• 대한안전경영과학회지
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• 제16권2호
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• pp.43-52
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• 2014

In weapon systems development, live fire tests have been frequently adopted to evaluate the performance of the systems under development. Therefore, it is necessary to ensure safety in the test ranges where the live fire tests can cause serious hazards. During the tests, a special care must be taken to protect the test and evaluation (T&E) personnel and also test assets from potential danger and hazards. Thus, the development and management of the range safety process is quite important in the tests of guided missiles and artillery considering the explosive power of the destruction. Note also that with a newly evolving era of weapon systems such as laser, EMP and non-lethal weapons, the test procedure for such systems is very complex. Therefore, keeping the safety level in the test ranges is getting more difficult due to the increased unpredictability for unknown hazards. The objective of this paper is to study on how to enhance the safety in the test ranges. To do so, an approach is proposed based on model-based systems engineering (MBSE). Specifically, a functional architecture is derived utilizing the MBSE method for the design of the range safety process under the condition that the derived architecture must satisfy both the complex test situation and the safety requirements. The architecture developed in the paper has also been investigated by simulation using a computer-aided systems engineering tool. The systematic application of this study in weapon live tests is expected to reduce unexpected hazards and test design time. Our approach is intended to be a trial to get closer to the recent theme in T&E community, "Testing at the speed of stakeholder's need and rapid requirement for rapid acquisition."

• 한국CDE학회논문집
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• 제1권2호
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• pp.133-149
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• 1996

In order to reduce the trial-and-errors in design and production of injection molded plastic parts, there has been much research effort not only on CAE systems which simulate the injection molding process, but also on CAD systems which support initial design and re-design of plastic parts and their molds. The CAD systems and CAE systems have been developed independently with being built on different basis. That is, CAD systems manipulate the part shapes and the design features in a complete solid model, while CAE systems work on shell meshes generated on the abstract sheet model or medial surface of the part. Therefore, it is required to support the two types of geometric models and feature information in one environment to integrate CAD and CAE systems for accelerating the design speed. A feature-based non-manifold geometric modeling system has been developed to provide an integrated environment for design and analysis of injection molding products. In this system, the geometric models for CAD and CAE systems are represented by a non-manifold boundary representation and they are merged into a single geometric model. The suitable form of geometric model for any application can be extracted from this model. In addition, the feature deletion and interaction problem of the feature-based design system has been solved clearly by introducing the non-manifold Boolean operation based on 'merge and selection' algorithm. The sheet modeling capabilities were also developed for easy modeling of thin plastic parts.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.525-525
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• 2000

In this paper, we present recursive algorithms for state space model identification using subspace extraction via Schur complement. It is shown that an estimate of the extended observability matrix can be obtained by subspace extraction via Schur complement. A relationship between the least squares residual and the Schur complement matrix obtained from input-output data is shown, and the recursive algorithms for the subspace-based state-space model identification (4SID) methods are developed. We also proposed the above algorithm for an instrumental variable (IV) based 4SID method. Finally, a numerical example of the application of the algorithms is illustrated.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.41-43
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• 2003

A radar scattering model is developed based on an empirical rough surface scattering model, the radiative transfer model (RTM), a numerical simulation algorithm of radar scattering from particles, and experimental data obtained by ground-based scatterometers and SAR systems. At first, the scattering matrices of scattering particles such as a leaf, a branch, and a trunk, have been modeled using the physical optics (PO) model and the numerical full-wave analysis. Then, radar scattering from a group of mixed particles has been modeled using the RTM, which leads to a general scattering model for earth surfaces. Finally, the scattering model has been verified with the experimental data obtained by scatterometers and SAR systems.

• 대한임베디드공학회논문지
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• 제4권4호
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• pp.164-173
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• 2009

Automotive systems have tended to be equipped with many electronic contents to satisfy safety, comport, convenience, and entertainment services over the past years. As a result, the amount of vehicle embedded software in electrical/electronic(E/E) systems is steadily increasing to manage these requirements. This leads to the traditional, document-based software development in the vehicle embedded systems being increasingly displaced by a model-based development in order to reduce software development time and cost. Due to the application of model-based development, a great evolution is being realized in the aspect of efficiency, but the development is being made without sufficient testing. So, erroneous automotive embedded software may cause serious problems such as car accidents which relate to human safety. Therefore, efficient methods for model-based test and validation are needed to improve software reliability in the stage of embedded software development. This paper presents the model-based development and test method for AUTOSAR embedded software to improve its reliability and safety, and it is demonstrated based on the case study.

