• International conference on construction engineering and project management
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• 2015.10a
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• pp.639-640
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• 2015

Traditional construction planning based on historical data and heuristic adjustment can no longer incorporate all the operational details and guarantee the expected performance. The variation between the expected and the actual production leads to cost overruns or delay. Although predicting reliable productivity on construction site is getting more important, the difficulty of this increases. In this regard, this paper suggested to develop BIM-integrated simulation framework. This framework could predict productivity dynamics by considering factors affecting on construction productivity at operational phase. We developed the following processes; 1) enabling a BIM model to produce input data for simulation; 2) developing the construction operation simulation; 3) running simulation using BIM data and obtaining productivity results. The BIM-integrated simulation framework was tested with structural steel erection model because steel erection work is one of the most critical process influencing on the whole construction budget and duration. We could improve to predict more dynamic productivity from this framework, and this reliable productivity helps construction managers to optimize resource allocation, increase schedule reliability, save storage cost, and reduce material loss.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.6 s.183
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• pp.88-95
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• 2006

In this paper, the gain-scheduled control design proposed in the previous paper has been applied to a target tracking system. In such system, it is needed to attenuate disturbance effectively as long as control input satisfies the given constraint on its magnitude. The scheduled gains are derived in the framework of linear matrix inequality(LMI) optimization by means of the MatLab toolbox. Its effectiveness is verified along with the simulation results compared with the conventional optimum constant gain and the scheduled gain control with constant Q matrix cases.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.12
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• pp.65-73
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• 2007

In this paper, the gain scheduled state feedback and disturbance feedforward control design proposed in the previous paper has been applied to a simple matching system and a turret stabilization system. In such systems, it is needed to attenuate disturbance response effectively as long as control input satisfies the given constraint on its magnitude. The scheduled control gains are derived in the framework of linear matrix inequality(LMI) optimization by means of the MatLab toolbox. Its effectiveness is verified along with the simulation results compared with the conventional optimum constant gain control and the scheduled state feedback control cases.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.455-468
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• 2018

The concepts of graph theory are applied to model and analyze dynamics of computer networks, biochemical networks and, semantics of social networks. The analysis of dynamics of complex networks is important in order to determine the stability and performance of networked systems. The analysis of non-stationary and nonlinear complex networks requires the applications of ordinary differential equations (ODE). However, the process of resolving input excitation to the dynamic non-stationary networks is difficult without involving external functions. This paper proposes an analytical formulation for generating solutions of nonlinear network ODE systems with functional decomposition. Furthermore, the input excitations are analytically resolved in linearized dynamic networks. The stability condition of dynamic networks is determined. The proposed analytical framework is generalized in nature and does not require any domain or range constraints.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.308-314
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• 1998

This paper studies the problem of computing the Hankel singular values and vectors in the input delay systems. It is shown that the Hankel singular values are solutions to a transcendental equation and the Hankel singular vectors are obtained from the kernel of the matrix. The computation is carried out in state space framework. Finally, Hankel approximation of a simple example shows the usefulness of this study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.137-143
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• 2017

In this paper, we investigate static control of Boolean networks described in the framework of semi-tensor product (STP) operation. The control objective is to determine control input nodes and their logical values so as to stabilize the considered Boolean network to a desired fixed point or cycle. Using topology of Boolean networks such as incidence matrix and hub nodes, a set of appropriate control input nodes is selected, and based on STP operations, we assign constant control inputs so that the controlled network can converge to a prescribed fixed point or cycle. To validate applicability of the proposed scheme, we conduct a numerical study on the problem of determining control input nodes for a Boolean network representing hierarchical differentiation of myeloid progenitors.

• Journal of the Korean Mathematical Society
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• v.59 no.2
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• pp.217-233
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• 2022

Although machine learning shows state-of-the-art performance in a variety of fields, it is short a theoretical understanding of how machine learning works. Recently, theoretical approaches are actively being studied, and there are results for one of them, margin and its distribution. In this paper, especially we focused on the role of margin in the perturbations of inputs and parameters. We show a generalization bound for two cases, a linear model for binary classification and neural networks for multi-classification, when the inputs have normal distributed random noises. The additional generalization term caused by random noises is related to margin and exponentially inversely proportional to the noise level for binary classification. And in neural networks, the additional generalization term depends on (input dimension) × (norms of input and weights). For these results, we used the PAC-Bayesian framework. This paper is considering random noises and margin together, and it will be helpful to a better understanding of model sensitivity and the construction of robust generalization.

• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.655-664
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• 2019

In this paper, we propose a novel way of combining multiple deep convolutional neural network (DCNN) architectures which work well for accurate video face identification by adopting a serial combination of 3D and 2D DCNNs. The proposed method first divides an input video sequence (to be recognized) into a number of sub-video sequences. The resulting sub-video sequences are used as input to the 3D DCNN so as to obtain the class-confidence scores for a given input video sequence by considering both temporal and spatial face feature characteristics of input video sequence. The class-confidence scores obtained from corresponding sub-video sequences is combined by forming our proposed class-confidence matrix. The resulting class-confidence matrix is then used as an input for learning 2D DCNN learning which is serially linked to 3D DCNN. Finally, fine-tuned, serially combined DCNN framework is applied for recognizing the identity present in a given test video sequence. To verify the effectiveness of our proposed method, extensive and comparative experiments have been conducted to evaluate our method on COX face databases with their standard face identification protocols. Experimental results showed that our method can achieve better or comparable identification rate compared to other state-of-the-art video FR methods.

• Structural Engineering and Mechanics
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• v.76 no.6
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• pp.709-724
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• 2020

The typical inerter system, the tuned viscous mass damper (TVMD), has been proven to be efficient. It is characterized by an energy-dissipation-enhancement effect, whereby the dashpot deformation of TVMD can be amplified for enhanced energy dissipation efficiency. However, existing studies related to TVMD have mainly been performed on elastic structures, so the working mechanism remains unclear for nonlinear structures. To deal with this, an energy-spectrum analysis framework is developed systematically for classic bilinear hysteretic structures with TVMD. Considering the soil effect, typical bedrock records are propagated through the soil deposit, for which the designed input energy spectra are proposed by considering the TVMD parameters and structural nonlinear properties. Furthermore, the energy-dissipation-enhancement effect of TVMD is quantitatively evaluated for bilinear hysteretic structures. The results show that the established designed input energy spectra can be employed to evaluate the total energy-dissipation burden for a nonlinear TVMD structure. Particularly, the stiffness of TVMD is the dominant factor in adjusting the total input energy. Compared with the case of elastic structures, the energy-dissipation-enhancement effect of TVMD for nonlinear structures is weakened so that the expected energy-dissipation effect of TVMD is replaced by the accumulated energy dissipation of the primary structure.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.19-36
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• 2011

Since the ambitious declaration of the vision of the Semantic Web, a growing number of studies on semantic search have recently been made. However, we recognize that our community has not so much accomplished despite those efforts. We analyze two underlying problems : a lack of a shared notion of semantic search that guides current research, and a lack of a comprehensive view that envisions future work. Based on this diagnosis, we start by defining semantic search as the process of retrieving desired information in response to user's input using semantic technologies such as ontologies. Then, we propose a classification framework in order for the community to obtain the better understanding of semantic search. The proposed classification framework consists of input processing, target source, search methodology, results ranking, and output data type. Last, we apply our proposed framework to prior studies and suggest future research directions.