• Korean Journal of Remote Sensing
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• v.13 no.2
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• pp.121-132
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• 1997

This paper describes the development of a stereo matching algorithm for extracting Digital Elevation Model(DEM) from satellite images. This matching algorithm is based on a non-linear least squares correlation estimation but has improved matching speed. The algorithm consists of three steps: matching execution, matching control and matching optimization. Each is described. The performance of the presented algorithm is quantitatively analyzed with experiments on matching probability, matching speed and matching convergence radius.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.1
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• pp.67-73
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• 2002

This paper presents the development of development of stabilization and tracking algorithms for a shipboard satellite antenna system. In order to stabilize the satellite antenna system designed in the previous work, a model for each control axis is derived and its parameters are estimated using a genetic algorithm, and the state feedback controller is designed based on the linearized model. Then a tracking algorithm is derived to overcome some drawbacks of the step tracking. The proposed algorithm searches for the best position using gradient-based formulae and signal intensities measured according to a search pattern. The effectiveness of both the stabilization and tracking algorithms is demonstrated through experiment using real-world data.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.944-952
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• 2001

Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it is desired to combine INS with external aids such as GPS. However GPS informations have a randomly abrupt jump due to a sudden corruption of the received satellite signals and environment, and moreover GPS can\`t provide navigation solutions. In this paper, smoothing and prediction schemes are proposed for GPS`s jump or unavailable GPS. The smoothing algorithm which is designed as a scalar adaptive filter, smooths abrupt jump. The prediction algorithm which is proved by Schuler error model of INS, estimates INS error in appropriate time. The outputs of proposed algorithm apply stable measurements to GPS aided INS Kalman filter. Simulations show that the proposed algorithm can effectively remove measurement jump and predict INS error.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.1
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• pp.9-15
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• 2019

Sensorless electric superchargers have recently been actively developed to provide a large amount of oxygen to engines in order assist the combustion process for miniaturizing the engines and improving fuel efficiency. The model-based sensorless method for surface-mounted permanent magnet synchronous motors has a disadvantage in that the system may become unstable due to parameter variations in low-speed operation and the rapid-acceleration section. An electric supercharger requires fast response to improve the engine response delay, such as the turbocharger turbo-rack. Therefore, the responsiveness must be improved to use the model-based sensorless system. The position compensation algorithm designed in this study is controlled by converting the position error into the beta, which is the angle formed by the d-axis and the stator current during sudden speed change. In this study, we improved the response of the model-based sensorless system through the algorithm and verified the algorithm validity by applying the algorithm to an actual dual-motor supercharger.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.87-97
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• 2000

Signal optimization model is divided bandwidth-maximizing model and delay-minimizing model. Bandwidth-maximizing model express model formulation as MILP(Mixed Integer Linear Programming) and delay-minimizing model like TRANSYT-7F use "hill climbing" a1gorithm to optimize signal times. This study Proposed optimization model using genetic algorithm one of evolution algorithm breaking from existing optimization model This Proposed model were tested by several scenarios and evaluated through NETSIM with TRANSYT-7F\`s outputs. The result showed capability that can obtain superior solution.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.72-82
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• 2023

The EOTS(Electro Optical Tracking System) must have stabilization performance to provide high-quality images under disturbance environment. In this paper, we present a controller that can minimize the LOS error and has a simple structure. Hence, to evaluate the performance of this controller, analysis in the frequency domain and LOS error measurement are performed. In order to measure the LOS error without a 'rate table' that requires a lot of facility investment, we propose a design method for disturbance model that considers the operating environment of the EOTS. Finally, the performance of the stabilization algorithm is evaluated by the proposed disturbance model.

• Journal of Microbiology and Biotechnology
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• v.14 no.6
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• pp.1163-1169
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• 2004

An unstructured mathematical model is presented for lactic acid fermentation based on the energy balance. The proposed model reflects the energy metabolic state and then predicts the cell growth, lactic acid production, and glucose consumption rates by relating the above rates with the energy metabolic rate. Fermentation experiments were conducted under various initial lactic acid concentrations of 0, 30, 50, 70, and 90 g/l. Also, a genetic algorithm was used for further optimization of the model parameters and included the operations of coding, initialization, hybridization, mutation, decoding, fitness calculation, selection, and reproduction exerted on individuals (or chromosomes) in a population. The simulation results showed a good fit between the model prediction and the experimental data. The genetic algorithm proved to be useful for model parameter optimization, suggesting wider applications in the field of biological engineering.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.115-123
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• 2005

This paper presents the development of an algorithm for position control of feed drives in machine tools. The algorithm is constructed through an experimental method based on proportional control with a ramp input. In the first step of designing, a tracking-error curve is generated with the proportional control, and then an error model is decided to reduce the tracking error, Next, the output signal of the error model is added to the current error signal to yield the actuating error signal. The effectiveness of the proposed scheme is confirmed through simulation and experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.11-19
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• 1994

The matching probability P(ο/$\lambda$), of the signal sequence(ο) observed for a finite time interval with a HMM (Hidden Markov Model $\lambda$) indicates the probability that signal comes from the given model. By utilizing the fact that the probability represents matching score of the observed signal with the model we can recognize an unknown signal pattern by comparing the magnitudes of the matching probabilities with respect to the known models. Because the algorithm however uses floating point variables during the computing process hardware implementation of the algorithm requires floating point units. This paper proposes an integer algorithm which uses positive integer numbers rather than float point ones to compute the matching probability so that we can economically realize the algorithm into hardware. The algorithm makes the model parameters integer numbers by multiplying positive constants and prevents from divergence of data through the normalization of variables at each step. The final equation of matching probability is composed of constant terms and a variable term which contains logarithm operations. A scheme to make the log conversion table smaller is also presented. To analyze the qualitive characteristics of the proposed algorithm we attatch simulation result performed on two groups of 10 hypothetic models respectively and inspect the statistical properties with repect to the model order the magnitude of scaling constants and the effect of the observation length.