• Journal of Information Processing Systems
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• v.14 no.3
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• pp.790-800
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• 2018

The simplified neutrosophic set (SNS) is a generalization of fuzzy set that is designed for some practical situations in which each element has truth membership function, indeterminacy membership function and falsity membership function. In this paper, we propose a new method to construct similarity measures of single valued neutrosophic sets (SVNSs) and interval valued neutrosophic sets (IVNSs), respectively. Then we prove that the proposed formulas satisfy the axiomatic definition of the similarity measure. At last, we apply them to pattern recognition under the single valued neutrosophic environment and multi-criteria decision-making problems under the interval valued neutrosophic environment. The results show that our methods are effective and reasonable.

• The Journal of Fisheries Business Administration
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• v.49 no.4
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• pp.19-36
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• 2018

Overstock in aquaculture is a matter of concern in aquaculture management. To sort fish based on fingerling size in case of overstocking is an important problem in aquaculture farm. This study aims to determine the amount of fry overstock and sorting time in aquaculture farm. This study builds a mathematical model that finds the value of decision variables to optimize objective function summing up the fingerling purchasing cost, aquaculture farm operating cost and feeding cost under mortality and farming period constraints. The proposed mathematical model involves following biological and economical variables and coefficients: (1) number of fingerlings, (2) sorting time, (3) fish growth rate and variation, (4) mortality, (5) price of a fry (6) feeding cost, and (7) possible sorting periods. Numerical simulation is presented herein. The objective of numerical simulation is to provide decision makers to analyse and comprehend the proposed model. When extensive biological data about growth function of fry becomes available, the proposed model can be widely applicable to real aquaculture farms.

• Asia Marketing Journal
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• v.14 no.3
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• pp.69-102
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• 2012

Owing to increased number of luxury brand users, both genuine and counterfeit luxury product consumption continues to increase every year. Luxury brand is defined as use or display of a particular branded products which brings the ownership prestige apart from its functional utility(Grossmand and Shapiro 1988). Some luxury brands have imitations sold in marketplace due to their popularity. These imitations or counterfeits have been jumping on the bandwagon of the upturn in sales of their originals. The purpose of our study is to understand consumer's underlying motives to consume luxury brands, genuine and or counterfeits. To do this, we propose functional theories of attitudes, decision-making styles, and life attitudes to form the determining causes for different consumption choices of luxury brands: genuine brands, counterfeit brands, both genuine and counterfeit brands, and no consumption on luxury brands types. In proposed causal pathways, we examine moderated effects of socio-psychological factors to further investigate if consumer profiles would exert influences in causal relationships. From the existing theories of functional attitudes: value-expressive and social-adjustive attitudes, we developed and introduced a new measure of rationality-consumptive attitude. From the existing eight decision-making characteristics of consumer styles inventory(CSI), three measures of high-quality, hedonic-shopping, and price-shopping styles were primarily applied in the study along with newly introduced measure of 'high-price' being added, which makes four total. Seven life attitude measures of life purpose, life control, will to meaning, goal seeking, future mean to fulfill, life satisfaction, and religiosity were applied. Finally, such socio-psychological measures as age, gender, marital status, income, and age-gap between couples were assumed to function as moderators. With 430 valid study samples, ages from 20s to 50s, with more females(316) than males(114), with average personal possessions of 5 genuine and 9 counterfeit luxury brands, we conducted questionnaire survey. Results indicated that social-adjustive function is totally disappeared in the relationship due to current social trend of widespread consumptions on both genuine and counterfeit brands which in turn, make consumers feel less special on wearing or carrying them unlike in the past. Self-expressive function and rationality-consumptive functions act as strong catalysts for genuine brand consumption and counterfeit brand consumption, respectively. On consumers' decision-making styles, high-price sublation is the most powerful indicator anticipating counterfeit consumption, even more powerful than personal incomes. In life attitude, the overall model fit was not validated, and only life control and life satisfaction are proven to be significant on both genuine and counterfeit product consumptions. Employment of socio-psychological factors in the model improved understanding of users further. Young consumers tend to go for genuine products over counterfeits. Consumers in different income groups; low, medium and high, all significantly consume genuine products for reasons of different decision-making styles. The results indicated that consumers whose personal disposition is predisposed to consume products in the form of reflection of his or her personality, go only for genuine brands for quality reason, while consumers who rationally consume products for its function or usability, go only for counterfeits for high-price sublation reason. Meanwhile, both product users support for high-price orientation who are not well off.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.98-103
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• 2000

