• Journal of Mechanical Science and Technology
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• 제14권10호
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• pp.1122-1130
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• 2000

Based on the value of the Lagrange multiplier and the degree of constraint activeness, a new update rule is proposed for penalty parameters of the ALM method. The theoretical exposition of this suggested update rule is presented by using the algorithmic interpretation and the geometric interpretation of the augmented Lagrangian. This interpretation shows that the penalty parameters can effect the performance of the ALM method. Also, it offers a lower limit on the penalty parameters that makes the augmented Lagrangian to be bounded. This lower limit forms the backbone of the proposed update rule. To investigate the numerical performance of the update rule, it is embedded in our ALM based dynamic response optimizer, and the optimizer is applied to solve six typical dynamic response optimization problems. Our optimization results are compared with those obtained by employing three conventional update rules used in the literature, which shows that the suggested update rule is more efficient and more stable than the conventional ones.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.916-921
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• 2004

The penalty method has been widely applied to analyses of incompressible fluid flow. However, we have not yet found any prior studies that employed penalty method to analyze compressible fluid flow. In this study, with an eye on the apparent similarity between the slight compressible formulation and the penalty formulation, we have proposed a new approximate approach that can analyze compressible packing process using the penalty parameter l. Based on the assumption of the isothermal flow, a set of reference solutions was obtained to verify the validity of the proposed scheme. Furthermore, we have applied the proposed scheme to the analysis of the packing process of different cases.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.49-51
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• 2000

ln this paper, a method by which penalty factors of all generators including slack bus can be directly derived is presented. With a simple re-assignment of angle reference bus to a bus where no generation exists, penalty factors for slack bus is obtained without any physical assumption. While previous Jacobian-based techniques for generator penalty factor calculation have been derived with basis upon reference bus, proposed method are not dependent on reference bus and calculated penalty factors can be substituted directly into the general ELD equation to compute the economic dispatch. Equations for system loss sensitivity, penalty factors and optimal generation allocation are solved simultaneously in normal power flow computation.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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• pp.105-110
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• 2005

The penalty method has been widely applied to analyses of incompressible fluid flow. However, we have not yet found any prior studies that employed penalty method to analyze compressible fluid flow. In this study, with an eye on the apparent similarity between the slight compressible formulation and the penalty formulation, we have proposed a modified approximate approach that can analyze compressible packing process using the penalty parameter, which is an improvement on an earlier formulation (KSME, 2004B). Based on the assumption of the isothermal flow, a set of reference solutions was obtained to verify the validity of the proposed scheme. Furthermore, we have applied the proposed scheme to the analysis of the packing process of different cases.

• Journal of applied mathematics & informatics
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• 제33권3_4호
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• pp.387-399
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• 2015

In this paper, a new smoothing approximation to the l₁ exact penalty function for constrained optimization problems (COP) is presented. It is shown that an optimal solution to the smoothing penalty optimization problem is an approximate optimal solution to the original optimization problem. Based on the smoothing penalty function, an algorithm is presented to solve COP, with its convergence under some conditions proved. Numerical examples illustrate that this algorithm is efficient in solving COP.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.166-169
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• 2003

Reinforcement learning if a kind of machine learning. It aims to adapt an agent to a given environment with a clue to a reward and a penalty. Q-learning [8] that is a representative reinforcement learning system treats a reward and a penalty at the same time. There is a problem how to decide an appropriate reward and penalty values. We know the Penalty Avoiding Rational Policy Making algorithm (PARP) [4] and the Penalty Avoiding Profit Sharing (PAPS) [2] as reinforcement learning systems to treat a reward and a penalty independently. though PAPS is a descendant algorithm of PARP, both PARP and PAPS tend to learn a local optimal policy. To overcome it, ion this paper, we propose the Multi Best method (MB) that is PAPS with the multi-start method[5]. MB selects the best policy in several policies that are learned by PAPS agents. By applying PS, PAPS and MB to a soccer game environment based on the SoccerBots[9], we show that MB is the best solution for the soccer game environment.

