transient under $I_{d}^{*}=0 A$. The JMAG simulation results confirmed the ineffectiveness of the PI current control occurs as parameter mismatch happened. The utilization of Ld and Ld map successfully reduces the mismatch and increases the robustness of PI current control.","PeriodicalId":446396,"journal":{"name":"2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132245834","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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