Pub Date : 2022-05-26DOI: 10.1109/icsse55923.2022.9947357
Presents the copyright information for the conference. May include reprint permission information.
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{"title":"Copyright Page","authors":"","doi":"10.1109/icsse55923.2022.9947357","DOIUrl":"https://doi.org/10.1109/icsse55923.2022.9947357","url":null,"abstract":"Presents the copyright information for the conference. May include reprint permission information.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"435 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129869232","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 : 2022-05-26DOI: 10.1109/ICSSE55923.2022.10154017
Wei-Tai Huang, Ze-Qi Chen, J. Chou
In this study, Taguchi’s robust process design optimizes the turning and milling combined processing. The quality characteristics are surface roughness and circularity. The experiment uses the $L 9left(3^{4}right)$ orthogonal table to find the parameters optimized for a target. The control factors used are tool speed (r.p.m.), axial depth of cut (mm), finishing allowance (mm), and C-axis brake pressure $left(mathrm{kg} / mathrm{cm}^{2}right)$, with roughness and circularity as characteristic targets, analyze and calculate the obtained signal-to-noise ratio (S/N) data to obtain the optimization of quality characteristics. The experimental results show that the optimized surface roughness of quality characteristics is $0.473 mathrm{~mm}$, and the optimized parameters are Al (5001pm), B2 $(2 mathrm{~mm}), mathrm{Cl}(0.6 mathrm{~mm})$, and D1 $left(20 mathrm{~kg} / mathrm{cm}^{2}right)$. The circularity is $0.0003 mathrm{~mm}$, and the optimized parameters are $A 3$ (900r.p.m.), B1 (1mm), C3 (1.4mm) and D2 $left(25 mathrm{~kg} / mathrm{cm}^{2}right)$. After optimization experiments, the circularity has increased by 67 %, and the surface roughness has increased by 28.8 %. It is also known that a higher tool speed will increase the cutting speed and the tool wear will be relatively greater. After comparing the tool wear of the two characteristic targets, it is found that the tool wear difference of the circularity is $0.039 mathrm{~mm}$, which is an increase of 59 %. The tool wear difference of the surface roughness is $0.025 mathrm{~mm}$, an increase of 39 %.
{"title":"Research on Optimizing of circularity and Surface Roughness for Turn-Mill Multitasking Machining","authors":"Wei-Tai Huang, Ze-Qi Chen, J. Chou","doi":"10.1109/ICSSE55923.2022.10154017","DOIUrl":"https://doi.org/10.1109/ICSSE55923.2022.10154017","url":null,"abstract":"In this study, Taguchi’s robust process design optimizes the turning and milling combined processing. The quality characteristics are surface roughness and circularity. The experiment uses the $L 9left(3^{4}right)$ orthogonal table to find the parameters optimized for a target. The control factors used are tool speed (r.p.m.), axial depth of cut (mm), finishing allowance (mm), and C-axis brake pressure $left(mathrm{kg} / mathrm{cm}^{2}right)$, with roughness and circularity as characteristic targets, analyze and calculate the obtained signal-to-noise ratio (S/N) data to obtain the optimization of quality characteristics. The experimental results show that the optimized surface roughness of quality characteristics is $0.473 mathrm{~mm}$, and the optimized parameters are Al (5001pm), B2 $(2 mathrm{~mm}), mathrm{Cl}(0.6 mathrm{~mm})$, and D1 $left(20 mathrm{~kg} / mathrm{cm}^{2}right)$. The circularity is $0.0003 mathrm{~mm}$, and the optimized parameters are $A 3$ (900r.p.m.), B1 (1mm), C3 (1.4mm) and D2 $left(25 mathrm{~kg} / mathrm{cm}^{2}right)$. After optimization experiments, the circularity has increased by 67 %, and the surface roughness has increased by 28.8 %. It is also known that a higher tool speed will increase the cutting speed and the tool wear will be relatively greater. After comparing the tool wear of the two characteristic targets, it is found that the tool wear difference of the circularity is $0.039 mathrm{~mm}$, which is an increase of 59 %. The tool wear difference of the surface roughness is $0.025 mathrm{~mm}$, an increase of 39 %.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132109052","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 : 2022-05-26DOI: 10.1109/ICSSE55923.2022.9948254
Sandeep Vara Sankar Diddi, L. Ko
Electroencephalography (EEG) is one of the most widely used noninvasive system in the field of brain-computer interfacing (BCI). Visual evoked potentials (VEPs) are the efficient BCI techniques designed to detect target/non-target events through brain responses. Fuzzy based entropy measures have received increased attention in analyzing the complex multichannel EEG signals. Although, fuzzy entropy performs robustly compared to non-fuzzy methods, it does not examine the time series signals over multiple temporal scales, which is crucial for multivariate signals. This study proposed an empirical mode decomposition (EMD) featured fuzzy entropy by coarse-graining the time-series signal at a multi-scale level (EMFuzzyEn) to increase the performance of the BCI during hybrid steady state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) BCI paradigm. The results showed that the EMFuzzyEn features achieved significantly higher classification performance of 89 ± 1% for 9 channel combination and 87 ± 2% for 2 channel combination. Further, the EMFuzzyEn also showed superior performance when compared to our published event related potential (ERP) based BCI technique and popular non-fuzzy entropy algorithms. Overall, the results demonstrated that EMFuzzyEn algorithm enhances the discrimination between target and non-target events efficiently by evaluating their complexity differences thereby improving the classification performance and can be a potential indicator to measure the BCI performance.
