• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.32-32
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• 2011

Plasma etching is used in microelectronic processing for patterning of micro- and nano-scale devices. Commonly, optical emission spectroscopy (OES) is widely used for real-time endpoint detection for plasma etching. However, if the viewport for optical-emission monitoring becomes blurred by polymer film due to prolonged use of the etching system, optical-emission monitoring becomes impossible. In addition, when the exposed area ratio on the wafer is small, changes in the optical emission are so slight that it is almost impossible to detect the endpoint of etching. For this reason, as a simple method of detecting variations in plasma without contamination of the reaction chamber at low cost, a method of measuring plasma impedance is being examined. The object in this research is to investigate the suitability of using plasma impedance monitoring (PIM) with statistical approach for real-time endpoint detection of $SiO_2$ etching. The endpoint was determined by impedance signal variation from I-V monitor (VI probe). However, the signal variation at the endpoint is too weak to determine endpoint when $SiO_2$ film on Si wafer is etched by fluorocarbon plasma on inductive coupled plasma (ICP) etcher. Therefore, modified principal component analysis (mPCA) is applied to them for increasing sensitivity. For verifying this method, detected endpoint from impedance analysis is compared with optical emission spectroscopy (OES). From impedance data, we tried to analyze physical properties of plasma, and real-time endpoint detection can be achieved.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.4
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• pp.49-54
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• 2005

Measuring defects on the inside and on the surface of a steel structure is very important technology in order to predict the life span of the structure. In particular, a place with a high probability that it may contain defects is a welded part and it is very important to check defects in the part, absence/presence of non-uniform substances, its shape, and the location. Many non-destructive tests can be applied, but the ultrasonic flow detection test is widely used with some advantages. The ultrasonic flow detection test, however, cannot be applied when there is a problem by a contact medium between PZT and a specimen, in case of a small and complicated shape or a moving object or when the specimen is hot. In this study, to solve the problems of the contact ultrasonic flow detection test, the non-contact ultrasonic flow detection test for sending/receiving ultrasonic waves using lasers was described. I intended to develop a non-destructive detection system applying the laser application ultrasonic test to a steel structure by detecting the defects inside of and on the surface of the specimen.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.100-103
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• 2010

In this paper we propose a method of detecting moving object in H.264/AVC bitstream by representing the $4{\times}4$ block partition units as nodes of graph. By constructing hierarchical graph by taking into account the relation between nodes and the spatial-temporal relations between graphs in frames, we are able to track small objects, distinguish two occluded objects, and identify objects that move and stop alternatively.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.297-302
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• 2009

In this paper, we propose advanced screen detector as a tool for selecting the object for tracking and estimating its distance from a screen using fuzzy logic in vision based pointing device. Our system classifies the line component of the input image into horizontal and vertical lines and applies the fuzzy rule to obtain the best line pair which constitute peripheral framework of the screen. The proposed system improves the detection ratio for detecting the screen in relative to the detector used in the previous works for hand-held type vision based pointing device. Also it allows to detect the screen even though a small part of it may be hidden behind other object.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.313-320
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• 2018

The importance of sensors on a self-driving vehicle has rising since it act as eyes for the vehicle. Lidar sensors based on laser technology tend to yield better image quality with more laser channels, thus, it has higher detection accuracy for obstacles, pedistrians, terrain, and other vechicles. However, incorporating more laser channels results higher unit price more than ten times, and this is a major drawback for using high channel lidar sensors on a vehicle for actual consumer market. To come up with this drawback, we propose a method of integrating multiple low channel, low cost lidar sensors acting as one high channel sensor. The result uses four 16 channels lidar sensors with small form factor acting as one bulky 64 channels sensor, which in turn, improves vehicles cosmetic aspects and helps widespread of using the lidar technology for the market.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.192-199
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• 2022

With the advent of autonomous ships, it is emerging as one of the very important issues not only to operate with a minimum crew or unmanned ships, but also to secure the safety of ships to prevent marine accidents. On-site inspection of the hull is mainly performed by the inspector's visual inspection, and video information is recorded using a small camera if necessary. However, due to the shortage of inspection personnel, time and space constraints, and the pandemic situation, the necessity of introducing an automated inspection system using artificial intelligence and remote inspection is becoming more important. Furthermore, research on hardware and software that enables the automated inspection system to operate normally even under the harsh environmental conditions of a ship is absolutely necessary. For automated inspection systems, it is important to review artificial intelligence technologies and equipment that can perform a variety of hull failure detection and classification. To address this, it is important to classify the hull failure. Based on various guidelines and expert opinions, we divided them into 6 types(Crack, Corrosion, Pitting, Deformation, Indent, Others). It was decided to apply object detection technology to cracks of hull failure. After that, YOLOv5 was decided as an artificial intelligence model suitable for survey and a common hull crack dataset was trained. Based on the performance results, it aims to present the possibility of applying artificial intelligence in the field by determining and testing the equipment required for survey.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2213-2220
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• 2017

This paper proposes an efficient approach for hardware implementation of moving object detection (MOD) processor using effective Gaussian mixture learning (EGML)-based background subtraction method. Arithmetic units used in background generation were implemented using LUT-based approximation to reduce hardware complexity. Hardware resources used for both background subtraction and Gaussian probability density calculation were shared. The MOD processor was verified by FPGA-in-the-loop simulation using MATLAB/Simulink. The MOD performance was evaluated by using six types of video defined in IEEE CDW-2014 dataset, which resulted the average of recall value of 0.7700, the average of precision value of 0.7170, and the average of F-measure value of 0.7293. The MOD processor was implemented with 882 slices and block RAM of $146{\times}36kbits$ on Virtex5 FPGA, resulting in 60% hardware reduction compared to conventional design based on EGML. It was estimated that the MOD processor could operate with 75 MHz clock, resulting in real-time processing of $800{\times}600$ video with a frame rate of 39 fps.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1305-1318
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• 2012

Image segmentation is one of the difficult research problems in machine vision and pattern recognition field. A commonly used segmentation method is the Otsu method. It is simpler and easier to implement but it fails if the histogram is unimodal or similar to unimodal. And if some target area is smaller than background object, then its histogram has the distribution close to unimodal. In this paper, we proposed an improved image segmentation method based on 1D Otsu method for a small target detection. To overcome drawbacks by unimodal histogram effect, we depressed the background histogram using a logarithm function. And to improve a signal to noise ratio, we used a local average value by the neighbor window for thresholding using 1D Otsu method. The experimental results show that our proposed algorithm performs better segmentation result than a traditional 1D Otsu method, and needs much less computational time than that of the 2D Otsu method.

• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.97-101
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• 2004

In this paper, we study the fishery detection system for protection of an aquamlture farm. The FDS(fishery detection system) will be recognize a robbing vessel with real time and variance the position of aquaculture farm. We try to develop the F-AIS(Fishery Automatic Identification System) which am be detect approaching object to aquaculture farm and distinguish our fishing boot from thief vessel. The F-AIS with low price and wideband responsibility am be adopt to the FDS, i. e. the identification for a small-sized fishing boots.