• Journal of Positioning, Navigation, and Timing
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• v.5 no.1
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• pp.1-9
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• 2016

Range-based wireless localization system must measure accurate range between a mobile node (MN) and reference nodes. However, non-line-of-sight (NLOS) error caused by the spatial structures disturbs the localization system obtaining the accurate range measurements. Localization methods using the range measurements including NLOS error yield large localization error. But filter-based localization methods can provide comparatively accurate location solution. Motivated by the accuracy of the filter-based localization method, a filter residual-based NLOS error estimation method is presented in this paper. Range measurement-based residual contains NLOS error. By considering this factor with NLOS error properties, NLOS error is mitigated. Also a process noise covariance matrix tuning method is presented to reduce the time-delay estimation error caused by the single dynamic model-based filter when the speed or moving direction of a MN changes, that is the used dynamic model is not fit the current dynamic of a MN. The presented methods are evaluated by simulation allowing direct comparison between different localization methods. The simulation results show that the presented filter is more accurate than the iterative least squares- and extended Kalman filter-based localization methods.

• Journal of Magnetics
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• v.21 no.4
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• pp.529-536
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• 2016

Magnetically suspended sensitive gyroscopes (MSSGs) provide an interesting alternative for achieving precious attitude angular measurement. To effectively reduce the measurement error caused by dynamic imbalance, this paper proposes a novel compensation method based on analysis and modeling of the error for a MSSG. Firstly, the angular velocity measurement principle of the MSSG is described. Then the analytical model of dynamic imbalance error has been established by solving the complex coefficient differential dynamic equations of the rotor. The generation mechanism and changing regularity of the dynamic imbalance error have been revealed. Next, a compensation method is designed to compensate the dynamic imbalance error and improve the measurement accuracy of the MSSG. The common issues caused by dynamic imbalance can be effectively resolved by the proposed method in gyroscopes with a levitating rotor. Comparative simulation results before and after compensation have verified the effectiveness and superiority of the proposed compensation method.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.99-108
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• 1999

A four-degree-of-freedom non-linear model with time varying mesh stiffness has been developed for the dynamic analysis of spur gear trains. The model includes a spur gear pair, two shafts, two inertias representing load and prime mover. In the model, developed several factors such as time varying mesh stiffness and damping, separation of teeth, teeth collision, various gear errors and profile modifications have been considered. Two computer programs are developed to calculate stiffness of a gear pair and transmission error and the dynamic analysis of modeled system using time integration method. Dynamic tooth and mesh forces, dynamic factors are calculated. Numerical examples have been given, which shows the time varying mesh stiffness ha a significant effect upon the dynamic tooth force and torsional vibrations.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.457-469
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• 2022

Hydraulic concrete buildings in the northwest of China are often subject to the combined effects of low-temperature frost damage, during drying and wetting cycles, and salt erosion, so the study of concrete deterioration prediction is of major importance. The prediction model of the relative dynamic elastic modulus (RDEM) of four different kinds of modified concrete under the special environment in the northwest of China was established using Grey residual Markov theory. Based on the available test data, modified values of the dynamic elastic modulus were obtained based on the Grey GM(1,1) model and the residual GM(1,1) model, combined with the Markov sign correction, and the dynamic elastic modulus of concrete was predicted. The computational analysis showed that the maximum relative error of the corrected dynamic elastic modulus was significantly reduced, from 1.599% to 0.270% for the BS2 group. The analysis error showed that the model was more adjusted to the concrete mixed with fly ash and mineral powder, and its calculation error was significantly lower than that of the rest of the groups. The analysis of the data for each group proved that the model could predict the loss of dynamic elastic modulus of the deterioration of the concrete effectively, as well as the number of cycles when the concrete reached the damaged state.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.40
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• pp.1-14
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• 1996

We develop a dynamic demand forecasting model compared to regression analysis model and AutoRegressive Integrated Moving Average(ARIMA) model. The dynamic model can apply to the current dynamic data to forecasts through introducing state equation. A multiple regression model and ARIMA model using given data are designed via the model analysis. The forecasting fitness evaluation between the designed models and the dynamic model is compared with the criterion of sum of squared error.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.75-83
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• 2016

We propose methodology to analyze the dynamic mechanisms of financial market contagion under market integration using a biological contagion analytical approach. We employ U-statistic to measure market integration, and a dynamic model based on an error correction mechanism (single equation error correction model) and latent factor model to examine market contagion. We also use quantile regression and Wald-Wolfowitz runs test to test market contagion. This methodology is designed to effectively handle heteroscedasticity and correlated errors. Our simulation results show that the single equation error correction model fits well with the linear regression model with a stationary predictor and correlated errors.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.128-133
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• 2010

This paper presents a method for estimating the parameters of dynamic models for induction motor dominating loads. Using particle swarm optimization, the method finds the adequate set of parameters that best fit the sampling data from the measurement for a period of time, minimizing the error of the outputs, active and reactive power demands and satisfying the steady-state error criterion.

• Proceedings of the KSR Conference
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• 1998.11a
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• pp.280-287
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• 1998

In this paper, Dynamic Fault Tree algorithm(DFT algorithm) is presented. This new algorithm provides a concise representation of dynamic fault tolerance system structure with redundancy, dynamic redundancy management and complex fault ＆ error recovery techniques. And it allows the modeler to define a dynamic fault tree model with the relative advantages of both fault tree and Markov models that captures the system structure and dynamic behavior. This algorithm applies to TMR and Dual-Duplex systems with the dynamic behavior and show that this algorithm captured the dynamic behavior in these systems with fault ＆ error recovery technique, sequence-dependent failures and the use dynamic spare. The DFT algorithm for solving the problems of the systems is more effective than the Markov and Fault tree analysis model.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.124-131
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• 1997

This paper presents a modified adaptive control scheme that improves the transient performance of the overall system while maintaining the asymptotic convergence of the output error. The proposed control scheme is characterized as the added outer dynamic feedback loop on the conventional adaptive control scheme. This control scheme enables various robust control methods that were developed for standard model reference adaptive controllers to be applied to the proposed controller. In contrast with the modified adaptive controllers that use augmented errors to provide additional dynamic feedback, the proposed controller uses tracking error directly, thereby reducing the tracking error significantly in the transient state and making the error insensitive to noise.

• The Journal of Fisheries Business Administration
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• v.41 no.2
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• pp.107-133
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• 2010

As policy makers are often concerned about dynamic effects of demand behavior and its welfare analysis by quantity changes, the paper shows how dynamic price formation systems can be built up to analyze the effect of policy options to the markets dynamically. The paper develops dynamic model of price formation for fish from the intertemporal optimization of the consumer choice problem. While the resulting model has a similar form of the error correction types of dynamic price formation system, it provides the rational demand behavior contrary to the myopic behavior of error correction demand models. The paper also develops appropriate tools of dynamic welfare analysis in quantity space using only short-run demand estimates both theoretically and empirically as a first attempt in the literature of price formation and fisheries. The empirical results of Korean fish markets show that the dynamic model and the welfare measures are reasonably plausible. The methodology and theory of this research can be applied and extended to the commodity aggregation, dynamic demand estimation, and dynamic welfare effects of regulation in the similar framework. Thus, it is hoped that this will enhance its applications to the demand-side economics.