• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• The Transactions of the Korean Institute of Electrical Engineers A
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• 제48권8호
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• pp.996-1003
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• 1999

This paper propose a position-speed controller with an estimator which can estimate states and disturbance. The overall control system consists of two parts: the position-speed controller and an estimator. The Kalman filter applied as state-feedback controller is an optimal state estimator applied to a dynamic system that involves random perturbations and gives a linear, unbiased and minimum error variance recursive algorithm to optimally estimate the unknown state. Therefore, we consider the error problem about the servo system modeling and the measurement noise as a stochastic system and implement a optimal state observer, and enhance the estimate performance of position and speed using that. Using two-degree-of freedom(TDOF) conception, we design the command input response and the closed loop characteristics independently. The servo system is to improve the closed loop characteristics without affecting the command imput response. The characteristics of the closed loop system is improved by suppressing disturbance torque effectively with the disturbance observer using a inverse-transfer matrix. Therefore, the performance of overall position-speed controller is enhanced. Finally, the performance of the proposed controller is exemplified by some simulations and by applying the real servo system.

• The Transactions of the Korean Institute of Power Electronics
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• 제17권5호
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• pp.423-430
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• 2012

An adaptive maximum power point tracking (MPPT) scheme employing a variable scaling factor is presented. A MPPT control loop was constructed analytically and the magnitude variation in the MPPT loop gain according to the operating point of the PV array was identified due to the nonlinear characteristics of the PV array output. To make the crossover frequency of the MPPT loop gain consistent, the variable scaling factor was determined using an approximate curve-fitted polynomial equation about linear expression of the error. Therefore, a desirable dynamic response and the stability of the MPPT scheme were maintained across the entire MPPT voltage range. The simulation and experimental results obtained from a 3 KW rated prototype demonstrated the effectiveness of the proposed MPPT scheme.

• Proceedings of the KIEE Conference
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1215-1217
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• 2007

This paper is proposed a novel method to approximate the maximum power for a photovoltaic inverter system. It is designed for power systems application and utilities. The proposed Maximum Power Point Tracking(MPPT) control has the advantage to provide a new simple way to approximate the optimal or rated voltage, the optimal or rated current and maximum power rating produced by a solar panel and the photovoltaic inverter. And this straightforward method will be named linear reoriented coordinates method(LRCM) with the advantage that Pmax and $V_{op}$ can be approximated using the same variable as the dynamic model without using complicate approximations or Taylor series. This paper is proposed MPPT using LRMC method using weather condition of domestic moderate program technique. This paper is proposed the experimental results to verify the effectiveness of the new methods.

• Proceedings of the KOR-KST Conference
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• 대한교통학회 2000년도 제37회 학술발표회논문집
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• pp.25-47
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• 2000

In route guidance systems fastest-path routing has typically been adopted because of its simplicity. However, empirical studies on route choice behavior have shown that drivers use numerous criteria in choosing a route. The objective of this study is to develop computationally efficient algorithms for identifying a manageable subset of the nondominated (i.e. Pareto optimal) paths for real-time vehicle routing which reflect the drivers' preferences and route choice behaviors. We propose two pruning algorithms that reduce the search area based on a context-dependent linear utility function and thus reduce the computation time. The basic notion of the proposed approach is that ⅰ) enumerating all nondominated paths is computationally too expensive, ⅱ) obtaining a stable mathematical representation of the drivers' utility function is theoretically difficult and impractical, and ⅲ) obtaining optimal path given a nonlinear utility function is a NP-hard problem. Consequently, a heuristic two-stage strategy which identifies multiple routes and then select the near-optimal path may be effective and practical. As the first stage, we utilize the relaxation based pruning technique based on an entropy model to recognize and discard most of the nondominated paths that do not reflect the drivers' preference and/or the context-dependency of the preference. In addition, to make sure that paths identified are dissimilar in terms of links used, the number of shared links between routes is limited. We test the proposed algorithms in a large real-life traffic network and show that the algorithms reduce CPU time significantly compared with conventional multi-criteria shortest path algorithms while the attributes of the routes identified reflect drivers' preferences and generic route choice behaviors well.

• Ocean Systems Engineering
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• 제8권4호
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• pp.361-380
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• 2018

The dynamic performance of the wave energy converter (WEC) rotor with different geometric parameters such as depth of submergence and beak angle has been assessed by considering the linear potential flow theory using WAMIT solver and along with the computational fluid dynamics (CFD). The effect of viscous damping is incorporated by conducting numerical free decay test using CFD. The hydrodynamic coefficients obtained from the WAMIT, viscous damping from the CFD and estimated PTO damping are used to solve the equation of motion to obtain the final pitch response, mean optimal power and capture width. The viscous damping is almost 0.9 to 4.6 times when compared to the actual damping. It is observed that by neglecting the viscous damping the pitch response and power are overestimated when compared to the without viscous damping. The performance of the pitch WEC rotor in the Jeju western coast at the Chagwido is analyzed using Joint North Sea Wave Project (JONSWAP) spectrum and square-root of average extracted power is obtained. The performance of WEC rotor with depth of submergence 2.8 m and beak angle $60^{\circ}$ found to be good compared to the other rotors.

• Smart Structures and Systems
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• 제31권5호
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• pp.437-454
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• 2023

Real-time hybrid simulation (RTHS), which has the advantages of a substructure pseudo-dynamic test, is widely used to investigate the rate-dependent mechanical response of structures under earthquake excitation. However, time delay in RTHS can cause inaccurate results and experimental instabilities. Thus, this study proposes a model-based adaptive control strategy using a Kalman filter (KF) to minimize the time delay and improve RTHS stability and accuracy. In this method, the adaptive control strategy consists of three parts-a feedforward controller based on the discrete inverse model of a servohydraulic actuator and physical specimen, a parameter estimator using the KF, and a feedback controller. The KF with the feedforward controller can significantly reduce the variable time delay due to its fast convergence and high sensitivity to the error between the desired displacement and the measured one. The feedback control can remedy the residual time delay and minimize the method's dependence on the inverse model, thereby improving the robustness of the proposed control method. The tracking performance and parametric studies are conducted using the benchmark problem in RTHS. The results reveal that better tracking performance can be obtained, and the KF's initial settings have limited influence on the proposed strategy. Virtual RTHSs are conducted with linear and nonlinear physical substructures, respectively, and the results indicate brilliant tracking performance and superb robustness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• Journal of Korean Society of Transportation
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• 제22권7호
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• pp.161-167
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• 2004

While most or fixed-time control systems such as UTCS produce the signal timing plans that either maximizing bandwidth or minimizing a disutility index of delay and stops, cannot consider the fluctuation of traffic flow. One category of the traffic-response control systems, which make small changes on a predefined signal plan such as SCOOT, cannot be easily modified for feedback real-time control schemes based on observation of variables other than traffic flow. The other category, which decide to whether switch the traffic lights or not at each step of time as in PRODYN, does not adequately consider the relations between traffic flows and traffic lights at each step of time. In this paper we present a complete formulation that adequately consider the relations between traffic flows and traffic lights at each step of time. The formulation is a binary mixed integer linear programing (BMILP) that obtain traffic lights at each step for minimizing delay. Since numarical examples for application of the proposed model illustrated that the model adequately produced dynamic traffic signal plans minimizing delay at each step, the model may be expected to contribute to advanced transportation management systems (ATMS) for dynamic traffic signal control.

• Journal of Intelligence and Information Systems
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• 제23권3호
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.