• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1463-1478
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• 2012

Based on the theory of local-world network, the composition self-similarity (CSS) of network traffic is presented for the first time in this paper for the study of DoS detection. We propose the concept of composition distribution graph and design the relative operations. The $(R/S)^d$ algorithm is designed for calculating the Hurst parameter. Based on composition distribution graph and Kullback Leibler (KL) divergence, we propose the composition self-similarity anomaly detection (CSSD) method for the detection of DoS attacks. We evaluate the effectiveness of the proposed method. Compared to other entropy based anomaly detection methods, our method is more accurate and with higher sensitivity in the detection of DoS attacks.

• Smart Structures and Systems
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• v.7 no.3
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• pp.229-240
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• 2011

Recent developments in Smart Structures with very large scale embedded sensors and actuators have introduced new challenges in terms of data processing and sensor fusion. These smart structures are dynamically classified as a large-scale system with thousands of sensors and actuators that form the musculoskeletal of the structure, analogous to human body. In order to develop structural health monitoring and diagnostics with data provided by thousands of sensors, new sensor informatics has to be developed. The focus of our on-going research is to develop techniques and algorithms that would utilize this musculoskeletal system effectively; thus creating the intelligence for such a large-scale autonomous structure. To achieve this level of intelligence, three major research tasks are being conducted: development of a Bio-Inspired data analysis and information extraction from thousands of sensors; development of an analytical technique for Optimal Sensory System using Structural Observability; and creation of a bio-inspired decision-making and control system. This paper is focused on the results of our effort on the first task, namely development of a Neuro-Morphic Engineering approach, using a neuro-symbolic data manipulation, inspired by the understanding of human information processing architecture, for sensor fusion and structural diagnostics.

• Management & Information Systems Review
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• v.34 no.2
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• pp.79-95
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• 2015

The purpose of this paper is to evaluate the effects of perceived risk and brand attitude on licensing brands comparing with non-licensed brands(virtual brand). Data was collected through a self-administered questionnaire in quasi-experimental design setting. I designed the experimental setting that there were two virtual companies to sell the luxury bags(symbolic goods) or cruise tour(experiential goods) and to launch their goods with own brand or licensed brand. The experimental groups were composed of women consumers who were familiar with consuming experiential goods and symbolic goods. Results from the experiment suggest that consumer's perceived risk on brands gives a negative impact on brand attitude. And congruence in goods types and licensed brand values leads to difference in the level of perceived risk. In experiential goods, brand licensing from famous and experiential brands can reduce perceived risk. But in symbolic goods, brand licensing effect which reduces the perceived risk is less effective than in experiential goods. This findings suggest that brand licensing may lower the level of consumer's perceived risk, but incongruity in goods type and brand value may result in strategic failure.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.4
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• pp.229-235
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• 2024

Recent years have seen active research on methods for efficiently processing and interpreting large volumes of data in the fields of artificial intelligence and machine learning. One of these data processing technologies, Vector Symbolic Architecture (VSA), offers an innovative approach to representing complex symbols and data using high-dimensional vectors. VSA has garnered particular attention in various applications such as natural language processing, image recognition, and robotics. This study quantitatively evaluates the characteristics and performance of VSA methodologies by applying five VSA methodologies to the MNIST dataset and measuring key performance indicators such as encoding speed, decoding speed, memory usage, and recovery accuracy across different vector lengths. BSC and VT demonstrated relatively fast performance in encoding and decoding speeds, while MAP and HRR were relatively slow. In terms of memory usage, BSC was the most efficient, whereas MAP used the most memory. The recovery accuracy was highest for MAP and lowest for BSC. The results of this study provide a basis for selecting appropriate VSA methodologies depending on the application area.

