• Proceedings of the Technology Innovation Conference
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• 1999.06a
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• pp.203-225
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• 1999

In this paper, I tried to analyze the relationship between the market structure of teleommunicatin industry and market performance using Economides and Lehr's results considering composite goods methods. And I took three policy scenarios into account. First, government tires to implement the policy which divides the integrated monopoly into network operator and service provider, then invite competition to service part. Second, government tries to implement the policy which invite the competition to service part only. Third, government tries to implement the policy which invite the competition to integrated duopoly. I gained the best result in the third scenario. So we can conclude that Parallel vertical integration is the best market structure. I also specially checked the relationship between the market structure and service quality. I gained the best service quality in the third scenario. So we can conclude that Parallel Vertical Integration is also the best market structure with regard to the service quality.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.519-521
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• 1998

Fuzzy inference systems have found many applications in recent years. The fuzzy inference system design procedure is related to an expert or a skilled human operator in many fields. Various attempts have been made in optimizing its structure using genetic algorithm automated designs. This paper presents a new approach to structurally optimized designs of FNN models. The messy genetic algorithm is used to obtain structurally optimized fuzzy neural network models. Structural optimization is regarded important before neural network based learning is switched into. We have applied the method to the problem of a time series estimation.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.772-774
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• 1998

MMIS System of KNGR is composed of systems by using of digital equipment in general, and also interface function among systems is completed by a network. According to this, KNGR PCS also got rid of many kinds of interface cards which have been used for hardwired interface to outside system, and most of function in these cards is to be programmed by PLC. This paper defines the function and method which is to be programmed to PLC. And this paper presents new function which is to be added for operator's interface by using network. It is expected that PCS logic cabinet will be more simplified, satisfy KNGR design concept, and operation convenience will be increased.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.446-449
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• 2001

A LLU system is developed for efficient use of existing local loop. By this system, new entrant ran use the local loop indifferently comparing with incumbent telecommunications operator. To implement the LLU, bottom-up typed LRIC model by network redesigning was accepted for costing system in Korea. In this paper, local loop redesigning methodology is presented to build a bottom-up typed LRIC model.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04a
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• pp.472-474
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• 2001

인터넷 사용자들의 요구에 부응하기 위해 인터넷 트래픽은 점차 멀티미디어화, 고대역화로 나아가고 있다. 이에 따라 초고속 통신망의 구축이 전 세계적으로 활발히 이루어지고 있으며 고속의 백본망에서 고속 라우터의 중요성이 대두되었다. 고속 라우터는 차세대 인터넷 환경의 핵심장비이며, 고속 라우터를 안정적이며 효율적으로 관리하는 것이 중요한 문제로 대두되고 있다. 아울러 현재 고속 라우터에서 제공하는 다양한 응용 서비스를 효율적이고, 편리한 방법으로 제공할 수 있도록 해주는 망관리 매니저의 역할이 중요하다. 본 논문에서는 고속 라우터 시스템의 Web 기반 SNMP(Simple Network Management Protocol) 망관리 메니저 기능과 EWS(Embedded Web Server)을 이용한 운용자 정합기능을 설계하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.40-48
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• 2023

Speech Emotions recognition has become the active research theme in speech processing and in applications based on human-machine interaction. In this work, our system is a two-stage approach, namely feature extraction and classification engine. Firstly, two sets of feature are investigated which are: the first one is extracting only 13 Mel-frequency Cepstral Coefficient (MFCC) from emotional speech samples and the second one is applying features fusions between the three features: Zero Crossing Rate (ZCR), Teager Energy Operator (TEO), and Harmonic to Noise Rate (HNR) and MFCC features. Secondly, we use two types of classification techniques which are: the Support Vector Machines (SVM) and the k-Nearest Neighbor (k-NN) to show the performance between them. Besides that, we investigate the importance of the recent advances in machine learning including the deep kernel learning. A large set of experiments are conducted on Surrey Audio-Visual Expressed Emotion (SAVEE) dataset for seven emotions. The results of our experiments showed given good accuracy compared with the previous studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1894-1915
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• 2023

Software-Defined Networking (SDN) offers several advantages in dynamic routing, flexible programmable control and custom application-driven network management. However, the programmability of the data plane in traditional SDN is limited. A network operator cannot change the ability of the data plane and perform complex packet processing on the data plane, which limits the flexibility and extendibility of SDN. In the paper, AP-SDN (Action Program enabled Software-Defined Networking) architecture is proposed, which extends the action set of SDN data plane. In the proposed architecture, a modified Open vSwitch is utilized in the data plane allowing the execution of action programs at runtime, thus enabling complex packet processing. An example action program is also implemented which transparently encrypts traffic for terminals. At last, a prototype system of AP-SDN is developed and experiments show its effectiveness and performance.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.268-276
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• 2005

This paper deals with optimum communication spanning tree(OCST) problem. Generally, OCST problem is known as NP-hard problem and recently, it is reveled as MAX SNP hard by Papadimitriou and Yannakakis. Nevertheless, many researchers have used polynomial approximation algorithm for solving this problem. This paper uses evolutionary algorithm. Especially, when an evolutionary algorithm is applied to tree network problem such as the OCST problem, representation and genetic operator should be considered simultaneously because they affect greatly the performance of algorithm. So, we introduce a new representation method to improve the weakness of previous representation which is proposed for solving the degree constrained minimum spanning tree problem. And we also propose a new decoding method to generate a reliable tree using the proposed representation. And then, for finding a suitable genetic operator which works well on the proposed representation, we tested three kinds of genetic operators using the information of network or the genetic information of parents. Consequently, we could confirm that the proposed method gives better results than the previous methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.102-110
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• 2015

The main source of noise in computed tomography (CT) images is a quantum noise, which results from statistical fluctuations of X-ray quanta reaching the detector. This paper proposes a neural network (NN) based hybrid filter for removing quantum noise. The proposed filter consists of bilateral filters (BFs), a single or multiple neural edge enhancer(s) (NEE), and a neural filter (NF) to combine them. The BFs take into account the difference in value from the neighbors, to preserve edges while smoothing. The NEE is used to clearly enhance the desired edges from noisy images. The NF acts like a fusion operator, and attempts to construct an enhanced output image. Several measurements are used to evaluate the image quality, like the root mean square error (RMSE), the improvement in signal to noise ratio (ISNR), the standard deviation ratio (MSR), and the contrast to noise ratio (CNR). Also, the modulation transfer function (MTF) is used as a means of determining how well the edge structure is preserved. In terms of all those measurements and means, the proposed filter shows better performance than the guided filter, and the nonlocal means (NLM) filter. In addition, there is no severe restriction to select the number of inputs for the fusion operator differently from the neuro-fuzzy system. Therefore, without concerning too much about the filter selection for fusion, one could apply the proposed hybrid filter to various images with different modalities, once the corresponding noise characteristics are explored.