• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.7-12
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• 1993

This paper presents a generalized truncated least, squares adaptive algorithm and a two-stage design method. The proposed algorithm is directly derived from the normal equation of the generalized truncated least squares method (GTLSM). The special case of the GTLSM, the truncated least squares (TLS) adaptive algorithm, has a distinct features which includes the case of minimum steps estimator. This algorithm seemed to be best in the deterministic case. For real applications in the presence of disturbances, the GTLS adaptive algorithm is more effective. The two-stage design method proposed here combines the adaptive control system design with a conventional control design method and each can be treated independently. Using this method, the validity of the presented algorithms are examined by the simulation studies of an indirect adaptive control.

• Structural Engineering and Mechanics
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• v.21 no.5
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• pp.507-518
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• 2005

In the shear-lag analysis of structures deterministic procedure is insufficient to provide complete information. Probabilistic analysis is a holistic approach for analyzing shear-lag effects considering uncertainties in structural parameters. This paper proposes an efficient and accurate algorithm to analyze shear-lag effects of structures with parameter uncertainties. The proposed algorithm integrated the advantages of the response surface method (RSM), finite element method (FEM) and Monte Carlo simulation (MCS). Uncertainties in the structural parameters can be taken into account in this algorithm. The algorithm is verified using independently generated finite element data. The proposed algorithm is then used to analyze the shear-lag effects of a simply supported beam with parameter uncertainties. The results show that the proposed algorithm based on the central composite design is the most promising one in view of its accuracy and efficiency. Finally, a parametric study was conducted to investigate the effect of each of the random variables on the statistical moment of structural stress response.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.217-220
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• 2000

This paper studies a real-time VBR traffic characterization. There are two big approaches to determine traffic. One is a statistic approach and the other is a deterministic approach. This paper proposes a new constraint function, what we called “Sub-Sum Constraint Function”(SSCF). This function is mainly based on a deterministic approach and uses a statistic approach. It predicts and calculates the next rate with a present information about the stream. SSCF captures the intuitive bounded by a rate lower than its peak rate and closer to its long-term average rate. This model makes a order of the constraint function much less than any other works (O(n)). It can also be mapped on a token bucket algorithm which consists of r (token rate) and b (token depth). We use a concept, EB(effective bandwidth) for a utility of our function and comparing with other techniques such as CBR, average VBR. We simulated 21 multimedia sources for verifying the utility of our function.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.3
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• pp.81-91
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• 1994

This paper is to build an economic production quantity model for situations, in which, during the stockout period, a fraction .betha.(backorder ratio) of the demand is backordered and remaining fraction (1-.betha.) is lost. This paper develops an objective function representing the average annual cost of a production system by defining a time-weighted backorder cost and a lost sales penalty cost per unit lost under the assumptions of deterministic demand rate and deterministic production rate, and provides an algorithm for its optimal solution. At the extreme .betha.= 1, the presented model reduces to the Fabrycky's model with complete backorders.

• Genomics & Informatics
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• v.1 no.1
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• pp.20-24
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• 2003

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive 'optimal' incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the 'globally optimal' binary partitions. In addition, the objects that have not been clustered at small non­zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.45.5-45
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• 2002

In this paper, a quasi-deadbeat minimax estimation (QME) is proposed as a new class of time-domain parameter estimations for deterministic generic linear models. Linearity, quasi-deadbeat property, FIR structure, and independency of the initial parameter information will be required in advance, in addition to a new performance criterion of a worst case gain between the disturbances and the current estimation error. The proposed QME is obtained in a closed form by directly solving an optimization problem. The QME is represented in both a batch form and an iterative form. A fast algorithm for the suggested estimation is also presented, which is remarkable in view...

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.6-13
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• 2015

이 연구는 초기 지구의 대기 환경에서 유기화합물(glycine)이 합성되는 실험(Urey-Miller 실험)에서의 반응 경로를 Deterministic한 방법의 중간체 sampling 방법으로 반응 네트워크를 구성하고 Yen's알고리즘으로 네트워크 내의 최단경로를 제시함으로써 반응물과 생성물이 결정되어 있을 때 최소한의 화학적 직관만을 이용하여 제시하는 것이 목표이다. 이 연구 결과는 2014년도 Nature Chemistry에 발표된 다른 방법론을 적용하여 제시된 Urey-Miller reaction path와 비교해 어떠한 반응이 상대적으로 더 타당한 경로를 제시했을지 알아보았다. 이 연구에서 나온 reaction path에서의 중간체들에 대해 GAMESS를 이용한 B3LYP/6-31g(d,p) DFT계산을 수행하였다. 결과를 분석해보면서 어떤 부분이 부족하며 이 연구에 적용한 방법론을 어떻게 발전시켜나가야 더 나은 결과를 얻을 수 있을지를 함께 고려해 보았다.

• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.81-87
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• 2016

This paper analysis the AM-SCS-MMA (Adaptive Modulus-Soft Constraint Satisfaction-MMA) based on the adaptive modulus and minimus-disturbance technique in order to improve the stability and robustness in low signal to noise power of current MMA adaptive equalization algorithm. In AM-SCS-MMA, it updates the filter coefficient applying the adaptive modulus and minimum-disturbance technique of deterministic optimization problem instead of LMS or gradient descend algorithm for obtain the minimize the cost function of adaptive equalization. It is possible to improve the equalizer filter stability, robustness to the various noise characteristic and simultaneous reducing the intersymbol interference due to the amplitude and phase distortion occurred at channel. The computer simulation were performed for confirming the improved performance of SCS-MMA. For these, the output signal constellation of equalizer, residual isi, MSE, EMSE (Excess MSE) which means the channel traking capability and SER which means the robustness were applied. As a result of computer simulation, the AM-SCS-MMA have slow convergence time and less residual quantities after steady state, more good robustness in the poor signal to noise ratio, but poor in channel tracking capabilities was confirmed than MMA.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.201-208
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• 1996

This paper proposes an efficient method for improving the training performance of the neural network using a hybrid of backpropagation algorithm and dynamic tunneling system.The backpropagation algorithm, which is the fast gradient descent method, is applied for high-speed optimization. The dynamic tunneling system, which is the deterministic method iwth a tunneling phenomenone, is applied for blobal optimization. Converging to the local minima by using the backpropagation algorithm, the approximate initial point for escaping the local minima is estimated by the pattern classification, and the simulation results show that the performance of proposed method is superior th that of backpropagation algorithm with randomized initial point settings.