• Journal of Institute of Control, Robotics and Systems
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• v.8 no.1
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• pp.60-66
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• 2002

In this paper, a new target pointing guidance algorithm is proposed by combining the optimal target pointing solution and a simple time-to-Go estimator. Also investigated are some properties of the guidance algorithm which include a relation to conventional PNG, convergence region and convergence trajectories of error states according to the time-to-go estimator gain. Some guidelines for designing the pointing guidance law are commented based on the convergence properties. A design example in the case of large initial heading errors is presented and its performance is investigated by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.468-473
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• 1993

Augmented proportional navigation requires the information of a target acceleration, which must be estimated by a filtering logic. The process necessary accompanies a time lag, which degrades the guidance performance. A trade-off study between augmented and conventional proportional navigation is conducted with the time lag taken into consideration. The result shows that the conventional proportional navigation has better performance against a target maneuver in the missile-target coplane, while the augmented has better performance against out-of-coplane maneuver.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.42-45
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• 1987

In this paper a target tracking algorithm of the centroid extraction from moving edges is proposed, It aims to avoid the difficulty of imahe segmentation in case of the centroid extraction from one frame. The performance of the proposed algorithmfor noisy and occluded images is discussed Finally it is also applied to a real time target tracker.

• Journal of Intelligence and Information Systems
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• v.4 no.2
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• pp.23-34
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• 1998

Travel time forecasting, especially public bus travel time forecasting in urban areas, is a difficult and complex problem which requires a prohibitively large computation time and years of experience. As the network of target area grows with addition of streets and lanes, computational burden of the forecasting systems exponentially increases. Even though the travel time between two neighboring intersections is known a priori, it is still difficult, if not impossible, to compute the travel time between every two intersections. For the reason, previous approaches frequently have oversimplified the transportation network to show feasibilities of the problem solving algorithms. In this paper, forecasting of the travel time between every two intersections is attempted based on travel time data between two neighboring intersections. The time stamps data of public buses which recorded arrival time at predetermined bus stops was extensively collected and forecast. At first, the time stamp data was categorized to eliminate white noise, uncontrollable in forecasting, based on wavelet conversion. Then, the radial basis neural networks was applied to remaining data, which showed relatively accurate results. The success of the attempt was confirmed by the drastically reduced relative error when the nodes between the target intersections increases. In general, as the number of the nodes between target intersections increases, the relative error shows the tendency of sharp increase. The experimental results of the novel approaches, based on wavelet conversion and neural network teaming mechanism, showed the forecasting methodology is very promising.

• International Journal of Quality Innovation
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• v.4 no.1
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• pp.191-204
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• 2003

The target costing technique, mathematically discussed by Sauers, only uses the $C_p index along with Taguchi loss function and $\bar{X}$-P control charts to setup goal control limits. The new specification limits derived from Taguchi loss function is linked through the $C_p value to $\bar{X}$-P control charts to obtain goal control limits. Studies have shown that the point estimator of the $C_p index, $C_p, could vary from time to time due to the sampling error. The suggested approach is to use confidence intervals, especially the lower confidence intervals, to replace the point estimator. Therefore, an improvement on target costing technique is presented by applying the lower confidence interval of the $C_p index and using both Taguchi and Spiring's loss functions together with $\bar{X}$-P charts to make this technique more robust in practice. An example is also provided to illustrate how the improved target costing technique works.

• Journal of Biomedical Engineering Research
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• v.24 no.1
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• pp.45-53
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• 2003

Based on the 4-compartmental pharmacokinetic model developed in PART1, target-controlled infusion(TCI) pump system was designed and evaluated. The TCI system consists of digital board including microcontroller and digital signal process(DSP), analog board, motor-driven actuator, user friendly interface, power management and controller. It provides two modes according to the drugs: plasma target concentration and effect target concentration. Anaesthetist controls the depth of anaesthesia for patients by adjusting the required concentration to maintain both plasma and effect site in drug concentration. The data estimated in DSP include infusion rate, initial load dose, and rotation number of motor encoder. During TCI operation, plasma concentration. effect site concentration, awaken concentration, context-sensitive decrement time and system error information are displayed in real time. Li-ion battery guarantees above 2 hours without power line failure. For high reliability of the system, two microprocessors were used to perform independent functions for both pharmacokinetic algorithm and motor control strategy.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.541-547
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• 1992

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three-dimensional adaptive tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track update illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver detector. A detailed simulation is conducted to test the validity of our tracking algorithm for encounter geometries under various conditions of maneuver.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1073-1080
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• 2011

In this study, a target detection algorithm was proposed for using hyperspectral imagery. The proposed algorithm is designed to have minimal processing time, low false alarm rate, and flexible threshold selection. The target detection procedure can be divided into two steps. Initially, candidates of target pixel are extracted using matching ratio of spectral pattern that can be calculated by spectral derivation. Secondly, spectral distance is computed only for those candidates using Euclidean distance. The proposed two-step method showed lower false alarm rate than the Euclidean distance detector applied over the whole image. It also showed much lower processing time as compared to the Mahalanobis distance detector.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.62-72
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• 1993

The phased array antenna has the ability to perform adaptive sampling by directing the radar beam without inertia in any direction. The adaptive sampling capability of the phased array antenna allows each sampling time interval to be varied for each target, depending on the acceleration of each target at any time. In this paper we design a three dimensional adaptive target tracking algorithm for the phased array radar system with a given set of measurement parameters. The tracking algorithm avoids taking unnecessarily frequent samples, while keeping the angular prediction error within a fraction of antenna beamwidth so that the probability of detection will not be degraded during a track updata illuminations. In our algorithm, the target model and the sampling rate are selected depending on the target range and the target maneuver status which is determined by a maneuver level detector. A detailed simulation is conducted to test the validity of our tracking algorithm for target trajectories under various conditions of maneuver.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.93-100
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• 2000

Target signal feature parameters are very important to classify target by active sonar. Two highly correlated broad band pulses separated by time T have a time separation pitch(TSP) of 1/T Hz which is equal to the trough-to-trough or peak-to-peak spacing of its spectrum. In this study, TSP informations which represent feature of each target signal were effectively extracted by the FFT. The extracted TSP feature parameters were also applied to the pattern recognition algorithm to classify target and to analyze their properties.