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.283-291
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• 2005

The development of mobile applications is fast in recent years. However, nearly all applications are for messaging, financial, locating services based on simple interactions with mobile users because of the limited screen size, narrow network bandwidth, and low computing power. Processing an algorithm for supporting a group decision process on mobile devices becomes impossible. In this paper, we introduce the mobile-oriented simple interactive procedure for support a group decision making process. The interactive procedure is developed for multiple objective linear programming problems to help the group select a compromising solution in the mobile Internet environment. Our procedure lessens the burden of group decision makers, which is one of necessary conditions of the mobile environment. Only the partial weak order preferences of variables and objectives from group decision makers are enough for searching the best compromising solution. The methodology is designed to avoid any assumption about the shape or existence of the decision makers's utility function. For the purpose of the experimental study of the procedure, we developed a group decision support system in the mobile Internet environment, MOBIGSS　and applied to an allocation problem of investor assets.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• The Journal of the Acoustical Society of Korea
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• v.27 no.2
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• pp.75-79
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• 2008

In this paper, we propose a novel speech enhancement technique using global soft decision (GSD) based on the probabilistic outputs of support vector machine (SVM). Generally, speech enhancement algorithms applied soft decision gain modification and noise power estimation have bettor performance than those employing hard decision. Especially, global speech absence probability (GSAP), which is known as an effective measure of the speech absence in each frame, has been adopted to SD-based speech enhancement methods. For this reason, we introduce a new GSAP estimated from the probabilistic output of SVM using sigmoid function. The performance of the proposed algorithm is evaluated by the PESQ and MOS test under various noise environments and yields better results compared with the conventional GSD scheme.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.5
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• pp.1-12
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• 1999

A new algorithm of constructing binary decision diagram(BDD) for design verification of switch-level circuits is proposed in this paper. In the switch-level circuit, functions are characterized by serial and parallel connections of switches and the final logic values may have high-impedance and unstable states in addition to the logic values of 0 and 1. We extend the BDD to represent functions of switch-level circuits as acyclic graphs so called switch-level binary decision diagram (SLBDD). The function representation of the graph is in the worst case, exponential to the number of inputs. Thus, the ordering of decision variables plays a major role in graph sizes. Under the existence of pass-transistors and domino-logic of precharging circuitry, we also propose an input ordering algorithm for the efficiency in graph sizes. We conducted several experiments on various benchmark circuits and the results show that our algorithm is efficient enough to apply to functional simulation, power estimation, and fault-simulation of switch-level design.

• The Research Journal of the Costume Culture
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• v.17 no.4
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• pp.723-732
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• 2009

The quick response(QR) system is very popular in Korean apparel companies. However, the usage of QR system was not known well. The purpose of this study is to identify the usage of the quick response decision support system(QR DSS) and postponement manufacturing in the Korean apparel company. The researched company was the only one which used the QR DSS. The researchers carried out the depth interview with the QR decision makers of the company. This company had 14 brands, and had used the QR DSS since January, 2008. The results are as follows: The QR DSS was supportive computer software program, and it helped the staffs to make agile decision about QR repeat production of clothing. The QR DSS automatically calculated the related data, and suggested the expected sales volume and the proper supply amounts of the styles. There were four functions in QR DSS : 'QR Alert', 'Proper Supply Amount Simulation', 'Sensible QR', and 'Supply/Sales Simulation by Item'. The men's clothing brands effectively used 'Supply/Sales Simulation by Item' function. And the women's clothing brands effectively used 'QR Alert' function. This company also used the postponement production system for QR repeat production. The postponement production was conducted with four methods : the yarn stocking, the grey fabric stocking, the dyed fabric stocking, and the fabric sourcing. The men's clothing brands usually used of the yarn stocking methods and the dyed fabric stocking methods. The women's clothing brands usually used the grey fabric stocking methods. By using QR DSS and postponement production system the company was able to shorten the lead time for QR decision making.