{"title":"Fuzzy Entropy based Complexity Analysis for Target Classification during Hybrid BCI Paradigm","authors":"Sandeep Vara Sankar Diddi, L. Ko","doi":"10.1109/ICSSE55923.2022.9948254","DOIUrl":"https://doi.org/10.1109/ICSSE55923.2022.9948254","url":null,"abstract":"Electroencephalography (EEG) is one of the most widely used noninvasive system in the field of brain-computer interfacing (BCI). Visual evoked potentials (VEPs) are the efficient BCI techniques designed to detect target/non-target events through brain responses. Fuzzy based entropy measures have received increased attention in analyzing the complex multichannel EEG signals. Although, fuzzy entropy performs robustly compared to non-fuzzy methods, it does not examine the time series signals over multiple temporal scales, which is crucial for multivariate signals. This study proposed an empirical mode decomposition (EMD) featured fuzzy entropy by coarse-graining the time-series signal at a multi-scale level (EMFuzzyEn) to increase the performance of the BCI during hybrid steady state visual evoked potential (SSVEP) and rapid serial visual presentation (RSVP) BCI paradigm. The results showed that the EMFuzzyEn features achieved significantly higher classification performance of 89 ± 1% for 9 channel combination and 87 ± 2% for 2 channel combination. Further, the EMFuzzyEn also showed superior performance when compared to our published event related potential (ERP) based BCI technique and popular non-fuzzy entropy algorithms. Overall, the results demonstrated that EMFuzzyEn algorithm enhances the discrimination between target and non-target events efficiently by evaluating their complexity differences thereby improving the classification performance and can be a potential indicator to measure the BCI performance.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126791652","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 : 2022-05-26DOI: 10.1109/ICSSE55923.2022.9948259
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{"title":"IEEE Copyright and Consent Form","authors":"","doi":"10.1109/ICSSE55923.2022.9948259","DOIUrl":"https://doi.org/10.1109/ICSSE55923.2022.9948259","url":null,"abstract":"Presents the copyright information for the conference. May include reprint permission information.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127010060","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 : 2022-05-26DOI: 10.1109/ICSSE55923.2022.9948253
Presents the copyright information for the conference. May include reprint permission information.
