• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.161-172
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• 2021

Neuromorphic architecture is drawing attention as a next-generation computing that supports artificial intelligence technology with low energy. However, FPGA embedded boards based on Neuromorphic architecturehave limited resources due to size and power. In this paper, we compared and evaluated the image reduction method using the interpolation method that rescales the size without considering the feature points and the DCT (Discrete Cosine Transform) method that preserves the feature points as much as possible based on energy. The scaled images were compared and analyzed for accuracy through CNN (Convolutional Neural Networks) in a PC environment and in the Nengo framework of an FPGA embedded board.. As a result of the experiment, DCT based classification showed about 1.9% higher performance than that of interpolation representation in both CNN and FPGA nengo environments. Based on the experimental results, when the DCT method is used in a limited resource environment such as an embedded board, a lot of resources are allocated to the expression of neurons used for classification, and the recognition rate is expected to increase.

• Journal of the Korean Society of Radiology
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• v.3 no.3
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• pp.5-11
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• 2009

When using Image Guided Radiation Therapy, the patient is placed using skin marker first and after confirming anatomical location using OBI, the couch is moved to correct the set up. Evaluation for the error made at that moment was done. Through comparing $0^{\circ}$ and $270^{\circ}$ direction DRR image and OBI image with 2D-2D matching when therapy planning, comparison between patient's therapy plan setup and actual treatment setup was made to observe the error. Treatment confirmation on important organs such as head, neck and spinal cord was done every time through OBI setup and other organs such as chest, abdomen and pelvis was done 2 ~ 3 times a week. But corrections were all recorded on OIS so that evaluation on accuracy could be made through using skin index which was divided into head, neck, chest and abdomen-pelvis on 160 patients. Average setup error for head and neck patient on each AP, SI, RL direction was $0.2{\pm}0.2cm$, $-0.1{\pm}0.1cm$, $-0.2{\pm}0.0cm$, chest patient was $-0.5{\pm}0.1cm$, $0.3{\pm}0.3cm$, $0.4{\pm}0.2cm$, and abdomen was $0.4{\pm}0.4cm$, $-0.5{\pm}0.1cm$, $-0.4{\pm}0.1cm$. In case of pelvis, it was $0.5{\pm}0.3cm$, $0.8{\pm}0.4cm$, $-0.3{\pm}0.2cm$. In rigid body parts such as head and neck showed lesser setup error compared to chest and abdomen. Error was greater on chest in horizontal axis and in AP direction, abdomen-pelvis showed greater error. Error was greater on chest in horizontal axis because of the curve in patient's body when the setup is made. Error was greater on abdomen in AP direction because of the change in front and back location due to breathing of patient. There was no systematic error on patient setup system. Since OBI confirms the anatomical location, when focus is located on the skin, it is more precise to use skin marker to setup. When compared with 3D-3D conformation, although 2D-2D conformation can't find out the rolling error, it has lesser radiation exposure and shorter setup confirmation time. Therefore, on actual clinic, 2D-2D conformation is more appropriate.

• Journal of Korea Game Society
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• v.16 no.5
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• pp.89-98
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• 2016

The purpose of this paper is to analyze corporate image of Korean game companies. The questionnaires such as corporate image, gaming playing period, dispute with parents included The lowest score was 2.75 on Han Game which is servicing web board games. Kakao which is a game platform got the highest score. Result through the tests showed that 5 companies were categorized into 3 groups, lower group(Han Game and NEXON), middle (NCSoft and Netmarble), higher (Kakao). Correlation with gender factor showed that only NEXON is significant. NEXON Score by female is higher than that of male. In addition, longer playing period on NEXON games has negative corporate image. Correlation between conflict with parents and negative corporate image of NEXON is also significant.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.339-348
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• 2017

Due to recent improvements in computer processing speed and image processing technology, researches are being actively carried out to combine information from a camera with existing GNSS (Global Navigation Satellite System) and dead reckoning. In this study, the mathematical model based on SPR (Single Photo Resection) is derived for image-based assistant algorithm for vehicle positioning. Simulation test is performed to analyze factors affecting SPR. In addition, GNSS/on-board vehicle sensor/image based positioning algorithm is developed by combining image-based positioning algorithm with existing positioning algorithm. The performance of the integrated algorithm is evaluated by the actual driving test and landmark's position data, which is required to perform SPR, based on simulation. The precision of the horizontal position error is 1.79m in the case of the existing positioning algorithm, and that of the integrated positioning algorithm is 0.12m at the points where SPR is performed. In future research, it is necessary to develop an optimized algorithm based on the actual landmark's position data.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.337-343
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• 1996

