• Journal of The Korean Society of Inherited Metabolic disease
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• v.2 no.1
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• pp.77-79
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• 2002

The urea cycle, consisting of a series of six enzymatic reactions, plays key roles to prevent the accumulation of toxic nitrogenous compound and synthesize arginine de novo. Five well characterized diseases have been described, resulting from an enzymatic defect in the biosynthesis of one of the normally expressed enzyme. This presentation will focus on two representative diseases; ornithine transcarbamylase(OTC) deficiency and citrullinemia(argininosuccinate synthetase deficiency). OTC deficiency is one of the most common inborn error of urea cycle, which is inherited in X-linked manner. We identified 17 different mutations in 20 unrelated Korean patients with OTC deficiency; L9X, R26P, R26X, T44I, R92X, G100R, R141Q, G195R, M205T, H214Y, D249G, R277W, F281S, 853 del C, R320X, V323M and 10 bp del at nt. 796-805. These mutations occur at well conserved nucleotide sequences across species or CpG hot spot. The L9X and R26X lead to the disruption of leader sequences, required for directing mitochondrial localization of the OTC precursor. Their phenotypes are severe, and neonatal onset. The G100R, R277W and V323M mutations were uniquely identified in patients with late onset OTC deficiency. The other genotypes are associated with neonatal onset. Out of 20 patients with OTC deficiency, only 6 patients are alive; two were liver transplanted, and normal in growth and development at 2, 4 years after transplantation respectively. Citrullinemia is an autosomal recessive disease, caused by the mutations in the argininosuccinate synthetase(ASS) gene. We identified in 3 major mutations in 11 unrelated Korean patients with citrullinemia; G324S, $IVS6^{-2}$ A to G, and 67 bp ins at nt 1125-1126. Among these, the 67 base pair insertion mutation is novel. The allele frequency of each mutation is; G324S(45%), IVS6-2 A to G(32%), and 67 base pair insertion(14%). All patients are diagnosed at neonatal or infantile age. Interestingly, two patients presented with stroke like episode. Out of 11 patients, 5 patients died. Among 6 patients alive, one patient was successfully liver transplanted.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.1
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• pp.64-73
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• 2012

In this paper, a new land preview system using laser range finder based on heave estimation algorithm is proposed. The proposed land preview system is an equipment which measures the shape of forward topography for autonomous vehicle. To implement this land preview system, the laser range finder is generally used because of its wide measuring range and robustness under various environmental condition. Then the current location of the vehicle has to be known to generate the shape of forward topography and sensors based on acceleration such as IMU and accelerometer are generally utilized to measure heave motion in the conventional land preview system. However the drawback to these sensors is that they are too expensive for low-cost vehicle such as mobile robot and their measurement error is increased for mobile robot with abrupt acceleration. In order to overcome this drawback, an algorithm that estimates heave motion using the information of odometer and previously measured topography is proposed in this paper. The proposed land preview system based on the heave estimation algorithm is verified through simulation and experiments for various terrain using a simulator and a real system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1C
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• pp.1-7
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• 2012

At the industrial work site, the manufacturing process is being automated to improve work efficiency. However, it is often difficult to automate the entire manufacturing process, and there are spaces in which workers there are constantly exposed to danger. To protect such workers from the danger, this paper studied a worker safety management system for the industrial work site which uses a location recognition system and which is based on the Ubiquitous-Wireless Sensor Network (U-WSN). Using wireless signals, the distance between two devices can be measured and the location of a worker can be calculated using triangularization in 3-D. But at the industrial work sites where there are a lot of steel and structures, errors occur due to signal reflection and multi-path, etc., which makes it difficult to get the accurate location. To address this problem the following was done: first, a circular polarization patch antenna appropriate to the work site was used to reduce the degree of error that may occur from the antenna emission pattern and the particular Line of Sight (LOS); second, a 3-D localization technique and a filtering algorithm were used to improve the accuracy of location determination. The developed system was tested by using it on a wharf crane to validate its accuracy and effectiveness. The proposed location recognition system is expected to contribute greatly in ensuring the safety of workers at industrial work sites.

• Progress in Medical Physics
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• v.24 no.4
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• pp.290-294
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• 2013

Stereotactic Radiosurgery require high accuracy and precision of patient positioning and target localization. We evaluate the real time positioning accuracy of isocenter using optic guided patient positioning system, ExacTrac (BrainLab, Germany), during spinal radiosurgery procedure. The system is based on real time detect multiple body markers attached on the selected patient skin landmarks. And a custom designed patient positioning verification tool (PPVT) was used to check the patient alignment and correct the patient repositioning before radiosurgery. In this study, We investigate the selected 8 metastatic spinal tumor cases. All type of tumors commonly closed to thoracic spinal code. To evaluate the isocenter positioning, real time patient alignment and positioning monitoring was carried out for comparing the current 3-dimensional position of markers with those of an initial reference positions. For a selected patient case, we have check the isocenter positioning per every 20 millisecond for 45 seconds during spinal radiosurgery. In this study, real time average isocenter positioning translation were $0.07{\pm}0.17$ mm, $0.11{\pm}0.18$ mm, $0.13{\pm}0.26$ mm, and $0.20{\pm}0.37$ mm in the x (lateral), y (longitudinal), z (vertical) directions and mean spatial error, respectively. And body rotations were $0.14{\pm}0.07^{\circ}$, $0.11{\pm}0.07^{\circ}$, $0.03{\pm}0.04^{\circ}$ in longitudinal, lateral, table directions and mean body rotation $0.20{\pm}0.11^{\circ}$, respectively. In this study, the maximum mean deviation of real time isocenter positioning translation during spinal radiosurgery was acceptable accuracy clinically.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.469-476
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• 1998

