• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.3
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• pp.345-350
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• 2016

It is the personal information protection law is prepared, in the government, to develop a personal information protection basic plan has been implemented. However, year after year, complaints caused by infringement of personal information in public institutions has increased. In this paper, the recognition level of analysis related to the protection of personal information of local governments, public institutions through a questionnaire survey, raised the need for improvement.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.449-451
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• 2006

With the increasing danger of personal information being exposed, a technique to protect personal information by identifying a non-user in case it is exposed. A study to construct a neural network recognizer for developing a economical and effective user protecting system. For this, time variables regarding user typing patterns from a pattern extraction device. With the variations in the standard deviation for the collected time variables, non-user patterns were generated. The recognition performance increased with the increase in the standard deviation and a higher recognition was achieved at 2.5. Also, five types of training data were generated and the recognition performance was examined as a function of the number of non-user patterns. With the increase in non-suer patterns, the recognition error quantified in the root mean square error (RMSE) was reduced. The smallest RMSE was obtained at the type 5 and 90 non-user patterns. In overall, the type 3 model yielded the highest recognition accuracy Particularly, a perfect recognition of 100% was achieved at 45 non-user patterns.

• Journal of Platform Technology
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• v.11 no.3
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• pp.56-67
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• 2023

Recently, de-identification of personal information, which has been a long-cherished desire of the data-based industry, was revised and specified in August 2020. It became the foundation for activating data called crude oil[2] in the fourth industrial era in the industrial field. However, some people are concerned about the infringement of the basic rights of the data subject[3]. Accordingly, a development study was conducted on the Batch De-Identification Tool, a personal information de-identification automation tool. In this study, first, we developed an image labeling tool to label human faces (eyes, nose, mouth) and car license plates of various resolutions to build data for training. Second, an object recognition model was trained to run the object recognition module to perform de-identification of personal information. The automated personal information de-identification tool developed as a result of this research shows the possibility of proactively eliminating privacy violations through online services. These results suggest possibilities for data-based industries to maximize the value of data while balancing privacy and utilization.

• Review of Korea Contents Association
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• v.12 no.3
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• pp.39-43
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• 2014

• Journal of Korean Library and Information Science Society
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• v.47 no.4
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• pp.385-409
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• 2016

This study is to examine the effect of book festivals on personal reading habituation and library revitalization. The survey of participants of book festivals is conducted. As a result, it appears that participation of book festivals has a impact on personal reading habituation and recognition of and using library. The followings are results of this survey: recognition of participants about book festivals has a impact on personal reading habituation and recognition of library and using library, but the effect(2.1~4.7%) is slight; Satisfaction of book fair(display and selling booths) has a impact on personal reading habituation(30.1%); Satisfaction of experience booths has a impact on recognition of library(22.4%) and using library(26.4%); Satisfaction of participating in book festivals also has a impact on participant's reading habituation(24.5%), recognition of library(20.1%) and using library(27.5%).

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.205-212
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• 2019

With the rapid spread of mobile phones, users are continuously interested in using the mobile phone in connection with personal activities. Also, increasingly users want to share (transmit and receive) and save data more easily and simply in the mobile environment. This paper suggests motion recognition of mobile phone to share personal information with any people located within a certain distance using location-based service with GCM service. The suggested application is based on Google Cloud Messaging which enables asynchronous communication with the mobile applications executed in Android operating system. The requirements of light-weight mechanism can be satisfied as it is possible to access sharing of personal information easily, simply and in real time through all mobile devices anywhere.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.96-99
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• 2020

According to brilliant development of smart devices, many related services are being devised. And, almost every service is designed to provide user-centric services based on personal information. In this situation, to prevent unintentional leakage of personal information is essential. Conventionally, ID and Password system is used for the user authentication. This is a convenient method, but it has a vulnerability that can cause problems due to information leakage. To overcome these problem, many methods related to face recognition is being researched. Through this paper, we investigated the trend of user authentication through biometrics and a representative model for face recognition techniques. One is DeepFace of FaceBook and another is FaceNet of Google. Each model is based on the concept of Deep Learning and Distance Metric Learning, respectively. And also, they are based on Convolutional Neural Network (CNN) model. In the future, further research is needed on the equipment configuration requirements for practical applications and ways to provide actual personalized services.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.206-208
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• 2022

As the big data industry has recently developed significantly, interest in privacy violations caused by personal information leakage has increased. There have been attempts to automate this through named entity recognition in natural language processing. In this paper, named entity recognition data is constructed semi-automatically by identifying sentences with de-identification information from de-identification information in Korean Wikipedia. This can reduce the cost of learning about information that is not subject to de-identification compared to using general named entity recognition data. In addition, it has the advantage of minimizing additional systems based on rules and statistics to classify de-identification information in the output. The named entity recognition data proposed in this paper is classified into twelve categories. There are included de-identification information, such as medical records and family relationships. In the experiment using the generated dataset, KoELECTRA showed performance of 0.87796 and RoBERTa of 0.88.

• Fashion & Textile Research Journal
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• v.4 no.4
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• pp.369-375
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• 2002

The present study attempts to examine the degree of recognition of the Personal Colors by the age of the woman, and to analyze the influence of the recognition on the factors for choosing specific colors for hair coloring and facial make-up and the consequent satisfaction. The data will be used as a basic material for research and marketing in the field of color consulting in the beauty industry. Collected data were statistically processed using the SPSS WIN program. Depending on the nature of the contents to be analyzed, either the percentage calculation or the Chi-square analysis or the ANOVA was carried out. The findings of the study are as follows; The overall recognition of the Personal Colors was generally low in terms of the knowledge, information and experiences. While the degree of recognition was the highest in teenagers, the necessity of diagnosing the Personal Colors was most deeply perceived by the women in their 30s. One of the factors for choosing a specific color for hair coloring was their favorite color for the teenagers, and the Personal Color or the advice of the professional for the women in their 30s. Meanwhile, the highest factor for those in their 20s was the colors in vogue. For the facial color make-up as well, this sensitivity to popular colors was also highest in the twenty-something women. The color choice in consideration of favorite colors and the Personal Colors was the most prominent in the teenagers. The tendency of utilizing the advice of sales people or the professionals was the highest in the women in their 30s. In the survey of satisfaction with the chosen colors for hair coloring and make-up, it was found that satisfaction was the highest in the cases of choosing the Personal Colors in all the age groups, while it was the lowest for the choice of popular colors.