• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.223-227
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• 2021

A treatment based on the algebraic operations on fuzzy numbers is used to replace the fuzzy problem into an equivalent crisp one. The finite difference technique is used to replace the continuous boundary value problem (BVP) of arbitrary order 1<α≤2, with fuzzy boundary parameters into an equivalent crisp (algebraic or differential) system. Three numerical examples with different behaviors are considered to illustrate the treatment of the singular perturbed case with different fractional orders of the BVP (α=1.8, α=1.9) as well as the classical second order (α=2). The calculated fuzzy solutions are compared with the crisp solutions of the singular perturbed BVP using triangular membership function (r-cut representation in parametric form) for different values of the singular perturbed parameter (ε=0.8, ε=0.9, ε=1.0). Results are illustrated graphically for the different values of the included parameters.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.295-302
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• 2007

It is very iterative and complicated work to enter the personal information every time one fills the form-based resume or one joins the new membership page on the internet. Although there are some systems that have the personal information on the computer and fill the membership page automatically, their accuracies are not often satisfactory in that the fields and their values do not match exactly. The research proposes and implements a system that has user's information on the computer and reasons and fills the information automatically that a membership web page(target page) requests using the personal information ontology. During the reasoning process, the target page is analyzed to extract the requested fields. Then the requested field names are converted to the standard field names using synonym ontology. The converted requested fields find the appropriate level in the personal information ontology using ontology match making to generate the final field value. The system not only finds the similar fields but also generates the exact field values by reasoning on the information ontology hierarchy. By experimenting with several membership pages on the web, the system showed higher accuracy over the existing systems. The system can be easily applicable to the cases where one iteratively fills the same information such as resume form.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.1-26
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• 2022

This study classified five types of online food subscription economy: replenishment, curation, surprise, membership, and visitation. An online survey was conducted with 314 customers who experienced 5 types of online subscription economy. This study selected the characteristics of the food subscription economy as convenience, perceived personalization, economic utility, and timeliness through previous studies. The effect of the four characteristics on perceived value (utilitarian and emotional) and the relationship between customer engagement and perceived value, which are dependent variables that have never been used in the food subscription economy, were verified through the S-O-R model. In this relationship, we demonstrated how consumers' personal tendencies, such as need for cognitive closure and self-efficacy, mediate between timeliness and perceived value related to online food delivery. The study results are as follows. Perceived personalization, convenience, and timeliness had a positive effect on the utilitarian value in the order. It also had a positive effect on emotional values in the order of perceived personalization and timeliness. On the other hand, economic utility had no significant effect on practical branches. Customer engagement had a positive effect in the order of emotional value and utilitarian value. The lower the need for cognitive closure the more positive the utilitarian value. The lower the self-efficacy, the more positive the emotional value was perceived. Through the above study, companies that want to operate or start an online food subscription economy need a strategic approach rather than unreasonable price discounts in pricing policy. In addition, it is necessary to focus on marketing activities that provide emotional value by focusing on perceived personalization, which is the satisfaction factor of online food subscription.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.60-69
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• 1998

This paper proposes a speech synthesis method using Fuzzy VQ, and then study how to make choice of fuzziness value which optimizes (controls) the performance of FVQ in order to obtain the synthesized speech which is closer to the original speech. When FVQ is used to synthesize a speech, analysis stage generates membership function values which represents the degree to which an input speech pattern matches each speech patterns in codebook, and synthesis stage reproduces a synthesized speech, using membership function values which is obtained in analysis stage, fuzziness value, and fuzzy-c-means operation. By comparsion of the performance of the FVQ and VQ synthesizer with simmulation, we show that, although the FVQ codebook size is half of a VQ codebook size, the performance of FVQ is almost equal to that of VQ. This results imply that, when Fuzzy VQ is used to obtain the same performance with that of VQ in speech synthesis, we can reduce by half of memory size at a codebook storage. And then we have found that, for the optimized FVQ with maximum SQNR in synthesized speech, the fuzziness value should be small when the variance of analysis frame is relatively large, while fuzziness value should be large, when it is small. As a results of comparsion of the speeches synthesized by VQ and FVQ in their spectrogram of frequency domain, we have found that spectrum bands(formant frequency and pitch frequency) of FVQ synthesized speech are closer to the original speech than those using VQ.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.288-295
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• 1998

This paper proposes a new data classification method based on the tolerant rough set that extends the existing equivalent rough set. Similarity measure between two data is described by a distance function of all constituent attributes and they are defined to be tolerant when their similarity measure exceeds a similarity threshold value. The determination of optimal similarity theshold value is very important for the accurate classification. So, we determine it optimally by using the genetic algorithm (GA), where the goal of evolution is to balance two requirements such that (1) some tolerant objects are required to be included in the same class as many as possible. After finding the optimal similarity threshold value, a tolerant set of each object is obtained and the data set is grounded into the lower and upper approximation set depending on the coincidence of their classes. We propose a two-stage classification method that all data are classified by using the lower approxi ation at the first stage and then the non-classified data at the first stage are classified again by using the rough membership functions obtained from the upper approximation set. We apply the proposed classification method to the handwritten numeral character classification. problem and compare its classification performance and learning time with those of the feed forward neural network's back propagation algorithm.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.515-518
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• 1998

