• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.700-713
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• 2009

Most of any existing classification algorithm in data mining area have focused on goals improving efficiency, which is to generate decision tree more rapidly by utilizing just less computing resources. In this paper, we focused on the efficiency as well as effectiveness that is able to generate more meaningful classification rules in application area, which might consist of the ontology automatic generation, business environment and so on. For this, we proposed not only novel function with the weight of support and confidence degree but also analyzed the characteristics of the weighted function in theoretical viewpoint. Furthermore, we proposed novel classification algorithm based on the weighted function and the characteristics. In the result of evaluating the proposed algorithm, we could perceive that the novel algorithm generates more classification rules with significance more rapidly.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• Explosives and Blasting
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• v.33 no.1
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• pp.1-12
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• 2015

In this study, the domestic bench blasting sites were researched to set the blast coefficient C according to the type of rock and type of industry. With the use of the experimental data on the representative industrial explosives and the data of the manufacturers'data on explosives, powder coefficient e was set up. The blast coefficient C was 0.21~0.30 when the average value for 5 representative kinds of rocks including granite was searched. The blast coefficient C for quarrying, mining and construction sites were 0.22, 0.13 and 0.26 respectively. On the other hand, powder coefficient e was obtained in four elements such as reactive energy, ballistic mortar test, VOD, Langefors'strength per unit weight. e value for emulsion which is one of the representative explosives was found to be 1 while those of high performance emulsion and ANFO were found to be 0.9 and 1, respectively.

• Tunnel and Underground Space
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• v.22 no.5
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• pp.354-364
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• 2012

In order to evaluate the physical properties of rock which might serve as a database for both mining and civil works, a lot of laboratory tests for Jecheon limestones were conducted to find unit weight, absorption ratio, porosity, elastic wave velocity, uniaxial compressive strength, Young's modulus, poisson's ratio, tensile strength, shore hardness, friction angle and cohesion. On investigation of the mechanical properties of both the gray limestone and the clayey limestone distributed in the studied region, the clayey limestone turned out to have more weak mechanical properties which might come from low unit weight, high absorption ratio and high porosity of rocks. The failure criteria of Jecheon limestones were discussed by means of both Mohr-Coulomb criterion and Hoek-Brown criterion. Regression analyses of the physical properties obtained from a lot of laboratory tests were also conducted by means of both linear and multiple regression analyses.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.9-17
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• 2011

This paper proposes an algorithm that predicts the state of user's next actions, exploiting the HMM (Hidden Markov Model) on user profile data stored in the ubiquitous home network. The HMM, recognizes patterns of sequential data, adequately represents the temporal property implicated in the data, and is a typical model that can infer information from the sequential data. The proposed algorithm uses the number of the user's action performed, the location and duration of the actions saved by "Activity Recognition System" as training data. An objective formulation for the user's interest in his action is proposed by giving weight on his action, and change on the state of his next action is predicted by obtaining the change on the weight according to the flow of time using the HMM. The proposed algorithm, helps constructing realistic ubiquitous home networks.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.717-730
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• 2004

Recently. many researches on the personalization of a web-site have been actively made. The web personalization predicts the sets of the most interesting URLs for each user through data mining approaches such as clustering techniques. Most existing methods using clustering techniques represented the web transactions as bit vectors that represent whether users visit a certain WRL or not to cluster web transactions. The similarity of the web transactions was decided according to the match degree of bit vectors. However, since the existing methods consider only whether users visit a certain URL or not, users' interestingness on the URL is excluded from clustering web transactions. That is, it is possible that the web transactions with different visit proposes or inclinations are classified into the same group. In this paper. we propose an enhanced transaction modeling with interestingness weight to solve such problems and a new similarity measuring method that exploits the proposed transaction modeling. It is shown through performance evaluation that our similarity measuring method improves the accuracy of the web transaction clustering over the existing method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.4
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• pp.569-577
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• 2018

In this paper, with the aid of information which is included within data, preprocessing algorithm-based black plastic classifier is designed. The slope and area of spectrum obtained by using laser induced breakdown spectroscopy(LIBS) are analyzed for each material and its ensuing information is applied as the input data of the proposed classifier. The slope is represented by the rate of change of wavelength and intensity. Also, the area is calculated by the wavelength of the spectrum peak where the material property of chemical elements such as carbon and hydrogen appears. Using informations such as slope and area, input data of the proposed classifier is constructed. In the preprocessing part of the classifier, Principal Component Analysis(PCA) and fuzzy transform are used for dimensional reduction from high dimensional input variables to low dimensional input variables. Characteristic analysis of the materials as well as the processing speed of the classifier is improved. In the condition part, FCM clustering is applied and linear function is used as connection weight in the conclusion part. By means of Particle Swarm Optimization(PSO), parameters such as the number of clusters, fuzzification coefficient and the number of input variables are optimized. To demonstrate the superiority of classification performance, classification rate is compared by using WEKA 3.8 data mining software which contains various classifiers such as Naivebayes, SVM and Multilayer perceptron.

• The Journal of Fisheries Business Administration
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• v.27 no.1
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• pp.67-90
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• 1996

As an important factor of capitalism economics, price of commodities represents a certain country's economic index. For having correct price policy, there should be an appropriate mechanism to make and use systematic statistical data on price. Price statistics are made by indexes and price indexes are categorized into producer price index(PPI) and consumer price index(CPI). The Bank of Korea is publishing producer price index every year, but the producer price index contains some problems. These include as follows : (a) the impractical selection of fisheries products sample (b) uncorrect measure of aquatic products weights (c) investigating sample places. This study try to substitute producer price index of aquatic products and change construction of fisheries producer price index with experimental research on representative fisheries, weight of each fisheries, and suitability of investigating sample places. It is possible to improve practical fisheries producer price index with the results of this research. The findings are as follow. (a) It will be helpful for the government to make the fisheries price policy. (b) It can be used to understand trends of accurate price and price increase of aquatic products, and it's possible to compare with it other industrial indexes including the mining, agricultural, and manufacturing industry and understand relative price movement. (c) When free sales systems of fisheries products as expected, it will be helpful to analyze price movement of producing fisheries cooperatives, producing fisheries market and consuming fisheries market, analysis of market, and formation and consideration of budget. (d) It can be an important index to determine labor wage.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.256-272
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural network and to apply the newly developed techniques to the study area of Boun in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs, field survey data, and a spatial database of the topography, soil type, timber cover, geology and land use. The landslide-related factors (slope, aspect, curvature, topographic type, soil texture, soil material, soil drainage, soil effective thickness, timber type, timber age, and timber diameter, timber density, geology and land use) were extracted from the spatial database. Using those factors, landslide susceptibility was analyzed by artificial neural network methods. For this, the weights of each factor were determinated in 3 cases by the backpropagation method, which is a type of artificial neural network method. Then the landslide susceptibility indexes were calculated and the susceptibility maps were made with a GIS program. The results of the landslide susceptibility maps were verified and compared using landslide location data. A GIS was used to efficiently analyze the vast amount of data, and an artificial neural network was turned out be an effective tool to maintain precision and accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1213-1220
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• 2011

In this paper, a new ensemble system using dynamic weighting method with added weight information into classifiers is proposed. The weights used in the traditional ensemble system are those after the training phase. Once extracted, the weights in the traditional ensemble system remain fixed regardless of the test data set. One way to circumvent this problem in the gating networks is to update the weights dynamically by adding processes making architectural hierarchies, but it has the drawback of added processes. A simple method to update weights dynamically, without added processes, is proposed, which can be applied to the already established ensemble system without much of the architectural modification. Experiment shows that this method performs better than AdaBoost.