• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.2809-2817
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• 2010

The scholarly concern of train industry has been improved, since country focuses on this field. Especially, train maintenance facility has been studied for construction, because the facility is important to inspect the train that transports passenger and cargo. Various operation plans and criteria are considered to build train maintenance facility. In this paper, the decision making of train maintenance facility for construction is studied and analyzed with simulation, because mathematical theory is restricted to consider various operation plans and criteria. Through simulation, we analyze warm-up after selecting dependent factors, and extract data. The data is used to select a alternative in this problem, Multiple Attribute Decision Making(MADM). Previous study is difficult, complex and subjective for decision making. However, entropy measurement we used help decision maker for decision making, easily, simply, and objectively. This method can have decision maker choose the best alternative in case the train maintenance facility construction is fast revitalized.

• Journal of Industrial Technology
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• v.3
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• pp.39-46
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• 1983

This paper presents a quantitative measure of the degree of part commonality and redundancy in a product line based on entropy measure of information theory. The several possible methods of analysis are discussed and the use of the entropy measure is discussed. These commonality and redundancy measure can be applied to analyze the usage pattern of part across a product line and to determine which parts have the broadest usage across the firm's product lines. An analysis of the results by entropy statistics is compared with the practical part usage in a simulation of several types of part usage's distributions.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.69-74
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• 2015

In this paper the patterns of information system is reduced so that control rules can guarantee fast response of queries in database. Generally an information system includes many kinds of necessary and unnecessary attribute. In particular, inconsistent information system is less likely to acquire the accuracy of response. Hence we are interested in the simple and understandable rules that can represent useful patterns by means of rough entropy and Bayesian posterior probability. We propose an algorithm which can reduce control rules to a minimum without inadequate patterns such that the implication between condition attributes and decision attributes is measured through the framework of rough entropy. Subsequently the validation of the proposed algorithm is showed through test information system of new employees appointment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.107-113
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• 2014

The derivation of optimal information from decision table is based on the concept of indiscernibility relation and approximation space in rough set. Because decision table is more likely to be susceptible to the superposition or inconsistency in decision table, the reduction of attributes is a important concept in knowledge representation. While complete subsets of the attribute's domain is considered in algebraic definition, incomplete subsets of the attribute's domain is considered in information-theoretic definition. Therefore there is a marked difference between algebraic and information-theoretic definition. This paper proposes a conditional entropy using rough set as information theoretical measures in order to deduct the optimal information which may contain condition attributes and decision attribute of information system and shows its effectiveness.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.160-168
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• 2016

The classification and evaluation of cooking that is being published on the internet are presented without scientific criteria based on individual subjective factors. In this paper, we objectified the degree of cooking difficulty based on the information entropy. And we measured the similarity by calculating the common entropy between recipes and constructed a social network based on the recipe similarity. As a result of measuring the cooking difficulty, 'Dongtae Haemul-jjim' (Korean) and 'Vegetarian Lasagna' (Italy) are the most difficult recipes and 'Gochu-jang' (Korean) and 'Tofu steak' (Italy) are the easiest recipes. Through the recipe network, the similarity between Korean and Asian cooking is higher than Western cuisine. We showed a similar recipe to a particular cooking, the group of similar recipes, and reasonable schedule when preparing the menu from the viewpoint of ease of cooking.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.269-276
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• 2008

Recently, constructing the database of road network is a main key in various social operation as like the transportation, management, security, disaster assesment, and the city plan in our life. However it need high expenses for constructing the data, and relies on many people for finishing the tasks. This study proposed the classification method for discriminating between the road and building points using the entropy theory, then detects the classes as a expecting road from the classified point group using the standard reflectance intensity of road and the characteristics restricted by raw. Hence the main object of this study is to develop a method which can detect the road in urban area using only the LiDAR data.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.12-21
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• 2012

IEEE 802.11 MAC protocol standard, DCF(CSMA/CA), is originally designed to ensure the fair channel access between mobile nodes sharing the local wireless channel. It has been, however, revealed that some misbehavior nodes transmit more data than other nodes through artificial means in hot spot area spreaded rapidly. The misbehavior nodes may modify the internal process of their MAC protocol or interrupt the MAC procedure of normal nodes to achieve more data transmission. This problem has been referred to as a selfish node problem and almost literatures has proposed methods of analyzing the MAC procedures of all mobile nodes to detect the selfish nodes. However, these kinds of protocol analysis methods is not effective at detecting all kinds of selfish nodes enough. This paper address this problem of detecting selfish node using Tsallis-Entropy which is a kind of statistical method. Tsallis-Entropy is a criteria which can show how much is the density or deviation of a probability distribution. The proposed algorithm which operates at a AP node of wireless LAN extracts the probability distribution of data interval time for each node, then compares the one with a threshold value to detect the selfish nodes. To evaluate the performance of proposed algorithm, simulation experiments are performed in various wireless LAN environments (congestion level, how selfish node behaviors, threshold level) using ns2. The simulation results show that the proposed algorithm achieves higher successful detection rate.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.26-46
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• 2020

An irrigated-maize agroecosystem is viewed as an open thermodynamic system upon which solar radiation impresses a large gradient that moves the system away from equilibrium. Following the imperative of the second law of thermodynamics, such agroecosystem resists and reduces the externally applied gradient by using all means of this nature-human coupled system acting together as a nonequilibrium dissipative process. The ultimate purpose of our study is to test this hypothesis by examining the energetics of agroecosystem growth and development. As a first step toward this test, we employed the eddy covariance flux data from 2003 to 2014 at the AmeriFlux NE1 irrigated-maize site at Mead, Nebraska, USA, and analyzed the energetics of this agroecosystem by scrutinizing its radiation, energy and entropy exchange. Our results showed: (1) more energy capture during growing season than non-growing season, and increasing energy capture through growing season until senescence; (2) more energy flow activity within and through the system, providing greater potential for degradation; (3) higher efficiency in terms of carbon uptake and water use through growing season until senescence; and (4) the resulting energy degradation occurred at the expense of increasing net entropy accumulation within the system as well as net entropy transfer out to the surrounding environment. Under the drought conditions in 2012, the increased entropy production within the system was accompanied by the enhanced entropy transfer out of the system, resulting in insignificant net entropy change. Drought mitigation with more frequent irrigation shifted the main route of entropy transfer from sensible to latent heat fluxes, yielding the production and carbon uptake exceeding the 12-year mean values at the cost of less efficient use of water and light.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.1-10
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• 2005

In this Paper, we propose a new block matching a1gorithm that extracts motion vectors from consecutive range data. The proposed method defines a matching metric that integrates intensity, hue, and range. Our algorithm begins matching with a small matching template. If the matching degree is not good enough, we slightly expand the size of a matching template and then repeat the matching process until our matching criterion is satisfied or the predetermined maximum size has been reached. As the iteration proceeds, we adaptively adjust weights of the matching metric by considering the importance of each feature. In the experiments, we show that our block matching approach can work as a promising solution by comparing the proposed method with previously known method in terms of performance.