• Fisheries and Aquatic Sciences
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• v.1 no.2
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• pp.260-268
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• 1998

A flow pattern and water exchange in Sagami Bay is examined using a barotropic hydraulic model. In the model experiments, the volume transports of the Kuroshio Through Flow were changed with time. The results of the model experiments show that when the volume transport is increased with time, water mass and vorticity are transferred to the inner part of the bay by wakes from the western part of the bay. In the case of decrease, as the wakes are ceased, the inner cyclonic circulation water is discharged to the outside of the bay by its southward extension through the Oshima eastern channel. It is found that the water exchange by the short-term variation of volume transport in time is about 20% of all the bay water.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.63-71
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• 2020

In this paper, we propose the new time-varying coefficient GARCH-in-Mean model. The benefit of our model is to allow the risk-return parameter in the mean equation to vary over time. At the end of 2019 to the beginning of 2020, the world witnessed two shocking events: COVID-19 pandemic and 2020 oil price war. So, we decide to use the daily data from December 2, 2019 to May 29, 2020, which cover these two major events. The purpose of this study is to find the dynamic movement between risk and return in four major oil markets: Brent, West Texas Intermediate, Dubai, and Singapore Exchange, during COVID-19 pandemic and 2020 oil price war. For the European oil market, our model found a significant and positive risk-return relationship in Brent during March 26-April 21, 2020. For the North America oil market, our model found a significant positive risk return relationship in West Texas Intermediate (WTI) during March 12-May 8, 2020. For the Middle East oil market, we found a significant and positive risk-return relationship in Dubai during March 12-April 14, 2020. Lastly, for the South East Asia oil market, we found a significant positive risk return relationship in Singapore Exchange (SGX) from March 9-May 29, 2020.

• Proceedings of the IEEK Conference
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• 2003.07b
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• pp.943-946
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• 2003

This paper proposes new SPICE Macro-Model of MTJ(Magnetic Tunnel Junction). This Macro-Model has five I/O terminals, reproduces MR characteristics including hysteresis and behaves correctly to time varying input signals. Furthermore, this Model can be easily modified to various MTJs with different characteristics by simply varying internal parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4240-4258
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• 2016

A practical iterative channel estimation technique is proposed for the multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system in the high-speed mobile environment, such as high speed railway scenario. In the iterative algorithm, the Kalman filter and data detection are jointed to estimate the time-varying channel, where the detection error is considered as part of the noise in the Kalman recursion in each iteration to reduce the effect of the detection error propagation. Moreover, the employed Kalman filter is from the canonical state space model, which does not include the parameters of the autoregressive (AR) model, so the proposed method does not need to estimate the parameters of AR model, whose accuracy affects the convergence speed. Simulation results show that the proposed method is robust to the fast time-varying channel, and it can obtain more gains compared with the available methods.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.289-294
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• 2011

Digital redesign is yet another efficient tool to convert a pre-designed analog controller into a sampled-data one to maintain the analog closed-loop performance in the sense of state matching. A rising difficulty in developing a digital redesign technique for trackers with time-varying references is the unavailability of a closed-form discrete-time model of a system, even if it is linear time-invariant. A way to resolve this is to approximate the time-varying reference as a piecewise constant one, which deteriorates the state matching performance. Another remedy may be to decrease a sampling period, which however could numerically destabilize the optimization-based digital redesign condition. In this paper, we develop a digital redesign condition for time-varying trackers by approximating the time-varying reference through a triangular hold and by introducing delta-operated discrete-time models. It is shown that the digitally redesigned sampled-data tracker recovers the performance of the pre-designed analog tracker under a fast sampling limit. Simulation results on the formation flying of satellites convincingly show the effectiveness of the development.

• Structural Engineering and Mechanics
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• v.37 no.2
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• pp.131-147
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• 2011

The logarithmic decrement method has been long used to estimate damping ratios in systems with only one modal component such as linear single degree of freedom (SDOF) mechanical systems. This paper presents an application of a methodology that uses joint time-frequency distribution (JTFD) as input, instead of the raw signal, to systems with several vibration modes. A most important feature of the present approach is that it can be applied to a system with time-varying damping ratio. Initially the precision and robustness of the method is determined using a synthetic model with multiple harmonic components, one of them displaying a time-varying damping ratio, subsequently the results obtained from experiments with a reduced model are presented. A comparison is made between the results obtained with this methodology and those using the classical technique of Least Squares Complex Exponential Method (LSCE) in order to highlight the advantages of the former, such as, good precision, robustness and excellent performance in extreme cases, e.g., when very low frequency components and time varying damping ratio are present.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1858-1859
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• 2006

This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The gradient descendant method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.3
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• pp.250-254
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• 2008

Robot actuators are significantly important with respect to whole control system performance. This paper presents an adaptive control approach for flexible robot actuators with time-varying spring and damping nature. We first represent a perturbed system model with assumption that its information are partially known. Nominal model reference control method is employed for deriving our adaptive control law. We carry out numerical simulation to evaluate the proposed control system and compare simulation results to a well-known control method for demonstrating its effectiveness.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.559-566
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• 2006

In the real system application, the interacting multiple model (IMM) based algorithm requires less computing resources as well as a good performance with respect to the various target maneuverings. And it further requires an easy design procedure in terms of its structures and parameters. To solve these problems, a fuzzy interacting multiple model (FIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as inputs of a fuzzy decision maker, is proposed. To verify the performance of the proposed algorithm, airborne target tracking is performed. Simulation results show that the FIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.387-393
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• 2014

Recurrent gap times are analyzed with diverse methods under several assumptions such as a marginal model or a frailty model. Several resampling techniques have been recently suggested to estimate the covariate effect; however, these approaches can be applied with a time-fixed covariate. According to simulation results, these methods result in biased estimates for a time-varying covariate which is often observed in a longitudinal study. In this paper, we extend a resampling method by incorporating new weights and sampling scheme. Simulation studies are performed to compare the suggested method with previous resampling methods. The proposed method is applied to estimate the effect of an educational program on traffic conviction data where a program participation occurs in the middle of the study.