• Journal of the Korean Society of Visualization
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• v.8 no.4
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• pp.31-37
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• 2010

In this study, a novel hybrid micro/nano PIV system combining defocusing and TIRFM technique has been developed for the multiscale flow measurement. With the developed system, both far and near field velocity fields have been measured simultaneously in a 2D straight microchannel and the particle trajectories were extracted by the nearest tracking algorithm. The shear rate values taken from experimental results have been estimated by comparing with the analytical solution of 2D Poiseuille flow and it is confirmed that the result shows good agreement with the theoretical value.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.299-305
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• 2014

In this paper, a simple version of the hybrid navigation system using Kalman filter is proposed. The implemented hybrid navigation system is composed of a GPS to measure the position and the velocity, and a IMU(inertial measurement unit) to measure the acceleration and the posture of a mobile robot. A discrete Kalman filter is applied to provide the position of the robot by fusing both of the sensor data. When GPS signal is available, the navigation system estimates the position of the robot from the Kalman filter using position and velocity from GPS, and acceleration from IMU. During the interval until next GPS signal arrives, the system calculates the position of the robot using acceleration from IMU and velocity obtained at the previous step. Performance of the navigation system is verified by comparing the real path and the estimated path of the mobile robot. From experiments, we conclude that the navigation system is acceptable for the mobile robot.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.535-544
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• 2017

In-flight aircraft engine performance estimation is one of the key techniques for advanced intelligent engine control and in-flight fault detection, isolation and accommodation. This paper detailed the current performance degradation estimation methods, and an improved hybrid Kalman filter via velocity-based LPV (VLPV) framework for these needs is proposed in this paper. Composed of a nonlinear on-board model (NOBM) and VLPV, the filter shows a hybrid architecture. The outputs of NOBM are used for the baseline of the VLPV Kalman filter, while the system performance degradation factors on-line estimated by the measured real system output deviations are fed back to the NOBM for its updating. In addition, the setting of the process and measurement noise covariance matrices' values are also discussed. By applying it to a commercial turbofan engine, simulation results show the efficiency.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.464-471
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• 2013

This paper proposes a hybrid searching algorithm based on parameter identification for power system load models. Hybrid searching was performed by the combination of particle swarm optimization (PSO) and a complex method, which enhances the convergence of solutions closer to minima and takes advantage of global searching with PSO. In this paper, the load model of interest is composed of a ZIP model and a third-order model for induction motors for stability analysis, and parameter sets are obtained that best-fit the output measurement data using the hybrid search. The origin of the hybrid method is to further apply the complex method as a local search for finding better solutions using the selected particles from the performed PSO procedure.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• Journal of Ocean Engineering and Technology
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• v.17 no.4
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• pp.73-80
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• 2003

This paper presents considerations on the results of the rotating arm test, which was carried out for assessment of an hybrid navigation system for a semi-autonomous underwater vehicle. The navigation system consists of an inertial measurement unit(IMU), an ultra-short baseline(USBL) acoustic navigation sensor and a doppler velocity log(DVL) accompanying a magnetic compass. A navigational systemmodel is derived to include the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters are 25 in the order. The extended Kalman filter was used to propagate the error covariance, The rotating arm tests were carried out in the Ocean Engineering Basin of KRISO, to generate circular motion. The hybrid underwater navigation system shows good tracking performance against the circular planar motion. Additionally this paper checked the effects of the sampling ratio of the navigation system and the possibility of the dead reckoning with the DVL and the magnetic compass to estimate the position of the vehicle.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.187-188
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• 2011

This paper proposes the hybrid Complex-PSO algorithm based on the complex search method and particle swarm optimization (PSO) for unconstrained optimization. This hybridization intends to produce faster and more accurate convergence to the optimum value. These hybrid will concentrate on determining the dynamic load model parameters, the ZIP model and induction motor model parameters. Measurement-based parameter estimation, which employs measurement data to derive load model parameters, is used. The theoretical foundation of the measurement-based approach is system identification. The main objective of this paper is to demonstrate how the standard particle swarm optimization and complex method can be improved through hybridization of the two methods and the results will be compared with that of their original forms.

• Advances in nano research
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• v.12 no.3
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• pp.263-279
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• 2022

In this research, the physical education training quality was investigated using the entropy model to compute variance associated with a random value (a strong tool). The entropy and undefined estimation principles are used to extract the greatest entropy of information dependent on the index system. In the study of tennis motion tracking from a dynamic viewpoint, such stages are utilized to improve the perception of the players' achievement (Lv et al. 2020). Six female tennis players served on the right side (50 cm from the T point). The initial flat serve from T point was the movement under consideration, and the entropy was utilized to weigh all indications. As a result, a multi-index measurement vector is stabilized, followed by the confidence level to determine the structural plane establishment range. As a result, the use of the unascertained measuring technique of information entropy showed an excellent approach to assessing athlete performance more accurately than traditional ways, enabling coaches and athletes to enhance their movements successfully.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.03a
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• pp.271-277
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• 2004

As one of the concepts of the laser/electric hybrid propulsion system, a feasibility study on possibilities of electrostatic acceleration of a laser ablation plasma induced from a solid target was conducted. Energy distributions of accelerated ions were measured by a Faraday cup. A time-of-flight measurement was also conducted for ion velocity measurement. It was found that an average speed of ions from a pure laser ablation in this case was about 20 km/sec for pulse energy of 40 $\mu$J/pulse with pulse width of 250 psec. On the other hand, through an electrostatic field with a + I ,000 V electrode, the speed could be accelerated up to 40 km/sec. It was shown that the electrode with positive potential was more effective than that with negative potential for positive-ion acceleration in laser induced plasma, or pulsed plasma, in which ions were induced with the Coulomb explosion following electrons. In addition, the ion-acceleration or deceleration strongly depended on conditions of pairs of inner diameter and electrodes gap.

• Transactions of the KSME C: Technology and Education
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• v.4 no.1
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• pp.19-26
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• 2016

This paper presents the measurement and analysis results for the performance of HGCHP system using a cooling tower as a supplemental heat rejector. In order to demonstrate the performance of the hybrid approach, we installed the monitoring equipments including sensors for measuring temperature and power consumption, and measured operation parameters from February 1, 2014 to February 28, 2015. Leaving load temperatures to building showed an average value of $11.7^{\circ}C$ for cooling and $39.5^{\circ}C$ for heating, respectively. From the analysis, the daily PF of hybrid GCHP system varied from 2.6 to 6.6 over the measurement period.