• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.436-436
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• 2011

팔머가뭄지수(Palmer Drought Severity Index, PDSI)는 미국의 서부 캔자스와 아이오와 지역을 기반으로 Palmer(1965)에 의해 개발된 가뭄지수이다. PDSI는 가뭄을 정량적으로 표현하기 위해 제안된 최초의 포괄적인 가뭄지수라 할 수 있으며, 하나의 기상인자가 아닌 수문순환을 구성하는 여러 가지 인자들의 복합적인 작용에 의해 가뭄이 나타난다는 점에 착안하고 있다. Palmer(1965)는 캔자스와 아이오와 지역의 자료를 이용하여 산정된 수분 편차지수를 이용하여 가뭄지속기간에 대한 합을 산정하고 각각의 지속기간별로 가장 작은 값의 분포를 검토한 후 이를 기반으로 가뭄심도 분류를 위한 가뭄단계를 제시하였으며, 수분편차지수와 이전 월의 PDSI를 이용하여 분석 대상 월의 PDSI 산정을 위한 관계식을 유도하여 제시하였다. Palmer(1965)는 최대 가뭄지속기간에 대한 검토를 통해 유도된 가뭄지수 산정공식을 가뭄기 및 습윤기에 관계없이 동일하게 적용하였다. 그러나 습윤기의 경우 가뭄기간에 대해 분석된 직선과는 다른 패턴을 보일 수 있으며, 이로 인해 습윤 상태에 대해 산정된 지수는 실제 상황과 다른 결과를 나타낼 가능성이 있다. Wells 등(2004)은 이러한 점을 고려하여 가뭄기와 습윤기에 대한 지속기간별 최대 수분편차지수의 합을 도시한 결과를 나타낸 후 가뭄기와 습윤기의 특성이 다르게 나타남을 제시하였으며, 최종적으로 가뭄기와 습윤기에 대해 PDSI 산정공식의 지속기간 인자(duration factor)를 분리하여 산정할 수 있는 방법론을 제시하였다. 본 연구에서는 Wells 등(2004)에 의해 제시된 PDSI 산정공식 유도방법을 우리나라의 관측자료에 적용한 후 그 결과를 검토하였다. 그 결과 가뭄기의 경우 기존 Palmer(1965)의 결과와 매우 유사한 가뭄지수 산정 결과를 얻을 수 있었으나 습윤기에 대해 산정된 결과는 매우 다른 특성을 나타내고 있음을 확인하였다. PDSI는 가뭄 모니터링 및 관리를 위한 지표로 널리 이용되고 있으므로 적절치 못한 지수 값이 제시될 경우 효율적인 가뭄 대책 수립을 어렵게 하는 요소로 작용할 수 있다. 따라서 본 연구에서 제시된 결과는 가뭄관리 및 모니터링을 위해 PDSI를 이용함에 있어 보다 정확한 지표를 제공하기 위해 유용한 정보로 활용될 수 있을 것으로 판단된다.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.407-418
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• 2007

Service science is a new application area that implements services in an interdisciplinary area of management, economics, and engineering. Service systems provide functionalities of traditional software systems, moreover the functionalities are more intellectual in that they require dynamic context awareness, analysis, and decision making based on the recognized and analyzed contexts. However, conventional software development approaches do not sufficiently provide methods to model the service requirements and to design service-intensive systems. Therefore, there is a great demand on effective methodologies for developing service systems. In this paper, we compare traditional software systems with service-intensive systems in order to identify characteristics of the service systems. And, we propose a step-wise process to model service systems, in terms of architecture, components, and workflows. Then, we show a case study on an emergency handling system which is a type of living assistant systems. We believe that the proposed approach can be used in developing high-quality service systems effectively.

• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.43-53
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• 2007

Intrusion detection system(IDS) has recently evolved while combining signature-based detection approach with anomaly detection approach. Although signature-based IDS tools have been commonly used by utilizing machine learning algorithms, they only detect network intrusions with already known patterns, Ideal IDS tools should always keep the signature database of your detection system up-to-date. The system needs to generate the signatures to detect new possible attacks while monitoring and analyzing incoming network data. In this paper, we propose a new outlier cluster detection algorithm with density (or influence) function, Our method assumes that an outlier is a kind of cluster with similar instances instead of a single object in the context of network intrusion, Through extensive experiments using KDD 1999 Cup Intrusion Detection dataset. we show that the proposed method outperform the conventional outlier detection method using Euclidean distance function, specially when attacks occurs frequently.

• Journal of the Korean Society for Railway
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• v.11 no.3
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• pp.257-262
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• 2008

Design and analysis of road subgrade system, which is exposed to repetitive loading condition, uses resilient modulus. The behavior of railway subgrade system will not be quite different from that of road system. Following this phenomenological feature of the subgrade system, this paper introduces the implementation of the resilient modulus based constitutive model on a commercial finite element software. The implementation of the resilient modulus models such as K-${\theta}$ and Uzan on a FE program has been conducted previously. These model assumes that the material state reaches to the nonlinear elastic condition and with further application of repetitive loads, the response of material is completed in elastic condition. According to the recent test results performed on cohesive subgrade soils, however, permanent deformation occurs with repetitive loads. With aids of previously suggested models the permanent deformation cannot be modeled. To overcome such limitation a plastic potential derived from the test results and simple failure criterion based constitutive model is developed. The comparison between the analysis and test results shows a good correlation.

• Korean Journal of Construction Engineering and Management
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• v.10 no.6
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• pp.117-126
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• 2009

Many work-related risk factors can cause construction site hazards. Considering variable and changeable characteristics of construction site, risk of same trade can be different by combination of inherent characteristics. But established safety management has performed like other industries not reflecting characteristics of construction. So, to suggest risk assessment methodology considering risk influence factors in construction site, this study extracts risk influence factors by literature reviews and surveys, builds the weighting system for classification of factors. And risk of trade is calculated based on frequency and severity of risk. Risk assessment methodology is suggested by integrating risk influence factors and risk of trade. From this, risk considering the conditions of construction sites can be derived and based on the derived risk, safety management can be performed more effectively. This study has meaning for assessing the risk which can reflect characteristics of construction site considering risk influence factors.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.22-22
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• 2015

가뭄은 적시에 경보해야 하는 홍수와 달리 진행속도가 느리고 시간적으로 대처할 여유가 있어 진행중일지라도 미리 감지만 한다면 그 피해를 최소화할 수 있다. 이로 인해 미국 등 수문기상 선진국에서는 수문기상 장기예보자료로부터 가뭄전망정보 생산기술을 개발하였으며, 특히 가뭄전망의 정확도 향상을 위해 여러 통계적 보정기법을 적용하고 있다. 국내의 경우 기상청에서 가뭄전망을 목적으로 2011년에 수치예보모델을 이용하여 가뭄전망정보를 생산한바 있으나, 전망정보의 불확실성 문제로 가뭄예보에 활용하는데 한계가 있어 이를 개선할 수 있는 기술개발이 요구되는 실정이다. 본 연구에서는 기후예측자료를 이용하여 가뭄전망정보 생산기술을 개발하고 정확도 개선을 위해 베이지안 기법을 연계하였다. GloSea5 (Global Seasonal forecast model 5) 장기예보자료를 이용하였으며, 베이지안 기법을 통해 과거 관측자료에 대한 사전분포, 모델의 전망정보로부터 우도함수를 유도하여 최종 사후분포를 추정하였다. 베이지안 기법 적용 전 후에 따른 가뭄지수를 산정하였다. 관측자료 기반의 가뭄지수와의 비교분석을 통해 선행기간 및 계절별 가뭄예측 성능을 평가하였으며, 실제 가뭄기간 동안에 가뭄의 재현성을 지역별로 분석하였다. 장기예보자료만을 활용한 기존 가뭄전망에서는 관측 자료가 포함된 1개월 전망에서도 불확실성이 매우 높았지만 베이지안 기법 적용으로 가뭄전망의 정확도가 크게 개선되었다. 특히, 1, 2개월 전망의 시계열 가뭄지수가 관측기반의 가뭄지수의 거동과 매우 유사하게 나타났으며, 지역별로도 베이지안 기법 적용시 실제 가뭄피해 상황을 적절히 재현하는 것으로 나타났다. 국내 가뭄예보에 있어 기후예측정보를 단순활용하기 보다는 베이지안과 같은 통계적 보정기법을 이용하여 가뭄전망정보를 생산하는 것이 바람직하며, 본 연구에서는 가뭄예보업무에 활용될 수 있도록 베이지안 기법에 대한 검증 및 평가를 지속적으로 수행할 계획이다.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.85-110
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• 2018

