• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.35-42
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• 2024

In this study, we propose a method for real-time recognition and analysis of dog behavior using a motion sensor and deep learning techonology. The existing home CCTV (Closed-Circuit Television) that recognizes dog behavior has privacy and security issues, so there is a need for new technologies to overcome them. In this paper, we propose a system that can analyze and care for a dog's behavior based on the data measured by the motion sensor. The study compares the MLP (Multi-Layer Perceptron) and CNN (Convolutional Neural Network) models to find the optimal model for dog behavior analysis, and the final model, which has an accuracy of about 82.19%, is selected. The model is lightened to confirm its potential for use in embedded environments.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.105-117
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• 2019

Bitcoin, which can be classified as a cryptocurrency, has attracted attention from various industries because it is an innovative digital currency and the beginning of a Blockchain system. However, as the research on Bitcoin progressed, several security vulnerabilities and possible attacks were analyzed. Among them, the security problem caused by the transparency of the Blockchain database prevents the Blockchain system from being applied to various fields. This vulnerability is further classified as the weak anonymity of participating nodes and privacy problem due to disclosure of transaction details. In recent years, several countermeasures have been developed against these vulnerabilities. In this paper, we first describe the main features of the public and private Blockchain, and explain privacy, unlinkability and anonymity. And, three public Blockchain platforms, Dash, Zcash and Monero which are derived from Bitcoin, and Hyperledger Fabric which is a private Blockchain platform, are examined. And we analyze the operating principles of the protocols applied on each platform. In addition, we classify the applied technologies into anonymity and privacy protection in detail, analyze the advantages and disadvantages, and compare the features and relative performance of the platforms based on the computational speed of the applied cryptographic mechanisms.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.43-53
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• 2023

In this detailed and comprehensive study, our primary focus has been placed on accurately gauging the number of visitors and their real-time locations in commercial spaces. Particularly, in a real cafe, using security cameras, we have developed a system that can offer live updates on available seating and predict future congestion levels. By employing YOLO, a real-time object detection and tracking algorithm, the number of visitors and their respective locations in real-time are also monitored. This information is then used to update a cafe's indoor map, thereby enabling users to easily identify available seating. Moreover, we developed a model that predicts the congestion of a cafe in real time. The sophisticated model, designed to learn visitor count and movement patterns over diverse time intervals, is based on Long Short Term Memory (LSTM) to address the vanishing gradient problem and Sequence-to-Sequence (Seq2Seq) for processing data with temporal relationships. This innovative system has the potential to significantly improve cafe management efficiency and customer satisfaction by delivering reliable predictions of cafe congestion to all users. Our groundbreaking research not only demonstrates the effectiveness and utility of indoor location tracking technology implemented through security cameras but also proposes potential applications in other commercial spaces.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.1
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• pp.150-163
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• 2022

Recently, the gated community has been increasing due to various reasons such as demand for differentiated areas and security, but various problems have been raised, including regional conflicts, traffic restrictions and disconnection of surrounding areas. Therefore, this study empirically considered what kind of spatial effect the gated community has on the surrounding area by analyzing the vitality using floating population big data and analyzing pedestrian accessibility using network analysis and social network analysis. As a result, it was found that the overall vitality in the study area was greatly affected by the land use and the building use. However, focusing on apartment complexes, even in the same land use, when the form of the complex is open to the outside, there is a lot of floating population, so the vitality is high. In terms of accessibility, assuming that the gated community is open, it was found that as the physical connectivity improved, there were more roads for pedestrians to choose from, and the accessibility improved as traffic and exchanges occurred in the disconnected space. The value of improving property rights and residential environment is also precious, but it is necessary to review how to reflect the improvement of local permeability in enhancing the publicness of cities and the value and direction of communities that can coexist with the region.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.209-227
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• 2023

The government continues to announce measures to revitalize smart construction technology based on BIM for productivity innovation in the construction industry. In the design phase, the goal is design automation and optimization by converging BIM Data and other advanced technologies. Accordingly, in the basic design of the Namhae Seomyeon-Yeosu Sindeok National Road Construction Project, a domestic undersea tunnel project, BIM-based design was carried out by developing tunnel design automation technology using 3D spatial information according to the tunnel design process. In order to derive the optimal alignment, more than 10,000 alignment cases were generated in 36hr using the generative design technique and a quantitative evaluation of the objective functions defined by the designer was performed. AI-based ground classification and 3D Geo Model were established to evaluate the economic feasibility and stability of the optimal alignment. AI-based ground classification has improved its precision by performing about 30 types of ground classification per borehole, and in the case of the 3D Geo Model, its utilization can be expected in that it can accumulate ground data added during construction. In the case of 3D blasting design, the optimal charge weight was derived in 5 minutes by reviewing all security objects on the project range on Dynamo, and the design result was visualized in 3D space for intuitive and convenient construction management so that it could be used directly during construction.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.137-152
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• 2004

