• Journal of Satellite, Information and Communications
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• v.10 no.4
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• pp.77-82
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• 2015

This paper proposed an accident prediction model in order to prevent accidents in mountain areas using a big data analysis. Data of accidents in mountain areas are shown as graphs. We have analyzed cases: the number of accidents per year, day of week, time of day to find patterns of the negligent accident in mountain areas. The proposed prediction model consists of weighted variables of the accident in mountain through visualized big data analysis. The model of danger index performance is demonstrated by showing accident-prone areas with weighted variables.

• The Journal of Economics, Marketing and Management
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• v.10 no.6
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• pp.17-26
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• 2022

Purpose: This study analyzes advanced cases of overseas smart cities and examines policy implications related to the creation of smart cities in Korea. Research design, data, and methodology: Analysis standards were established through the analysis of best practices. Analysis criteria include Technology, Privacy, Security, and Governance. Results: In terms of technology, U-City construction experience and communication infrastructure are strengths. Korea's ICT technology is inferior to major countries. On the other hand, mobile communication, IoT, Internet, and public data are at the highest level. The privacy section created six principles: legality, purpose limitation, transparency, safety, control, and accountability. Security issues enable urban crime, disaster and catastrophe prediction and security through the establishment of an integrated platform. Governance issues are handled by the Smart Special Committee, which serves as policy advisory to the central government for legal system, standardization, and external cooperation in the district. Conclusions: Private technology improvement and participation are necessary for privacy and urban security. Citizens should participate in smart city governance.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.513-515
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• 2022

본 연구는 범죄를 발생시키는데 관련된 여러가지 요인들을 기반으로 범죄 예측 모델을 생성하고 설명 가능 인공지능 기술을 적용하여 인천 광역시를 대상으로 범죄 발생에 영향을 미치는 요인들을 분석하였다. 범죄 예측 모델 생성을 위해 XG Boost 알고리즘을 적용하였으며, 설명 가능 인공지능 기술로는 Shapley Additive exPlanations (SHAP)을 사용하였다. 기존 관련 사례들을 참고하여 범죄 예측에 사용된 변수를 선정하였고 변수에 대한 데이터는 공공 데이터를 수집하였다. 실험 결과 성매매단속 현황과 청소년 실종 가출 신고 현황이 범죄 발생에 큰 영향을 미치는 주요 요인으로 나타났다. 제안하는 모델은 범죄 발생 지역, 요인들을 미리 예측하여 제시함으로써 범죄 예방에 사용되는 인력자원, 물적자원 등을 용이하게 쓸 수 있도록 활용할 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.393-400
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• 2016

Recently, there have been various researches on efficient and automatic analysis on urban environment methods that utilize the computer vision and machine learning technology. Among many new analyses, urban safety analysis has received a major attention. In order to predict more accurately on safety score and reflect the human visual perception, it is necessary to consider the generic and local information that are most important to human perception. In this paper, we use Double-column Convolutional Neural network consisting of generic and local columns for the prediction of urban safety. The input of generic and local column used re-sized and random cropped images from original images, respectively. In addition, a new learning method is proposed to solve the problem of over-fitting in a particular column in the learning process. For the performance comparison of our Double-column Convolutional Neural Network, we compare two Support Vector Regression and three Convolutional Neural Network models using Root Mean Square Error and correlation analysis. Our experimental results demonstrate that our Double-column Convolutional Neural Network model show the best performance with Root Mean Square Error of 0.7432 and Pearson/Spearman correlation coefficient of 0.853/0.840.

• Korean Security Journal
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• no.30
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• pp.139-169
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• 2012

The purpose of this study is to contribute to establishing the scientific policing policies through deriving the time series models that can forecast the occurrence of major crimes such as murder, robbery, burglary, rape, violence and identifying the occurrence of major crimes using the models. In order to achieve this purpose, there were performed the statistical methods such as Generation of Time Series Model(C) for identifying the forecasting models of time series, Generation of Time Series Model(C) and Sequential Chart of Time Series(N) for identifying the accuracy of the forecasting models of time series on the monthly incidence of major crimes from 2002 to 2010 using IBM PASW(SPSS) 19.0. The following is the result of the study. First, murder, robbery, rape, theft and violence crime's forecasting models of time series are Simple Season, Winters Multiplicative, ARIMA(0,1,1)(0,1,1), ARIMA(1,1,0 )(0,1,1) and Simple Season. Second, it is possible to forecast the short-term's occurrence of major crimes such as murder, robbery, burglary, rape, violence using the forecasting models of time series. Based on the result of this study, we have to suggest various forecasting models of time series continuously, and have to concern the long-term forecasting models of time series which is based on the quarterly, yearly incidence of major crimes.

• Fire Science and Engineering
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• v.33 no.6
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• pp.72-79
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• 2019

Through this study, we developed a model for predicting chemical accidents lead to casualties. The model was derived from the logistic regression analysis model and applied to the variables affecting the accident. The accident data used in the model was analyzed by studying the statistics of past chemical accidents, and applying independent variables that were statistically significant through data analysis, such as the type of accident, cause, place of occurrence, status of casualties, and type of chemical accident that caused the casualties. A significance of p < 0.05 was applied. The model developed in this study is meaningful for the prevention of casualties caused by chemical accidents and the establishment of safety systems in the workplace. The analysis using the model found that the most influential factor in the occurrence of casualty in accidents was chemical explosions. Therefore, there is an urgent need to prepare countermeasures to prevent chemical accidents, specifically explosions, from occurring in the workplace.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.510-512
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• 2019

Recently, there have been growing requirements in the public safety sector to ensure safety through detection of hazardous situations or preemptive predictions. It is noteworthy that various sensor data can be analyzed and utilized as a result of mobile device's dissemination, and many advantages can be used in terms of safety and security. An effective modeling technique is needed to combine sensor data generated by smart-phones and wearable devices to analyze users' moving patterns and behavioral patterns, and to ensure public safety by fusing location-based crime risk data provided.

• Convergence Security Journal
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• v.22 no.3
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• pp.79-86
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• 2022

Voice phishing is a cyber crime in which fake financial institutions, the Public Prosecutor's Office, and the National Police Agency are impersonated to find out an individual's Certification number and credit card number or withdraw a deposit. Recently, voice phishing has been carried out in a subtle and secret way. Analyzing the trend of voice phishing that occurred in '18~'21, it was found that there is a seasonality that occurs rapidly at a time when the movement of money is intensifying in the trend of voice phishing, giving ambiguity to time series analysis. In this research, we adjusted seasonality using the X-12 seasonality adjustment methodology for accurate prediction of voice phishing occurrence trends, and predicted the occurrence of voice phishing in 2022 using the ARIMA model.

• Korean Journal of Culture and Social Issue
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• v.10 no.2
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• pp.39-77
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• 2004

This literature review introduced the concept of psychopathy which has never been defined academically in Korea. Also it is reviewed how this concept could be applied as latent factor of criminal behavior in the forensic settings. For this purpose, first of all, the periodical change of psychopathy definition was explored. Then it was investigated which determinants might develop psychopathy and what would be the behavioral characteristics of psychopaths. Finally, risk assessment tools measuring psychopathy were introduced and their predictive efficacy and applicability in Korean criminal justice system was discussed.