• Journal of Digital Contents Society
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• v.18 no.2
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• pp.411-422
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• 2017

Current policies for sharing bandwidth increase average throughput and improve utilization of the bandwidth in the local network. However, with these policies, a central administer, which is responsible for allocating bandwidth to each network flow, cannot allocate resources based on user characteristics. Thus, it leads to unfair bandwidth allocation because it does not guarantee services based on user characteristics. Therefore, we propose a novel negotiation method to share the bandwidth in a limited bandwidth network, in which, a user negotiates with other users to gain more resource. Ideally, we use a fuzzy system to infer and determine whether a device will request bandwidth or not based on the current usage of the given device. We conduct two experiments consisting of a video streaming simulation in OPNET and a real-time video streaming in WebRTC. The results of the experiment indicate that the proposed method can flexibly share the bandwidth utilization based on user's requirement in the network.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1072-1079
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• 2003

This study is purported for suggesting a new clustering algorithm that enables incremental categorization of numerous documents. The suggested algorithm adopts the natures of the spherical k-means algorithm, which clusters a mass amount of high-dimensional documents, and the fuzzy ART(adaptive resonance theory) neural network, which performs clustering incrementally. In short, the suggested algorithm is a combination of the spherical k-means vector space model and concept vector and fuzzy ART vigilance parameter. The new algorithm not only supports incremental clustering and automatically sets the appropriate number of clusters, but also solves the current problems of overfitting caused by outlier and noise. Additionally, concerning the objective function value, which measures the cluster's coherence that is used to evaluate the quality of produced clusters, tests on the CLASSIC3 data set showed that the newly suggested algorithm works better than the spherical k-means by 8.04% in average.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.32-40
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• 2008

Fuzzy rules are very useful and efficient to describe classification rules especially when the attribute values are continuous and fuzzy in nature. However, it is generally difficult to determine membership functions for generating efficient fuzzy classification rules. In this paper, we propose a method of automatic generation of efficient fuzzy classification rules using evolutionary algorithm. In our method we generate a set of initial membership functions for evolutionary algorithm by supervised clustering the training data set and we evolve the set of initial membership functions in order to generate fuzzy classification rules taking into consideration both classification accuracy and rule comprehensibility. To reduce time to evaluate an individual we also propose an evolutionary algorithm with data partition evaluation in which the training data set is partitioned into a number of subsets and individuals are evaluated using a randomly selected subset of data at a time instead of the whole training data set. We experimented our algorithm with the UCI learning data sets, the experiment results showed that our method was more efficient at average compared with the existing algorithms. For the evolutionary algorithm with data partition evaluation, we experimented with our method over the intrusion detection data of KDD'99 Cup, and confirmed that evaluation time was reduced by about 70%. Compared with the KDD'99 Cup winner, the accuracy was increased by 1.54% while the cost was reduced by 20.8%.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.423-433
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• 2008

The surface curvatures extracted from the face contain the most important personal facial information. In particular, the face shape using the depth information represents personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple face regions using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area and has to take into consideration of the orientated frontal posture to normalize. Multiple areas are extracted by the depth threshold values from reference point, nose tip. And then, we calculate the curvature features: principal curvature, gaussian curvature, and mean curvature for each region. The second step of approach concerns the application of eigenface and Linear Discriminant Analysis(LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for each region. In the experimental results, using the depth threshold value 40 (DT40) show the highest recognition rate among the regions, and the maximum curvature achieves 98% recognition rate, incase of fuzzy integral.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.6
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• pp.1325-1336
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• 2012

In the past about 10 different kinds of malicious code were found in one day on the average. However, the number of malicious codes that are found has rapidly increased reachingover 55,000 during the last 10 year. A large number of malicious codes, however, are not new kinds of malicious codes but most of them are new variants of the existing malicious codes as same functions are newly added into the existing malicious codes, or the existing malicious codes are modified to evade anti-virus detection. To deal with a lot of malicious codes including new malicious codes and variants of the existing malicious codes, we need to compare the malicious codes in the past and the similarity and classify the new malicious codes and the variants of the existing malicious codes. A former calculation method of the similarity on the existing malicious codes compare external factors of IPs, URLs, API, Strings, etc or source code levels. The former calculation method of the similarity takes time due to the number of malicious codes and comparable factors on the increase, and it leads to employing fuzzy hashing to reduce the amount of calculation. The existing fuzzy hashing, however, has some limitations, and it causes come problems to the former calculation of the similarity. Therefore, this research paper has suggested a new comparison method for malicious codes to improve performance of the calculation of the similarity using fuzzy hashing and also a classification method employing the new comparison method.

