• Journal of Korean Institute of Industrial Engineers
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• v.31 no.3
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• pp.257-264
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• 2005

This paper suggests a heuristic algorithm for the clustering problem. Clustering involves grouping similar objects into a cluster. Clustering is used in a wide variety of fields including data mining, marketing, and biology. Until now there are a lot of approaches using Self-Organizing Feature Maps(SOFMs). But they have problems with a small output-layer nodes and initial weight. For example, one of them is a one-dimension map of k output-layer nodes, if they want to make k clusters. This approach has problems to classify elaboratively. This paper suggests one-dimensional output-layer nodes in SOFMs. The number of output-layer nodes is more than those of clusters intended to find and the order of output-layer nodes is ascending in the sum of the output-layer node's weight. We can find input data in SOFMs output node and classify input data in output nodes using Euclidean distance. We use the well known IRIS data as an experimental data. Unsupervised clustering of IRIS data typically results in 15 - 17 clustering error. However, the proposed algorithm has only six clustering errors.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.4
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• pp.181-192
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• 1994

In an order-oriented production system such as mold manufacturing, the production starts with an order acceptance, and the production planning is set up according to the accepted order information. Such a work can be done through a dynamic process management system which can reflect shop floor situation dynamically. In this paper, so called the Quick order acceptance evaluation system that can investigate order confirmation quickly, is discussed. When an order is asked, this system must consider the time constraint to determine whether to accept or not, and must be reliable when the determined results are used in the shop floor. For this system, firstly, we simplified the machines by grouping based on their operation capabilities, secondly, we conducted load analysis to calculate available capacities during given periods using heuristic method instead of mathematical algorithm, thirdly, expert can input his experienced knowledge into the system interactively when simulation results don't meet the required due dates. As a case study, we applied this system to an injection mold manufacturing factory.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.397-400
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• 1999

As the result of enhancement of CAD, Design Automation and manufacturing technology, it's on the increasing complexity, integration ratio, data signals, and pin count to IC chips. This brings about difficulties of testing, and incresing test time. Now One of the most cost-consuming procedure as integration ratio increases is the testing step. In this paper, we propose a new method, “Efficient TP(test point) assignment algorithm” using “input grouping”, This is helpful method to reducing test length without losing fault coverage. Experimental results show that proposed method reduces test length remarkably and doesn't miss fault coverage, with low hardware overhead Increasing.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.266-272
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• 2005

This study is to empirically analyze the effects on job performance of Accounting Information Systems, Analysis methods were used to Cronbach's Alpha analysis, Factor analysis, analysis of variance(ANOVA) and regression in odor by the contingency grouping method. The results of this study are as follows : First, The regression analysis had effects on AIS utility and job performance. Second, The Analysis of variance(ANOVA) had non-effects on systems operating degree and systems satisfaction. Third, The input variables of information accuracy and systems satisfaction had additional effect about IT capability.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.77-85
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• 2018

Multi-User Multiple-Input Multiple-Output (MU-MIMO) is the core technology for improving the channel capacity compared to Single-User MIMO (SU-MIMO) by using multiuser gain and spatial diversity. Key problem for the MU-MIMO is the user selection which is the grouping the users optimally. To solve this problem, we adopt Extreme Value Theory (EVT) at the beginning of the proposed algorithm, which defines a primary user set instead of a single user that has maximum channel power according to a predetermined threshold. Each user in the primary set is then paired with all of the users in the system to define user groups. By comparing these user groups, the group that produces a maximum sum rate can be determined. Through computer simulations, we have found that the proposed method outperforms the conventional technique yielding a sum rate that is 0.81 bps/Hz higher when the transmit signal to noise ratio (SNR) is 30 dB and the total number of users is 100.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1137-1139
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• 2003

This paper proposed a practical method for building detection and extraction using airborne laser scanning data. The proposed method consists mainly of two processes: low and high level processes. The major distinction from the previous approaches is that we introduce a concept of pseudogrid (or binning) into raw laser scanning data to avoid the loss of information and accuracy due to interpolation as well as to define the adjacency of neighboring laser point data and to speed up the processing time. The approach begins with pseudo-grid generation, noise removal, segmentation, grouping for building detection, linearization and simplification of building boundary , and building extraction in 3D vector format. To achieve the efficient processing, each step changes the domain of input data such as point and pseudo-grid accordingly. The experimental results shows that the proposed method is promising.

• Journal of KIISE
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• v.44 no.11
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• pp.1209-1218
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• 2017

A variety of sensors is widely used these days, and it has become much easier to acquire spatial big data streams from various sources. Since spatial data streams have inherently skewed and dynamically changing distributions, the system must effectively distribute the load among workers. Previous studies to solve this load imbalance problem are not directly applicable to processing spatial data. In this research, we propose Adaptive Spatial Key Grouping (ASKG). The main idea of ASKG is, by utilizing the previous distribution of the data streams, to adaptively suggest a new grouping scheme that evenly distributes the future load among workers. We evaluate the validity of the proposed algorithm in various environments, by conducting an experiment with real datasets while varying the number of workers, input rate, and processing overhead. Compared to two other alternative algorithms, ASKG improves the system performance in terms of load imbalance, throughput, and latency.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.79-88
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• 2007

As the mobile computers such as Personal Digital Assistants (PDAs) or Smart Phone has been widespread in our daily life, the demand of an extensive data input will gradually be increased. Consequently, many alternative soft keyboards have been proposed to satisfy this requirement. However none of them can provide an absolute solutions for that, the segregated guideline for keyboard arrangement and usage still cause much confusion to the user. Therefore, an integrated guideline is seriously required. In order to come up with the integrated guideline for Korean Soft keyboard GUI, the questionnaire and the experimental test compared with five soft keyboards were conducted in this study. As a result this paper presents five guidelines for improving an input speed of soft keyboard on the mobile computing; following familiarity for effective screen use, using a single key rather than grouping key, considering position of prediction word, considering prompt feedback, finally, considering optimized keyboard size.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.234-239
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• 2021

Recently, as the installation of a large number of high-performance CCTVs for crime prevention and traffic control has increased rapidly, the problem of increasing system requirements for response to privacy infringement factors and analysis of high-definition image information transmitted from multiple cameras has been actively emerging. Accordingly, there is a need for a method for responding to privacy infringement and a method for efficiently processing surveillance images input from multiple cameras. In this paper, in order to reduce the processing cost of the input image and improve the processing speed, an integrated image is generated by grouping images input from a plurality of cameras. After analyzing the pre-generated integrated video, it detects a preset privacy event or an event that highlights interest. Depending on whether or not an event is detected, you will perform an editing operation corresponding to the event.

• Journal of Korea Multimedia Society
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• v.10 no.8
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• pp.981-996
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• 2007

In this paper, we describe a method that can recognize gestures by obtaining motion features information with principal factor analysis from sequential gesture images. In the algorithm, firstly, a two dimensional silhouette region including human gesture is segmented and then geometric features are extracted from it. Here, global features information which is selected as some meaningful key feature effectively expressing gestures with principal factor analysis is used. Obtained motion history information representing time variation of gestures from extracted feature construct one gesture subspace. Finally, projected model feature value into the gesture space is transformed as specific state symbols by grouping algorithm to be use as input symbols of HMM and input gesture is recognized as one of the model gesture with high probability. Proposed method has achieved higher recognition rate than others using only shape information of human body as in an appearance-based method or extracting features intuitively from complicated gestures, because this algorithm constructs gesture models with feature factors that have high contribution rate using principal factor analysis.