• ETRI Journal
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• 제15권2호
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• 한국IT서비스학회지
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• 제17권3호
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• pp.171-189
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• 2018

To remove software defects and improve performance of software, many developers perform code inspections and use static analysis tools. A code inspection is an activity that is performed manually to detect software defects in the developed source. However, there is no clear criterion which source codes are inspected. A static analysis tool can automatically detect software defects by analyzing the source codes without running the source codes. However, it has disadvantage that analyzes only the codes in the functions without analyzing the relations among source functions. The functions in the source codes are interconnected and formed a social network. Functions that occupy critical locations in a network can be important enough to affect the overall quality. Whereas, a static analysis tool merely suggests which functions were called several times. In this study, the core functions will be elicited by using social network analysis and DEA (Data Envelopment Analysis) for CUBRID open database sources. In addition, we will suggest clear criteria for selecting the target sources for code inspection and will suggest ways to find core functions to minimize defects and improve performance.

• 지식경영연구
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• 제17권3호
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• pp.23-44
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• 2016

This study examined the cause-and-effect relationships among developmental network characteristics, developmental functions, and career identity. The study analyzed the data of employees with more than two years of experience and a college degree or over qualifications, from domestic companies. For analysis, the structural equation model was applied. Most of all, it was substantiated that the structural relationships among the developmental network characteristics, developmental functions, and career identity. The specific results were as follows. First, tie strength had a positive effect on all of developmental functions but psychosocial support mediated the relationship between the tie strength and career identity. Second, an indirect relationship between indicators of network diversity and identity was not found or the indirect relationship was very weak.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.49-55
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• 2018

In this paper, we propose schemes of blockchain utilization in tactical communication environment. The military tactical communication environment has similar characteristics with blockchain network such as distributed architecture, decentralization, and the need for data integrity. A communication node constituting a tactical communication network is constituted by a system capable of configuring and connecting a network for each node. When a communication node, having such capabilities, is configured as a node of blockchain network, various functions could be performed. In this paper, we propose utilization schemes of authentication, integrity, record management, and privilege control based blockchain technology. Functions for authentication, integrity verification, and record management need to ensure the stored data and could track history. The requirement of function's characteristics are matched to blockchain which is storing data sequentially and difficult to hack data, so that it could perform functionally and sufficiently well. Functions for authority control should be able to assign different privileges according to the state of the requestor. Smart contract will function when certain conditions are satisfied and it will be able to perform its functions by using it. In this paper, we will look over functions and utilization schemes of blockchain technology which could reliably share and synchronize data in a tactical communication environment composed of distributed network environment.

• Transactions on Control, Automation and Systems Engineering
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• 제2권3호
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• pp.169-174
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• 2000

In this paper, Haar wavelet-based neural network is described for the identification and control of discrete-time nonlinear dynamical systems. Wavelets are suited to depict functions with local nonlinearities and fast variations because of their intrinsic properties of finite support and self-similarity. Due to the orthonormal properties of Haar wavelet functions, wavelet neural networks result in a greatly simplified training problem. This wavelet-based scheme performs adaptively both the identification of nonlinear functions and the control of the overall system, while the multilayer neural network is applied to the control system just after its sufficient learning of the unknown functions. Simulation shows that the wavelet network can be a good alternative to a multilayer neural network with backpropagation.

• Journal of Applied and Pure Mathematics
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• 제4권5_6호
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• pp.269-286
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• 2022

In this article we exhibit univariate and multivariate quantitative approximation by Kantorovich-Choquet type quasi-interpolation neural network operators with respect to supremum norm. This is done with rates using the first univariate and multivariate moduli of continuity. We approximate continuous and bounded functions on ℝ^N , N ∈ ℕ. When they are also uniformly continuous we have pointwise and uniform convergences. Our activation functions are induced by the arctangent, algebraic, Gudermannian and generalized symmetrical sigmoid functions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 추계학술대회 논문집 학회본부
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• pp.142-145
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• 1997

This paper presents an modified orthogonal neural network(MONN) based on orthogonal functions and applies the network to nonlinear system control. The accuracy of orthogonal neural network is essentially dependent on the choice of basic orthogonal functions. Modified orthogonal neural network is modified model of orthogonal neural network with input transformation to adapt its basic orthogonal functions. The results show that the modified orthogonal neural network has the excellent performance of approximating and controlling nonlinear systems and the input transformation make the ability of modified orthogoneural neural network better than one of orthogonal neural network.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권2호
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• pp.211-216
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• 2004

Fuzzy neural networks have been successfully applied to analyze/generate predictive rules for medical or diagnostic data. However, most approaches proposed so far have not considered the weights for the membership functions much. This paper presents a neural network with weighted fuzzy membership functions. In our approach, the membership functions can capture the concentrated and essential information that affects the classification of the input patterns. To verify the performance of the proposed model, well-known Iris data set is performed. According to the results, the weighted membership functions enhance the prediction accuracy. The architecture of the proposed neural network with weighted fuzzy membership functions and the details of experimental results for the data set is discussed in this paper.

• 디지털산업정보학회논문지
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• 제9권4호
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• pp.1-10
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• 2013

Recently many documents have been written by HTML due to the expansion of the Internet and WWW. HTML allows scripting languages to interact between an user and an browser. Sometimes there might have problems with some functions of the script language. Some functions have the same name although the different contents and vice versa. It presents tremendous difficulties to maintain the functions. And, a file includes many functions shared among documents. If a user tries to see a HTML document, the file will be sent to the web browser in the user system. The network resource is wasted due to moving all data in the file through the network although some functions in the file is not used. So, we should design and implement the code library of a script language with a database. Each function has the name and arguments. A system of the code library manages functions with them. The system has the functions consistency and reduces the network resource consumption because there are only necessary functions in documents. Beside it is easy for the system to modify and reuse the functions.

• 방송공학회논문지
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• 제17권1호
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• pp.37-48
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• 2012

최근 홈 네트워크 시장에서는 가전 업체를 중심으로 DLNA(Digital Living Network Alliance) 기술 기반 홈 미디어 네트워크 서비스가 활발히 제공되기 시작하고 있다. 현재 DLNA 미디어 기기와 함께 일반적으로 제공되는 DLNA 네트워킹 소프트웨어는 서버 기능, 렌더러 기능 및 디바이스 제어 기능 모두를 포함한 통합 소프트웨어의 성격이 강하다. 그러나 홈 미디어 네트워크 환경에서는 하나의 기기가 여러 기능을 동시에 내장할 필요가 없고 자주 사용하는 기능이 정해져 있는 경우가 많다. 스마트TV의 재생 기능, NAS(Network Attached Storage)의 미디어 저장 기능과 같이 각각의 기기가 특정 역할을 수행하는 홈 미디어 네트워크 환경에서 DLNA 통합 소프트웨어 활용은 오히려 네트워킹 및 프로세싱 측면에서 낭비를 유발한다. 본 논문에서는 현재의 통합 기능 DLNA 네트워킹 소프트웨어를 분석하고, DLNA 디바이스 클래스를 근거로 홈 미디어 기기의 자원을 효율적으로 이용하는 기기 별 DLNA 소프트웨어 모델을 제시한다. 또한 제시한 DLNA 소프트웨어 모델 중 스마트TV 및 스마트폰 용 DLNA 소프트웨어를 구현해 일반적인 DLNA 네트워킹 소프트웨어와의 자원 효율성을 비교한다.