• Journal of Wetlands Research
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• v.13 no.3
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• pp.547-565
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• 2011

Different types of schemes have been used in stage prediction involving conceptual and physical models. Nevertheless, none of these schemes can be considered as a single superior model. To overcome disadvantages of existing physics based rainfall-runoff models for stage predicting because of the complexity of the hydrological process, recently the data-derived models has been widely adopted for predicting flood stage. The objective of this study is to evaluate model performance for stage prediction of the Neuro-Fuzzy and regression analysis stage prediction models in these data-derived methods. The proposed models are applied to the Wangsukcheon in Han river watershed. To evaluate the performance of the proposed models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient(NSEC), mean absolute error(MAE), adjusted coefficient of determination($R^{*2}$). The results show that the Neuro-Fuzzy stage prediction model can carry out the river flood stage prediction more accurately than the regression analysis stage prediction model. This study can greatly contribute to the construction of a high accuracy flood information system that secure lead time in medium and small streams.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.293-298
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• 2010

The key of USN(Ubiquitous Sensor Network) technology is low power wireless communication technology and proper resource allocation technology for efficient routing. The distinguished resource allocation method is needed for efficient routing in sensor network. To solve this problems, we propose an algorithm that can be adopted in USN with making up for weak points of PQ and WRR in this paper. The proposed algorithm produces the control discipline by the fuzzy theory to dynamically assign the weight of WRR scheduler with checking the Queue status of each class in sensor network. From simulation results, the proposed algorithm improves the packet loss rate of the EF class traffic to 6.5% by comparison with WRR scheduling method and that of the AF4 class traffic to 45% by comparison with PQ scheduling method.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.76-84
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• 2003

Now days, It is based on GIS and GPS, it can search for the shortest path and estimation of arrival time by using the internet and cell phone to driver. But, even though good car navigation system does not create which is the shortest path when there average vehicle speed is 10 -20 Km. Therefore In order to reduce vehicle waiting time and average vehicle speed, we suggest optimal green time algorithm using fuzzy adaptive control, where there are different traffic intersection length and lane. In this paper, it will be able to forecast the optimal traffic information, estimation of destination arrival time, under construction road, and dangerous road using internet.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.13-16
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• 1999

이산 HMM을 이용한 음성인식을 할 때, 관측심볼은 훈련 데이터의 양자화과정을 수행하여 얻게 된다. 훈련 데이터는 선정된 몇몇 화자에 의해서 얻어지게 되는데, 이러한 이유로 인하여 충분하지 못한 훈련 데이터가 얻어지므로, 관측 심볼에 따라 출력확률분포값이 영(zero)이나, 거의 영에 가까운 값을 가지게 된다. 이러한 요인은 인식률의 저하를 초래하므로, 본 논문에서는 fuzzy smoothing 기법을 채택하여, 출력확률분포값이 영(zero)의 값을 가지는 것을 방지하여, 새로 구해진 파라메터로 인식실험을 하였다. Smoothing과정을 수행한 후의 인식률이 smoothing을 하진 않은 인식율에 비해 평균 $1.46\%$ 향상되었다.

• Journal of the Korea Institute of Military Science and Technology
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• v.4 no.1
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• pp.41-51
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• 2001

In this paper, the mathematical modeling and the design of controllers were performed for the dynamic performance simulation of the diesel engine for underwater vehicle. Nonlinear equations are acquired through the mathematical modeling using mean torque production model technique. Three kinds of controllers were designed for the perform simulation of the engine model. As the result of simulation, it was confirmed that each controller can be applied with regard to system characteristics and desired conditions etc.