• 대한전기학회논문지
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• 제40권7호
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• pp.718-724
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• 1991

In this paper, we proposed an algorithm to improve the memory efficiency by means of learning thresholds in spite of correlations among input patterns to be memorized. The proposed algorithm does not need preprocess correlations among input patterns but processes them with a threshold on a neural network. When memory contents are destroyed by correlation, nearly all patterns can be properly recovered with past learning. Through experiments we show how out algorithm can improve the memory efficiency.

• 한국측량학회지
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• 제37권6호
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• pp.445-452
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• 2019

본 연구에서는 대용량 위성영상의 무감독분류를 위해 k-means clustering 알고리즘의 병렬처리 코드를 개발하여 PC-cluster에서 구현하였다. 이를 위해 OpenMP (Open Multi-Processing)를 기반으로 CPU (Central Processing Unit)의 다중코어를 이용하는 intra-node 코드와 message passing interface를 기반으로 PC-cluster를 이용하는 inter-nodes 코드, 그리고 이 둘을 병용하는 hybrid 코드를 구현하였다. 본 연구에 사용한 PC-cluster는 한 대의 마스터 노드와 여덟 대의 슬래이브 노드로 구성되어 있고 각 노드에는 여덟 개의 다중코어가 장착되어 있다. PC-cluster에는 Microsoft Windows와 Canonical Ubuntu의 두 가지 운영체제를 설치하여 병렬처리 성능을 비교하였다. 실험에 사용한 자료는 두 가지 다중분광 위성영상으로서 중용량인 LANDSAT 8 OLI (Operational Land Imager) 영상과 대용량인 Sentinel 2A 영상이다. 병렬처리의 성능을 평가하기 위하여 speedup과 efficiency를 측정한 결과 전반적으로 speedup은 N/2 이상, efficiency는 0.5 이상으로 나타났다. Microsoft Windows와 Canonical Ubuntu를 비교한 결과 Ubuntu가 2-3배의 빠른 결과를 나타내었다. 순차처리와 병렬처리 결과가 일치하는지 확인하기 위해 각 클래스의 밴드별 중심값과 분류된 화소의 수를 비교하고 결과 영상간 화소대 화소 비교도 수행하였다. Intra-node 코드를 구현할 때에는 OpenMP에 의한 false sharing이 발생하지 않도록 주의해야 하고, PC-cluster에서 대용량 위성영상을 처리하기 위해서는 파일 I/O에 의한 성능저하를 줄일 수 있도록 코드 및 하드웨어를 설계해야 함을 알 수 있었다. 또한 PC-cluster에 설치된 운영체제에 따라서도 성능 차이가 발생함을 알 수 있었다.

• 경영과학
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• 제32권3호
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• pp.37-54
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• 2015

This paper suggests energy efficiency which can be the foundation on corporate profit and effective energy management following by change of global climate and of energy-related regulations. Using comparable financial information and information related to energy use, an DEA (Data Envelopment Analysis) model has been used to identify energy efficiency with DMU (Decision Making Unit)s which are companies subjected to reduce greenhouse gas emission in 2009. Through this research, different from existing researches, environmental variables which can influence on energy efficiency are identified. The results show as follows. First, most of companies follow IRS, which means scale of economy exists among units so that they have more opportunity to increase efficiency by increasing scale of inputs. Second, this research identified that depending on the difference of environmental characters such as the emission structure and the size of companies, energy efficiency of the companies turns out differently.

• KIEAE Journal
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• 제2권3호
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• pp.11-16
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• 2002

Indoor air environment is one of the most important factors that affect resident's health and comfort level. In this paper, the influence of ventilation efficiency with different types of furniture arrangement at breathing zone in a room was analyzed by numerical simulation based on computational fluid dynamics(CFD). The furniture layout of students' bedroom have been classified by three different patterns so that SVE3(scale for ventilation efficiency3) in the rooms was analyzed for air flow distribution. According to the results of the study, SVE3 has the maximum value in spaces between furnitures and each comer of the room. The furniture arrangement influences the ventilation efficiency. It was con finned that ventilation effective in a room is not uniformly distributed as compared the breathing zone with all the area in a room. It means that a study of ventilation efficiency was considered relatively with target zone(a residential or breathing zone) and all the area in a space.