In this paper, we investigate the defect detection by making use of pre-made reference image data and classify the defects by using the artificial neural network. The approach is composed of three main parts. The first step consists of a proper generation of two reference image data by using a low level morphological technique. The second step proceeds by performing three times logical bit operations between two ready-made reference images and just captured image to be tested. This results in defects image only. In the third step, by extracting four features from each detected defect, followed by assigning them into the input nodes of an already trained artificial neural network we can obtain a defect class corresponding to the features. All of the image data are formed in a bit level for the reduction of data size as well as time saving. Experimental results show that proposed algorithms are found to be effective for flexible defect detection, robust classification, and high speed process by adopting a simple logic operation.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.361-369
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• 1998

Recently, the improvement of on-board satellite sensors covering hyperspectral image sensors, high spatial resolution sensors provide data on earth in diverse aspect. The application field relating remotely sensed data also varies depending on what type of job one wants. The various resolution of sensors from low to extremely high is also available on the market with a user defined specific location. The expense to purchase remote sensed data is going down compare to the cost it need past few years ago in terms of research or private use. Now, the satellite remote sensed data is used on the field of forecasting, forestry, agriculture, urban reconstruction, geology, or other research field in order to extract meaningful information by applying special techniques of image processing. There are many image processing packages available worldwide and one common aspect is that they are expensive. There need to be a advanced satellite data processing package for people who can not afford commercial packages to apply special remote sensing techniques on their data and produce valued-added product. The study was carried out with the purpose of developing a special satellite data processing package which covers almost every satellite produced data with normal image processing functions and also special functions needed on specific research field with friendly graphical user interface (GUI). And for the people with any background of remote sensing with windows platform.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.556-561
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• 2006

It is very important to classify the DNA Chip image pattern in order to acquire useful information about genetic disease of people. In this paper, we developed the novel pattern classification method of DNA Chip image using MLP based back-propagation and Self organizing Map learning algorithm. And then we compared and analyzed these classified pattern results. Also we carried out experiment in the MV2440 board using CPU Cote for S3C2440(ARM 920T) and PC environment, and displayed its results in order to give the genetic information to user mote easily in various environment.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.10
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• pp.1701-1716
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• 2010

This is a follow-up study of "A study on the color image of baby's wear brands". This study was performed to classify the style images and analyze the style and color images of 7 baby wear brands that suggests how their harmony and consistency identify their clothing images. In order to classify the style image, questionnaires were organized with 40 pieces of the style board of 7 brands and 20 baby wear images adjectives. The total 324 copies were used to survey students who majored in fashion design. Questionnaires were analyzed by factor analysis from the SPSS 12.0 package program. The results of this study are as follows: First, the style image of baby wear brands was classified by 4 factors, 'loveliness', 'chic', 'liveliness', and 'pureness'. This was similar with the results of the color images that were surveyed in advance of this study. There were different characteristics for the style image of 7 baby wear brands. Second, gender (boy, girl, and new-borns) affected the style image and not season. Third, the harmony and consistency of the style and color image of 7 baby wear brands were different. It was possible to identify the clothing image of 5 baby wear brands by consistency and harmony in style and color images. However, it was evaluated that 2 baby wear brands were needed to plan the style and color image closely as well as harmoniously to make their clothing image definitive.

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.

• The Journal of Korean Society for Radiation Therapy
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• v.19 no.1
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• pp.1-5
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• 2007

Purpose: The accuracy and advantages of OBI(On Board Imager) against the conventional method like film and EPID for the setup error correction were evaluated with the analysis of the accumulated data which were produced in the process of setup error correction using OBI. Materials and Methods: The results of setup error correction using OBI system were analyzed for the 130 patients who had been planned for 3 dimensional conformal radiation therapy during March 2006 and May 2006. Two kilo voltage images acquired in the orthogonal direction were fused and compared with reference setup images. The setup errors in the direction of vertical, lateral, longitudinal axis were recorded and calculated the distance from the isocenter. The corrected setup error were analyzed according to the lesion and the degree of shift variations. Results: There was no setup error in the 41.5% of total analyzed patients and setup errors between 1mm and 5mm were found in the 52.3%. 6.1% patients showed the more than 5mm shift and this error were verified as a difference of setup position and the movement of patient in a treatment room. Conclusion: The setup error analysis using OBI in this study verified that the conventional setup process in accordance with the laser and field light was not enough to get rid of the setup error. The KV images acquired using OBI provided good image quality for comparing with simulation images and much lower patients' exposure dose compared with conventional method of using EPID. These advantages of OBI system which were confirmed in this study proved the accuracy and priority of OBI system in the process of IGRT(Image Guided Radiation Therapy).