• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1952-1953
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• 2011

본 논문에서는 다항식 기반 Radial Basis Function(RBF)신경회로망(Polynomial based Radial Basis function Neural Network)을 설계하고 이를 n-클래스 패턴 분류 문제에 적용한다. 제안된 다항식기반 RBF 신경회로망은 입력층, 은닉층, 출력층으로 이루어진다. 입력층은 입력 벡터의 값들을 은닉층으로 전달하는 기능을 수행하고 은닉층과 출력층사이의 연결가중치는 상수, 선형식 또는 이차식으로 이루어지며 경사 하강법에 의해 학습된다. Networks의 최종 출력은 연결가중치와 은닉층 출력의 곱에 의해 퍼지추론의 결과로서 얻어진다. 패턴분류기의 최적화는 PSO(Particle Swarm Optimization)알고리즘을 통해 이루어진다. 그리고 제안된 패턴분류기는 실제 얼굴인식 시스템으로 응용하여 직접 CCD 카메라로부터 입력받은 데이터를 영상 보정, 얼굴 검출, 특징 추출 등과 같은 처리 과정을 포함하여 서로 다른 등록인물의 n-클래스 분류 문제에 적용 및 평가되어 분류기로써의 성능을 분석해본다.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.608-612
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• 2006

센서네트워크는 유비쿼터스 컴퓨팅 기술의 핵심으로 자리 잡아가고 있다. 이러한 센서네트워크는 네트워크가 갖는 특성으로 인하여 일반 네트워크보다 보안에 취약하므로 안전한 통신을 위하여 센서 노드 간 키를 설정하는 것은 보안을 위한 기본적인 요구사항이 되고 있다. 센서네트워크를 클러스터링하여 각 클러스터별로 유일한 다항식을 할당하는 클러스터기반 키 분배에서 클러스터 헤더가 할당하는 다항식 부분정보(polynomial share)인 일변수 다항식 정보는 키 분배시 도청에 의하여 클러스터 헤더의 키인 이변수 다항식을 계산할 수 있었다. 따라서 본 논문에서는 클러스터헤더가 동일한 클러스터내의 센서노드들에게 다항식 부분정보를 배분할 때 다항식을 계산한 상수값을 분배함으로써 클러스터에 도청으로부터 다항식을 알아낼 수 없게 하여 센서노드 간 안전한 통신을 할 수 있게 하였다.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.1
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• pp.101-121
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• 1997

Consider a Jackson type closed queueing network in which each queue has a single exponential server. Assume that N customers are moving among .kappa. queues. We propose a candidata procedure which yields a lower bound of the network throughput which is sharper than those which are currently available : Let (.rho.$_{1}$, ... .rho.$_{\kappa}$) be the loading vector, let x be a real number with 0 .leq. x .leq. N, and let y(x) denote that y is a function of x and be the unique positive solution of the equation. .sum.$_{i = 1}$$^{\kappa}$y(x) .rho.$_{i}$ (N - y(x) x $p_{i}$ ) = 1 Whitt [17] has shown that y(N) is a lower bound for the throughput. In this paper, we present evidence that y(N -1) is also a lower bound. In dosing so, we are led to formulate a rather general conjecture on 'quot;Migrating Critical Points'quot; (MCP). The .MCP. conjecture asserts that zeros of successive derivatives of certain rational functions migrate at an accelerating rate. We provide a proof of MCP in the polynomial case and some other special cases, including that in which the rational function has exactly two real poles and fewer than three real zeros.tion has exactly two real poles and fewer than three real zeros.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2313-2321
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• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.1883-1892
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• 1998

This paper considers the efficient design of routing path and wavelength assignment asignment in the sigle-hop WDM optical transport networks. The connecton demands between node-pairs are given and a connection must be made by only one lightpath. It is assumed that no wavelength conversion is allowed and the physical topology of the network is given. This paper proposes a method to find the routes of lightpaths and assign wavelengths to the routes, which minimizes the number of total wavelength to satisfy all connection demands. We establish a new optimization model that finds the minimum number of wavelengths. A heuristic algorithm with polynomial iterations is developed for the problem. The algorithm is implemented and applied to the netowrks with real problem size. The results of the application are compared with the commericial optimization solver, GAMS/OSL and Wauters & Demeester [8].

• Geomechanics and Engineering
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• v.1 no.1
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• pp.53-74
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• 2009

In recent years, several computer-aided pattern recognition and data mining techniques have been developed for modeling of soil behavior. The main idea behind a pattern recognition system is that it learns adaptively from experience and is able to provide predictions for new cases. Artificial neural networks are the most widely used pattern recognition methods that have been utilized to model soil behavior. Recently, the authors have pioneered the application of genetic programming (GP) and evolutionary polynomial regression (EPR) techniques for modeling of soils and a number of other geotechnical applications. The paper reviews applications of pattern recognition and data mining systems in geotechnical engineering with particular reference to constitutive modeling of soils. It covers applications of artificial neural network, genetic programming and evolutionary programming approaches for soil modeling. It is suggested that these systems could be developed as efficient tools for modeling of soils and analysis of geotechnical engineering problems, especially for cases where the behavior is too complex and conventional models are unable to effectively describe various aspects of the behavior. It is also recognized that these techniques are complementary to conventional soil models rather than a substitute to them.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.73-78
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• 2019

This paper proposes polynomial-time rule for maximum weighted independent set(MWIS) problem that is well known NP-hard. The well known distributed algorithm selects the maximum weighted node as a element of independent set in a local. But the merged independent nodes with less weighted nodes have more weights than maximum weighted node are frequently occur. In this case, existing algorithm fails to get the optimal solution. To deal with these problems, this paper constructs maximum weighted independent set in local area. Application result of proposed algorithm to various networks, this algorithm can be get the optimal solution that fail to existing algorithm.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.177-182
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• 2022

A deep neural network (DNN) includes variables whose values keep on changing with the training process until it reaches the final point of convergence. These variables are the co-efficient of a polynomial expression to relate to the feature extraction process. In general, DNNs work in multiple 'dimensions' depending upon the number of channels and batches accounted for training. However, after the execution of feature extraction and before entering the SoftMax or other classifier, there is a conversion of features from multiple N-dimensions to a single vector form, where 'N' represents the number of activation channels. This usually happens in a Fully connected layer (FCL) or a dense layer. This reduced 2D feature is the subject of study for our analysis. For this, we have used the FCL, so the trained weights of this FCL will be used for the weight-class correlation analysis. The popular DNN models selected for our study are ResNet-101, VGG-19, and GoogleNet. These models' weights are directly used for fine-tuning (with all trained weights initially transferred) and scratch trained (with no weights transferred). Then the comparison is done by plotting the graph of feature distribution and the final FCL weights.

• ETRI Journal
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• v.44 no.5
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• pp.746-758
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• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.467-478
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• 2016

In this paper, we try to find the optimal locations of NeNB(Nomadic evolved NodeB)s for maximizing the overall throughput of the PS-LTE networks. Since finding optimal locations of all NeNBs in a given area is NP-hard(Non-deterministic Polynomial time-hard) problem, we proposed a PSO-based heuristic approach. In order to evaluate the performance, we conducted two experiments. We compared performance with other schemes such as Exhaustive Search, Random Walk Search, and locating neighboring NeNBs with the same NeNB-to-NeNB distance. The proposed method showed the similar results to the exhaustive search method in terms of locating optimal position and user's data throughput. The proposed method, however, has the fast and consistent convergence time.