• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.518-523
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• 2017

This paper proposes a novel denoising method based on non-local(NL) means. The NL-means algorithm is effective for removing an additive Gaussian noise, but the denoising parameter should be controlled depending on the noise level for proper noise elimination. Therefore, the proposed method optimizes the denoising parameter according to the noise levels. The proposed method consists of two processes: off-line and on-line. In the off-line process, the relations between the noise level and the denoising parameter of the NL-means filter are analyzed. For a given noise level, the various denoising parameters are applied to the NL-means algorithm, and then the qualities of resulting images are quantified using a structural similarity index(SSIM). The parameter with the highest SSIM is chosen as the optimal denoising parameter for the given noise level. In the on-line process, we estimate the noise level for a given noisy image and select the optimal denoising parameter according to the estimated noise level. Finally, NL-means filtering is performed using the selected denoising parameter. As shown in the experimental results, the proposed method accurately estimated the noise level and effectively eliminated noise for various noise levels. The accuracy of noise estimation is 90.0% and the highest Peak Signal-to-noise ratio(PSNR), SSIM value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.525-531
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• 2018

This paper proposes a bi-component dye, including the unripe fruit of Diospyros kaki THUNB and Ecklonia cava, to substitute for traditional persimmon dyeing because fabrics dyed with persimmon juice become stiffer and natural persimmon is insufficient for dyeing. This study examined the color difference and fabric stiffness depending on the ratio of Ecklonia cava for in a one-bath dye solution with Diospyros kaki THUNB and showed that 6% of Ecklonia cava in the bi-component dye was the optimum for decreasing the fabric stiffness. Based on these results, a bi-component dye constituting of 94% Diospyros kaki THUNB and 6% Ecklonia cava was prepared. The particle size was found to be smaller than both single dyes and it maintained a similar amount of Catechin to Diospyros kaki THUNB dye. Finally, cotton fabric dyed with a bi-component dye was much improved in terms of the fabric hand and the surface color was similar to that of the traditional persimmon-dyed fabric. These results could help to develop the natural persimmon dyeing industry.

• Resources Recycling
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• v.22 no.2
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• pp.53-61
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• 2013

Since the 2000s, to start inducement of SCR(Selective Catalytic Reduction) denitrification facility by large scale companies which are emitted large amount of nitrogen oxides such as power plants, combined heat and power plant, incinerators and chemical plants due to take effect the regulation of stationary sources of nitrogen oxide(NOx), and the total amount of discharged pollutants, such as regulatory gradually emissions regulations are being strengthened and the expanded coverage due to the use of SCR denitrification catalyst is a growing trend. Since 2010 due to the new catalysts to replace the already installed power plants and incinerators due to inactive, and catalytic denitrification SCR waste catalyst waste as a resource rather than the development of technologies for recycling situation is urgently needed. In this study, analyzed paper and patent for recycling technologies of waste catalyst. The range of search was limited in the open patents of USA (US), European Union (EP), Japan (JP), Korea (KR) and SCI journals from 1975 to 2012. Patents and journals were collected using key-words searching and filtered by filtering criteria. The trends of the patents and journals was analyzed by the years, countries, companies, and technologies.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.6
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• pp.271-280
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• 2017

In this paper, we propose a new safety system composed of wearable devices, driver's seat belt, and integrating controllers. The wearable device and driver's seat belt capture driver's biological information, while the integrating controller analyzes captured signal to alarm the driver or directly control the car appropriately according to the status of the driver. Previous studies regarding driver's safety from driver's seat, steering wheel, or facial camera to capture driver's physiological signal and facial information had difficulties in gathering accurate and continuous signals because the sensors required the upright posture of the driver. Utilizing wearable sensors, however, our proposed system can obtain continuous and highly accurate signals compared to the previous researches. Our advanced wearable apparatus features a sensor that measures the heart rate, skin conductivity, and skin temperature and applies filters to eliminate the noise generated by the automobile. Moreover, the acceleration sensor and the gyro sensor in our wearable device enable the reduction of the measurement errors. Based on the collected bio-signals, the criteria for identifying the driver's condition were presented. The accredited certification body has verified that the devices has the accuracy of the level of medical care. The laboratory test and the real automobile test demonstrate that our proposed system is good for the measurement of the driver's condition.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.259-274
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• 2014

