• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.572-574
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• 2011

본 논문에서는 USN(Ubiquitous Sensor Network) 환경에서 많이 활용되는 운영체제로 국내외 많은 연구기관, 대학, 기업체에서 활용하고 있는 TinyOS와 지능형 무선센서에 대해서 살펴보고, 이를 제어하기 위해 활용되는 nesC 프로그래밍 기법 및 특성을 살펴보았다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 심포지엄 논문집 정보 및 제어부문
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• pp.98-101
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• 2004

Linear Programming(LP) is the term used for defining a wide range of optimization problems in which the objective function to be minimized or maximized is linear in the unknown variables and the constraints are a combination of linear equalities and inequalities. LP problems occur in many real-life economic situations where profits are to be maximized or costs minimized with constraint limits on resources. While the simplex method introduced in a later reference can be used for hand solution of LP problems, computer use becomes necessary even for a small number of variables. Problems involving diet decisions, transportation, production and manufacturing, product mix, engineering limit analysis in design, airline scheduling, and so on are solved using computers. This technique is called Sequential Linear Programming (SLP). This paper describes LP's problems and solves a LP's problems using the neural networks.

• Geomechanics and Engineering
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• 제18권4호
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• pp.373-389
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• 2019

It is important to study the problem of durability for tunnel structures. As a main influence on the durability of tunnel structures, carbonation-induced corrosion is studied. For the complicated environment of tunnel structures, based on the data samples from real engineering examples, the intelligent method (genetic programming) is used to construct the service life prediction model of tunnel structures. Based on the model, the prediction of service life for tunnel structures in carbonation environments is studied. Using the data samples from some tunnel engineering examples in China under carbonation environment, the proposed method is verified. In addition, the performance of the proposed prediction model is compared with that of the artificial neural network method. Finally, the effect of two main controlling parameters, the population size and sample size, on the performance of the prediction model by genetic programming is analyzed in detail.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• 한국관개배수논문집
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• 제3권1호
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• pp.32-39
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• 1996

Analyses of flow rates and pressures in pipe networks are crucial in determining pipe diameters. In this study the nonlinear programming has been applied to compute pipe Hon in a looped pipe network. The required theoretical background is described to app

• Korean Journal of Mathematics
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• 제4권1호
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• pp.7-16
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• 1996

The optimization problems with quadratic constraints often appear in various fields such as network flows and computer tomography. In this paper, we propose an algorithm for solving those problems and prove the convergence of the proposed algorithm.

• 한국위성정보통신학회논문지
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• 제7권3호
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• pp.130-134
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• 2012

본 논문에서는 소형 임베디드 시스템을 홈 네트워크 서비스 제어 시스템으로 활용하여 가정의 전기를 스마트폰을 이용하여 원격으로 제어함으로 에너지를 절약할 수 있는 시스템을 제안한다. 제안 시스템의 구성은 OpenWrt 임베디드 리눅스 기반의 소형 유무선공유기를 임베디드 시스템 개발 플랫폼으로 홈 네트워크 서비스 제어 시스템을 구현하였으며, Android 스마트폰 어플리케이션을 통한 원격 제어 기능 구현하였으며, 가정의 조명 시스템은 기존 인터페이스 보드에 LED를 연결하여 구성하였다. 프로토타입 시스템은 OpenWrt 운영체제가 설치된 Buffalo의 WZR-HP-G450H 유무선공유기, LED를 연결한 Arduino의 Uno 인터페이스 보드, Android 애플리케이션 개발 환경은 한백전자의 HBE-SM5-S4210 개발 키트를 이용하여 구현하였다. 동작 검증은 Android 기반 원격제어를 위한 TCP/IP 프로그래밍, 유무선공유기와 Android 개발 키트 인터페이스를 위한 소켓통신 프로그래밍, 유무선공유기와 인터페이스 보드 연결을 위한 UART 통신 프로그램으로 실행하였다. 구현 결과 소형 유무선공유기를 이용한 저 비용의 홈 네트워크 시스템의 가능성을 보여주었다.

• 한국경영과학회지
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• 제19권2호
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• pp.1-19
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• 1994

A generalized network problem is a special class of linear programming problem whose coefficient matrix contains at most two nonzero elements per column. A generalized network problem with 0-1 flow restrictions is called an integer generalized network(IGN) problem. In this paper, we presented a branch and bound algorithm for the IGN that uses network relaxation. To improve the procedure, we develop various strategies, each of which employs different node selection criterion and/or branching variable selection criterion. We test these solution strategies and compare their efficiencies with LINDO on 70 randomly generated problems.

• 산업경영시스템학회지
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• 제13권21호
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• pp.51-60
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• 1990

In this paper we deal with the miniaml cost network flow problem with uncertain arc capacity constraints. When the arc capacities are fuzzy with linear L-R type membership function, using parametric programming procedure, we reduced it to the deterministic minimal cost network flow problem which can be solved by various typical network flow algorithms. A modified Algorithm using the Out-of-kilter algorithm is developed.

• 정보처리학회 논문지
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• 제13권3호
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• pp.122-129
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• 2024

최근 프로그래밍 교육의 중요성이 강조되고 있지만, 초·중·고교 학생들은 프로그래밍 교육에 어려움을 겪고 있다. 대부분의 프로그래밍 환경이 블록 코딩을 바탕으로 이루어지고 있는데 이는 텍스트 코딩으로의 이행에 방해가 된다. 전통적인 PC 환경도 유지 관리 문제 등 어려움이 있다. 이러한 상황에서 모바일 앱은 대안적 프로그램 교육환경으로 생각해 볼 수 있다. 이 논문에서는 이동형 기기에서 프로그램을 작성할 수 있는 모바일 앱 설계하고 구현하였다. 첫 사례로 Scheme 인터프리터 모바일 앱을 구현하였는데, Scheme은 다중 패러다임 프로그래밍을 지원하는 교육용 언어로 MIT의 프로그래밍 교과에 사용되고 있다. 구현된 앱은 독립형 앱으로 설계되어 네트워크를 사용하지 않아도 된다는 장점이 있다. 벤치마크 수행결과, PC 수행 시간에 대한 안드로이드 기기 수행 시간은 Derivative 벤치마크 131%와 Tak 벤치마크 157%로 나타났다. 또한, 안드로이드 기기에서 벤치마크 프로그램의 수행 시간 최댓값은 Derivative 벤치마크 19.8ms, Tak 벤치마크 131.15ms로 나타났다. 이는 안드로이드 기기를 프로그래밍 교육용으로 선택 시 실습에 큰 제약이 되지 않음을 나타낸다.