• ETRI Journal
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• 제39권6호
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• pp.841-850
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• 2017

We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

• 한국통신학회논문지
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• 제13권5호
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• pp.379-389
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• 1988

본 논문에서는 통신방의 2-상태 스취칭 실패함수를 구하기 위한 알고리즘과 단섭간 신뢰도 계성방법을 제시하였다. 통신망을 그래프로 모형화하고, 이 그래프에 대한 일련의 행열변환을 함으로서 모든 통신통로를 차단하는 최초컷-\ulcornerV 행열을 구하였으며 최소 컷-\ulcornerV행열로부터 2-상태 스위칭 실패함수를 구하였다. 또한 2-상태 스위칭 실패함수에 확률변수를 대응시켜 통신망의 단점간 신뢰도를 계정하였다. 제안된 알고리즘을 설명하기 위하여 몇가지 예를 보였으며 복잡하고 규모가 큰 망에 대한 단점간 신뢰도를 계정하기 위해서 전산기 프로그램을 제시하였다.

• 한국지능시스템학회논문지
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• 제13권5호
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• pp.606-613
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• 2003

As a technical method for controlling chaotic dynamics, this paper presents a predictive control for chaotic systems based on radial basis function networks(RBFNs). To control the chaotic systems, we employ an on-line identification unit and a nonlinear feedback controller, where the RBFN identifier is based on a suitable NARMA real-time modeling method and the controller is predictive control scheme. In our design method, the identifier and controller are most conveniently implemented using a gradient-descent procedure that represents a generalization of the least mean square(LMS) algorithm. Also, we introduce a projection matrix to determine the control input, which decreases the control performance function very rapidly. And the effectiveness and feasibility of the proposed control method is demonstrated with application to the continuous-time and discrete-time chaotic nonlinear system.

• 대한안전경영과학회지
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• 제22권4호
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• pp.45-52
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• 2020

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

• 정보처리학회논문지D
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• 제16D권4호
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• pp.631-642
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• 2009

정보화에 대한 꾸준한 정책적 지원과 투자를 통하여 우리나라의 전자정부는 세계 6위 수준으로 평가되는 등 괄목할 성과를 이루었으나, 각 부처별로 정보시스템을 구축 운영함으로써 중복투자 및 정보자원 공동 활용 미흡 등의 문제가 발생되고 있다. 따라서 전자정부 시스템 정보자원에 대한 통합적 관리노력을 추진 중이나, 현재 시스템 아키텍처에 대한 서술 방식이 상이하여 이해관계자간의 의사소통을 저해하는 요인으로 작용하고 있다. 이에 따라 공동활용 가능한 정보자원을 명확히 식별할 수 있는 시스템 아키텍처의 표준적인 서술(standardized architecture description) 방안 수립이 절실히 요구된다. 본 연구에서는 전자정부 시스템 아키텍처의 표준적 서술 기반을 제공하는 기능-네트워크 매트릭스 모형(function-network matrix model)을 제안하였다. 기능-네트워크 매트릭스는 기능계층 구분을 열로, 네트워크 영역 구분을 행으로 나타내어, 기능계층과 네트워크 영역을 통합하여 표현할 수 있는 모형이다. 기능-네트워크 매트릭스를 활용하여 전자정부 민원행정시스템 및 행정업무시스템을 대상으로 적용한 사례를 제시하였다. 이를 통하여 서로 다른 시스템간의 아키텍처의 공통점과 차이점을 명확히 파악하고, 시스템간 정보자원의 공동활용 및 시스템 통합을 위한 정보자원 식별의 가능성을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.686-710
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• 2019

This work proposes a model to solve the problem of Network Coding over a one-session multicast network. The model is based on a system of restrictions that defines the packet flows received in the sink nodes as functions of the outgoing flows from the source node. A multicast network graph is used to derive a directed labeled line graph (DLLG). The successive powers of the DLLG adjacency matrix to the convergence in the null matrix permits the construction of the jump matrix Source-Sinks. In its reduced form, this shows the dependency of the incoming flows in the sink nodes as a function of the outgoing flows in the source node. The emerging packets for each outgoing link from the source node are marked with a tag that is a linear combination of variables that corresponds to powers of two. Restrictions are built based on the dependence of the outgoing and incoming flows and the packet tags as variables. The linear independence of the incoming flows to the sink nodes is mandatory. The method is novel because the solution is independent of the Galois field size where the packet contents are defined.

• International Journal of Control, Automation, and Systems
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• 제6권6호
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• pp.915-927
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• 2008

Networked control systems(NCSs) have gained increasing attention in recent years due to their advantages and potential applications. The network Quality-of-Service(QoS) in NCSs always fluctuates due to changes of the traffic load and available network resources. To handle the network QoS variations problem, this paper presents an intelligent scheduling control method for NCSs, where the sampling period and the control parameters are simultaneously scheduled to compensate the effect of QoS variation on NCSs performance. For NCSs with network-induced delays and packet dropouts, a discrete-time switch model is proposed. By defining a sampling-period-dependent Lyapunov function and a common quadratic Lyapunov function, the stability conditions are derived for NCSs in terms of linear matrix inequalities(LMIs). Based on the obtained stability conditions, the corresponding controller design problem is solved and the performance optimization problem is also investigated. Simulation results are given to demonstrate the effectiveness of the proposed approaches.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.436-439
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• 1995

In this paper we present a method to construct fuzzy model with multi-dimension input membership function, which can construct fuzzy inference system on one node of the network directly. This method comes from a common framework called Universal Learning Network (ULN). The fuzzy model under the framework of ULN is called Universal Learning Network-based Fuzzy Inference System (ULNFIS), which possesses certain advantages over other networks such as neural network. We also introduce how to imitate a real system with ULN and a control scheme using ULNFIS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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• pp.3701-3727
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• 2016

Recently Service Function Chaining (SFC) is promising to innovate the network service mode in modern networks. However, a feasible implementation of SFC is still difficult due to the need to achieve functional equivalence with traditional modes without sacrificing performance or increasing network complexity. In this paper, we present a configurable network service chaining (CNSC) mechanism to provide services for network traffics in a flexible and optimal way. Firstly, we formulate the problem of network service chaining and design an effective service chain construction framework based on integrating software-defined networking (SDN) with network functions virtualization (NFV). Then, we model the service path computation problem as an integer liner optimization problem and propose an algorithm named SPCM to cooperatively combine service function instances with a network utility maximum policy. In the procedure of SPCM, we achieve the service node mapping by defining a service capacity matrix for substrate nodes, and work out the optimal link mapping policies with segment routing. Finally, the simulation results indicate that the average request acceptance ratio and resources utilization ratio can reach above 85% and 75% by our SPCM algorithm, respectively. Upon the prototype system, it is demonstrated that CNSC outperforms other approaches and can provide flexible and scalable network services.