On Aug 18, 2013, Sakurajima volcano in Japan erupted on a relatively large-scale. Geostationary Ocean Color Imager (GOCI) had used to detect volcanic ash in the surrounding area on the next day of this eruption. The geomagnetic variation has been analyzed using geomagnetic data from Cheongyang observatory in Korea and several geomagnetic observatories in Japan. First, we reconstruct geomagnetic data by principal component analysis and conduct semblance analysis by wavelet transform. Secondly, we minimize the error of solar effect by using wavelet based semblance filtering with Kp index. As a result of this study, we could confirm that the geomagnetic variation usually occur at the moment of Sakurajima volcano eruption. However, we cannot rule out the possibilities that it could have been impacted by other factors besides volcanic eruption in other variation's cases. This research is an exceptional study to analyze geomagnetic variation related with abroad volcanic eruption uncommonly in Korea. Moreover, we expect that it can help to develop further study of geomagnetic variation involved in earthquake and volcanic eruption.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.168-177
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• 2020

Seabed multiple reflections (seabed multiples) are the main cause of misinterpretations of primary reflections in both shot gathers and stack sections. Accordingly, seabed multiples need to be suppressed throughout data processing. Conventional model-driven methods, such as prediction-error deconvolution, Radon filtering, and data-driven methods, such as the surface-related multiple elimination technique, have been used to attenuate multiple reflections. However, the vast majority of processing workflows require time-consuming steps when testing and selecting the processing parameters in addition to computational power and skilled data-processing techniques. To attenuate seabed multiples in seismic reflection data, input gathers with seabed multiples and label gathers without seabed multiples were generated via numerical modeling using the Marmousi2 velocity structure. The training data consisted of normal-moveout-corrected common midpoint gathers fed into a U-Net neural network. The well-trained model was found to effectively attenuate the seabed multiples according to the image similarity between the prediction result and the target data, and demonstrated good applicability to field data.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.117-130
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• 2020

Noises can distort acquired geophysical data, leading to their misinterpretation. Potential noises sources include anthropogenic activity, natural phenomena, and instrument noises. Conventional denoising methods such as wavelet transform and filtering techniques, are based on subjective human investigation, which is computationally inefficient and time-consuming. Recently, many researchers attempted to implement neural networks to efficiently remove noise from geophysical data. This study aims to review and analyze different types of neural networks, such as artificial neural networks, convolutional neural networks, autoencoders, residual networks, and wavelet neural networks, which are implemented to remove different types of noises including seismic, transient electromagnetic, ground-penetrating radar, and magnetotelluric surveys. The review analyzes and summarizes the key challenges in the removal of noise from geophysical data using neural network, while proposes and explains solutions to the challenges. The analysis support that the advancement in neural networks can be powerful denoising tools for geophysical data.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.31-40
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• 2012

u-GIS DSMSs have been researched to deal with various sensor data from GeoSensors in ubiquitous environment. Also, they has been more important for high availability. The data from GeoSensors have some characteristics that increase explosively. This characteristic could lead memory overflow and data loss. To solve the problem, various load shedding methods have been researched. Traditional methods drop the overloaded tuples according to a particular criteria in a single server. Tuple deletion sensitive queries such as aggregation is hard to satisfy accuracy. In this paper a dual processing load shedding method is suggested to improve the accuracy of aggregation in clustering environment. In this method two nodes use replicated stream data for high availability. They process a stream in two nodes by using a characteristic they share stream data. Stream data are synchronized between them with a window as a unit. Then, processed results are merged. We gain improved query accuracy without data loss.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.277-287
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• 2014

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.4
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• pp.173-180
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• 2014

Compressive sensing is a new signal acquisition paradigm that enables sparse/compressible signal to be sampled under the Nyquist-rate. To fully benefit from its much simplified acquisition process, huge efforts have been made on improving the performance of compressive sensing recovery. However, concerning color images, compressive sensing recovery lacks in addressing image characteristics like energy distribution or human visual system. In order to overcome the problem, this paper proposes a new group-sparsity hard thresholding process by preserving some RGB-grouped coefficients important in both terms of energy and perceptual sensitivity. Moreover, a smoothed group-sparsity iterative hard thresholding algorithm for compressive sensing of color images is proposed by incorporating a frame-based filter with group-sparsity hard thresholding process. In this way, our proposed method not only pursues sparsity of image in transform domain but also pursues smoothness of image in spatial domain. Experimental results show average PSNR gains up to 2.7dB over the state-of-the-art group-sparsity smoothed recovery method.