• Journal of Industrial Convergence
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• v.13 no.3
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• pp.57-76
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• 2015

The purpose of this paper is to examine the moderate effects of self monitoring and the ways of thinking on the relationships and the moderate effects of brand credibility and brand attachment on the relationships between the negative information about a brand and the customer attitude holistic and analytic on the relationships between the negative information about a brand and customer attitude. To accomplish these purposes, this research divided negative information about a brand into corporate ability and corporate social responsibility. In addition, research also divided product type into functional product and symbolic product. participants are classified as having Low or High self monitoring. and the ways of thinking divided into holistic and analytic on the relationships between the negative information about a brand and customer attitude. The following are the summary of hypothesis test: (1)the consumers with low(high) level of self monitering are more likely to reveal high level of preference for negative information of corporate ability. (2)the consumers with analytic(holistic) ways of thinking are more likely to reveal high level of preference for negative information of corporate ability. (3)the consumers with low(high) level of self monitering are more likely to reveal high level of preference for functional product. (4)the consumers with analytic(holistic) ways of thinking aren't more likely to reveal high level of preference for functional(symbolic) product.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2089-2090
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• 2006

본 논문은 기호 코딩 및 정보입자를 이용한 유전자 알고리즘의 전방향 퍼지 집합 기반 뉴럴네트워크 (Information Granules and Symbolic Encoding-based Fuzzy Set Polynomial Neural Networks ; IG and SE based FSPNN)의 모델 설계를 제안한다. 기존 퍼지 집합기반 다항식 뉴럴네트워크(FSPNN)의 구조 최적화를 위해 이진코딩을 사용하였다. 그러나 이진코딩에서 스트링의 길이가 길면 길수록 인접한 두 수 사이에 발생하는 급격한 비트 차이라는 해밍절벽이 발생하였다. 이에 제안된 모델에서는 해밍절벽의 문제를 해결하기 위해 기호코딩을 사용하였다. 제안된 모델은 각 입력에 대해 MFs의 개수 만큼 규칙을 생성하는 Fuzzy 집합기반 다항식 뉴럴네트워크(FSPNN)를 그대로 사용한다. 그리고 IG based gFSPNN의 평가을 위해 실험적 예제를 통하여 제안된 모델의 성능 및 근사화 능력의 우수함을 보인다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.7 no.1
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• pp.25-28
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• 1982

A method for finding the symbolic reliability expressision of a conbinational logic circuit is presented. The evaluation of the probabilities of the outputs can be symbolically evaluated by the Boolean operation named sharp operation, provided that every input of such a circuit can be treated as random variables with values set(0, 1) and the output of a circuit can be represented by a Boolean sum of produt expression.

• Journal of the Korean Home Economics Association
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• v.49 no.8
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• pp.97-111
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• 2011

This study aimed to present marketing communication strategy for lifestyle groups. Consumers' preference on advertisement media/information source, advertisement appeal types, and on-line media use were examined by lifestyle groups. This study was executed by web survey and off-line survey. A total of 141 data was obtained and data were analyzed by PASW 18.0. Results were as follows: First, 4 types of lifestyle groups were made up by holistic approach; 'price oriented', 'traditional symbolic', 'positive life', and 'open mind enjoyment'. 'Positive life type' preferred every type of ads media. 'Traditional symbolic type' liked magazine and 'price oriented type' and 'open mind enjoyment type' thought off-line store display more important. For ads appeal types, 'positive life type' preferred social oriented appeal type. Every group except 'price oriented type' preferred emotional appeal type and especially 'open mind enjoyment type' liked the most emotional ads appeal type.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.648-651
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• 2017

프로그램의 코드에 있어서 실행되지 않은 영역은 미지의 영역으로써 각종 에러와 오류의 잠재적 가능성을 지니고 있다. 개발자는 이러한 영역을 모두 검증, 테스팅 해봐야 이후 프로그램의 실행에서 예상치 못한 치명적 오류들에 대응할 수 있을 것이다. 우리는 본 논문에서 소프트웨어 테스팅의 두 가지 기법에 대하여 소개를 하고 이 두 가지를 이용하여 미실행된 영역을 실행시킬 수 있는 방법론을 제안하고자 한다. 실험에서 JaCoCo와 SPF를 사용하여 방법론을 적용하였고 이를 통하여 미실행 영역이 커버되는 테스트 케이스를 자동으로 얻어 낼 수 있었다.