• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.39.2-39.2
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• 2018

GW170817/GRB170817A establishes a double neutron star merger as the progenitor of a short gamma-ray burst, starting 1.7 s post-coalescence. GRB170817A represents prompt or continuous emission from a newly formed hyper-massive neutron star or black hole. We report on a deep search for broadband extended gravitational-wave emission in spectrograms up to 700 Hz of LIGO O2 data covering this event produced by butterfly filtering comprising a bank of templates of 0.5 s. A detailed discussion is given of signal-to-noise ratios in image analysis of spectrograms and confidence levels of candidate features. This new pipeline is realized by heterogeneous computing with modern graphics processor units (GPUs). (Based on van Putten, M.H.PM., 2017, PTEP, 093F01.)

• Journal of KSNVE
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• v.9 no.5
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• pp.991-997
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• 1999

It is getting higher the demand on a good sound in a car. Like some other sensory qualities, achieving a good sound is still treated as a black-box followed by the individual taste of tuning engineers. This study aims to define the attributes of a good car audio sound and the assessment factors affecting the sound quality. With a variety of surveys on professional listening groups and car audio installers, 26 most frequently used vocabularies are selected as typical presentations of car audio sound quality. After repetitions of listening tests with deliberately selected test CD, pre-recorded 15 different digital car-recordings, headphones replay system, and three different listening groups, it is proved that there are at least 5 different assessment factors which significantly affect the car audio sound quality. They are high fidelity, spectral balance, tonal accuracy, sound stage and acoustic fault.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.2
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• pp.26-38
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• 2021

Image coloration refers to adding plausible colors to a grayscale image or video. Image coloration has been used in many modern fields, including restoring old photographs, as well as reducing the time spent painting cartoons. In this paper, a method is proposed for colorizing grayscale images using a convolutional neural network. We propose an encoder-decoder model, adapting FusionNet to our purpose. A proper loss function is defined instead of the MSE loss function to suit the purpose of coloring. The proposed model was verified using the ImageNet dataset. We quantitatively compared several colorization models with ours, using the peak signal-to-noise ratio (PSNR) metric. In addition, to qualitatively evaluate the results, our model was applied to images in the test dataset and compared to images applied to various other models. Finally, we applied our model to a selection of old black and white photographs.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.1-11
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• 2021

This paper presents a procedure to extract lane line models using a set of proposed methods. Firstly, an image warping method based on homography is proposed to transform a target image into an image which is efficient to find lane pixels within a certain region in the image. Secondly, a method to use the combination of the results of edge detection and HSL (Hue, Saturation, and Lightness) transform is proposed to detect lane candidate pixels with reliability. Thirdly, erroneous candidate lane pixels are eliminated using a selection area method. Fourthly, a method to fit lane pixels to quadratic polynomials is proposed. In order to test the validity of the proposed procedure, a set of black-box images captured under varying illumination and noise conditions were used. The experimental results show that the proposed procedure could overcome the problems of color-only and edge-only based methods and extract lane pixels and model the lane line geometry effectively within less than 0.6 seconds per frame under a low-cost computing environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1380-1386
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• 2007

This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is blacker white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm. Experiments using real images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed.

• Journal of Radiation Protection and Research
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• v.36 no.2
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• pp.79-85
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• 2011

OThe purpose of this article was to measure and compare the value of the metal artifact reduction (MAR) algorithm by Dual energy(kVp switching) CT (Computed Tomography) for non using MAR and we introduced new variable Dual energy CT applications through a clinical scan. The used equipment was GE Discovery 750HD with Dual-Energy system(kVp switching). CT scan was performed on the neck and abdomen area subject for patients. Studies were from Dec 20 2010 to Feb 10 2011 and included 25 subject patients with prosthesis. We were measured the HU (Hounsfield Unit) and noise value at metal artifact appear(focal loss of signal and white streak artifact area) according to the using MAR algorithm. Statistical analyses were performed using the paired sample t-test. In patient subject case, the statistical difference of showing HU was p=0.01 and p=0.04 respectively. At maximum black hole artifact area and white streak artifact area according to the using MAR algorithm. However noise was p=0.05 and p=0.04 respectively; and not the affected black hole and white streak artifact area. Dual Energy CT with the MAR algorithm technique is useful reduce metal artifacts and could improve the diagnostic value in the diagnostic image evaluation of metallic implants area.

• Journal of Korea Multimedia Society
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• v.16 no.8
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• pp.962-971
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• 2013

In this paper, we propose a method to infer the user's behavior and situation through collected data from multi-sensor equipped with a smart clothing and it was implemented as a smart-phone App. This smart-clothes is able to monitor wearer users' health condition and activity levels through the gyro, temp and acceleration sensor. Sensed vital signs are transmitted to a bluetooth-enabled smart-phone in the smart-clothes. Thus, users are able to have real time information about their user condition, including activities level on the smart-application. User context reasoning and behavior determine is very difficult using multi-sensor depending on the measured value of the sensor varies from environmental noise. So, the reasoning and the digital filter algorithms to determine user behavior reducing noise and are required. In this paper, we used Multi-black Filter and SVM processing behavior for 3-axis value as a representative value of one.

• Publications of The Korean Astronomical Society
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• v.22 no.2
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• pp.43-54
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• 2007

Many gravitational wave detectors are now being built or under operation throughout the world. In particular, LIGO has taken scientific data several times, although current sensitivity is not sufficient to detect the weak signals routinely. However, the sensitivities have been improving steadily over past years so that the real detection will take place in the near future. Data analysis is another important area in detecting the gravitational wave signal. We have carried out the basic research in order to implement data analysis software in Korea@home environment. We first studied the LIGO Science Collaboration Algorithm Library(LAL) software package, and extracted the module that can generate the virtual data of gravitational wave detector. Since burst sources such as merging binaries of neutron stars and black holes are likely to be detected first, we have concentrated on the simulation of such signals. This module can generate pure gravitational wave forms, noise suitable for LIGO, and combination of the signal and noise. In order to detect the gravitational signal embedded in the noisy data, we have written a simple program that employs 'matched filtering' method which is very effective in detecting the signal with known waveform. We found that this method works extremely well.

• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.587-599
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• 2011

As one measure of image interpretability, NIIRS(National Imagery Interpretability Rating Scale) has been used. Unlike MTF(Modulation Transfer Function), SNR(Signal to Noise Ratio), and GSD(Ground Sampling Distance), NIIRS can describe the quality of overall image at user's perspective. NIIRS is observed with human observation directly or estimated by edge analysis. For edge analysis specially manufactured artificial target is used commonly. This target, formed with a tarp of black and white patterns, is deployed on the ground and imaged by the satellite. Due to this, the artificial target-based method needs a big expense and can not be performed often. In this paper, we propose a new edge analysis method that enables to estimate NIIRS accurately. In this method, natural targets available in the image are used and characteristics of the target are considered. For assessment of the algorithm, various experiments were carried out. The results showed that our algorithm can be used as an alternative to the artificial target-based method.

• 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