• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.767-780
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• 2015

In recent years, as smart phone penetration rate is growing explosively, new forms of cyber crime data is poured out beyond the limits of management system for cyber crime investigation. These new forms of data are collected and stored in police station but, some of data are not systematically managed. As a result, investigators sometimes miss the hidden data which can be critical for a case. Crime data is usually generated by computer which produces complex and huge data and records many logs automatically, so it is necessary to simplify a collected data and cluster by crime pattern. In this paper, we categorize all kinds of cyber crime and simplify crime database and extract critical clues relative to other cases. Through data mining and network-visualization, we found there is correlation between clues of a case. From this result, we conclude cyber crime data mining helps crime prevention, early blocking and increasing the efficiency of the investigation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2A
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• pp.105-112
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• 2012

Face detection at a distance faces is very challenging since images are often degraded by blurring and noise as well as low resolution. This paper proposes an improved face detection method with AdaBoost filtering and sequential testing stages with color and shape information. The conventional AdaBoost filter detects face regions but often generates false alarms. The face detection method is improved by adopting sequential testing stages in order to remove false alarms. The testing stages comprise skin-color test and variable edge-mask filtering. The skin-color filtering is composed of two steps, which involve rectangular window regions and individual pixels to generate binary face clusters. The size of the variable edge-mask is determined by the ellipse which is estimated from the face cluster. The validation of the horizontal and vertical ratio of the mask is also investigated. In the experiments, the efficacy of the proposed algorithm is proved by images captured by a CCTV and a smart-phone

• Journal of Digital Convergence
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• v.14 no.2
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• pp.271-278
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• 2016

University-industry cooperation is indispensable for a short-term problem solving, as well as sustainable growth in the economy of the Republic of Korea. In order to embody and implement that, the government has run a variety of financial assistance programs and policies for the promotion of the cooperation for the last 10 years, and Korea Sanhak Foundation has made progress in academic and research development support, creative personnel training and scholarships, International Support and other projects. In this paper, window-type university-industry convergence and practices, and analyzing the best practices of national and international IT-based fusion technology, corporate cooperation were established to support measures taken in conjunction type and model of the university. Industry utilizing the latest IT technology based on this model, academia, proposed a collaboration system between workers building measures.

• The KIPS Transactions:PartC
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• v.12C no.2 s.98
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• pp.157-168
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• 2005

Group key agreement protocols are designed to provide a group of parties securely communicating over a public network with a session key. The mobile computing architecture is asymmetric in the sense of computational capabilities of participants. That is, the protocol participants consist of the stationary server(application servers) with sufficient computational Power and a cluster of mobile devices(clients) with limited computational resources. It is desirable to minimize the amount of computation performed by each group member in a group involving low-power mobile devices such as smart cards or personal digital assistants(PDAs). Furthermore we are required to update the group key with low computational costs when the members need to be excluded from the group or multiple new members need to be brought into an existing group. In this paper, we propose a dynamic group key protocol which offers computational efficiency to the clients with low-power mobile devices. We compare the total communicative and computational costs of our protocol with others and prove its suity against a passive adversary in the random oracle model.

• Smart Structures and Systems
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• v.7 no.4
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• pp.241-262
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• 2011

Cast iron pipe has been used as a water distribution technology in North America since the early nineteenth century. The first cast iron pipes were made of grey cast iron which was succeeded by ductile iron as a pipe material in the 1940s. These different iron alloys have significantly different microstructures which give rise to distinct mechanical properties. Insight into the non-destructive structural condition assessment of aging pipes can be advantageous in developing mitigation strategies for pipe failures. This paper examines the relationship between the small-strain and large-strain properties of exhumed cast iron water pipes. Nondestructive and destructive testing programs were performed on eight pipes varying in age from 40 to 130 years. The experimental program included microstructure evaluation and ultrasonic, tensile, and flexural testing. New applications of frequency domain analysis techniques including Fourier and wavelet transforms of ultrasonic pulse velocity measurements are presented. A low correlation between wave propagation and large-strain measurements was observed. However, the wave velocities were consistently different between ductile and grey cast iron pipes (14% to 18% difference); the ductile iron pipes showed the smaller variation in wave velocities. Thus, the variation of elastic properties for ductile iron was not enough to define a linear correlation because all the measurements were practically concentrated in single cluster of points. The cross-sectional areas of the specimens tested varied as a result of minor manufacturing defects and levels of corrosion. These variations affect the large strain testing results; but, surface defects have limited effect on wave velocities and may also contribute to the low correlations observed. Lamb waves are typically not considered in the evaluation of ultrasonic pulse velocity. However, Lamb waves were found to contribute significantly to the frequency content of the ultrasonic signals possibly resulting in the poor correlations observed. Therefore, correlations between wave velocities and large strain properties obtained using specimens manufactured in the laboratory must be used with caution in the condition assessment of aged water pipes especially for grey cast iron pipes.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.675-687
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• 2010

