• Proceedings of the KSME Conference
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• 2005.11a
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• pp.85.2-85.2
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• 2005

• Journal of Science of Art and Design
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• v.3
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• pp.225-247
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• 2001

The analysis of art work that is objective and theoretical needs the help of the cognitive psychology, for the pictorial semiotics requires psychology. The first step to the analysis of art work is about the visual elements and their relations. But the semiotics is lack of the method of the analysis of art work and the some authors don't have treated or been interested in psychological analysis. The main problem of visual semiotics is the density of pictorial representation. It makes the semantic of art work impossible at the very early process of analysis. But the density is not only a matter of visual representation, verbal language also has this problem. The point is that art work functions more art than denotation, but verbal language does more denotation than art. This difference makes difficult to apply the method of language or semiotics to visual art. The possibility of pictorial syntax or perceptual semantics should begin considering the unification of perception and semantics. In principles these two field can be unified. At atomism and holism these are parallel. Therefore perceptual semantics is possible The cognitive psychology can help to formulation of perceptual semantics. At first, the visual representation is incremental and it can be divided at three steps. In these steps each sensation, perception and cognition level has their own role. Perceptual representation of art work should be specified at these three levels. And each of these levels, the special properties of art work should be drawn and examined in the possibility of semiotics. The investigation of psychological levels and semiotic level should be circulated. It will help to formulate the method of analysis of art work.

• Journal of KIISE
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• v.45 no.1
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• pp.15-21
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• 2018

Low-Rank Representation (LRR) based methods are widely used in many practical applications, such as face clustering and object detection, because they can guarantee high prediction accuracy when used to constructing graphs in graph - based semi-supervised learning. However, in order to solve the LRR problem, it is necessary to perform singular value decomposition on the square matrix of the number of data points for each iteration of the algorithm; hence the calculation is inefficient. To solve this problem, we propose an improved and faster LRR method based on the recently published Fast LRR (FaLRR) and suggests ways to introduce and optimize additional constraints on the underlying optimization goals in order to address the fact that the FaLRR is fast but actually poor in classification problems. Our experiments confirm that the proposed method finds a better solution than LRR does. We also propose Fast MLRR (FaMLRR), which shows better results when the goal of minimizing is added.

• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.55-61
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• 2021

The character motion control based on physics simulation using reinforcement learning continue to being carried out. In order to solve a problem using reinforcement learning, the network structure, hyperparameter, state, action and reward must be properly set according to the problem. In many studies, various combinations of states, action and rewards have been defined and successfully applied to problems. Since there are various combinations in defining state, action and reward, many studies are conducted to analyze the effect of each element to find the optimal combination that improves learning performance. In this work, we analyzed the effect on reinforcement learning performance according to the state representation, which has not been so far. First we defined three coordinate systems: root attached frame, root aligned frame, and projected aligned frame. and then we analyze the effect of state representation by three coordinate systems on reinforcement learning. Second, we analyzed how it affects learning performance when various combinations of joint positions and angles for state.

• Korean Journal of Mathematics
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• v.19 no.2
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• pp.111-127
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• 2011

We prove the uniqueness solvability of the Tricomi problem for the elliptic - hyperbolical equation of the second type by using a new representation of the solution in the generalized class R.

• Journal of applied mathematics & informatics
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• v.34 no.1_2
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• pp.95-106
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• 2016

In this paper, according to the classical LSQR algorithm forsolving least squares (LS) problem, an iterative method is proposed for finding the minimum-norm pure imaginary solution of the quaternionic least squares (QLS) problem. By means of real representation of quaternion matrix, the QLS's correspongding vector algorithm is rewrited back to the matrix-form algorthm without Kronecker product and long vectors. Finally, numerical examples are reported that show the favorable numerical properties of the method.

• Korean Management Science Review
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• v.18 no.1
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• pp.105-118
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• 2001

The common shortest path problem is to find the shortest route between two specified nodes in a transportation network with only one traffic mode. The public transportation network with multiple traffic mode is a more realistic representation of the transportation system in the real world, but it is difficult for the conventional shortest path algorithms to deal with. The genetic algorithm (GA) is applied to solve this problem. The objective function is to minimize the sum of total service time and total transfer time. The individual description, the coding rule and the genetic operators are proposed for this problem.

• Journal of the Korean Mathematical Society
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• v.54 no.1
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• pp.227-248
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• 2017

In this paper, we reduce the classification problem of equivariant (topological complex) vector bundles over a simple graph to the classification problem of their isotropy representations at vertices and midpoints of edges. Then, we solve the reduced problem in the case when the simple graph is homeomorphic to a circle. So, the paper could be considered as a generalization of [3].

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.388-393
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• 1990

A robot manipulator and an obstacle are described mathematically in joint space, with the mathematical representation for the collision between the robot manipulator and the obstacle. Using these descriptions, the robot motion planning problem is formulated which can be used to avoide a time varying obstacle. To solve the problem, the constraints on motion planning are discretized in joint space. An analytical method is proposed for planning the motion in joint space from a given starting point to the goal point. It is found that solving the inverse kinematics problem is not necessary to get the control input to the joint motion controller for collision avoidance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.60-64
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• 2001

In this paper, a new learning methodology for Kernel Methods is suggested that results in a sparse representation of kernel space from the training patterns for classification problems. Among the traditional algorithms of linear discriminant function(perceptron, relaxation, LMS(least mean squared), pseudoinverse), this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epochs. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.