• The Journal of the Acoustical Society of Korea
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• v.38 no.4
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• pp.387-395
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• 2019

TMA (Target Motion Analysis) in passive sonar is generally conducted with the bearing only or the bearing frequency. In order to conduct TMA fast and accurately, it is essential to estimate a initial target maneuver precisely. The accuracy of TMA can be improved by using SNR (Signal to Noise Ratio) information and acoustic propagation model additionally. This method assumes that the radiated noise level of the target is known, but the accuracy of TMA can be degraded due to a mismatch between the assumed radiated noise level and the actual radiated noise level. In this paper, TMA with the acoustic propagation model, bearing measurements, and SNR information is conducted in the ocean environment of South Korea (East Sea/ Yellow Sea/ South Sea). And the performance analysis of TMA for the mismatch in the radiated noise is presented.

• Journal of Information Technology Applications and Management
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• v.27 no.6
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• pp.141-151
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• 2020

The job mismatch between job seekers and SMEs is becoming more and more intensifying with the serious difficulties in youth employment. In this study, a bi-directional content-based metric learning model is proposed to recommend suitable jobs for job seekers and suitable job seekers for SMEs, respectively. The proposed model not only enables bi-directional recommendation, but also enables HR matching without relearning for new job seekers and new job offers. As a result of the experiment, the proposed model showed superior performance in terms of precision, recall, and f1 than the existing collaborative filtering model named NCF+GMF. The proposed model is also confirmed that it is an evolutionary model that improves performance as training data increases.

• Journal of the Korean Regional Science Association
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• v.36 no.2
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• pp.3-12
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• 2020

Recently, metropolitanization and suburbization have been occurring mainly in large cities, and spatial miss-match between residential and employment areas has increased. Spatial miss-match is different in the metropolitan cities and other metropolitan cities in Korea. Seoul and other metropolitan cities have grown to become centers of business functions, while other metropolitan cities have been transformed into residential function centers. Accordingly, The reverse commuting phenomenon is occurring in the rural metropolitan. The reverse commuting phenomenon limits the employment opportunities of specific classes among urban residents, and cause various problems such as environmental pollution and traffic congestion. Therefore, many studies on spatial mismatch and reverse commuting have been conducted, but a number of studies have been conducted on the Seoul metropolitan area, and research on other metropolitan areas is insufficient. The purpose of this study is to analyze the cause of the commuting phenomenon in the metropolitan area of the non-metropolitan area by considering spatial mismatch and understanding the reverse commutation situation in the local metropolitan area. This study is analyzed by a multi-level model and suggests the need for management of industrial location and expansion of residence in suburban.

• Journal of Electrical Engineering and Technology
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• v.1 no.2
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• pp.161-169
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• 2006

Load representation has a significant impact on power system analysis and control results. In this paper, composite load models are developed based on on-line measurement data from a practical power system. Three types of static-dynamic load models are derived: general ZIP-induction motor model, Exponential-induction motor model and Z-induction motor model. For the dynamic induction motor model, two different third-order induction motor models are studied. The performances in modeling real and reactive power behaviors by composite load models are compared with other dynamic load models in terms of relative mismatch error. In addition, numerical consideration of ill-conditioned parameters is addressed based on trajectory sensitivity. Numerical studies indicate that the developed composite load models can accurately capture the dynamic behaviors of loads during disturbance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.660-665
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• 1993

An elastic model is developed to predict the average thermal residual stresses in the matrix and fiber of a misoriented short fiber composite. The thermal residual stresses are induced by the mismatch in the coefficient of the thermal expansion of the matrix and fiber when the composite is subjected to a uniform temperature change. The model considers two special cases of fiber misorientation ; two-dimensional in-plane and three-dimensional axisymmetric. The analytical formulation of the model is based on Eshelby's equivalent inclusion method and is nuque in that it is able to account for interactions among fibers. The model is more general than past models and it is able to treat prior analyses of the simpler composite systems as extram cases. The present model is to investigate the effects of fiber volume fraction, distribution type, distribution cut-off angle, and aspect ratio on thermal residual stress for both in-plane and axisymmetric fiber misorientation. Fiber volum fraction, aspect ratio, and disturbution cut-off angle are shown to have more significant effects on the magnitude of the thermal residual stress than fiber distrubution type for both in-plane and axisymmetric misorientation.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.958-961
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• 2001

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2164-2166
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• 2002

This paper present a controller design scheme for time-delay system. The Smith Predictor has been proposed to solve the problem of time-delay. But this structure has a condition that parameters of plant and model have to be matched accurately. Because of this condition, it is not applied broadly in practical industrial process field. In this paper, the 2nd-order model with dead-time is used as plant model of the Smith Predictor and a main controller is designed by using the effect of mismatch between plant and model.

• Journal of Power Electronics
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• v.19 no.2
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• pp.497-508
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• 2019

The predictive speed control (PSC) strategy can realize the simultaneous control of speed and current by using one cost function. As a model-based control method, the performance of the PSC is vulnerable to model mismatches such as load torque disturbances and parameter uncertainties. To solve this problem, this paper presents a robust predictive speed control (RPSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed RPSC uses extended state observers (ESOs) to estimate the lumped disturbances caused by load torque changes and parameter mismatches. The observer-based prediction model is then compensated by using the estimated disturbances. The introduction of ESOs can achieve robustness against predictive model uncertainties. In addition, a modified cost function is designed to further suppress load torque disturbances. The performance of the proposed RPSC scheme has been corroborated by experimental results under the condition of load torque changes and parameter mismatches.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.543-548
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• 2014

Multiple-model based speech recognition framework (MMSR) has been known to be very successful in speech recognition. Since it uses multiple hidden Markov modes (HMMs) that corresponds to various noise types and signal-to-noise ratio (SNR) values, the selected acoustic model can have a close match with the test noisy speech. However, since the number of HMM sets is limited in practical use, the acoustic mismatch still remains as a problem. In this study, we experimentally determined the optimal SNR mapping between the test noisy speech and the HMM set to mitigate the mismatch between them. Improved performance was obtained by employing the SNR mapping instead of using the estimated SNR from the test noisy speech. When we applied the proposed method to the MMSR, the experimental results on the Aurora 2 database show that the relative word error rate reduction of 6.3% and 9.4% was achieved compared to a conventional MMSR and multi-condition training (MTR), respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.3
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• pp.613-620
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• 2010

Additive noise and channel distortion strongly degrade the performance of speaker verification systems, as it introduces distortion of the features of speech. This distortion causes a mismatch between the training and recognition conditions such that acoustic models trained with clean speech do not model noisy and channel distorted speech accurately. This paper presents a phase-related feature extraction method in order to improve the robustness of the speaker verification systems. The instantaneous frequency is computed from the phase of speech signals and features from the histogram of the instantaneous frequency are obtained. Experimental results show that the proposed technique offers significant improvements over the standard techniques in both clean and adverse testing environments.