• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.240-245
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• 1990

A on-line optimization scheme based on model in a binary distillation process is proposed. A reduced-order model utilized the concept of collocation is used as a process model and the recursive prediction error method is employed to identify the reduced-order model. The concentrations of end products are controlled by nonlinear adaptive predictive control algorithm. The objective function is constructed to find optimum operate condition for saving utility cost. The proposed optimization is scheme is tested through simulation studies in 13-staged water-methanol distillation column.

• 전기학회논문지
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• 제56권2호
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• pp.396-406
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• 2007

In this paper, we proposed development of improved model reduction and design of hierarchical controller using reduction model. The model reduction is considered that it is the transient response and the steady-state response through the use of nyquist curve. The hierarchical controller selected tuning of PID controller to ensure specified gain and phase margin and hybrid smith-predictor fuzzy controller using reduction model. Simulation examples are given to show the better performance of the proposed method than conventional methods.

• 한국시뮬레이션학회논문지
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• 제7권2호
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• pp.63-78
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• 1998

The hierarchical cell structure consists of the macrocell and microcells to increase the system capacity and to achieve broad coverage. The hierarchical cell structure provides services for users in different mobility. In this paper, an analytical queueing model in mobile networks is proposed for the performance evaluation of the hierarchical cell structure. The model for networks with the multiple levels can simplify multi-dimensional ones into one-dimensional queueing model. The computational advantage will be growing as the layers are constructed in multiple levels. The computer simulation is provided for validating the proposed analytical model.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1985년도 하계학술회의논문집
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• pp.127-130
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• 1985

A stochastic model to predict radio interference field as caused by corona discharges on high voltage transmission lines has been developed. This model is based on corona discharge distributed randomly in time and space. A stochastic model for the corona current induced by corona discharges on power lines is proposed. On the basis of the proposed corona current model, a rigorous analysis is presented to evaluate the radio interference (RI) field caused by corona discharges on a single conductor using the stochastic method.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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• pp.802-807
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• 2005

For linear projects, it has long been known that resource utilization is important in improving work efficiency. However, most existing scheduling techniques cannot satisfy the need for solving such issues. This paper presents an optimization model for solving linear scheduling problems involving resource assignment tasks. The proposed model adopts constraint programming (CP) as the searching algorithm for model formulation, and the proposed model is designed to optimize project total cost. Additionally, the concept of outsourcing resources is introduced here to improve project performance.

• 한국지능시스템학회논문지
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• 제15권2호
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• pp.251-256
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• 2005

Up to now, a number of models have been proposed and discussed to describe a wide range of inelastic behaviors of materials. The fetal problem of using such models is however the existence of model errors, and the problem remains inevitably as far as a material model is written explicitly. In this paper, the authors define the implicit constitutive model and propose an implicit viscoplastic constitutive model using neural networks. In their modeling, inelastic material behaviors are generalized in a state space representation and the state space form is constructed by a neural network using input output data sets. A technique to extract the input-output data from experimental data is also described. The proposed model was first generated from pseudo-experimental data created by one of the widely used constitutive models and was found to replace the model well. Then, having been tested with the actual experimental data, the proposed model resulted in a negligible amount of model errors indicating its superiority to all the existing explicit models in accuracy.

• Advances in concrete construction
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• 제1권4호
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• pp.289-304
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• 2013

A reliable concrete constitutive material model is critical for an accurate numerical analysis simulation of reinforced concrete structures under extreme dynamic loadings including impact or blast. However, the formulation of concrete material model is challenging and entails numerous input parameters that must be obtained through experimentation. This paper presents a damage scale analytical model to characterize concrete material for its pre- and post-peak behavior. To formulate the damage scale model, statistical regression and finite element analysis models were developed leveraging twenty existing experimental data sets on concrete compressive strength. Subsequently, the proposed damage scale analytical model was implemented in the finite element analysis simulation of a reinforced concrete pier subjected to vehicle impact loading and the response were compared to available field test data to validate its accuracy. Field test and FEA results were in good agreement. The proposed analytical model was able to reliably predict the concrete behavior including its post-peak softening in the descending branch of the stress-strain curve. The proposed model also resulted in drastic reduction of number of input parameters required for LS-DYNA concrete material models.