This paper proposes a new hybrid algorithm for auto-tuning fuzzy controllers improving the performance. The presented algorithm estimates automatically the optimal values of membership functions, fuzzy rules, and scaling factors for fuzzy controllers, using a genetic-MGM algorithm. The object of the proposed algorithm is to promote search efficiency by a genetic and modified gradient optimization techniques. The proposed genetic and MGM algorithm is based on both the standard genetic algorithm and a gradient method. If a maximum point don't be changed around an optimal value at the end of performance during given generation, the genetic-MGM algorithm searches for an optimal value using the initial value which has maximum point by converting the genetic algorithms into the MGM(Modified Gradient Method) algorithms that reduced the number of variables. Using this algorithm is not only that the computing time is faster than genetic algorithm as reducing the number of variables, but also that can overcome the disadvantage of genetic algorithms. Simulation results verify the validity of the presented method.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.3
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• pp.13-20
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• 2020

The purpose of this study is to analyze the fuzzy pattern that is reflected on the inside of the value evaluator in measuring the economic value of architectural aesthetic using the fuzzy-contingent valuation method. The main results of analyzing the relationship between architectural aesthetic and fuzzy patterns by typing 307 fuzzy patterns collected from visitors at Dongdaemun Design Plaza are as follows: First, low levels of architectural aesthetic can be a primary cause of extreme refusal of payment. However, it was confirmed that the extreme refusal of payment could partially involve mentality of free-ride on public goods or mentality that would not give value to past events that are not future. Second, if the architectural aesthetic score is 77.5, the most perfect form of fuzzy pattern is formed. It is confirmed that the fuzzy form, which is the standard in the relationship between architectural aesthetic and money value, is made at 77.5 points. This means that it is most efficient to have 77.5 points of architectural aesthetic to secure balanced data by membership in the study of architectural aesthetic value measurement through fuzzy pattern. Third, according to the architectural aesthetic score, respondents can be interpreted as follows: no monetary willingness arises before or after 52.4, starts to respond to the amount before and after 65.6, severe conflict over payments around 70.6~71.7, stronger willingness to pay around 77.6, want to pay for sure around 80.0.

• Journal of Korea Trade
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• v.23 no.6
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• pp.145-158
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• 2019

Purpose - Alliance members have constantly revised market strategies over time by withdrawing membership from a current alliance, joining another alliance, or constructing a new alliance. From the perspective of the signaling effect, the purpose of this study is to analyze the impacts of partner volatility (new member, old member, and new group) on firm value. Design/methodology - To analyze the impact of partner volatility on firm value, companies in strategic alliances are classified into the three groups of new partner, existing partner, and new alliance, and the effects on company value are verified through an event study and the signaling effect analysis. Findings - This study proved that new partners and newly formed strategic alliances have higher expectation effects than old partner company groups, and have a more positive effect on the relevant firms' stock prices. In addition, the result of the study showed the same valid results as the alliance levels, and showed that investors' expectations were higher with new partners and new alliances than with old partners. Research Implications - A new perspective on the signaling effects of strategic alliances among shipping lines was presented in this study by grouping alliance types including new member, old member, and new group. The results provide useful insights for selecting partners and firm values of alliance announcement times. Originality/value - This study analyzed partner volatility on relevant companies' stock prices from the perspective of investors from the global shipping conference reorganization in 2017. Strategic alliances were classified into the three categories of new partner, old partner, and new alliance, and the effects on firm value were verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• International Journal of Safety
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• v.11 no.2
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• pp.15-21
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• 2012

This paper is aimed to develop an electrical fire detection system (EFDS) which can analyze the possibility of electrical fire for overcurrent, leakage current and arc signals of panel board in traditional market shop. The EFDS adopted fuzzy logic and precursory data for overcurrent, leakage current and arc signals to evaluate the possibility of electrical fire. The signals are obtained directly from panel board in traditional market shops and fuzzy membership function is obtained from experiment, simulation, expert's advice. The overcurrent data is acquired by thermal data of normal and abnormal states (partial disconnection) on the insulated electrical wire, in accordance with the increase of the current signal, The leakage current data is obtained under various environments. The arc signal is acquisited by waveforms of instantaneous value in time domain and frequency band in frequency domain. The Fuzzy algorithm for DB of EFDS consists of fuzzification, inference engine by Mamdani's method and defuzzification by center of gravity method. In order to verify the performance and reliability of EFDS, it was applied to Jeon-Ju traditional market shops (90 shops) in Korea. Results show that EFDS in this paper is useful in alarming the fire case, which will prevent severe damage to human beings and properties, and reduce the electrical fires in a vulnerable area of electrical disaster.