• ETRI Journal
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• v.38 no.3
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• pp.510-517
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• 2016

Recent developments in the field of separation of mixed signals into music/voice components have attracted the attention of many researchers. Recently, iterative kernel back-fitting, also known as kernel additive modeling, was proposed to achieve good results for music/voice separation. To obtain minimum mean square error (MMSE) estimates of short-time Fourier transforms of sources, generalized spatial Wiener filtering (GW) is typically used. In this paper, we propose an advanced music/voice separation method that utilizes a generalized weighted ${\beta}$-order MMSE estimation (WbE) based on iterative kernel back-fitting (KBF). In the proposed method, WbE is used for the step of mixed music signal separation, while KBF permits kernel spectrogram model fitting at each iteration. Experimental results show that the proposed method achieves better separation performance than GW and existing Bayesian estimators.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.701-707
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• 1987

This paper is concerned with an accurate delay time modling of the ED MOS logic gates and its application to the multiple delay logic simulator. The proposed delay model of the ED MOS logic gate takes account of the effects of not only the loading conditions but also the slope of the input waveform. Defining delay as the time spent by the current imbalance of the active inverter to charge and discharge the output load, with respect to physical reference levels, rise and fall model delay times are obtained in an explicit formulation, using optimally weighted imbalance currents at the end points of the voltage transition. A logic simulator which uses multiple rise/fall delays based on the model as decribed in the above has been developed. The new delay model and timing verification method are evaluated with repect to delay accuracy and execution time.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.123-128
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• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.75-86
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• 2008

A remarshalling is one of the operational strategies considered importantly at a port container terminal for the fast ship operations and heighten efficiency of slacking yard. The remarshalling rearranges the containers scattered at a yard block in order to reduce the transfer time and the rehandling time of container handling equipments. This Paper deals with the rearrangement problem, which decides to where containers are transported considering time value of each operations. We propose the mixed integer programming model minimizing the weighted total operation cost. This model is a NP-hard problem. Therefore we develope the heuristic algorithm for rearrangement problem to real world adaption. We compare the heuristic algorithm with the optimum model in terms of the computation times and total cost. For the sensitivity analysis of configuration of storage and cost weight, a variety of scenarios are experimented.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.12
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• pp.1333-1339
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• 2009

A MWR(Missile Warning Radar) of GVES(Ground Vehicle Equipment System) has to effectively decide the threat for a detected target. Linear Approximation Fitting(LAF) and Weighted Linear Approximation Fitting(WLAF) algorithm is proposed as algorithm for a threat decision method. The target is classified into a threat or non-threat using a boundary condition of the angular rate, and the boundary condition is determined using probability model simulation. This paper confirms the performance of proposed threat decision algorithm using measurement.

• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.1
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• pp.37-44
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• 1983

A class of adaptive control algorithms applied to discrete-time single-input single-output deterministic linear systems is analyzed by using direct model reference adaptive control. Controller parameters are identified with weighted least square Method. And computer simulations reveal that proposed weighted least square method in which the value of depends on the identification error can be used regardless of the sufficient condition of reference input signal.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2911-2913
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• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.2
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• pp.108-116
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• 2011

Non-line-of-sight (NLOS) error is the most common and also a major source of errors in wireless location system. A novel weighting factor (NWF) method is presented in this paper, based on the RSS(Received Signal Strength) measurements, path loss model and Circular Disk of Scatterers Model (CDSM). The proposed positioning method effectively weighted the TOA distance measurements for each Base Station (BS). Simulation results show that the proposed method efficiently weighted the distance measurements and achieve higher localization accuracy than that of Linear Line of Position (LLOP) and Believable Factor Algorithm (BFA).

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.4
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• pp.44-52
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• 2016

The departure flow management is the planning tool to optimize the schedule of the departure aircraft and allows them to join smoothly into the overhead traffic flow. To that end, the arrival time prediction to the merge point for the cruising aircraft is necessary to determined. This paper proposes a trajectory prediction model for the cruising aircraft based on the machine learning approach. The proposed method includes the trajectory vectored from the procedural route and is applied to the historical data to evaluate the prediction performances.

• Management Science and Financial Engineering
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• v.16 no.1
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• pp.59-79
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• 2010

This paper considers bandwidth allocation and scheduling problems on Ethernet Passive Optical Networks (EPON). EPON is one of the good candidates for the optical access network. This paper formulates the bandwidth allocation problem as a nonlinear mathematical one and characterizes the optimal bandwidth allocation which maximizes weighted sum of throughput and fairness. Based upon the characterization, two heuristic algorithms are suggested with various numerical tests. The test results show that our algorithms can be used for efficient bandwidth allocation on the EPON. This paper also shows that the WSPT (Weighted Shortest Processing Time) rule is optimal for minimization the total delay time in transmitting the traffic of the given allocated bandwidth.