• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.366-371
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• 2009

A realization for an accurate position detecting system of a moving target in two dimensional plane using dual line CCDs and photometric interpolation is presented. The system is realized that the infrared LEDs are utilized for lighting source, a target size is recognized by the scanned data from CCD owing to blocking the radiated light path by placing the target between CCD and lighting source, a coordinate on the plane is found by plane trigonometry formed by the moving target and two CCD sensors, and the former scan data is used for the coordinate iteratively and the photometric interpolation is applied to sub-pixel of scanned image. The experimental results show that the experiment results in a success rate about 3 different size targets, 3, 5 and 7mmm on the test plane $210{\times}373mm$. The moving target positioning detected success rate is 93% in 3mm target, 5mm is 95.3%, and 7mm is 95.8% respectively. The photometric interpolation is enhanced to 1.5% in comparison to be unused.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1086-1092
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• 2009

Stereoscopic conversion has been an important and challenging issue for many 3-D video applications. Usually, there are two different stereoscopic conversion approaches, i.e., image motion-based conversion that uses motion information and object-based conversion that partitions an image into moving or static foreground object(s) and background and then converts the foreground in a stereoscopic object. As well, since the input sequence is MPEG-1/2 compressed video, motion data stored in compressed bitstream are often unreliable and thus the image motion-based conversion might fail. To solve this problem, we present the utilization of key-motion that has the better accuracy of estimated or extracted motion information. To deal with diverse motion types, a transform space produced from motion vectors and color differences is introduced. A key-motion is determined from the transform space and its associated stereoscopic image is generated. Experimental results validate effectiveness and robustness of the proposed method.

• Journal of Biosystems Engineering
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• v.25 no.5
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• pp.415-420
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• 2000

It is very difficult to mechanize tomato harvesting because identifying a target tomato which is partly covered by leaves and stalks is not easy. This research was conducted to develop tomato harvesting robot which can identifying a target tomato, determining its dimensional position, and harvesting it in a limited time. Followings were major findings in this study. The first visual system of the robot was composed of two CCD cameras, however, which could not detect tomato not placed on the center of lens and partly covered by leaves or stalks. Secondary visual device, combined with two cameras and pan tilting was designed which could decreased the positioning errors within $\pm$10mm but still not enough for covered tomato by any obstacles. Finally, laser detector was added to the visual system that could reduce the position detecting errors within 10mm in X-Y direction and 5mm in Z direction for the covered tomatoes.

• Smart Structures and Systems
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• v.29 no.4
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• pp.625-640
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• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.4
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• pp.125-132
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• 2021

As the layers of artificial neural network deepens, and the dimension of data used as an input increases, there is a problem of high arithmetic operation requiring a lot of arithmetic operation at a high speed in the learning and recognition of the neural network (NN). Thus, this study proposes a data dimensionality reduction method to reduce the dimension of the input data in the NN. The proposed Line-segment Feature Analysis (LFA) algorithm applies a gradient-based edge detection algorithm using median filters to analyze the line-segment features of the objects existing in an image. Concerning the extracted edge image, the eigenvalues corresponding to eight kinds of line-segment are calculated, using 3×3 or 5×5-sized detection filters consisting of the coefficient values, including [0, 1, 2, 4, 8, 16, 32, 64, and 128]. Two one-dimensional 256-sized data are produced, accumulating the same response values from the eigenvalue calculated with each detection filter, and the two data elements are added up. Two LFA256 data are merged to produce 512-sized LAF512 data. For the performance evaluation of the proposed LFA algorithm to reduce the data dimension for the recognition of handwritten numbers, as a result of a comparative experiment, using the PCA technique and AlexNet model, LFA256 and LFA512 showed a recognition performance respectively of 98.7% and 99%.

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.30-38
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• 2017

In this paper, we propose an underwater acoustic sensor fault detection method for passive sonar systems. In general, a passive sonar system displays processed results of array signals obtained from tens of the acoustic sensors as a two-dimensional image such as displays for broadband or narrowband analysis. Since detection result display in the operation software is to display the accumulated result through the array signal processing, it is difficult to determine the possibility where signal may be contaminated by the fault or failure of a single channel sensor. In this paper, accordingly, we propose a detection method based on the analysis of RMSCR (Root Mean Square Crossing-Rate), and the processing techniques for the faulty sensors are analyzed. In order to evaluate the performance of the proposed method, the precision of detecting fault sensors is measured by using signals acquired from real array being operated in several coastal areas. Besides, we compare performance of fault processing techniques. From the experiments, it is shown that the proposed method works well in underwater environments with high average RMS, and mute (set to zero) shows the best performance with regard to fault processing techniques.

