Pub Date : 2021-01-01DOI: 10.7236/JIIBC.2021.21.2.103
M. Lee, Jeong Su Kim
About the orthogonal Hadamard matrix announced by Hadamard in France in 1893, Professor Moon Ho Lee newly defined it as Center Weight Hadamard in 1989 and announced it, and discovered the Jacket matrix in 1998. The Jacket matrix is a generalization of the Hadamard matrix. In this paper, we propose a method of obtaining the Symmetric Jacket matrix, analyzing important properties and patterns, and obtaining the Jacket matrix's determinant and Eigenvalue, and proved it using Eigen decomposition. These calculations are useful for signal processing and orthogonal code design. To analyze the matrix system, compare it with DFT, DCT, Hadamard, and Jacket matrix. In the symmetric matrix of Galois Field, the element-wise inverse relationship of the Jacket matrix was mathematically proved and the orthogonal property AB=I relationship was derived.
{"title":"Characteristics of Jacket Matrix for Communication Signal Processing","authors":"M. Lee, Jeong Su Kim","doi":"10.7236/JIIBC.2021.21.2.103","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.2.103","url":null,"abstract":"About the orthogonal Hadamard matrix announced by Hadamard in France in 1893, Professor Moon Ho Lee newly defined it as Center Weight Hadamard in 1989 and announced it, and discovered the Jacket matrix in 1998. The Jacket matrix is a generalization of the Hadamard matrix. In this paper, we propose a method of obtaining the Symmetric Jacket matrix, analyzing important properties and patterns, and obtaining the Jacket matrix's determinant and Eigenvalue, and proved it using Eigen decomposition. These calculations are useful for signal processing and orthogonal code design. To analyze the matrix system, compare it with DFT, DCT, Hadamard, and Jacket matrix. In the symmetric matrix of Galois Field, the element-wise inverse relationship of the Jacket matrix was mathematically proved and the orthogonal property AB=I relationship was derived.","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"62 1","pages":"103-109"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76192357","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.3.15
TaeYoung Kim, Joongi Hong, Mingu Kang, Seounghan Song, Jeong-Hoon Lee, Sun Tae Kim
{"title":"Integrity Support System for Blockchain-based explainable CCTV Video","authors":"TaeYoung Kim, Joongi Hong, Mingu Kang, Seounghan Song, Jeong-Hoon Lee, Sun Tae Kim","doi":"10.7236/JIIBC.2021.21.3.15","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.3.15","url":null,"abstract":"","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"117 1","pages":"15-21"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79088004","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.3.81
Xilong Ding, Qiutan Li, Xufei Wang, Le Chen, Jinku Son, JeongYoung Song
{"title":"Apple Detection Algorithm based on an Improved SSD","authors":"Xilong Ding, Qiutan Li, Xufei Wang, Le Chen, Jinku Son, JeongYoung Song","doi":"10.7236/JIIBC.2021.21.3.81","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.3.81","url":null,"abstract":"","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"47 2","pages":"81-89"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72619058","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.4.97
Eun-Jin Jang, Seung-Jung Shin
{"title":"Proposal of User Filtering System for Black Consumer Extraction -focusing on Shared Electric Kickboard Users-","authors":"Eun-Jin Jang, Seung-Jung Shin","doi":"10.7236/JIIBC.2021.21.4.97","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.4.97","url":null,"abstract":"","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"34 1","pages":"97-102"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89373982","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.4.75
Seung-Gag Lim
This paper relates with the Hybrid-DSE-MMA (Hybrid-Dithered Signed Error-MMA) that is possible to improving the equalization performance by using the adaptive modulus and adaptive stepsize in DSE-MMA adaptive equalizer. The DSE-MMA possible to improve the robustness performance to external noise of SE-MMA by using the sign after adding the dither signal for get the error signal in order to update the tap coefficient. But it has a drawback of performance degradation in convergence speed and residual isi by using the fixed modulus and fixed stepsize. In this paper, it was confirmed that this equalization performance degradation was improved by applying the adaptive modulus and stepsize in DSE-MMA propotional to the output power of equalizer by computer simulation. In order to compare the improved equalization performance to currently DSE-MMA, the recovered signal constellation that is the output of the equalizer, residual isi, Maximum Distortion, MSE and the SER were used as a performance index. As a result of computer simulation, the Hybrid-DSE-MMA improve the equalization performance in every index, but gives slower convergence speed compared to DSE-MMA.
