• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.31-38
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• 1994

This paper proposed signal decomposition and multiresolution representation through wavelet transform using wavelet orthonormal basis. And it suggested most appropriate filter for scaling function in multiresoltion representation and compared two compression method, arithmetic coding and Huffman coding. Results are as follows 1. Daub18 coefficient is most appropriate in computing time, energy compaction, image quality. 2. In case of image browsing that should be small in size and good for recognition, it is reasonable to decompose to 3 scale using pyramidal algorithm. 3. For the case of progressive transmittion where requires most grateful image reconstruction from least number of sampls or reconstruction at any target rate, I embedded the data in order of significance after scaling to 5 step. 4. Medical images such as information loss is fatal have to be compressed by lossless method. As a result from compressing 5 scaled data through arithmetic coding and Huffman coding, I obtained that arithmetic coding is better than huffman coding in processing time and compression ratio. And in case of arithmetic coding I could compress to 38% to original image data.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1062-1070
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• 2009

The strip shape for the stainless steel process has made an issue of the strip quality, and hence the shape control method is developed at the Sendzimir rolling mill (ZRM). ZRM is a stainless cold rolling mill and has actuators for the shape control. They are first intermediate rolls and top crown rolls, which are controlled horizontally and vertically, respectively. The shape control of the stainless steel rolling process has difficulty in obtaining the symmetrical shape. The objective of the shape control is to minimize the shape deviation and to maintain stable state, which keeps symmetrical shape pattern in the lateral direction. The method of the shape recognition employs a least squares method and neural network. The shape deviation is the difference between the target shape and actual shape and is controlled by the fuzzy shape control. The fuzzy shape control using operator's informative knowledge is proposed in this paper. The experiments are carried out online for various stainless materials and sizes. The productivity of the rolling process has increased from 9.0 to 9.4 tons per hour.

• Journal of Forest and Environmental Science
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• v.33 no.3
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• pp.239-242
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• 2017

Although camera traps have been widely used to understand the abundance of wildlife in recent decades, the effort has been restricted to small sub-set of wildlife which can mark-and-recapture. The Random Encounter Model shows an alternative approach to estimate the absolute abundance from camera trap detection rate for any animals without the need for individual recognition. Our study aims to examine the feasibility and validity of the Random Encounter Model for the density estimation of endangered leopard cats (Prionailurus bengalensis) in Maekdo riparian park, Busan, South Korea. According to the model, the estimated leopard cat density was $1.76km^{-2}$ (CI 95%, 0.74-3.49), which indicated 2.46 leopard cats in $1.4km^2$ of our study area. This estimate was not statistically different from the previous leopard cat population count ($2.33{\pm}0.58$) in the same area. As follows, our research demonstrated the application and usefulness of the Random Encounter Model in density estimation of unmarked wildlife which helps to manage and protect the target species with a better understanding of their status.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.9
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• pp.1718-1726
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• 2006

In this paper, we proposed a fuzzy controller and algorithm for efficiently obstacle avoidance in unknown space. The ultrasonic sensor is used for position and distance recognition of obstacle, and fuzzy controller is used for left and right wheels angular velocity control. The fuzzification is used singleton method and the control rule is each wheel forty-nine. The fuzzy inference is used simplified Mamdani's reasoning and defuzzification is used SCOG(Simplified Center Of Gravity). The computer simulation based on mobile robot modelling was performed for the capacity of fuzzy controller and the really applicable possibility revaluation of the proposed avoidance algorithm and fuzzy controller. As a result, mobile robot was exactly reached in target and it avoided obstacle efficiently.

• Journal of The Korean Digital Architecture Interior Association
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• v.13 no.2
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• pp.5-16
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• 2013

With regard to the new town developments which have supplied lots of houses in a short period of time in Korea this study aims to evaluation and finding out problems of the developments finally to suggest the direction. A new town's competitiveness model set in the previous paper takes a role of research frame to recognize the problems and to show the direction. The model explains that new town's competitiveness is composed of 4 factors; Self-sufficiency, Innovativenss, Identity and Sustainability. Problems of the developments are as follows; incongruity of spatial structure especially in the capital region, deficiency of self-sufficiency resulted from single-use development, restriction on mixed development by a number of regulations in capital region, low business value, grand scale of land compensation, house oriented planning guidance, unfair share of infrastructure fee, and physical structure depending mainly on fossil energy. Based on this recognition this study conclusively suggests corresponding direction such as role performance as a means of urban growth management, promotion of quality of life by accumulating social capital, introduction of socially sustainable management program for the new towns, discovery and creation of town's value, reexamination of self-sufficiency's meaning or target, selective deregulation of metropolitan development, institutional strategy for cost reduction, changeover from house index to urban function oriented index, and pursuit of low-carbon green town.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.990-995
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• 2010