• Journal of the Korea Society for Simulation
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• v.31 no.4
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• pp.23-31
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• 2022

The development of innovative technology through the 4th Industrial Revolution is actively used in the defense field. In particular, surveillance and reconnaissance capabilities using drones will be of great help to the development of military combat capabilities, such as preparing for future military personnel reductions and reinforcing alert capabilities. In this study, we analyze the combat efficiency of drones how helpful drones can be to the military operations through simulations. Drones and enemy move in the efficient shortest path within a two-dimensional space in which operational areas are mapped into number such as detection probability. Based on the detection probability of an enemy infiltrating along the path with the lowest detection probability, the detection probability change that occurs whenever a drone is additionally deployed is presented, and we analyze the combat efficiency according to the additional drone input. Simulation proves that the increase in combat efficiency decreases as more drones are added in small operational areas such as company-level operational areas. This study is expected to contribute to the efficient operation of a limited number of drones in company-level units and to help determine the most desirable quantity of drones for additional combat power improvement.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.3
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• pp.54-65
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• 1997

To quantify the LII signals from soot particle of flames in diesel engine cylinder, a new method has been proposed for correcting LII signal attenuated by soot particles between the measuring point and the detector. It has been verified by an experiment on a laminar jet ethylene-air diffusion flame. Being proportional to the attenuation, the ratio of LII signal at two different detection wavelengths can be used to correct the measured LIIsignal and obtain the unattenuated LII signal, from which the soot volume fraction in the flame can be estimated. Both the 1064-nm and frequency-doubled 532-nm beams from the Nd : YAG laser are used. Single-shot, one-dimensional(1-D) line images are recorded on the intensified CCD camera, with the rectangular-profile laser beam using 1-mm-diameter pinhole. Two broadband optical interference filters having the center wavelengths of 647 nm and 400 nm respectively and a bandwidth of 10 nm are used. This two-wavelength correction has been applied to the ethylene-air coannular laminar diffusion flame, previously studied on soot formation by the laser extinction method in this laboratory. The results by the LII measurement technique and the conventional laser extinction method at the height of 40 nm above the jet exit agreed well with each other except around outside of the peaks of soot concentration, where the soot concentration was relatively high and resulting attenuation of the LII signal was large. The radial profile shape of soot concentration was not changed a lot, but the absolute value of the soot volume fraction around outside edge changed from 4ppm to 6.5 ppm at r=2.8mm after correction. This means that the attenuation of LII signal was approximately 40% at this point, which is higher than the average attenuation rate of this flame, 10~15%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.125-133
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• 2008

In this paper, we propose active queue management mechanism (Active-WRED) to guarantee quality of the high priority service class in multi-class traffic service environment. In congestion situation, this mechanism increases drop probability of low priority traffic and reduces the drop probability of the high priority traffic, therefore it can improve the quality of the high priority service. In order to analyze the performance of our mechanism we introduce the stochastic analysis of a discrete-time queueing systems for the performance evaluation of the Active Queue Management (AQM) based congestion control mechanism called Weighted Random Early Detection (WRED) using a two-state Markov-Modulated Bernoulli arrival process (MMBP-2) as the traffic source. A two-dimensional discrete-time Harkov chain is introduced to model the Active-WRED mechanism for two traffic classes (Guaranteed Service and Best Effort Service) where each dimension corresponds to a traffic class with its own parameters.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.939-948
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• 2004

In this paper. we Propose a robust and fast image registration technique for motion correction in brain CT-CT angiography obtained from same patient to be taken at different time. First, the feature points of two images are respectively extracted by 3D edge detection technique, and they are converted to locally weighted 3D distance map in reference image. Second, we search the optimal location whore the cross-correlation of two edges is maximized while floating image is transformed rigidly to reference image. This optimal location is determined when the maximum value of cross-correlation does't change any more and iterates over constant number. Finally, two images are registered at optimal location by transforming floating image. In the experiment, we evaluate an accuracy and robustness using artificial image and give a visual inspection using clinical brain CT-CT angiography dataset. Our proposed method shows that two images can be registered at optimal location without converging at local maximum location robustly and rapidly by using locally weighted 3D distance map, even though we use a few number of feature points in those images.