• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.7
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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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.445-452
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• 2019

In this study, parallel processing codes of k-means clustering algorithm were developed and implemented in a PC-cluster for unsupervised classification of large satellite images. We implemented intra-node code using multicores of CPU (Central Processing Unit) based on OpenMP (Open Multi-Processing), inter-nodes code using a PC-cluster based on message passing interface, and hybrid code using both. The PC-cluster consists of one master node and eight slave nodes, and each node is equipped with eight multicores. Two operating systems, Microsoft Windows and Canonical Ubuntu, were installed in the PC-cluster in turn and tested to compare parallel processing performance. Two multispectral satellite images were tested, which are a medium-capacity LANDSAT 8 OLI (Operational Land Imager) image and a high-capacity Sentinel 2A image. To evaluate the performance of parallel processing, speedup and efficiency were measured. Overall, the speedup was over N / 2 and the efficiency was over 0.5. From the comparison of the two operating systems, the Ubuntu system showed two to three times faster performance. To confirm that the results of the sequential and parallel processing coincide with the other, the center value of each band and the number of classified pixels were compared, and result images were examined by pixel to pixel comparison. It was found that care should be taken to avoid false sharing of OpenMP in intra-node implementation. To process large satellite images in a PC-cluster, code and hardware should be designed to reduce performance degradation caused by file I / O. Also, it was found that performance can differ depending on the operating system installed in a PC-cluster.

• Korean Management Science Review
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• v.32 no.3
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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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• v.2 no.3
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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.

• Journal of Korea Multimedia Society
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• v.20 no.12
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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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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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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• v.18 no.2
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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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• v.31 no.4
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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.