• 한국컴퓨터정보학회논문지
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• 제21권3호
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• pp.65-71
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• 2016

To reduce the expenses for development a novel drug, systems biology has been studied actively. Target prediction, a part of systems biology, contributes to finding a new purpose for FDA(Food and Drug Administration) approved drugs and development novel drugs. In this paper, we propose a classification model for predicting novel target genes based on relation between target genes and disease related genes. After collecting known target genes from TTD(Therapeutic Target Database) and disease related genes from OMIM(Online Mendelian Inheritance in Man), we analyzed the effect of target genes on disease related genes based on PPI(Protein-Protein Interactions) network. We focused on the distinguishing characteristics between known target genes and random target genes, and used the characteristics as features for building a classifier. Because our model is constructed using information about only a disease and its known targets, the model can be applied to unusual diseases without similar drugs and diseases, while existing models for finding new drug-disease associations are based on drug-drug similarity and disease-disease similarity. We validated accuracy of the model using LOOCV of ten times and the AUCs were 0.74 on Alzheimer's disease and 0.71 on Breast cancer.

• 제어로봇시스템학회논문지
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• 제4권1호
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• pp.76-81
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• 1998

This paper presents an on-line scheme for parameter estimation of continuous-time systems, based on the model adjustment technique and the genetic algorithm technique. To deal with the initialisation and unmeasurable signal problems in on-line parameter estimation of continuous-time systems, a discrete-time model is obtained for the linear differential equation model and approximations of unmeasurable states with the observable output and its time-delayed values are obtained for the nonlinear state space model. Noisy observations may affect these approximation processes and degrade the estimation performance. A digital prefilter is therefore incorporated to avoid direct approximations of system derivatives from possible noisy observations. The parameters of both the model and the designed filter are adjusted on-line by a genetic algorithm, A set of simulation works for linear and nonlinear systems is carried out to demonstrate the effectiveness of the proposed method.

• 제어로봇시스템학회논문지
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• 제2권3호
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• pp.148-157
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• 1996

Availability of input trajectories corresponding to desired output trajectories is often important in designing control systems for batch and other transient processes. In this paper, we propose a predictive control-type model-based iterative learning algorithm which is applicable to finding the nominal input trajectories of a linear time-invariant batch process. Unlike the other existing learning control algorithms, the proposed algorithm can be applied to nonsquare systems and has an ability to adjust noise sensitivity as well as convergence rate. A simple model identification technique with which performance of the proposed learning algorithm can be significantly enhanced is also proposed. Performance of the proposed learning algorithm is demonstrated through numerical simulations.

• 제어로봇시스템학회논문지
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• 제7권7호
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• pp.632-639
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• 2001

This paper proposes an improved architecture of web-based monitoring systems for monitor of processes in plants from the soft real-time point of view. The suggested model is designed to be able to guarantee the temporal and spatial consistency and transmit the monitoring data periodically via the intranet and the Internet. The model generates one thread for monitoring management, one DB thread, one common memory, and corresponding monitoring threads to clients. The monitoring thread is executed during the smaller time than the execution time of the process used in the conventional methods such as CGI and servlet method. The Java API for the server API, VRML, EAI(External Authoring Interface) and Java Applets for efficient dimensional WEB monitoring are used. The proposed model is implemented and tested for a FMS plant, Some examples show that the proposed model is useful one.

• 드라이브 ㆍ 컨트롤
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• 제19권1호
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• pp.51-61
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• 2022

This paper presents a model-free system based on a framework of a backstepping sliding mode control (BSMC) with a radial basis function neural network (RBFNN) and adaptive mechanism for electro-hydraulic systems (EHSs). First, an EHS mathematical model was dedicatedly derived to understand the system behavior. Based on the system structure, BSMC was employed to satisfy the output performance. Due to the highly nonlinear characteristics and the presence of parametric uncertainties, a model-free approximator based on an RBFNN was developed to compensate for the EHS dynamics, thus addressing the difficulty in the requirement of system information. Adaptive laws based on the actor-critic neural network (ACNN) were implemented to suppress the existing error in the approximation and satisfy system qualification. The stability of the closed-loop system was theoretically proven by the Lyapunov function. To evaluate the effectiveness of the proposed algorithm, proportional-integrated-derivative (PID) and improved PID with ACNN (ACPID), which are considered two complete model-free methods, and adaptive backstepping sliding mode control, considered an ideal model-based method with the same adaptive laws, were used as two benchmark control strategies in a comparative simulation. The simulated results validated the superiority of the proposed algorithm in achieving nearly the same performance as the ideal adaptive BSMC.