• Smart Structures and Systems
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• v.15 no.3
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• pp.897-911
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• 2015

This study presents a novel approach based on advancements in Evolutionary Computation for data-driven modeling of complex multi-dimensional memory-dependent systems. The investigated example is a benchmark coupled three-dimensional system that incorporates 6 Bouc-Wen elements, and is subjected to external excitations at three points. The proposed technique of this research adapts Genetic Programming for discovering the optimum structure of the differential equation of an auxiliary variable associated with every specific degree-of-freedom of this system that integrates the imposed effect of vibrations at all other degrees-of-freedom. After the termination of the first phase of the optimization process, a system of differential equations is formed that represent the multi-dimensional hysteretic system. Then, the parameters of this system of differential equations are optimized in the second phase using Genetic Algorithms to yield accurate response estimates globally, because the separately obtained differential equations are coupled essentially, and their true performance can be assessed only when the entire system of coupled differential equations is solved. The resultant model after the second phase of optimization is a low-order low-complexity surrogate computational model that represents the investigated three-dimensional memory-dependent system. Hence, this research presents a promising data-driven modeling technique for obtaining optimized representative models for multi-dimensional hysteretic systems that yield reasonably accurate results, and can be generalized to many problems, in various fields, ranging from engineering to economics as well as biology.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1130-1134
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• 1990

In this paper, we propose a mthod to decouple the multi-input multi-output two-dimensional system. Then, we analyze the realization dimension of the feedback, feedforward given to decouple. Moreover, we consider the possibility of the reduction of the dynamical dimension needed to decouple. Besides, in order to stabilize the decoupled two-dimensional system, we suggest a method to assign the poles of each entry of the transfer function matrix to the desired positions.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.52-57
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• 2007

In this paper we proposed multi-directional matching windows combined by multi-dimensional feature vector matching, which uses not only intensity values but also multiple feature values, such as variance, first and second derivative of pixels. Multi-dimensional feature vector matching has the advantage of compensating the drawbacks of area-based stereo matching using one feature value, such as intensity. We define matching cost of a pixel by the minimum value among eight multi-dimensional feature vector distances of the pixels expanded in eight directions having the interval of 45 degrees. As best stereo matches, we determine the two points with the minimum matching cost within the disparity range. In the experiment we used aerial imagery and IKONOS satellite imagery and obtained more accurate matching results than that of conventional matching method.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3206-3212
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• 2023

We developed a miniaturized multi-dimensional radiation sensor system consisting of an inorganic scintillator array and plastic optical fibers. This system can be applied to remotely obtain the radioactivity distribution and identify the radionuclides in radioactive waste by utilizing a scanning method. Variation in scintillation light was measured in two-dimensional regions of interest and then converted into radioactivity distribution images. Outliers present in the images were removed by using a digital filter to make the hot spot location more accurate and cubic interpolation was applied to make the images smoother and clearer. Next, gamma-ray spectroscopy was performed to identify the radionuclides, and three-dimensional volume scanning was also performed to effectively find the hot spot using the proposed array sensor.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.214-218
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• 1992

In this paper, we propose a method for obtaining state-space realization form of two-dimensional transfer function matrices (2DTFM). It contains free parameters. And, we perform various consideration about it. Moreover, we present the conditions so that the state-space realization form exists.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.156-161
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• 2003

It is an interest problem to predict substance distributions in three-dimensional space. Recently, a research field as Geostatistics is advanced. It is a kind of inter- or extrapolation mathematically. Some useful means for the inter- and extrapolation are known, in which slide window method with neural networks is hopeful one. We propose multi-dimensional extrapolation using multi-layer neural networks and the slide-window method. The multi-dimensional extrapolation is not similar to one-dimension. It has plural algorithms. We researched line predictors and local-plain predictors I two-dimensional space. The both predictors are equivalent; however, in multi-dimensional extrapolation, it is very important to find the direction of predictions. Especially, since the slide window method requires information to predict the future in sampling data, if they are not ordered appropriately in the direction, the predictor cannot operate. We tested the extrapolation for typical two-dimensional functions, and found an excellent character of slide-window method based on local-plain. By using the method, we can extrapolate the function until twice-outer regions of the definitions.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.397-397
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• 2000

There exists an increasing need of study for generalized consecutive k-out-of-n systems. This paper demonstrates that a recursive formula for multi-dimensional consecutive k-out-of-n systems can be systematically developed by means of conventional structure function analysis. By taking advantage of notational convenience, the formulae expressed in the same recursive fashion just as the one dimensional consecutive k-out-of-n system. With the aids of the recursive formulae, not only the exact reliability of the system, but also the optimal arrangement of components is obtainable in a straightforward way.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.211-223
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• 2018

Mobile relay systems have been adopted by $4^{th}$ generation mobile systems as an alternative method to extend cell coverage as well as to enhance the system throughput at cell-edges. In order to achieve such performance gains, the mobile relay systems require path selection and resource allocation schemes that are specifically designed for these systems which make use of additional radio resources not needed in single-hop systems. This paper proposes an integrated path selection and resource allocation scheme for LTE-Advanced relay systems using collaborative communication. We first define the problem of maximizing the downlink throughput of LTE-Advanced relay systems using collaborative communication and transform it into a multi-dimensional multi-choice backpacking problem. The proposed Lagrange multiplier-based heuristic algorithm is then applied to derive the approximate solution to the maximization problem. It is shown through simulations that the approximate solution obtained by the proposed scheme can achieve a near-optimal performance.

• Journal of Power Electronics
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• v.17 no.5
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• pp.1160-1172
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• 2017

A novel space vector modulation strategy in the non-orthogonal three-dimensional coordinate system for multi-level three-phase four-wire inverters is proposed in this paper. This new non-orthogonal three-dimensional space vector modulation converts original trigonometric functions in the orthogonal three-dimensional space coordinate into simple algebraic operations, which greatly reduces the algorithm complexity of three-dimensional space vector modulation and preserves the independent control of the zero-sequence component. Experimental results have verified the correctness and effectiveness of the proposed three-dimensional space vector modulation in the new non-orthogonal three-dimensional coordinate system.