• 품질경영학회지
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• 제34권2호
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• pp.129-135
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• 2006

In the advance of computer technology, it is possible to keep all the related informations for monitoring equipments in control and huge amount of real time manufacturing data in a data base. Thus, the statistical analysis of large data sets with hundreds of thousands observations and hundred of independent variables whose some of values are missing at many observations is needed even though it is a formidable computational task. A tree structured approach to classification is capable of screening important independent variables and their interactions. In a Six Sigma project handling large amount of manufacturing data, one of the goals is to screen vital few variables among trivial many variables. In this paper we have reviewed and summarized CART, C4.5 and CHAID algorithms and proposed a simple method of screening vital few variables by selecting common variables screened by all the three algorithms. Also how to develop a logistics regression model on a large data set is discussed and illustrated through a large finance data set collected by a credit bureau for th purpose of predicting the bankruptcy of the company.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권1호
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• pp.1-6
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• 2007

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. Recently, with the objective of generalizing the SVDD which treats all training data with equal importance, the so-called D-SVDD (density-induced support vector data description) was proposed incorporating the idea that the data in a higher density region are more significant than those in a lower density region. In this paper, we consider the problem of further improving the D-SVDD toward the use of a partial reference set for testing, and propose an LMI (linear matrix inequality)-based optimization approach to solve the improved version of the D-SVDD problems. Our approach utilizes a new class of density-induced distance measures based on the RSDE (reduced set density estimator) along with the LMI-based mathematical formulation in the form of the SDP (semi-definite programming) problems, which can be efficiently solved by interior point methods. The validity of the proposed approach is illustrated via numerical experiments using real data sets.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.147-147
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• 2018

In this article, we use an open source software library: TensorFlow, developed for the purposes of conducting very complex machine learning and deep neural network applications. However, the system is general enough to be applicable in a wide variety of other domains as well. The proposed model based on a deep neural network model, LSTM (Long Short-Term Memory) to predict the river water level at Okcheon Station of the Guem River without utilization of rainfall - forecast information. For LSTM modeling, the input data is hourly water level data for 15 years from 2002 to 2016 at 4 stations includes 3 upstream stations (Sutong, Hotan, and Songcheon) and the forecasting-target station (Okcheon). The data are subdivided into three purposes: a training data set, a testing data set and a validation data set. The model was formulated to predict Okcheon Station water level for many cases from 3 hours to 12 hours of lead time. Although the model does not require many input data such as climate, geography, land-use for rainfall-runoff simulation, the prediction is very stable and reliable up to 9 hours of lead time with the Nash - Sutcliffe efficiency (NSE) is higher than 0.90 and the root mean square error (RMSE) is lower than 12cm. The result indicated that the method is able to produce the river water level time series and be applicable to the practical flood forecasting instead of hydrologic modeling approaches.

• 한국항행학회논문지
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• 제23권5호
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• pp.339-344
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• 2019

정보통신 기술 발전에 따른 데이터 보안 위협의 상승에 대비하기 위하여 유도탄 점검 장비에 저장된 자료의 안전성을 보장할 수 있는 기술은 중요하다. 이를 위하여 자료가 누출 되더라도 복원할 수 없게 데이터 저장 시 암호화를 수행하여야 하고, 해당 데이터를 복호화한 후에도 무결성이 보장되어야 한다. 본 논문에서는 데이터 저장 시 대칭키 암호시스템인 AES 알고리즘을 유도탄 점검장비에 적용하고, 각 AES의 각 비트 별 데이터 양에 따른 암호화 복호화 시간을 측정하였다. 또한 기존 점검 시스템에 AES Rijndael 알고리즘을 구현하여 암호화 수행으로 인한 영향을 분석하였고 제안한 암호화 알고리즘을 기존 시스템에 적용하는 것이 적합한지 확인 하였다. 용량별 / 알고리즘 비트수별로 분석한 결과 제안한 알고리즘 적용이 시스템 운용에 영향 없음을 확인하였고, 최적의 알고리즘을 도출할 수 있었다. 추가로 복호화 결과를 초기 데이터와 비교하였고, 해당 알고리즘이 데이터 무결성을 보장할 수 있음을 확인할 수 있었다.

