• Convergence Security Journal
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• v.18 no.5_1
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• pp.45-51
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• 2018

As data utilization and importance becomes important, data-related accidents and damages are gradually increasing. Especially, insider threats are the most harmful threats. And these insider threats are difficult to detect by traditional security systems, so rule-based abnormal behavior detection method has been widely used. However, it has a lack of adapting flexibly to changes in new attacks and new environments. Therefore, in this paper, we propose an adaptive anomaly movement detection framework based on a statistical Markov model to detect insider threats in advance. This is designed to minimize false positive rate and false negative rate by adopting environment factors that directly influence the behavior, and learning data based on statistical Markov model. In the experimentation, the framework shows good performance with a high F2-score of 0.92 and suspicious behavior detection, which seen as a normal behavior usually. It is also extendable to detect various types of suspicious activities by applying multiple modeling algorithms based on statistical learning and environment factors.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.9-16
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• 2019

In this paper, it will analyze security problems, so technology's potential can apply to business security area. First, in order to deep learning do security tasks sufficiently in the business area, deep learning requires repetitive learning with large amounts of data. In this paper, to acquire learning ability to do stable business tasks, it must detect abnormal IP packets and attack such as normal software with malicious code. Therefore, this paper will analyze whether deep learning has the cognitive ability to detect various attack. In this paper, to deep learning to reach the system and reliably execute the business model which has problem, this paper will develop deep learning technology which is equipped with security engine to analyze new IP about Session and do log analysis and solve the problem of mathematical role which can extract abnormal data and distinguish infringement of system data. Then it will apply to business model to drop the vulnerability and improve the business performance.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.6
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• pp.223-234
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• 2021

Recent advances in the 4th Industrial Revolution have accelerated the change of the shopping behavior from offline to online. Search queries show customers' information needs most intensively in online shopping. However, there are not many search query research in the field of search, and most of the prior research in the field of search query research has been studied on a limited topic and data-based basis based on researchers' qualitative judgment. To this end, this study defines the type of search query with data-based quantitative methodology by applying machine learning to search research query field to define the 15 topics of search query by conducting topic modeling based on search query and clicked document information. Furthermore, we present a new classification system of new search query types representing searching behavior characteristics by extracting key variables through principal component analysis and analyzing. The results of this study are expected to contribute to the establishment of effective search services and the development of search systems.

• Proceedings of the ESK Conference
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• 1996.10a
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• pp.47-55
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• 1996

This study developed a comprehensive and easily applicable nuclear reactor control system evaluation method using reactor operators behavioral and mental workload database. A proposed control panel design cycle consists of the 5 steps: (1) finding out inconvenient, erroneous, and mentally stressful factors for the proposed design through evaluative experiments, (2) drafting improved design alternatives considering detective factors found out in the step (1), (3) comparative experiements for the design alternatives, (4) selecting a best design alternative, (5) returning to the step (1) and repeating the design cycle. Reactor operators behavioral and mental workload database collected from evaluative experiments in the step (1) and comparative experiments in the step (3) of the design cycle have a key roll in finding out defective factors and yielding the criteria for selection of the proposed reactor control systems. The behavioral database was designed to include the major informations about reactor operators' control behaviors: beginning time of operations, involved displays, classification of observational behaviors, dehaviors, decisions, involved control devices, classification of control behaviors, communications, emotional status, opinions for man-machine interface, and system event log. The database for mental workload scored from various physiological variables-EEG, EOG, ECG, and respir- ation pattern-was developed to indicate the most stressful situation during reactor control operations and to give hints for defective design factors. An experimental test for the evaluation method applied to the Compact Nuclear Simulator (CNS) installed in Korea Atomic Energy Research Institute (KAERI) suggested that some defective design factors of analog indicators should be improved and that automatization of power control to a target level would give relaxation to the subject operators in stressful situation.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.326-326
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• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.33 no.6
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• pp.529-532
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• 2000