Harris corner detector is commonly used to detect feature points for recognizing 2-D or 3-D objects. However, the feature points calculated from both of query and target objects need to be same positions to guarantee accurate recognitions. In order to check the positions of calculated feature points, we generate a Huffman tree which is based on adjacent feature values as inputs. However, the structures of two Huffman trees will be same as long as both of a query and targets have same feature values no matter how different their positions are. In this paper, we sort feature values and calculate the Euclidean distances of coordinates between two adjacent feature values. The Huffman Tree is generated with these Euclidean distances. As a result, the information of point locations can be included in the generated Huffman tree. This is the main strategy for accurate recognitions. We call this system as the HRM(Hybrid Retrieval Machine). This system works very well even when artificial random noises are added to original data. HRM can be used to recognize biological data such as proteins, and it will curtail the costs which are required to biological experiments.

• BMB Reports
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• v.41 no.1
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• pp.23-28
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• 2008

The altered amino acid (AA) levels as neurotransmitter closely correlate to neurodegenerative conditions including Alzheimer's disease (AD). Target profiling analysis of nineteen AAs in brain cortex samples from three Tg2576 mice as AD model and three littermate mice as control model was achieved as their N(O,S)-ethoxycarbonyl/tert-butyldimethylsilyl derivatives by gas chromatography. Subsequently, star pattern recognition analysis was performed on the brain AA levels of AD mice after normalization to the corresponding control median values. As compared to control mice, $\gamma$-aminobutyric acid among ten AAs found in brain samples was significantly reduced (P < 0.01) while leucine was significantly elevated (P < 0.02) in AD mice. The normalized AA levels of the three AD mice were transformed into distorted star patterns which was different from the decagonal shape of control median. The present method allowed visual discrimination of the three AD mice from the controls based on the ten normalized AA levels.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.6
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• pp.118-123
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• 2008

Weakness process can be called ferrule array and epoxy filling process at connector manufacturing process, and a lot of problems happen as think as general manufacturing process at early investment. Wished to improve this and working environment mend of worker on childhood(planning phase) and problem that is happened at done ferrule array and epoxy injection by emphasis target. By ferrule sorting and Improvement of epoxy filling process, bring authoritativeness elevation of product by fraction defective decrease of product by sized work along with productivity elevation. On the other hand, working jigs are various in characteristics of optical connector manufacturing line. There have been lots of restriction in practice because the applicability of this system is only for single type model though the network should be smooth between lines. Thus, it is not only needed the recognition of necessity in industrial line but also the development of automation system arraying ferrule and filling epoxy in the manufacturing process. It is found that the present system development enhances productivity fairly and prevents industrial disaster in the optical connector manufacturing system.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.163-171
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• 2016

Lane detection is a widely researched topic. Although simple road detection is easily achieved by previous methods, lane detection becomes very difficult in several complex cases involving noisy edges. To address this, we use a Convolution neural network (CNN) for image enhancement. CNN is a deep learning method that has been very successfully applied in object detection and recognition. In this paper, we introduce a robust lane detection method based on a CNN combined with random sample consensus (RANSAC) algorithm. Initially, we calculate edges in an image using a hat shaped kernel, then we detect lanes using the CNN combined with the RANSAC. In the training process of the CNN, input data consists of edge images and target data is images that have real white color lanes on an otherwise black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) of 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged to form a deep structure. Our proposed lane detection algorithm successfully eliminates noise lines and was found to perform better than other formal line detection algorithms such as RANSAC

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1199-1203
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• 2007

As technologies are developed and systems are complicated, hazards embedded in the system are also increasing. proving safety and managing the safetyis more scientific and organizational domain so that safety management system is pursuing to be activeformation detecting the factors of hazard and managing them beyond passiveway. In the future, in order to establish and manage national safety management system, it is important to have effective system and manage it and also more important that all the people related to target system has to change their recognition and to playroles in it. Many railway safety measures reduce railway fatalities into half for last 10 years. But more improvement in railway safety is required to meet the social need after railway fire accident in Daegue. After the Daegue subway train fire accident, the Korean government has been trying to prepare a nation-wide railway safety program, a safety organization, and a Safety Act. To construct a nation-wide railway safety management program, system architecture was established.