• Proceedings of the KSME Conference
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• 2001.06e
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• pp.329-334
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• 2001

In order to investigate the operating characteristics of a supersonic steam ejector, the axisymmetric, compressible, Reynolds-averaged, Navier-Stokes computations are performed using a finite volume method. The secondary and back pressures of the ejector system with a second throat are changed to investigate their effects on the suction mass flow. Three operation modes of the steam ejector system, the critical mode, subcritical mode and back flow mode, are discussed to predict the critical suction mass flow. The present computations are validated with some experimental results. The secondary and back pressures of the supersonic steam ejector significantly affect the critical suction mass flow. The present computations predict the experimented critical mass flow with fairly good accuracy. A good correlation is obtained for the critical suction mass flow. The present results show that provided the primary nozzle configuration and secondary pressure are known, we can predict the critical mass flow with good accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.212-217
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• 1993

In this paper, characteristics of the motions of a human arm are investigated experimentally. When the conditions of the target point are restricted, human adjusts its trajectory and velocity pattern of the arm to fit the conditions skillfully. The purpose of this work is to examine the characteristics of the trajectory, velocity pattern, and the size of the duration in the following cases. First, we examine the case of point-to-point motion. The results are consistent with the minimum jerk theory. However, individual differences in the length of the duration can be observed in the experiment. Second, we examine the case which requires accuracy of positioning at the target point. It is found that the velocity pattern differs from the bell shaped pattern explained by the minimum jerk theory, and has its peak in the first half of the duration. When higher accuracy of the positioning is required, learning effects can be observed. Finally, to examine the case which requires constraint of the arm posture at the target point, we conduct experiments of a human trying to grasp a cup. It is considered that this motion consists of two steps : one is the positioning motion of the person in order to start the grasping motion, the other is the grasping motion of the human's hand approaching toward the cup and grasping it. In addition, two representative velocity patterns are observed : one is the similar velocity pattern explained in the above experiment, the other is the velocity pattern which has its relative maximum in the latter half of the duration.

• Journal of Electrical Engineering and Technology
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• v.6 no.1
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• pp.1-7
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• 2011

This paper proposes an enhanced noise robust algorithm for fault location on double-circuit transmission line for the case of single line-to-ground (SLG) fault, which uses distributed parameter line model that also considers the mutual coupling effect. The proposed algorithm requires the voltages and currents from single-terminal data only and does not require adjacent circuit current data. The fault distance can be simply determined by solving a second-order polynomial equation, which is achieved directly through the analysis of the circuit. The algorithm, which employs the faulted phase network and zero-sequence network with source impedance involved, effectively eliminates the effect of load flow and fault resistance on the accuracy of fault location. The proposed algorithm is tested using MATLAB/Simulink under different fault locations and shows high accuracy. The uncertainty of source impedance and the measurement errors are also included in the simulation and shows that the algorithm has high robustness.

• Journal of Sensor Science and Technology
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• v.29 no.5
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• pp.303-311
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• 2020

Nondispersive infrared CO₂ gas sensor was developed after the simulation of optical cavity structure and assembling the optical components: IR source, concave reflectors, Fresnel lens, a hollow disk, and IR detectors. By placing a hollow disk in front of reference IR detector, the output voltages are almost constant value, near to 70.2 mV. The absorbance of IR light, F_a, shows the second order of polynomial according to ambient temperatures at 1,500 ppm. The differential output voltages and the absorbance of IR light give a higher accuracy in estimations of CO₂ concentrations with less than ± 1.5 % errors. After implementing the parameters that are dependent upon the ambient temperatures in microcontroller unit (MCU), the measured CO₂ concentrations show high accuracies (less than ± 1.0 %) from 281 K to 308 K and the time constant of developed sensor is about 58 sec at 301 K. Even though the estimation errors are relatively high at low concentration, the developed sensor is competitive to the commercial product with a high accuracy and the stability.

• Journal of Korean Society for Geospatial Information Science
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• v.7 no.1 s.13
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• pp.41-52
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• 1999

In order to correct mismatches between neighboring digital maps, the middle line method has been widely used. However, it may result in not only a corruption of the topological consistency between the objects near to boundaries but also degeneration of accuracy. In this paper, we propose two edge-matching methods to overcome the problem of the middle line method. The first method is based on the rubber sheeting, which performs an elastic transformation for the objects located around the boundaries. The second method transforms the geometry of objects by the function of the distance from the boundary. These methods have important advantages that they preserve the topology of the original maps and improve tile accuracy, compared with the previous methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.3
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• pp.179-188
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• 2015

We propose obstacle classification method based on 2D LIDAR(Light Detecting and Ranging) database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width, intensity, variance of range, variance of intensity data. The first classification was processed by the width data, and the second classification was processed by the intensity data, and the third classification was processed by the variance of range, intensity data. The classification was processed by comparing to database, and the result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.

• The Journal of Information Systems
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• v.27 no.3
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.72-78
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• 2007

Determination of the local vertical is not trivial for a moving vehicle and in general will require corrections for the Earth geophysical deflection. The vehicle's local vertical can be estimated by INS integration with initial alignment in SDINS(Strap Down INS) system. In general, the INS has drift error and it cause the performance degradation. In order to compensate the drift error, GPS/INS augmented system is widely used. And in the event that GPS is denied or unavailable, celestial navigation using star tracker can be a backup navigation system especially for the military purpose. In this celestial navigation system, the vehicle's position determination can be achieved using more than two star trackers, and the accuracy of position highly depends on accuracy of local vertical direction. Modern tilt sensors or accelerometers are sensitive to the direction of gravity to arc second(or better) precision. The local gravity provides the direction orthogonal to the geoid and, appropriately corrected, toward the center of the Earth. In this paper the relationship between direction of center of the Earth and actual gravity direction caused by geophysical deflection was analyzed by using precision orbit simulation program embedded the JGM-3 geoid model. And the result was verified and evaluated with mathematical gravity vector model derived from gravitational potential of the Earth. And also for application purpose, the performance variation of pure INS navigation system was analyzed by applying precise gravity model.

• Journal of IKEEE
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• v.18 no.4
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• pp.581-585
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• 2014

In this paper, we propose a pedestrian detection by applying the HOG feature using ROI. Conventional HOG method has high accuracy, but shows the disadvantage of slow processing speed. By applying the ROI to the conventional method reduce computations for unnecessary area. Therefore proposed method improves the processing speed. In order to set the ROI area, we propose a structure that combined odd frames and even frames. Odd frame is in charge of operation for the entire area. And even frame does the operation for the ROI area. Implementation results of proposed method maintaining the same accuracy as the conventional method show a 20% improved performance of 8.3 frames per second.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.