Structural Health Monitoring (SHM) is the science and technology of monitoring and assessing the condition of aerospace, civil and mechanical infrastructures using a sensing system integrated into the structure. Impedance-based SHM measures impedance of a structure using a PZT (Lead Zirconate Titanate) patch. This paper presents a low-power wireless autonomous and active SHM node called Autonomous SHM Sensor 2 (ASN-2), which is based on the impedance method. In this study, we incorporated three methods to save power. First, entire data processing is performed on-board, which minimizes radio transmission time. Considering that the radio of a wireless sensor node consumes the highest power among all modules, reduction of the transmission time saves substantial power. Second, a rectangular pulse train is used to excite a PZT patch instead of a sinusoidal wave. This eliminates a digital-to-analog converter and reduces the memory space. Third, ASN-2 senses the phase of the response signal instead of the magnitude. Sensing the phase of the signal eliminates an analog-to-digital converter and Fast Fourier Transform operation, which not only saves power, but also enables us to use a low-end low-power processor. Our SHM sensor node ASN-2 is implemented using a TI MSP430 microcontroller evaluation board. A cluster of ASN-2 nodes forms a wireless network. Each node wakes up at a predetermined interval, such as once in four hours, performs an SHM operation, reports the result to the central node wirelessly, and returns to sleep. The power consumption of our ASN-2 is 0.15 mW during the inactive mode and 18 mW during the active mode. Each SHM operation takes about 13 seconds to consume 236 mJ. When our ASN-2 operates once in every four hours, it is estimated to run for about 2.5 years with two AAA-size batteries ignoring the internal battery leakage.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.192-202
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• 2021

In the past, various studies have been conducted on safety facility installation standards and road safety indices. But there are limitations in applying them to the field, such as using many survey items and variables that are difficult to use. Therefore, this study attempted to develop road safety judgment indicators considering the applicability of the research results and to prepare criteria for installing safety facilities. As part of the study, data of related systems were reviewed, and the use of variables already in use was figured out. Furthermore, the road safety judgment indicators reflecting traffic, road, and accident factors were developed through correlation and factor analysis. Later, the criteria score for determining the installation of safety facilities was derived through cluster analysis. The analyses suggested, that the installation judgment criterion score at the intersection was lower than that of the single road(crosswalk), and the road risk at the intersection was higher.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.45-67
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• 2017

According to the Internet Usage Research performed in 2016, the number of internet users and the internet usage have been increasing. Smartphone, compared to the computer, is taking a more dominant role as an internet access device. As the number of smart devices have been increasing, some views that the demand on high-speed internet will decrease; however, Despite the increase in smart devices, the high-speed Internet market is expected to slightly increase for a while due to the speedup of Giga Internet and the growth of the IoT market. As the broadband Internet market saturates, telecom operators are over-competing to win new customers, but if they know the cause of customer exit, it is expected to reduce marketing costs by more effective marketing. In this study, we analyzed the relationship between the cancellation rates of telecommunication products and the factors affecting them by combining the data of 3 cities, Anyang, Gunpo, and Uiwang owned by a telecommunication company with the regional data from KOSIS(Korean Statistical Information Service). Especially, we focused on the assumption that the neighboring areas affect the distribution of the cancellation rates by coupling type, so we conducted spatial cluster analysis on the 3 types of cancellation rates of each region using the spatial analysis tool, SatScan, and analyzed the various relationships between the cancellation rates and the regional data. In the analysis phase, we first summarized the characteristics of the clusters derived by combining spatial information and the cancellation data. Next, based on the results of the cluster analysis, Variance analysis, Correlation analysis, and regression analysis were used to analyze the relationship between the cancellation rates data and regional data. Based on the results of analysis, we proposed appropriate marketing methods according to the region. Unlike previous studies on regional characteristics analysis, In this study has academic differentiation in that it performs clustering based on spatial information so that the regions with similar cancellation types on adjacent regions. In addition, there have been few studies considering the regional characteristics in the previous study on the determinants of subscription to high-speed Internet services, In this study, we tried to analyze the relationship between the clusters and the regional characteristics data, assuming that there are different factors depending on the region. In this study, we tried to get more efficient marketing method considering the characteristics of each region in the new subscription and customer management in high-speed internet. As a result of analysis of variance, it was confirmed that there were significant differences in regional characteristics among the clusters, Correlation analysis shows that there is a stronger correlation the clusters than all region. and Regression analysis was used to analyze the relationship between the cancellation rate and the regional characteristics. As a result, we found that there is a difference in the cancellation rate depending on the regional characteristics, and it is possible to target differentiated marketing each region. As the biggest limitation of this study and it was difficult to obtain enough data to carry out the analyze. In particular, it is difficult to find the variables that represent the regional characteristics in the Dong unit. In other words, most of the data was disclosed to the city rather than the Dong unit, so it was limited to analyze it in detail. The data such as income, card usage information and telecommunications company policies or characteristics that could affect its cause are not available at that time. The most urgent part for a more sophisticated analysis is to obtain the Dong unit data for the regional characteristics. Direction of the next studies be target marketing based on the results. It is also meaningful to analyze the effect of marketing by comparing and analyzing the difference of results before and after target marketing. It is also effective to use clusters based on new subscription data as well as cancellation data.