展示会议的版权信息。可能包括转载许可信息。
{"title":"IEEE Copyright and Consent Form","authors":"","doi":"10.1109/ICSSE55923.2022.9948253","DOIUrl":"https://doi.org/10.1109/ICSSE55923.2022.9948253","url":null,"abstract":"Presents the copyright information for the conference. May include reprint permission information.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"04 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128494434","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 : 2022-05-26DOI: 10.1109/ICSSE55923.2022.9948256
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{"title":"IEEE Copyright and Consent Form","authors":"","doi":"10.1109/ICSSE55923.2022.9948256","DOIUrl":"https://doi.org/10.1109/ICSSE55923.2022.9948256","url":null,"abstract":"Presents the copyright information for the conference. May include reprint permission information.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124045145","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 : 2022-05-26DOI: 10.1109/icsse55923.2022.9948231
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展示会议的版权信息。可能包括转载许可信息。
{"title":"IEEE Copyright and Consent Form","authors":"","doi":"10.1109/icsse55923.2022.9948231","DOIUrl":"https://doi.org/10.1109/icsse55923.2022.9948231","url":null,"abstract":"Presents the copyright information for the conference. May include reprint permission information.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122895718","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 : 2022-05-26DOI: 10.1109/ICSSE55923.2022.9948265
Po-Jen Lai, Chuan-Pin Lu
In Taiwan, health insurance payments for cancer treatment are determined based on the patient's recovery. After medical personnel obtains a patient's cell examination results, they can check the decrease in the number or atrophy of cancer cells in the patient through methods such as flow cytometry. Medical personnel generally use fluorescence microscopes to view and count the number of nuclei. However, this method is time-consuming, has a high error rate, and the inspection results are highly inconsistent. Previous studies used convolutional neural networks for cell nuclei localization, automatic counting, and micronucleus analysis to solve the aforementioned problems. However, convolutional neural networks (YOLOV4) are to mis-positions of small-scale dual-nucleus cell images. In this study, the image geometric analysis algorithm is proposed to solve this problem. Using this method, YOLOV4 is used to perform 20X optical magnification for small-scale cell nuclei image localization, and the proposed algorithm was modified to improve the accuracy of cell nuclei localization. To demonstrate small-scale nucleus image localization problems and verify the efficacy of the proposed modified method, the results of the localization of small-scale nucleus image of the YOLO and Faster R-CNN algorithms were compared. The proposed method is shown to correct cell nucleus localization errors. This paper describes the proposed method structure and process in the following sections.
{"title":"Development of the Modified Method Based on Convolutional Neural Network of Cancer Cell Nucleus Image Localization","authors":"Po-Jen Lai, Chuan-Pin Lu","doi":"10.1109/ICSSE55923.2022.9948265","DOIUrl":"https://doi.org/10.1109/ICSSE55923.2022.9948265","url":null,"abstract":"In Taiwan, health insurance payments for cancer treatment are determined based on the patient's recovery. After medical personnel obtains a patient's cell examination results, they can check the decrease in the number or atrophy of cancer cells in the patient through methods such as flow cytometry. Medical personnel generally use fluorescence microscopes to view and count the number of nuclei. However, this method is time-consuming, has a high error rate, and the inspection results are highly inconsistent. Previous studies used convolutional neural networks for cell nuclei localization, automatic counting, and micronucleus analysis to solve the aforementioned problems. However, convolutional neural networks (YOLOV4) are to mis-positions of small-scale dual-nucleus cell images. In this study, the image geometric analysis algorithm is proposed to solve this problem. Using this method, YOLOV4 is used to perform 20X optical magnification for small-scale cell nuclei image localization, and the proposed algorithm was modified to improve the accuracy of cell nuclei localization. To demonstrate small-scale nucleus image localization problems and verify the efficacy of the proposed modified method, the results of the localization of small-scale nucleus image of the YOLO and Faster R-CNN algorithms were compared. The proposed method is shown to correct cell nucleus localization errors. This paper describes the proposed method structure and process in the following sections.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125142264","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 : 2022-05-26DOI: 10.1109/ICSSE55923.2022.9948244
Yu-Hsuan Lee, Wenjie Wang, Sheng-Kai Huang
This article proposes an algorithm to predict the cluster centers and their locations in unlabeled data in which we do not know how many clusters in advance. The proposed method is a recursive algorithm and has good performance to deal with the clustering problem in data with or without noise.
{"title":"A Prediction for the Cluster Centers in Unlabeled Data","authors":"Yu-Hsuan Lee, Wenjie Wang, Sheng-Kai Huang","doi":"10.1109/ICSSE55923.2022.9948244","DOIUrl":"https://doi.org/10.1109/ICSSE55923.2022.9948244","url":null,"abstract":"This article proposes an algorithm to predict the cluster centers and their locations in unlabeled data in which we do not know how many clusters in advance. The proposed method is a recursive algorithm and has good performance to deal with the clustering problem in data with or without noise.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114682480","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 : 2022-05-26DOI: 10.1109/ICSSE55923.2022.9948241
Presents the copyright information for the conference. May include reprint permission information.
展示会议的版权信息。可能包括转载许可信息。
{"title":"IEEE Copyright and Consent Form","authors":"","doi":"10.1109/ICSSE55923.2022.9948241","DOIUrl":"https://doi.org/10.1109/ICSSE55923.2022.9948241","url":null,"abstract":"Presents the copyright information for the conference. May include reprint permission information.","PeriodicalId":220599,"journal":{"name":"2022 International Conference on System Science and Engineering (ICSSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132799606","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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