Assesment of 30 nutritional minerals and 8 toxic elements in hair are very important not only for determining adequacy, deficiencies and unbalance, but also for assessing their relative relationships in the body. A test has been developed that serves this purpose exceedingly well. This test is known as tissue mineral analysis(TMA). TMA is very popular method in hair mineral analysis for health care professionals in over 46 countries' medical center. However, there are some problems. First, they do not have database which is suitable for korean to do analyze. Second, as the TMA results from TEI-USA is composed of english documents and graphic files prohibited to open, its usability is very low. Third, some of them has low level database which is related to TMA, so hairs are sent to TEI-USA for analyzing and medical services. it bring about an severe outflow of dollars. Finally, TMA results are based on the database of american health and mineral standards, it is possibly mislead korean mineral standards. The purposes of this research is to develope the first Intelligent Medical Expert System(IMES) of TMA, in Korea, which makes clear the problems mentioned earlier IMES can analyze the tissue mineral data with multiple stage decision tree classifier. It is also constructed with multiple fuzzy rule base and hence analyze the complex data from Korean database by fuzzy inference methods. Pilot test of this systems are increased of business efficiency and business satisfaction 86% and 92% respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.2
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• pp.1-9
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• 2008

As the security requirements of mobile phones have been increasing, there have been extensive researches using one biometric feature (e.g., an iris, a fingerprint, or a face image) for authentication. Due to the limitation of uni-modal biometrics, we propose a method that combines face and iris images in order to improve accuracy in mobile environments. This paper presents four advantages and contributions over previous research. First, in order to capture both face and iris image at fast speed and simultaneously, we use a built-in conventional mega pixel camera in mobile phone, which is revised to capture the NIR (Near-InfraRed) face and iris image. Second, in order to increase the authentication accuracy of face and iris, we propose a score level fusion method based on SVM (Support Vector Machine). Third, to reduce the classification complexities of SVM and intra-variation of face and iris data, we normalize the input face and iris data, respectively. For face, a NIR illuminator and NIR passing filter on camera are used to reduce the illumination variance caused by environmental visible lighting and the consequent saturated region in face by the NIR illuminator is normalized by low processing logarithmic algorithm considering mobile phone. For iris, image transform into polar coordinate and iris code shifting are used for obtaining robust identification accuracy irrespective of image capturing condition. Fourth, to increase the processing speed on mobile phone, we use integer based face and iris authentication algorithms. Experimental results were tested with face and iris images by mega-pixel camera of mobile phone. It showed that the authentication accuracy using SVM was better than those of uni-modal (face or iris), SUM, MAX, NIN and weighted SUM rules.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.785-802
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• 2017

As cyber threats using malicious code continue to increase, many security and vaccine companies are putting a lot of effort into analysis and detection of malicious codes. However, obfuscation techniques that make software analysis more difficult are applied to malicious codes, making it difficult to respond quickly to malicious codes. In particular, commercial obfuscation tools can quickly and easily generate new variants of malicious codes so that malicious code analysts can not respond to them. In order for analysts to quickly analyze the actual malicious behavior of the new variants, reverse obfuscation(=de-obfuscation) is needed to disable obfuscation. In this paper, general analysis methodology is proposed to de-obfuscate the software used by a commercial obfuscation tool, Themida. First, We describe operation principle of Themida by analyzing obfuscated executable file using Themida. Next, We extract original code and data information of executable from obfuscated executable using Pintool, DBI(Dynamic Binary Instrumentation) framework, and explain the implementation results of automated analysis tool which can deobfuscate to original executable using the extracted original code and data information. Finally, We evaluate the performance of our automated analysis tool by comparing the original executable with the de-obfuscated executable.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.8
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• pp.759-766
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• 2016

This paper is on a system design of recognizing sound sources and tracing locations from detecting a section of sound sources which is strong in surrounding environmental sounds about sound sources occurring in an abnormal situation by using signals within the section. In detection of the section with strong sound sources, weighted average delta energy of a short section is calculated from audio signals received. After inputting it into a low-pass filter, through comparison of values of the output result, a section strong in background sound is defined. In recognition of sound sources, from data of the detected section, using an HMM(: Hidden Markov Model) as a traditional recognition method, learning and recognition are realized from creating information to recognize sound sources. About signals of sound sources that surrounding background sounds are included, by using energy of existing signals, after detecting the section, compared with the recognition through the HMM, a recognition rate of 3.94% increase is shown. Also, based on the recognition result, location grasping by using TDOA(: Time Delay of Arrival) between signals in the section accords with 97.44% of angles of a real occurrence location.