• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.315-316
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• 1998

The purpose of this study is a potentiality examination of an infrared camera system for the 3D navigator which can be used in the field of neurosurgery. Nowadays, CAS(computer aided surgery) technique using 3D navigator is rapidly spread into the neurosurgeric operation such as brain and spine surgery. Several techniques which can detect 3D position of a probe have been proposed. These include mechnical arm, magnetic field, optical and ultrasonic sensing methods. In this study, we decided that using optical sensing method and tested a conventional CCD camera with a infrared filter and LEDs. Pulnix TM-300 camra has sufficient spectral response in the range of near infrared. Acquired image of infrared LEDs also sufficient quality.

• Korean Journal of Bronchoesophagology
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• v.4 no.1
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• pp.43-51
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• 1998

Background： Three-dementional imaging with spiral CT(3D spiral CT) is a well established imaging modality which has been investigated in various clinical settings. However the 3D spiral CT in upper airway disease is rarely reported and its results are still obscure. Objectives： To access the usefulness of 3D spiral CT imaging in patients with upper airway diseases. Materials and Methods We performed 3D spiral CT in fourteen patients In whom upper airway diseases were clinically suspected. Nine of these patients had upper airway stenosis, two had laryngeal cartilage fracture, and three had laryngo-hypopharyngeal cancer. For evaluation of location and extent of the lesions, we compared the findings of 3D imaging with those of air tracheogram, conventional 2D CT images, endoscopic and operative findings. Results： In case of stenosis, 3D spiral CT provide significant useful information, particularly the site and length of the stenotic segment. But, it was difficult to define the fracture of the laryngeal framework and to detect the cartilagenous invasion by head and neck cancer using the 3D imaging. Conclusion : The 3D spiral CT was an useful adjunctive method to assess some kind of upper airway disease but not in others. So, we should compare the findings of 3D images with those of other diagnostic tools for accurate diagnosis of the upper airway disease.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.41-51
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• 2018

Recently, in the field of video surveillance, deep learning based learning method is applied to intelligent video surveillance system, and various events such as crime, fire, and abnormal phenomenon can be robustly detected. However, since occlusion occurs due to the loss of 3d information generated by projecting the 3d real-world in 2d image, it is need to consider the occlusion problem in order to accurately detect the object and to estimate the pose. Therefore, in this paper, we detect moving objects by solving the occlusion problem of object detection process by adding depth information to existing RGB information. Then, using the convolution neural network in the detected region, the positions of the 14 keypoints of the human joint region can be predicted. Finally, in order to solve the self-occlusion problem occurring in the pose estimation process, the method for 3d human pose estimation is described by extending the range of estimation to the 3d space using the predicted result of 2d keypoint and the deep neural network. In the future, the result of 2d and 3d pose estimation of this research can be used as easy data for future human behavior recognition and contribute to the development of industrial technology.

• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.415-423
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• 2009

Because of the fast changing car design and increasing facilities, manufacturing process of cars is getting more complex now a days. Particularly, car manufacturing system that consist of automated devices, applies various simulation techniques to validate device motion and detect collision. To cope with this problem, traditional manufacturing system deployed test-run with the real devices. However, increased computing power in a contemporary manufacturing system changes it into realistic 3D simulation environment. Similarly, managed device data that was generated using 2D traditionally, can be converted to 3D realistic simulation. The existing problem with 3D simulation is disjoint data interaction between different work stations. Consequently, JIGs, fixing the car part accurately, are changed according to fixing position on the part or a part shape properties. In practice, the 3D JIG data has to be managed according to kinematic information, but not of its features. However, generating kinematic information to the 3D model repeatedly according to frequent change in part is not explained in current literatures. To fill this knowledge gap, this paper suggests an improving method of rendering 3D JIG kinematics information to simulation model. Thereafter, it shows the result of implementation.

• Journal of the Korea Society of Computer and Information
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• v.15 no.3
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• pp.111-119
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• 2010

An artificial light source has a character that a light emits from a point to all directions with many radial and straight ray shapes. It is very difficult and complex to render those emitting lights. Also, users have difficulty in expressing exactly 3D objects because of colors varying with changing of a light and having many parameters. In this paper, to solve those problems we design an artificial light source using X3D to create a model that represents easily many radial and straight ray shapes and propose the online system that each factors of colors to be reflected by a light is separated and then users can control them to detect object's colors by a mouse. Various light sources with reality can be easily created using proposed system.

• Journal of Magnetics
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• v.18 no.3
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• pp.342-347
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• 2013

Local wall thinning in pipelines affects the structural integrity of industries, such as nuclear power plants (NPPs). In the present study, a development of pulsed eddy current (PEC) technology that detects the wall thinning of pipelines covered with insulation is reviewed. The methods and experimental results, which have two kinds of probe with a single and double core, were compared. For this purpose, the single and double core probes having one and two excitation coils have been devised, and the differential probe with two Hall sensors has been fabricated to measure the wall thinning in insulated pipelines. The test sample is a stainless steel having different thickness, laminated by plastic insulation to simulate the pipelines in NPPs. The excitation coils in the probe is driven by a rectangular current pulse, the difference of two Hall sensors has been measured as a resultant PEC signal. The peak value of the detected signal is used to describe the wall thinning. The double core probe has better performance to detect the wall thinning covered with insulation; the single core probe can detect the wall thinning up to an insulation thickness of 18 mm, whereas the double probe can detect up to 25 mm. The results show that the double core PEC probe has the potential to detect the wall thinning in an insulated pipeline of the NPPs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.494-504
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• 2004

The researches about gaze detection have been much developed with many applications. Most previous researches only rely on image processing algorithm, so they take much processing time and have many constraints. In our work, we implement it with a computer vision system setting a IR-LED based single camera. To detect the gaze position, we locate facial features, which is effectively performed with IR-LED based camera and SVM(Support Vector Machine). When a user gazes at a position of monitor, we can compute the 3D positions of those features based on 3D rotation and translation estimation and affine transform. Finally, the gaze position by the facial movements is computed from the normal vector of the plane determined by those computed 3D positions of features. In addition, we use a trained neural network to detect the gaze position by eye's movement. As experimental results, we can obtain the facial and eye gaze position on a monitor and the gaze position accuracy between the computed positions and the real ones is about 4.2 cm of RMS error.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.130-135
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• 2012

In this paper, we propose a technique to detect the size and location of the small target in images by using Gaussian kernel repeatedly. In order to detect the size and location of the small target, we find the local maximum value by applying the facet model and then use the $3{\times}3$ Gaussian kernel repeatedly. we determine the size of small target by comparing the local maximum value $D_2$ according to the number of iteration. To reduce the computational complexity, we use the Gaussian pyramid when using the kernel repeatedly. Through the experiment, we verified that the size and location of the small target is detected by the number of iterations and results show improvements from conventional methods.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.168-174
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• 2022

Hydrogen sulfide(H₂S) gas is a high-risk gas that can cause suffocation or death in severe cases, depending on the concentration of exposure. Various studies to detect this gas are still in progress. In this study, we demonstrate a colorimetric sensor that can detect H₂S gas using its direct color change. The proposed nanofiber sensor containing a dye material named Lead(II) acetate, which changes its color according to H₂S gas reaction, is fabricated by electrospinning. The performance of this sensor is evaluated by measuring RGB changes, ΔE value, and gas selectivity. It has a ΔE value of 5.75 × 10^-3 ΔE/s·ppm, showing improved sensitivity up to 1.4 times that of the existing H₂S color change detection sensor, which is a result of the large surface area of the nanofibers. The selectivity for H₂S gas is confirmed to be an excellent value of almost 70 %.