• Journal of Korean Society of Industrial and Systems Engineering
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• v.14 no.24
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• pp.141-147
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• 1991

This paper presents extended model for the vehicle routing problem, which allows multiple visits to a node by multiple vehicles. Multiple visits enables us split delivery. After formulating this multiple visits model mathematically, a two stage heuristic algorithm is developed by decomposition approach. This model consists of two sub-problem. The one is fixed cost transportation problem and the other is transportation problem.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.811.1-818
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• 2001

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.4
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• pp.81-92
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• 2018

Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.2
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• pp.279-286
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• 2015

EMD is a fully data-driven signal processing method without using any predetermined basis function and requiring any user parameters setting. However EMD experiences a problem of mode mixing which interferes with decomposing the signal into similar oscillations within a mode. To overcome the problem, EEMD method was introduced. The algorithm performs the EMD method over an ensemble of the signal added independent identically distributed white noise of the same standard deviation. Even so EEMD created problems when the decomposition is complete. The ensemble of different signal with added noise may produce different number of modes and the reconstructed signal includes residual noise. This paper propose an modified EEMD method to overcome mode mixing of EMD, to provide an exact reconstruction of the original signal, and to separate modes with lower cost than EEMD's. The experimental results show that the proposed method provides a better separation of the modes with less number of sifting iterations, costs 20.87% for a complete decomposition of the signal and demonstrates superior performance in the signal reconstruction, compared with EEMD.

• Smart Structures and Systems
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• v.30 no.1
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• pp.75-88
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• 2022

Engineering structures in operation essentially belong to time-varying or nonlinear structures and the resultant response signals are usually non-stationary. For such time-varying structures, it is of great importance to extract time-dependent dynamic parameters from non-stationary response signals, which benefits structural health monitoring, safety assessment and vibration control. However, various traditional signal processing methods are unable to extract the embedded meaningful information. As a newly developed technique, variational mode decomposition (VMD) shows its superiority on signal decomposition, however, it still suffers two main problems. The foremost problem is that the number of modal components is required to be defined in advance. Another problem needs to be addressed is that VMD cannot effectively separate non-stationary signals composed of closely spaced or overlapped modes. As such, a new method named generalized adaptive variational modal decomposition (GAVMD) is proposed. In this new method, the number of component signals is adaptively estimated by an index of mean frequency, while the generalized demodulation algorithm is introduced to yield a generalized VMD that can decompose mode overlapped signals successfully. After that, synchrosqueezing wavelet transform (SWT) is applied to extract instantaneous frequencies (IFs) of the decomposed mono-component signals. To verify the validity and accuracy of the proposed method, three numerical examples and a steel cable with time-varying tension force are investigated. The results demonstrate that the proposed GAVMD method can decompose the multi-component signal with overlapped modes well and its combination with SWT enables a successful IF extraction of each individual component.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.133-138
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• 1986

In this paper, we are modeling the problem of the reliability evaluation in the network with delay. The triconnected decomposition and factoring algorithm for the network reliability, known as the most efficient algorithm, does not work in this constrained problem. So, we propose some ideas that reduce the above constrained problem to the general network reliability problem. We also present an algorithm for the reliability evaluation in the network with delay based on these ideas.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.780-785
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• 1998

In this paper, it is shown that the conventional methods for dealing with the singularity problem of a manipulator can be generalized as a local minimization problem with differently weighted objective functions. A new damping method proposed in this article automatically determines the damping amounts for singular values, which are inversely proportional to the magnitude of the singular values. Furthermore, this can be done without explicitly computing the singular values. The proposed method can be applied to all the manipulators with revolute joints.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2009.05a
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• pp.311-314
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• 2009

A two-dimensional thermal response and ablation simulation code for predicting charring material ablation and shape change on solid rocket nozzle is presented. For closing the problem of thermal analysis, Arrhenius' equation and Zvyagin's ablation model are used. The moving boundary problem and endothermic reaction in thermal decomposition are solved by rezoning and effective specific heat method. For simulation of complicated thermal protection systems, this method is integrated with a three-dimensional finite-element thermal and structure analysis code through continuity of temperature and heat flux.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.623-625
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• 2000

For the finite element analysis of highly saturated rotating machines involving rotation of a rotor such as dynamic analysis. cogging torque analysis and etc, so much time is needed because a new system matrix equation should be solved for each iteration and time step. It is proved in this paper that. in linear systems. the computational time can be greatly reduced by using the domain decomposition method (DDM). In nonlinear systems. however. this advantage vanishes because the stiffness matrix changes at each iteration especially when using the Newton-Raphson (NR) method. The transmission line modeling (TLM) method resolves this problem because in TLM method the stiffness matrix does not change throughout the entire analysis. In this paper, a new technique for FEA of rotating machines including rotation of rotor and non-linearity is proposed. This method is applied to a test problem. and compared with the conventional method.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.204-208
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• 2003

In this paper, a new matrix vector formulation for a wavelet-based subband decomposition is introduced. This formulation provides a means to compute a regular multi-resolution analysis over many levels of decomposition. With this approach. any single channel linear space-invariant filtering problem can be cast into a multi-channel framework. This decomposition Is applied to the linear space-invariant image restoration problem and propose a frequency-adaptive constrained least squares(CLS) filter. In the proposed filter, we use different parameters adaptively according to subband characteristics. Experimental results are presented for the proposed frequency-adaptive CLS filter These experiments show that if accurate estimates of the subband characteristics are available, the proposed frequency adaptive CLS filter provides significant improvements over the traditional single channel filter.