本文讨论了在DSE-MMA自适应均衡器中使用自适应模量和自适应步长来提高均衡性能的Hybrid-DSE-MMA (hybrid - dired Signed Error-MMA)。利用加入抖动信号后的符号来获取误差信号以更新抽头系数，可以提高SE-MMA对外部噪声的鲁棒性。但由于采用固定模量和固定步长，存在收敛速度和残差性能下降的缺点。本文通过计算机仿真，证实了将DSE-MMA比例中的自适应模量和步长应用于均衡器的输出功率，可以改善均衡器的性能退化。为了将改进后的均衡性能与目前的DSE-MMA进行比较，将均衡器输出的恢复信号星座、剩余isi、最大失真、MSE和SER作为性能指标。计算机仿真结果表明，Hybrid-DSE-MMA在各指标上均提高了均衡性能，但收敛速度较DSE-MMA慢。
{"title":"A Performance Analysis of Hybrid-DSE-MMA Adaptive Equalization Algorithm based on Adaptive Modulus and Adaptive Stepsize","authors":"Seung-Gag Lim","doi":"10.7236/JIIBC.2021.21.4.75","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.4.75","url":null,"abstract":"This paper relates with the Hybrid-DSE-MMA (Hybrid-Dithered Signed Error-MMA) that is possible to improving the equalization performance by using the adaptive modulus and adaptive stepsize in DSE-MMA adaptive equalizer. The DSE-MMA possible to improve the robustness performance to external noise of SE-MMA by using the sign after adding the dither signal for get the error signal in order to update the tap coefficient. But it has a drawback of performance degradation in convergence speed and residual isi by using the fixed modulus and fixed stepsize. In this paper, it was confirmed that this equalization performance degradation was improved by applying the adaptive modulus and stepsize in DSE-MMA propotional to the output power of equalizer by computer simulation. In order to compare the improved equalization performance to currently DSE-MMA, the recovered signal constellation that is the output of the equalizer, residual isi, Maximum Distortion, MSE and the SER were used as a performance index. As a result of computer simulation, the Hybrid-DSE-MMA improve the equalization performance in every index, but gives slower convergence speed compared to DSE-MMA.","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"20 1","pages":"75-80"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85091063","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.3.107
Seon-Min Kim, Byunghyun Han, Junyeong Heo
Recently artificial intelligence technology has been introduced in various fields and various machine learning models have been operated in various frameworks as academic interest has increased. However, these frameworks have different data formats, which lack interoperability, and to overcome this, the open neural network exchange format, ONNX, has been proposed. In this paper we describe how to transform multiple machine learning models to ONNX, and propose algorithms and inference systems that can determine machine learning techniques in an integrated ONNX format. Furthermore we compare the inference results of the models before and after the ONNX transformation, showing that there is no loss or performance degradation of the learning results between the ONNX transformation.
{"title":"Model Transformation and Inference of Machine Learning using Open Neural Network Format","authors":"Seon-Min Kim, Byunghyun Han, Junyeong Heo","doi":"10.7236/JIIBC.2021.21.3.107","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.3.107","url":null,"abstract":"Recently artificial intelligence technology has been introduced in various fields and various machine learning models have been operated in various frameworks as academic interest has increased. However, these frameworks have different data formats, which lack interoperability, and to overcome this, the open neural network exchange format, ONNX, has been proposed. In this paper we describe how to transform multiple machine learning models to ONNX, and propose algorithms and inference systems that can determine machine learning techniques in an integrated ONNX format. Furthermore we compare the inference results of the models before and after the ONNX transformation, showing that there is no loss or performance degradation of the learning results between the ONNX transformation.","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"104 1","pages":"107-114"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82756445","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.3.175
Youngghyu Sun, Jiyoung Lee, Soohyun Kim, Soohwan Kim, Heung-Jea Lee, Jinyoung Kim
This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.
{"title":"Prediction Technique of Energy Consumption based on Reinforcement Learning in Microgrids","authors":"Youngghyu Sun, Jiyoung Lee, Soohyun Kim, Soohwan Kim, Heung-Jea Lee, Jinyoung Kim","doi":"10.7236/JIIBC.2021.21.3.175","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.3.175","url":null,"abstract":"This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"34 1","pages":"175-181"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90845570","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.4.81
K. Gwak, Young J. Rho
Artificial intelligence technology is developing as it enters the fourth industrial revolution. Active researches are going on; visual-based models using CNNs. U-net is one of the visual-based models. It has shown strong performance for semantic segmentation. Although various U-net studies have been conducted, studies on tracking objects with unclear outlines such as gases and smokes are still insufficient. We conducted a U-net study to tackle this limitation. In this paper, we describe how 3D cameras are used to collect data. The data are organized into learning and test sets. This paper also describes how U-net is applied and how the results is validated.
{"title":"Tracking Method of Dynamic Smoke based on U-net","authors":"K. Gwak, Young J. Rho","doi":"10.7236/JIIBC.2021.21.4.81","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.4.81","url":null,"abstract":"Artificial intelligence technology is developing as it enters the fourth industrial revolution. Active researches are going on; visual-based models using CNNs. U-net is one of the visual-based models. It has shown strong performance for semantic segmentation. Although various U-net studies have been conducted, studies on tracking objects with unclear outlines such as gases and smokes are still insufficient. We conducted a U-net study to tackle this limitation. In this paper, we describe how 3D cameras are used to collect data. The data are organized into learning and test sets. This paper also describes how U-net is applied and how the results is validated.","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"14 1","pages":"81-87"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84165339","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.3.193
Sangmi Yoo, Hyoungbum Kim
{"title":"An exploratory study on the factors of creative problem-solving ability","authors":"Sangmi Yoo, Hyoungbum Kim","doi":"10.7236/JIIBC.2021.21.3.193","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.3.193","url":null,"abstract":"","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"26 1","pages":"193-200"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78612501","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 : 2021-01-01DOI: 10.7236/JIIBC.2021.21.4.177
Sang-Un Lee
{"title":"Algorithm for Grade Adjust of Mixture Optimization Problem","authors":"Sang-Un Lee","doi":"10.7236/JIIBC.2021.21.4.177","DOIUrl":"https://doi.org/10.7236/JIIBC.2021.21.4.177","url":null,"abstract":"","PeriodicalId":22795,"journal":{"name":"The Journal of the Institute of Webcasting, Internet and Telecommunication","volume":"174 1","pages":"177-182"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80728307","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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