• 수산해양기술연구
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• 제52권2호
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• pp.149-161
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• 2016

Fishing characteristics of Korean tuna purse seine fishery in the Pacific Ocean were investigated using logbook data compiled from captain onboard and the statistical data from 1980 to 2014. Changes in fishing ground and correlation between marine environmental factors and fishing patterns were investigated using Oceanographic index. The proportion of unassociated set was higher than that of associated set. The catch proportion of yellowfin was higher in the unassociated set, while that of skipjack and bigeye was higher in the associated set. Due to vessels, fishing gears and Korean captains' high-level of skills in fishing technology optimized for the unassociated set and preference of large fishes, especially large yellowfin tuna, it showed unique fishing characteristics focusing on the unassociated set. As for fishing distributions of Korean tuna purse seine fishery and impacts of oceanographic conditions on the fishery, the main fishing ground was concentrated on the area of $5^{\circ}N{\sim}10^{\circ}S$, $140^{\circ}E{\sim}180^{\circ}$ through the decades. When stronger El-nino occurred, the range of fishing ground tended to expand and main fishing ground moved to the eastern part of western and central Pacific Ocean. During this season, yellowfin tuna had high CPUE and catch proportion of yellowfin tuna in the eastern part also increased. As for the proportion of fishing effort by set type, proportion of log associated set was high during El-nino season while that of FAD associated set was high during La-nina season.

• Journal of Crop Science and Biotechnology
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• 제11권3호
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• pp.157-162
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• 2008

A total of 2,765 accessions were used as the initial set having both seed coat color and 100-seed weight data. As a result of molecular profiling using six SSR markers followed by stratification based on their usages, 335 accessions(12.1%) were selected by clustering based on UPGMA. Since 75 out of 335 accessions were mixed in phenotypic traits as a result of characterization, 260 accessions were finally set as a core set. This core set revealed nearly the same diversity compared with the other results on morphological traits of Korean soybean landraces. In total, 115 alleles(19.2 alleles per locus) were detected in the initial set and 79 alleles(13.2 alleles per locus) were detected in the core set. All 30 major alleles were present in the initial set and in the core set as well. In allele coverage, the core set was 71.4% of the initial set. These comparisons of number of alleles, gene diversity and coverage indicated that the core set represented the entire set well.

• 디지털융복합연구
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• 제12권7호
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• pp.285-290
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• 2014

병원에 직접 가지 않아도 집에서 진료결과를 쉽게 확인할 수 있고 환자가 원하는 최적의 서비스를 최적의 타이밍에 제공받을 수 있다는 많은 장점에도 불구하고 현재 사용되고 있는 개인 헬스케어 기기는 제조사들이 만든 독자적인 소프트웨어 하드웨어 프로토콜로 인해 기기들 간의 호환성이 거의 없다. 이러한 문제 때문에 개인 헬스케어 기기와 셋톱박스 간의 표준화가 매우 필요하며, 이를 위해 본 연구에서는 IEEE P11073 표준을 이용하여 개인 헬스케어 기기와 셋톱박스 간의 생체 데이터 전송이 가능한 헬스케어 셋톱박스를 설계하였다. IEEE P11073 표준을 이용한 셋톱박스는 다양한 헬스케어 기기에 대한 독립적 데이터 전송을 가능케하여 헬스케어 시장의 활성화에 큰 기여를 할 것이라 기대한다.

• 한국멀티미디어학회논문지
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• 제23권2호
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.308-313
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• 1998

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general description of concepts from specific instances of these concepts. In many real life situations, however, new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overcall set of instances. The method of learning presented here is base don a rough set concept proposed by Pawlak[2][11]. It is shown an algorithm to find minimal set of rules using reduct change theorems giving criteria for minimum recalculation with an illustrative example. Finally, the proposed learning algorithm is applied to fuzzy system to learn sampled I/O data.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 추계종합학술대회
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• pp.183-187
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• 2003

In this paper, we describe an implementation method of "set constraints logic language" using the logic language Prolog. "Set constraints logic language" is a programming language with a new paradigm that uses the "set theory" in programming. In this paper, we explain "set constraints problem solver" that has been proposed by A. Dovier and his researchers and we describe an implementation method of this solver using Prolog. We ran easily implement the "set constraints problem solver" in Prolog, since Prolog easily implements nondeterministic problems and provides a data structure railed list. We have applied the language to several application fields to show the usefulness of the language.