• Structural Engineering and Mechanics
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• v.16 no.4
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• pp.391-404
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• 2003

Local reinforcement in dowel type timber joints is essential to improve ductility, to increase load carrying capacity and to reduce the risk of brittle failure, especially in the case of using solid dowel. In many types of reinforcing materials available today, DVW (densified veneer wood) has been demonstrated to be the most advantages in terms of compatibility, embedding performance and ductility. Preliminary studies show that using appropriately sized DVW discs bonded into the timber interfaces may be an effective way to reinforce the connection. In this paper, non-linear 3-dimensional finite element models, incorporating orthotropic and non-linear material behaviour, have been developed to simulate structural performance of the timber joints locally reinforced by DVW discs. Different contact algorithms were applied to simulate contact conditions in the joints. The models were validated by the corresponding structural tests. Correlation between the experimental results and the finite element simulations is reasonably good. Using validated finite element models, parametric studies were undertaken to investigate effects of the DVW disc sizes and the end distances on shear stresses and normal stresses in a possible failure plane in the joint.

• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.126-134
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• 2006

The object oriented co-modeling methodology, previously introduced, employs a layered approach in war game models development in which an upper layer models abstract behavior of an object and a lower one models details of the object. Within the methodology military domain experts and simulation experts models an object at the upper and the lower layers, respectively in concurrent manner. This paper proposes a method of constructing a war game simulator using parametric behavior modeling technique, which provides a means for military domain experts/users to change model's detailed behavior with no knowledge on modeling semantics. The proposed simulator would support new algorithms or strategies with minimal cost and could be modified even by the users who are ignorant about modeling technique. To demonstrate the effectiveness of the proposed framework, a naval war game simulator is exemplified.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.4
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• pp.375-382
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• 2011

Nowadays computer graphic softwares have opened a lot of potential by providing parametric modeling and generative algorithms which are useful not only to describe various geometrical shapes but also to implement a designer's intent in terms of modules systematically. This study has proposed a way of developing a module for generating preliminary structural configuration using such potential computer graphics. Especially parametric modeling and generative algorithm are utilized to define various design alternatives, and moreover use of dynamic graphics enables designers to generate a structural form on one side and a force flow diagram correspondingly provided on the other. This ultimately leads to rational preliminary design of a structural form considering its force flow.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.3002-3010
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• 2000

ATM forum defined several ATM oriented services such as ABR, VBR, CBR, UBR, and others. Among them, ABR service is considered as one of the most appropriate ones in the LAN environment. ABR controls network traffic by using RM cells according to ATM forum standard. In particular, the point-to-multipoint ABR service in general uses consolidation algorithms to solve feedback implosion at the branch point. Consolidation algorithms alleviate propagation delay and network congestion problem: thus, the efficiency of consolidation algorithm is closely related to the network performance. In this paper, we describe the limitation of existing consolidation algorithms based on thorough analysis, and then propose an algorithm that performs better than the existing algorithms. We show that the proposed algorithm improves network performance through a parametric simulation, In our simulation the performance measure is the cell rate which is controlled by exploiting the congestion depth. We also found that the proposed algorithm not only improves efficiency of network, but also eliminates the consolidation noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.401-410
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• 2007

In this paper, we propose water quality monitoring system using vision camera and multiple objects tracking method. The proposed system analyzes object individually using vision camera unlike monitoring system using sensor method. The system using vision camera consists of individual object segmentation part and objects tracking part based on interrelation between successive frames. For real-time processing, we make background image using non-parametric estimation and extract objects using background image. If we use non-parametric estimation, objects extraction method can reduce large amount of computation complexity, as well as extract objects more effectively. Multiple objects tracking method predicts next motion using moving direction, velocity and acceleration of individual object then carries out tracking based on the predicted motion. And we apply exception handling algorithms to improve tracking performance. From experiment results under various conditions, it shows that the proposed system can be available for real-time water quality monitoring system since it has very short processing time and correct multiple objects tracking.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.9-15
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• 2013

Recent works on ad hoc network study have shown that achievable throughput can be made to scale linearly with the number of receive antennas even if the transmitter has only a single antenna. In this paper, we propose a non-parametric linear minimum mean square error (MMSE) receiver for achieving further gain in performance when the channel state information at receiver (CSIR) of interferers is imperfect. The key feature to make our approach effective is to exploit the autocorrelation of the received signal. In fact, by incorporating the desired channel information on top of the observations including interference and noise only, the proposed method achieves large fraction of the optimal MMSE transmission capacity without transmission rate loss. From the SINR analysis as well as transmission capacity simulations in realistic ad hoc network system, we show that the proposed non-parametric linear MMSE receiver brings substantial performance gain over existing multiple receive antenna algorithms.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.34-42
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• 2010

The DOA(direction of arrival), which is based on parametric and nonparametric estimation algorithm, and adaptive beamforming algorithm for mobile communication environments are researched and analyzed. In parametric estimation algorithm, eigenvalues of the signal component and the noise component are obtained from correlation matrix of received signal by array antenna and power spectrum of the received signal is discriminated from them. Otherwise, in nonparametric estimation algorithm, we minimize a regularized objective function for finding a estimate of the signal energy as a function of angle, using nonquadratic norm which leads to supper resolution and noise suppression. And then, DOA is estimated by the signal and noise spatial steering vector, and adaptive beam-forming pattern is improved by weight vectors obtained from the spatial vector. Therefore, the improved directional estimation algorithm with regularizing sparsity constraints offers super-resolution and noise suppression compared to other algorithms.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.12-14
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• 2005

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) by means of genetically optimized fuzzy polynomial neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms(GAs). The conventional SOFPNNs hinges on an extended Group Method of Data Handling(GMDH) and exploits a fixed fuzzy inference type in each FPN of the SOFPNN as well as considers a fixed number of input nodes located in each layer. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, a collection of the specific subset of input variables, and the number of membership function) and addresses specific aspects of parametric optimization. Therefore, the proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series).

• Journal of Biosystems Engineering
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• v.38 no.2
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• pp.149-162
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• 2013

Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.

• Journal of Korean Association for Spatial Structures
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• v.21 no.1
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• pp.79-86
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• 2021

Control performance of a smart tuned mass damper (TMD) mainly depends on control algorithms. A lot of control strategies have been proposed for semi-active control devices. Recently, machine learning begins to be applied to development of vibration control algorithm. In this study, a reinforcement learning among machine learning techniques was employed to develop a semi-active control algorithm for a smart TMD. The smart TMD was composed of magnetorheological damper in this study. For this purpose, an 11-story building structure with a smart TMD was selected to construct a reinforcement learning environment. A time history analysis of the example structure subject to earthquake excitation was conducted in the reinforcement learning procedure. Deep Q-network (DQN) among various reinforcement learning algorithms was used to make a learning agent. The command voltage sent to the MR damper is determined by the action produced by the DQN. Parametric studies on hyper-parameters of DQN were performed by numerical simulations. After appropriate training iteration of the DQN model with proper hyper-parameters, the DQN model for control of seismic responses of the example structure with smart TMD was developed. The developed DQN model can effectively control smart TMD to reduce seismic responses of the example structure.