• Geomechanics and Engineering
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• 제15권4호
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• pp.947-955
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• 2018

In this study, we propose a three-dimensional simplified slope stability analysis using a hybrid-type penalty method (HPM). In this method, a solid element obtained by the HPM is applied to a column that divides the slope into a lattice. Therefore, it can obtain a safety factor in the same way as simplified methods on the slip surface. Furthermore, it can obtain results (displacement and strain) that cannot be obtained by conventional limit equilibrium methods such as the Hovland method. The continuity condition of displacement between adjacent columns and between elements for each depth is considered to incorporate a penalty function and the relative displacement. For a slip surface between the bottom surface and the boundary condition to express the slip of slope, we introduce a penalty function based on the Mohr-Coulomb failure criterion. To compute the state of the slip surface, an r-min method is used in the load incremental method. Using the result of the simple three-dimensional slope stability analysis, we obtain a safety factor that is the same as the conventional method. Furthermore, the movement of the slope was calculated quantitatively and qualitatively because the displacement and strain of each element are obtained.

• 대한토목학회논문집
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• 제38권6호
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• pp.941-947
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• 2018

교통카드기반 도시철도네트워크에서 역사환승은 중간환승역이 아닌 출발역과 도착역의 노드에서 발생하는 환승보행이동을 의미한다. 철도네트워크에서 최적의 경로를 탐색하기 위해서는 차내통행시간 이외에 환승보행이동에 대한 페널티를 반영하는 방안이 요구된다. 그러나 기존 링크표지기반 경로탐색기법은 링크와 링크의 사이에서 나타나는 환승페널티가 인식되도록 설계되었다. 따라서 출발역과 도착역에서 나타나는 역사환승페널티를 반영하지 못하는 한계가 발생한다. 역사환승페널티를 반영하기 위해 가상링크를 도입하여 네트워크를 확장하는 방안이 있으나 링크표지기반알고리즘을 효과적으로 유지하지 못하는 단점이 발생된다. 본 연구는 역사환승페널티를 반영하기 위하여 네트워크확장없이 최적경로를 탐색하는 알고리즘을 제안한다. 이를 위해 단말기ID와 링크의 노선을 비교하여 노선환승페널티를 직접 적용하는 방안을 제안한다.

• Communications for Statistical Applications and Methods
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• 제20권6호
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• pp.481-490
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• 2013

Classification is an important research area as data can be easily obtained even if the number of predictors becomes huge. The support vector machine(SVM) is widely used to classify a subject into a predetermined group because it gives sound theoretical background and better performance than other methods in many applications. The SVM can be viewed as a penalized method with the hinge loss function and penalty functions. Instead of $L_2$ penalty function Fan and Li (2001) proposed the smoothly clipped absolute deviation(SCAD) satisfying good statistical properties. Despite the ability of SVMs, they have drawbacks of non-robustness when there are outliers in the data. We develop a robust SVM method using a weight function with the SCAD penalty function based on the local quadratic approximation. We compare the performance of the proposed SVM with the SVM using the $L_1$ and $L_2$ penalty functions.

• Communications for Statistical Applications and Methods
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• 제24권6호
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• pp.673-683
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• 2017

The least absolute shrinkage and selection operator (LASSO) has been a popular regression estimator with simultaneous variable selection. However, LASSO does not have the oracle property and its robust version is needed in the case of heavy-tailed errors or serious outliers. We propose a robust penalized regression estimator which provide a simultaneous variable selection and estimator. It is based on the rank regression and the non-convex penalty function, the smoothly clipped absolute deviation (SCAD) function which has the oracle property. The proposed method combines the robustness of the rank regression and the oracle property of the SCAD penalty. We develop an efficient algorithm to compute the proposed estimator that includes a SCAD estimate based on the local linear approximation and the tuning parameter of the penalty function. Our estimate can be obtained by the least absolute deviation method. We used an optimal tuning parameter based on the Bayesian information criterion and the cross validation method. Numerical simulation shows that the proposed estimator is robust and effective to analyze contaminated data.