In the case of acquisition and displaying medical Images such as ultrasonography and endoscopy on VGA monitor of PC system, image degradation of tear-drop appears through scan conversion. In this study, we compare several methods which can solve this degradation and implement the hardware system that resolves this problem in real-time with PC. It is possible to represent high quality image display and real-time processing and acquisition with specific de-interlacing device and PCI bridge on our hardware system. Image quality is improved remarkably on our hardware system. It is implemented as PC-based system, so acquiring, saving images and describing text comment on those images and PACS networking can be easily implemented.metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.267-277
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• 2019

Photogrammetry and computer vision are identical in determining the three-dimensional coordinates of images taken with a camera, but the two fields are not directly compatible with each other due to differences in camera lens distortion modeling methods and camera coordinate systems. In general, data processing of drone images is performed by bundle block adjustments using computer vision-based software, and then the plotting of the image is performed by photogrammetry-based software for mapping. In this case, we are faced with the problem of converting the model of camera lens distortions into the formula used in photogrammetry. Therefore, this study described the differences between the coordinate systems and lens distortion models used in photogrammetry and computer vision, and proposed a methodology for converting them. In order to verify the conversion formula of the camera lens distortion models, first, lens distortions were added to the virtual coordinates without lens distortions by using the computer vision-based lens distortion models. Then, the distortion coefficients were determined using photogrammetry-based lens distortion models, and the lens distortions were removed from the photo coordinates and compared with the virtual coordinates without the original distortions. The results showed that the root mean square distance was good within 0.5 pixels. In addition, epipolar images were generated to determine the accuracy by applying lens distortion coefficients for photogrammetry. The calculated root mean square error of y-parallax was found to be within 0.3 pixels.

• Journal of IKEEE
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• v.27 no.3
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• pp.345-349
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• 2023

In this paper, we propose a study on the development of deep learning structure for defective pixel detection of next-generation smart LED display board using imaging device. In this research, a technique utilizing imaging devices and deep learning is introduced to automatically detect defects in outdoor LED billboards. Through this approach, the effective management of LED billboards and the resolution of various errors and issues are aimed. The research process consists of three stages. Firstly, the planarized image data of the billboard is processed through calibration to completely remove the background and undergo necessary preprocessing to generate a training dataset. Secondly, the generated dataset is employed to train an object recognition network. This network is composed of a Backbone and a Head. The Backbone employs CSP-Darknet to extract feature maps, while the Head utilizes extracted feature maps as the basis for object detection. Throughout this process, the network is adjusted to align the Confidence score and Intersection over Union (IoU) error, sustaining continuous learning. In the third stage, the created model is employed to automatically detect defective pixels on actual outdoor LED billboards. The proposed method, applied in this paper, yielded results from accredited measurement experiments that achieved 100% detection of defective pixels on real LED billboards. This confirms the improved efficiency in managing and maintaining LED billboards. Such research findings are anticipated to bring about a revolutionary advancement in the management of LED billboards.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.414-421
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• 2024

In this paper, we design and verify a system that can detect piping leakage noise in an environment with significant reverberation and reflection using a multi-channel acoustic sensor module as a technology to prevent major plant accidents caused by leakage. Four-channel microphones arranged in a tetrahedron are designed as a single sensor module to measure three-dimensional sound intensity vectors. In an environment with large effects of reverberation and reflection, the measurement error of each sensor module increases on average, so after placing multiple sensor modules in the field, measurement results showing locations with large errors due to effects such as reflection are excluded. Using the intersection between three-dimensional vectors obtained from several pairs of sensor modules, the coordinates where the sound source is located are estimated, and outliers (e.g., positions estimated to be outside the site, positions estimated to be far from the average position) are detected and excluded among the points. For achieving aforementioned goal, an excluding algorithm by deciding the outliers among the estimated positions was proposed. By visualizing the estimated location coordinates of the leakage sound on the site drawing within 1 second, we construct and verify a system that can detect the location of the leakage sound in real time and enable immediate response. This study is expected to contribute to improving accident response capabilities and ensuring safety in large plants.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.455-468
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• 1998

For the objective interpretation of cerebral metabolic patterns in epilepsy patients, we developed computer-aided classifier using artificial neural network. We studied interictal brain FDG PET scans of 257 epilepsy patients who were diagnosed as normal(n=64), L TLE (n=112), or R TLE (n=81) by visual interpretation. Automatically segmented volume of interest (VOI) was used to reliably extract the features representing patterns of cerebral metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.