A chatbot is an interactive assistant that utilizes many communication modes: voice, images, video, or text. It is an artificial intelligence-based application that responds to users' needs or solves problems during user-friendly conversation. However, the current version of the chatbot is focused on understanding and performing tasks requested by the user; its ability to generate personalized conversation suitable for relationship-building is limited. Recognizing the need to build a relationship and making suitable conversation is more important for social chatbots who require social skills similar to those of problem-solving chatbots like the intelligent personal assistant. The purpose of this study is to propose a text analysis method that evaluates relationships between chatbots and users based on content input by the user and adapted to the communication situation, enabling the chatbot to conduct suitable conversations. To evaluate the performance of this method, we examined learning and verified the results using actual SNS conversation records. The results of the analysis will aid in implementation of the social chatbot, as this method yields excellent results even when the private profile information of the user is excluded for privacy reasons.

• Journal of the Korea Convergence Society
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• v.10 no.8
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• pp.15-20
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• 2019

Music therapy has shown many benefits in the treatment of disabled children and the mind. Today's music therapy system is a situation where no specific treatment system has been built. In order for the music therapist to make an accurate treatment, various music therapy cases and treatment history data must be analyzed. Although the most appropriate treatment is given to the client or patient, in reality a number of difficulties are followed due to several factors. In this paper, we propose a music therapy knowledge management model which convergence the existing therapy data and text mining technology. By using the proposed model, similar cases can be searched and accurate and effective treatment can be made for the patient or the client based on specific and reliable data related to the patient. This can be expected to bring out the original purpose of the music therapy and its effect to the maximum, and is expected to be useful for treating more patients.

• Journal of Creative Information Culture
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• v.7 no.4
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• pp.235-242
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• 2021

Recently, many human activity recognition(HAR) researches using smartphone sensor data have been studied. HAR can be utilized in various fields, such as life pattern analysis, exercise measurement, and dangerous situation detection. However researches have been focused on recognition of basic human behaviors or efficient battery use. In this paper, exercising activities performed indoors and outdoors were defined and recognized. Data collection and pre-processing is performed to recognize the defined activities by SVM, random forest and gradient boosting model. In addition, the recognition result is determined based on voting class approach for accuracy and stable performance. As a result, the proposed activities were recognized with high accuracy and in particular, similar types of indoor and outdoor exercising activities were correctly classified.

• The Journal of Bigdata
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• v.7 no.1
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• pp.89-97
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• 2022

Various efforts are needed to prevent accidents because ship collisions can cause various negative situations such as economic losses and casualties. Therefore, research to prevent accidents is being actively conducted, and in this study, new leading indicators for preventing ship collision accidents is proposed. In previous studies, the risk of collision was expressed in consideration of the distance between ships in a specific sea area, but there is a disadvantage that a new model needs to be developed to apply this to other sea areas. In this study, the density-based ship domain DESD (Density-based Empirical Ship Domain) including the environment and operating characteristics of the sea area was defined using AIS (Automatic Identification System) data, which is ship operation information. Deep clustering is applied to two-dimensional DESDs created for each sea area to cluster the seas with similar operating environments. Through the analysis of the relationship between clustered sea areas and ship collision accidents, it was statistically tested that the occurrence of accidents varies by characteristic of each sea area, and it was proved that DESD can be used as a leading indicator of accidents.