• Korean Journal of Cognitive Science
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• v.23 no.3
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• pp.295-322
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• 2012

In this paper, we cluster similar word senses applying vector space model and HAL (Hyperspace Analog to Language). HAL measures corelation among words through a certain size of context (Lund and Burgess 1996). The similarity measurement between a word pair is cosine similarity based on the vector space model, which reduces distortion of space between high frequency words and low frequency words (Salton et al. 1975, Widdows 2004). We use PCA (Principal Component Analysis) and SVD (Singular Value Decomposition) to reduce a large amount of dimensions caused by similarity matrix. For sense similarity clustering, we adopt supervised and non-supervised learning methods. For non-supervised method, we use clustering. For supervised method, we use SVM (Support Vector Machine), Naive Bayes Classifier, and Maximum Entropy Method.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.

• Journal of Korean Society of Transportation
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• v.23 no.8 s.86
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• pp.193-202
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• 2005

Due to the importance of SCM(Supply Chain management) in business logistics. many studies related to the SCM performance measurement have been conducted. However, previous SCM performance measurement models have not reflected both ways, operational efficiency and response for market shift. The aim of this research is to suggest a dynamic model to measure SCM performance both with operational efficiency and response for market shift, based on previous SCOR model. To achieve this aim, we developed a cognitive map-based model described eleven KPIs (Key Performance Indicators) with different weight values. And, to measure response level for market shift, we used the concept of entropy-elasticities. Finally, through some actual cases, merits that have no previous models were shown.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.883-889
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• 2013

The distribution of a response usually depends on the distribution of a variable. When the distribution of a variable has two different modes, the response also follows a distribution with two different modes. In most reliability analysis methods, the number of modes is irrelevant, but not the type of distribution. However, in actual problems, because information is often provided with two or more modes, it is important to estimate the distributions with two or more modes. Recently, some reliability analysis methods have been suggested for bimodal distributions. In this paper, we review some methods such as the Akaike information criterion (AIC) and maximum entropy principle (MEP) and compare them with the Monte Carlo simulation (MCS) using mathematical examples with two different modes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.459-466
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• 2006

Numerical values of thermodynamic properties such as temperature, pressure, dryness, volume, enthalpy and entropy are required in numerical analysis on evaluating the thermal performance. But the steam table itself cannot be used without modelling. From this point of view the neural network with function approximation characteristics can be an alternative. the multi-layer neural networks were made for saturated vapor region and superheated vapor region separately. For saturated vapor region the neural network consists of one input layer with 1 node, two hidden layers with 10 and 20 nodes each and one output layer with 7 nodes. For superheated vapor region it consists of one input layer with 2 nodes, two hidden layers with 15 and 25 nodes each and one output layer with 3 nodes. The proposed model gives very successful results with ${\pm}0.005%$ of percentage error for temperature, enthalpy and entropy and ${\pm}0.025%$ for pressure and specific volume. From these successful results, it is confirmed that the neural networks could be powerful method in function approximation of the steam table.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.11
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• pp.622-633
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• 2002

Authentication and Key Agreement using password provide convenience and amenity, but what human can remember has extremely low entropy. To overcome its defects, AMP(Authentiration and key agreement via Memorable Password) which performs authentication and key agreement securely via low entropy password are presented. AMP uses Diffie-Hellman problem that depends on discrete logarithm problem. Otherwise, this thesis applies elliptic curve cryptosystem to AMP for further efficiency That is, this thesis presents EC-AMP(Elliptic Curve-AMP) protocol based on elliptic curve discrete logarithm problem instead of discrete logarithm problem, and shows its high performance through the implementation. EC-AMP secures against various attacks in the random oracle model just as AMP Thus, we nay supply EC-AMP to the network environment that requires authentication and key agreement to get both convenience and security from elliptic curve discrete logarithm problem.

