• 대한수학회지
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• 제58권5호
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• pp.1059-1079
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• 2021

We show that given any chain transitive set of a C¹ generic diffeomorphism f, if a diffeomorphism f has the eventual shadowing property on the locally maximal chain transitive set, then it is hyperbolic. Moreover, given any chain transitive set of a C¹ generic vector field X, if a vector field X has the eventual shadowing property on the locally maximal chain transitive set, then the chain transitive set does not contain a singular point and it is hyperbolic. We apply our results to conservative systems (volume-preserving diffeomorphisms and divergence-free vector fields).

• 산업경영시스템학회지
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• 제36권4호
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• pp.123-129
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• 2013

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

• Structural Engineering and Mechanics
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• 제74권4호
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• pp.559-565
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• 2020

This paper intends to progress models to accurately estimate the behavior of fresh concrete under vibration using artificial neural networks (ANNs). To this end, behavior of a full scale precast concrete mold was investigated numerically. Experimental study was carried out under vibration with the use of a computer-based data acquisition system. In this study measurements were taken at three points using two vibrators. Transducers were used to measure time-dependent lateral displacements at these points on mold while both mold is empty and full of fresh concrete. Modeling of empty and full mold was made using ANNs. Benefiting ANNs used in this study for modeling fresh concrete, mold design can be performed. For the modeling of ANNs: Experimental data were divided randomly into two parts such as training set and testing set. Training set was used for ANN's learning stage. And the remaining part was used for testing the ANNs. Finally, ANN modeling was compared with measured data. The comparisons show that the experimental data and ANN results are compatible.

• 대한임베디드공학회논문지
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• 제11권6호
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• pp.353-359
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• 2016

In this paper, we propose a high performance L1 cache structure for the high clock CPU. The proposed cache memory consists of three parts, i.e., a direct-mapped cache to support fast access time, a two-way set associative buffer to reduce miss ratio, and a way-select table. The most recently accessed data is stored in the direct-mapped cache. If a data has a high probability of a repeated reference, when the data is replaced from the direct-mapped cache, the data is stored into the two-way set associative buffer. For the high performance and fast access time, we propose an one way among two ways set associative buffer is selectively accessed based on the way-select table (WST). According to simulation results, access time can be reduced by about 7% and 40% comparing with a direct cache and Intel i7-6700 with two times more space respectively.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2000년도 추계학술대회
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• pp.36-44
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• 2000

The purpose of this study is to introduce a more efficient forecasting technique, which could help result the reduction of cost in removing the waste of airline in-flight meals. We will use a neural network approach known to many researchers as the “Outstanding Forecasting Technique”. We employed a multi-layer perceptron neural network using a backpropagation algorithm. We also suggested using other related information to improve the forecasting performances of neural networks. We divided the data into three sets, which are training data set, cross validation data set, and test data set. Time lag variables are still employed in our model according to the general view of time series forecasting. We measured the accuracy of our model by “Mean Square Error”(MSE). The suggested model proved most excellent in serving economy class in-flight meals. Forecasting the exact amount of meals needed for each airline could reduce the waste of meals and therefore, lead to the reduction of cost. Better yet, it could enhance the cost competition of each airline, keep the schedules on time, and lead to better service.

• Asia Marketing Journal
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• 제22권2호
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• pp.1-18
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• 2020

We estimate a choice-based aggregate demand model accounting for consumers' consideration sets, and study its implications on market structure. In contrast to past research, we model and estimate consumer demand using aggregate-level consumer browsing data in addition to aggregate-level choice data. The use of consumer browsing data allows us to study consumer demand in a realistic setting in which consumers choose from a subset of products. We calibrate the proposed model on both data sets, avoid biases in parameter estimates, and compute the price elasticity measures. As an empirical application, we estimate consumer demand in the camcorder category and study its implications on market structure. The proposed model predicts a limited consumer price response and offers a more discriminating competitive landscape from the one assuming universal consideration set.

• 디지털융복합연구
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• 제10권6호
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• pp.253-258
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• 2012

유비쿼터스 컴퓨팅 환경에서 상황 정보 추론에 필요한 상황 정보 표현은 육하원칙에 의해 분류되어 4W1H의 상황 정보와 추론된 Why의 상황을 통합하여 상위 상황들을 추론하였다. 본 논문에서는 추론된 상황 정보에 Whom(특정 정보나 서비스)과 How much(정확성)를 추가한 팔하원칙(6W2H)에 의해 특정 상황과 서비스를 분류하고 분류된 상황들과 부정확한 지식에 대한 표현과 추론을 위해 러프집합 개념을 도입하여 특정 상황에 적합한 서비스 결정 방법을 제안한다. 서비스 제공의 정확성을 표현할 때 0과 1이라는 일반집합으로 표현하는 것은 한계가 있기 때문에 이를 위해 퍼지집합의 개념을 도입하였다. 또한, 러프 집합 개념을 이용하여 상황 속성에 대한 리덕션(reduction) 과정을 통해 불필요한 속성들을 제거함으로서 각 사용자에게 가장 적합한 서비스를 신속하게 제공할 수 있도록 하는 것에 그 의의가 있다.

• 한국음향학회지
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• 제23권2호
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• pp.173-179
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• 2004

음소 질의어 집합은 문맥 속에서 비슷한 조음 효과를 보이는 음소들을 분류해 놓은 것으로서, 음성 인식 시스템 학습 시 결정트리를 기반으로 HMM (hidden Markov model)의 상태들을 클러스터링할 때 사용된다. 현재까지의 음소 질의어 집합은 대부분 음성학자나 언어학자들에 의해 수작업으로 제시되어 왔는데, 이러한 지식 기반음소 질의어들은 언어 또는 유사음소 단위 (PLU: phone like unit)에 종속될 뿐 아니라 생성된 클러스터 내의 동질성을 저하시킬 수 있다는 단점이 있다. 본 논문에서는 이와 같은 문제점들을 해결하기 위해 음성 데이터를 사용하여 측정한 음소들 사이의 유사도를 기반으로 언어나 유사음소단위에 상관없이 자동으로 음소 질의어 집합을 생성하는 알고리즘을 제안한다. 실험결과, 제안한 방법으로 생성된 음소 질의어들을 사용한 인식기의 에러율이 약 14.3%감소하여 데이터 기반의 음소 질의어 집합이 상태 클러스터링에 효율적임을 관측하였다.

• 인터넷정보학회논문지
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• 제21권6호
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• pp.33-39
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• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

• 한국경영과학회지
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• 제39권4호
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• pp.75-84
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• 2014

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.