• 한국전산구조공학회논문집
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• 제21권1호
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• pp.51-58
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• 2008

본 연구에서는 입자강화 복합재료(particle-reinforced composites)의 거동을 예측하기 위하여 Lee and Pyo(2007)에 의해 제안된 계면손상을 고려한 복합재료의 미세역학 탄성모델과 Karihaloo and Fu(1989)의 미세균열 생성모델을 결합하여, 보강입자의 계면손상(imperfect interface)과 기지 내 미세균열을 고려하여 탄성구성모델(constitutive model)의 거동해석을 수행하였다. 제안된 탄성구성모델의 적용성 검증과 주요손상변수가 거동예측에 미치는 영향을 알아보기 위해 일축 하중 하에서의 응력-변형률 관계를 수치적으로 나타내었다. 또한, 기존의 관련 실험결과와 본 해석결과와의 비교를 통하여 제안된 모델의 정확도를 검증하였다.

• 대한교통학회지
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• 제13권1호
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• pp.185-203
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• 1995

The purpose of this paper is to develop a microscopic traffic simulation model which is able to both analyze and the evaluate signlaized urban network and to verify its usefulness in comparison with the other model which has alfeady been released. This simulation model adopts the General Motor's 5th model for car-following and introduces an unique lanechanging rule using acceptable gap. It analyzes single and dual-ring signal phases and generates detector information . So it could be applied to dynamic route guidance systems as wel as real time signal control systems. The results derived from Netsim and the observed data from the real network have been used to test the validit of the proposed model. The result of the test has shown that there are no significant differences between the NETSIM model and the proposed model in estimating travel speed and stopped delay. In optimum offset estimatin , it has shown the same results with NETSIM. the measure of effectiveness , however, derived from this model is slightly better than that of the real network situation. This may be due to the fact that the proposed model does not take into account side frictions from interferences and obstacles.

• Korean Journal of Radiology
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• 제24권6호
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• pp.541-552
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• 2023

Objective: Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using supine and erect abdominal radiography. Materials and Methods: A model that can utilize "pneumoperitoneum" and "non-pneumoperitoneum" classes was developed through knowledge distillation. To train the proposed model with limited training data and weak labels, it was trained using a recently proposed semi-supervised learning method called distillation for self-supervised and self-train learning (DISTL), which leverages the Vision Transformer. The proposed model was first pre-trained with chest radiographs to utilize common knowledge between modalities, fine-tuned, and self-trained on labeled and unlabeled abdominal radiographs. The proposed model was trained using data from supine and erect abdominal radiographs. In total, 191212 chest radiographs (CheXpert data) were used for pre-training, and 5518 labeled and 16671 unlabeled abdominal radiographs were used for fine-tuning and self-supervised learning, respectively. The proposed model was internally validated on 389 abdominal radiographs and externally validated on 475 and 798 abdominal radiographs from the two institutions. We evaluated the performance in diagnosing pneumoperitoneum using the area under the receiver operating characteristic curve (AUC) and compared it with that of radiologists. Results: In the internal validation, the proposed model had an AUC, sensitivity, and specificity of 0.881, 85.4%, and 73.3% and 0.968, 91.1, and 95.0 for supine and erect positions, respectively. In the external validation at the two institutions, the AUCs were 0.835 and 0.852 for the supine position and 0.909 and 0.944 for the erect position. In the reader study, the readers' performances improved with the assistance of the proposed model. Conclusion: The proposed model trained with the DISTL method can accurately detect pneumoperitoneum on abdominal radiography in both the supine and erect positions.