• 한국멀티미디어학회논문지
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• 제20권12호
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• pp.1960-1969
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• 2017

There have been ongoing researches to identify and analyze the patterns of electric power IoT data inside sensor nodes to supplement the stable supply of power and the efficiency of energy consumption. This study set out to propose an analysis process for electric power IoT data with the K-means algorithm, which is an unsupervised learning technique rather than a supervised one. There are a couple of problems with the old K-means algorithm, and one of them is the selection of cluster number K in a heuristic or random method. That approach is proper for the age of standardized data. The investigator proposed an analysis process of selecting an automated cluster number K through principal component analysis and the space division of normal distribution and incorporated it into electric power IoT data. The performance evaluation results show that it recorded a higher level of performance than the old algorithm in the cluster classification and analysis of pitches and rolls included in the communication bodies of utility poles.

• 산업경영시스템학회지
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• 제37권4호
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• pp.239-244
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• 2014

The conventional clustering approaches are mostly based on minimizing total dissimilarity of input and output. However, the clustering approach may not be helpful in some cases of clustering decision making units (DMUs) with production feature converting multiple inputs into multiple outputs because it does not care converting functions. Data envelopment analysis (DEA) has been widely applied for efficiency estimation of such DMUs since it has non-parametric characteristics. We propose a new clustering method to identify groups of DMUs that are similar in terms of their input-output profiles. A real world example is given to explain the use and effectiveness of the proposed method. And we calculate similarity value between its result and the result of a conventional clustering method applied to the example. After the efficiency value was added to input of K-means algorithm, we calculate new similarity value and compare it with the previous one.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.2
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• pp.1002-1006
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• 2004

The studies on OLED(Organic Light-Emitting Diode) materials and structures have been researched in other to improve luminescence efficiency of OLED. Electrons and holes are injected into the devices, transported across the layer and recombine to form excitons, their profiles are sensitive to mobility velocity of electrons and holes. A suggested means of improving the efficiency of LEDs would be to balance the injection of electrons and holes into light emission layer of the device. In this paper, we demonstrate the difference of velocity between hole and electron by experiments, and compare with a data of simulation and experiment changing hole carrier transport layer thickness, so we get the optimal we improve luminescence efficiency. We improve understanding of the various luminescence efficiency through experiments and numerical analysis of luminescence efficiency in the hole carrier transport layer's thicknes.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2012년도 제46차 하계학술발표논문집 20권2호
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• pp.243-246
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• 2012

A new multi inner stage(MIS) cyclone was designed to remove the acidic gas and minute particles of harmful materials produced from electronic industry. To characterize gas flow in MIS cyclone, pressure and velocity distribution were calculated by means of computational fluid dynamics(CFD) commercial program. Also, the flow locus of particles and particle removal efficiency were analyzed by Lagrangian method. When outlet pressure condition was -1,000 Pa, the efficiency was the best in this study. Based on the CFD simulation result, the pressure loss and destruction removal efficiency was measured through MIS cyclone experiment.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.481-488
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• 2007

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values prohibits it from being used to cluster real world data containing categorical values. The k-modes algorithm is to extend the k-means paradigm to categorical domains. The algorithm requires a pre-setting or random selection of initial points (modes) of the clusters. This paper improved the problem of k-modes algorithm, using the Max-Min method that is a kind of methods to decide initial values in k-means algorithm. we introduce new similarity measures to deal with using the categorical data for clustering. We show that the mushroom data sets and soybean data sets tested with the proposed algorithm has shown a good performance for the two aspects(accuracy, run time).

• Journal of the Korean Statistical Society
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• 제31권4호
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• pp.519-532
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• 2002

K-means clustering is a well-known partitioning method of multivariate observations. Recently, the method is implemented broadly in data mining softwares due to its computational efficiency in handling large data sets. However, it does not yield a suitable visual display of multivariate observations that is important especially in exploratory stage of data analysis. The aim of this study is to develop a K-means clustering method that enables visual display of multivariate observations in a low-dimensional space, for which the projection pursuit method is adopted. We propose a computationally inexpensive and reliable algorithm and provide two numerical examples.