• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• The Plant Pathology Journal
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• v.35 no.1
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• pp.71-76
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• 2019

$SdhB^{P225F}$ and $SdhB^{H272R}$ mutations have been found associated with boscalid resistance in Botrytis cinerea from strawberry in Shanghai, China. For rapid detection of two mutations, tetra-primers were designed and optimized to gain the relatively high accuracy and specificity based on the ARMS-PCR technique, by which resistance can be identified with different lengths of products on agarose gels. The tetra-primer ARMS-PCR systems for $SdhB^{P225F}$ and $SdhB^{H272R}$ were validated by 9 SdhB-squenced strains repeatedly. Then, sensitivity of 30 more strains were also tested by the methods, which were accordant with genotypes by sequencing and the sensitivity of conidial germination to boscalid by 100%. Thus, the methods developed in this study are proved to be rapid, inexpensive, accurate and practical for resistance detection of Botrytis cinerea caused by $SdhB^{P225F}$ and $SdhB^{H272R}$ mutations.

• Structural Engineering and Mechanics
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• v.48 no.3
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• pp.291-308
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• 2013

Structural health monitoring is crucial to maintain the structural performance safely. Moreover, the Kullback-Leibler divergence (KLD) is applied usually to asset the similarity between different probability density functions in the pattern recognition. In this study, the KLD is employed to detect the damage. However the asymmetry of the KLD is a shortcoming for the damage detection, to overcoming this shortcoming, two other divergences and one statistic distribution are proposed. Then the damage identification by the KLD and its three descriptions from the symmetric point of view is investigated. In order to improve the reliability and accuracy of the four divergences, the gapped smoothing method (GSM) is adopted. On the basis of the damage index approach, the new damage index (DI) for detect damage more accurately based on the four divergences is developed. In the last, the grey relational coefficient and hypothesis test (GRCHT) is utilized to obtain the more precise damage identification results. Finally, a clear remarkable improvement can be observed. To demonstrate the feasibility and accuracy of the proposed method, examples of an isotropic beam with different damage scenarios are employed so as to check the present approaches numerically. The final results show that the developed approach successfully located the damaged region in all cases effect and accurately.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.479-493
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• 2016

LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.3
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• pp.206-211
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• 2005

The device makers want to make higher density chips on the wafer through scale-down. The change of WSix/poly-Si gate film thickness is one of the key issues under 100 nm device structure. As a new device etching process is applied, end point detection(EPD) time delay was occurred in DPS+ poly chamber of Applied Materials. This is a barrier of device shrink because EPD time delay made physical damage on the surface of gate oxide. To investigate the EPD time delay, the experimental test combined with OES(Optical Emission Spectroscopy) and SEM(Scanning Electron Microscopy) was performed using patterned wafers. As a result, a EPD delay time is reduced by a new chamber seasoning and a new wavelength line through plasma scan. Applying a new wavelength of 252 nm makes it successful to call corrected EPD in WSix/poly-Si stack-down gate etching in the DPS+ poly chamber for the current and next generation devices.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.659-666
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• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.319-327
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• 2022

Transformers are the most famous deep learning models that has achieved great success in natural language processing and also showed good performance on computer vision. In this survey, we categorized transformer-based models for computer vision, particularly object detection tasks and perform comprehensive comparative experiments to understand the characteristics of each model. Next, we evaluated the models subdivided into standard transformer, with key point attention, and adding attention with coordinates by performance comparison in terms of object detection accuracy and real-time performance. For performance comparison, we used two metrics: frame per second (FPS) and mean average precision (mAP). Finally, we confirmed the trends and relationships related to the detection and real-time performance of objects in several transformer models using various experiments.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2002.11a
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• pp.130-136
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• 2002

Device makers want to make higher density chips as devices shrink, especially WSix poly stack down is one of the key issues. However, EPD (End Point Detection) time delay was happened in DPS+ poly chamber which is a barrier to achieve device shrink because EPD time delay killed test pattern and next generation device. To investigate the EPD time delay, a test was done with patterned wafers. This experimental was carried out combined with OES(Optical Emission Spectroscopy) and SEM (Scanning Electron Microscopy). OES was used to find corrected wavelength in WSix stack down gate etching. SEM was used to confirm WSix gate profile and gate oxide damage. Through the experiment, a new wavelength (252nm) line of plasma is selected for DPS+ chamber to call correct EPD in WSix stack down gate etching for current device and next generation device.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2781-2784
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• 2007

Early detection of a disease is important to tackle treatment issues in a better manner. Several diagnostic techniques are in use, these days; for such purpose and tremendous research is going on to develop newer and newer methods. However, more work is required to be done to develop cheap and reliable early detection techniques. Micro-fluidic chips are also playing key role to deliver new devices for better health care. The present study focuses on a review of recent developments in the interrogation of different techniques and present state-of-the-art of microfluidic sensor for better, quick, easy, rapid, early, inexpensive and portable POCT (Point of Care testing device) device for a particular study, in this case, bone disease called osteoporosis. Some simulations of the microchip are also made to enable feasibility of the development of a blood-chip-based system. The proposed device will assist in early detection of diseases in an effective and successful manner.