• Applied Chemistry for Engineering
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• v.34 no.3
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• pp.279-284
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• 2023

Isothermal titration calorimetry (ITC) is a useful technique to obtain thermodynamic binding properties such as enthalpy, Gibbs free energy, entropy, and stoichiometry of the chelation reaction. A single independent binding site model was used to evaluate the thermodynamic binding properties in BaCl₂ and ethylenediaminetetraacetic acid (EDTA) in Trince and HEPES buffers. ITC enables us to elucidate the binding mechanism and find an optimal chelation condition for BaCl₂ and EDTA in the pH range of 7~11. Chelation of BaCl₂ and EDTA is a spontaneous endothermic reaction. As pH increased, entropic contributions dominated. The optimal pH range is narrow around pH 9.0, where 1:1 binding between BaCl₂ and EDTA occurs.

• Journal of Navigation and Port Research
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• v.44 no.4
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• pp.291-297
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• 2020

In recent years, an increase in deaths and injuries of port cargo handling workers, has raised the need for more effective accident management. The purpose of this study was to evaluate the accident risk for port cargo handling workers and assess ports with high accident risk within the Korean alternative ports using the Entropy & GRA (Grey Relational Analysis). To achieve this purpose, first, 11 Korean ports were selected and the evaluative factors for their outranking evaluation by brainstorming were extracted. Second, the Grey Relational Coefficient of 11 alternative ports was calculated using the GRA. This paper, finally, determined the priority orders of accident risk through calculation of the Grey Relational Grade as the link Grey Relational Coefficient method and the weights of the evaluative factors were calculated by using the Entropy method. In the proposed model, eight criteria such as cargo worker, old cargo worker, work hours, facilities environment, steel cargo volumes, cargo volumes, injury numbers, and death numbers were collected. Busan port was identified as highest accident risk port, and so it should be a top priority to develop a plan to mitigate the risk.

• Journal of Wetlands Research
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• v.19 no.2
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• pp.202-210
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• 2017

Shear stress is one of the most important mechanical factors used in various fields and is important for the design of artificial channels. Current shear stresses have been used in the past, but there are factors that are difficult to actually measure or calculate, such as bed shear stress and energy slope in the equation used. In particular, the energy slope is a very difficult factor to estimate, and it is difficult to estimate the slope and flow velocity of the boundary layer although the energy slope can be used to obtain the shear stress distribution. In addition, the bed shear stress among the shear stress distribution is very difficult to measure directly, and the research is somewhat slower than the velocity. In this study, we have studied the simple calculation of the average flow velocity and the shear stress distribution using entropy M without reflecting the energy gradient, and we used existing laboratory data to demonstrate the utility of the applied equation. The stress distribution in the graphs was comparatively analyzed. In the case of the uniform flow and the non-uniform flow, the correlation coefficient was almost identical to 0.930-0.998.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.581-591
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• 2005

Collaborative filtering systems have problems involving sparsity and the provision of recommendations by making correlations between only two users' preferences. These systems recommend items based only on the preferences without taking in to account the contents of the items. As a result, the accuracy of recommendations depends on the data from user-rated items. When users rate items, it can be expected that not all users ran do so earnestly. This brings down the accuracy of recommendations. This paper proposes a collaborative recommendation method for extracting typical group preferences using user-item matrix optimization and user profiles in collaborative tittering systems. The method excludes unproven users by using entropy based on data from user-rated items and groups users into clusters after generating user profiles, and then extracts typical group preferences. The proposed method generates collaborative user profiles by using association word mining to reflect contents as well as preferences of items and groups users into clusters based on the profiles by using the vector space model and the K-means algorithm. To compensate for the shortcoming of providing recommendations using correlations between only two user preferences, the proposed method extracts typical preferences of groups using the entropy theory The typical preferences are extracted by combining user entropies with item preferences. The recommender system using typical group preferences solves the problem caused by recommendations based on preferences rated incorrectly by users and reduces time for retrieving the most similar users in groups.