To establish a food safety of dried layer, heat treatment effect on the bacterial density of dried layers was investigated. And a modified process developing experiment for dried layer products using closing type drying oven was carried out. tittle bacterial density difference on the dried layer products were found before and after heat treatment at $90^{\circ}C$ for 6 hrs called Hwaip treatment having been used for long term storage. Direct or indirect heat treatment of dried lavers using gas burner and frying pan reduced about 1 to 3 log cycle of viable cell count from $10^8\;CFU/g\;to\;10^5\;CFU/g$. Heat treatment by direct surface contact type cooking machine being used in the market place for cooked dried layer products could reduce the viable cell count on the layer product from $2.2{\times}10^5{\~}5.2{\times}10^7\;CFU/g\;to\;7.0{\times}10^2{\~}5.0{\times}10^5\;CFU/g$, Ultraviolet irradiation (20 W, 30 cm) to one or both side of the dried laver products reduced the viable cell count from $2.2{\times}10^6\;CFU/g\;to\;8.0{\times}10^5\;CFU/g\;and\;2.0{\times}10^5\;CFU/g$, respectively. The viable cell count of the dried layer products produced by modified process using a closing type dryer was about $10^3\;CFU/g$ and lower 3 log cycle than that in the products collected in market place and made by open type dryer.

• Journal of Food Hygiene and Safety
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• v.36 no.5
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• pp.400-406
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• 2021

The purpose of this study was to evaluate the microbial contamination level of Korean traditional rice cakes (Garaetteok, Injeolmi, Gyeongdan), as well as manufacturing environment of small-sized businesses in Korea. The contamination levels of total aerobic bacteria, coliforms, and Bacillus cereus in raw materials were 3.76-4.48, 2.21-4.14, and 1.02-1.15 log CFU/g respectively. On the other hand, Escherichia coli was not found. It has been found that the contamination level of total aerobic bacteria, coliforms, and B. cereus in the raw material decreased after the washing process, but it increased again during the soaking and grinding process. However, after the steaming stage, the contamination level increased again during the molding and cooling process, suggesting the need to take cautions in managing cooling water and molded rice cakes in the process. These results suggest that the safe management of cooling water and taking cautions in the drying process after steaming of rice cakes are necessary for controlling cross-contamination. No E. coli was detected during the manufacturing process involving all tested rice cakes. The microbial contamination level of manufacturing environment such as rice grinder and rice cake forming machine was high. Therefore, in terms of food safety strategy, it is necessary to consider introducing systematic cleansing and disinfection procedure to processing equipment and environment for the sake of reducing microbiological risks.

• Journal of Korean Society of Forest Science
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• v.105 no.2
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• pp.231-237
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• 2016

This research was conducted in order to examine whether the Woodgrab short-wood logging method, most widely used logging method in Korea, is more favorable than other logging methods in terms of productivity and profitability. For the comparative purposes, whole-tree logging methods with cable yarding system using a swing yarder and a tower yarder were evaluated. The productivity and the profitability of the logging operations by the machine types on a L. kaempferi stand were estimated by simulating logging processes based on bucking patterns and the results were compared. As a result, the Woodgrab short-wood logging system showed the most favorable results in terms of skidding productivity and operating cost. On the contrary, the system was the least profitable among the three logging methods. Main reason is that while the system may be beneficial in terms of operation productivity, it is restricted to produce only short logs mainly for low quality raw materials such as pulp, bolts, etc. which are sold at cheap prices.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.154-159
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• 2015

Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.99-110
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• 2011

Mobile environments rapidly changing and digital convergence widely employed, mobile devices including smart phones have been playing a critical role that changes users' lifestyle in the areas of entertainments, businesses and information services. The various services using mobile devices are developing to meet the personal needs of users in the mobile environments. Especially, an LBS (Location-Based Service) is combined with other services and contents such as augmented reality, mobile SNS (Social Network Service), games, and searching, which can provide convenient and useful services to mobile users. In this paper, we design and implement the prototype of mobile personal assistant (PA) agents. Our personal assistant agent helps users do some tasks by hiding the complexity of difficult tasks, performing tasks on behalf of the users, and reflecting the preferences of users. To identify user's preferences and provide personalized services, clustering and classification algorithms of data mining are applied. The clusters of the log data using clustering algorithms are made by measuring the dissimilarity between two objects based on usage patterns. The classification algorithms produce user profiles within each cluster, which make it possible for PA agents to provide users with personalized services and contents. In the experiment, we measured the classification accuracy of user model clustered using clustering algorithms. It turned out that the classification accuracy using our method was increased by 17.42%, compared with that using other clustering algorithms.