• Economic and Environmental Geology
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• v.14 no.3
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• pp.193-201
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• 1981

The problem of automatic inversion of apparent resistivity sounding curves resulting from horizontally layered earth models is solved using the least-squares technique. This method, which makes use of damped least-squares algorithm in conjunction with digital filtering technique, is found to be speedier and more accurate than the conventional curve-matching method. Four sounding curves were chosen to test the inversion scheme. The analysis of the theoretical sounding data associated with a three-layer model illustrates clear advantages over the conventional curve-matching method. The usefulness of the inversion method is also shown when applied to the actual field data. It was found that the best fit earth models coincide with the subsurface structures confirmed by drilling.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.743-749
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• 2007

Today, we can use radio communication device anywhere-anytime. Sometimes, we use the device in acoustic noise environment. The acoustic noise makes many problems in communication system. In acoustic noise environment, speaker cannot send clear information to receiver, because the received signal includes both speech signal and noise signal. A digital filter is useful to remove noise to get desired signal. One of methods is the adaptive digital filter using the adaptive noise canceller that automatically adjust filter parameters. This thesis addresses articulation algorithms against actual acoustic noises by means of two adaptive filtering methods. One is the adaptive noise canceller with two input channels and another is the spectral subtraction filter with one input channel. The experimental result from the proposed filter shows that the adaptive noise canceller is useful to reduce the non-stationary noises, while the spectral amplitude filter is effective for stationary noises.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.403-409
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• 2016

Recently, harmful content (such as images and photos of nudes) has been widely distributed. Therefore, there have been various studies to detect and filter out such harmful image content. In this paper, we propose a new method using Haar-like features and an AdaBoost algorithm for robustly extracting navel areas in a color image. The suggested algorithm first detects the human nipples through color information, and obtains candidate navel areas with positional information from the extracted nipple areas. The method then selects real navel regions based on filtering using Haar-like features and an AdaBoost algorithm. Experimental results show that the suggested algorithm detects navel areas in color images 1.6 percent more robustly than an existing method. We expect that the suggested navel detection algorithm will be usefully utilized in many application areas related to 2D or 3D harmful content detection and filtering.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.175-182
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• 2017

Noises are usually generated by various external causes and low quality devices in image data acquisition and recording as well as by channel interference in image transmission. Since these noise signals result in the loss of information, subsequent image processing is subject to the corruption of the original image. In general, image processing is performed in the mixed noise environment where common types of noise, known to be Gaussian and impulse, are present. This study proposes an iterative weighted mean filter for reducing mixed type of noise. Impulse noise pixels are first turned off in the input image, then $3{\times}3$ sliding window regions are processed by replacing center pixel with the result of weighted mean mask operation. This filtering processes are iterated until all the impulse noise pixels are replaced. Applied to images corrupted by Gaussian noise with ${\sigma}=10$ and different levels of impulse noise, the proposed filtering method improved the PSNR by up to 12.98 dB, 1.97 dB, 1.97 dB respectively, compared to SAWF, AWMF, MMF when impulse noise desities are less than 60%.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.4
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• pp.97-105
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• 2016

Radiography image detector produces digital images by collecting the charges from the incident x-ray photons and converting it to the voltage signals and then the digital signals. The fixed-pattern noise from the nonuinform amplifier gains in the employed multiple readout circuits. In order to correct the nonuniform gains, a gain-correction technique which is based on the gain map is conventionally used. Since the photon noise remains in the designed gain map, the noise contaminates the gain-corrected images. In this paper, experimental observations are conducted for filtering the remained noise in the gain map, and a filter optimization algorithm is proposed to efficiently remove the noise. For acquired x-ray images from detectors, the filtered gain maps are evaluated and it is shown that optimization algorithm can improve the filtering performance even for relatively strong fixed-pattern noises, which cannot be removed by a simple filter.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6708-6714
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• 2015

Various types of multimedia contents have been widely spread and distributed with the Internet that is easy to use. Meanwhile, Multimedia contents can bright a social problem because juveniles can access such harmful contents easily through the Internet. This paper proposes a method to determine if an input image is harmful or not, using an neural network. The proposed method first detects a face region from an input image through MCT features. The method then extracts skin color regions using color features and obtains candidate nipple areas from the extracted skin regions. Subsequently, we determine if the input image is harmful, by filtering out non-nipple regions using the artificial neural network. Experimental results show that the proposed method can effectively determine the harmfulness of input images.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2005.11a
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• pp.81-104
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• 2005

Accurate 3D models in urban areas are essential for a variety of applications, such as virtual visualization, CIS, and mobile communications. LiDAR(Light Detection and Ranging) is a relatively new technology for directly obtaining 3D points. Because Manual 3D data reconstruction from LiDAR data is very costly and time consuming, many researchs is focused on the automatic extraction of the useful data. In this paper, we classified ground and non-ground points data from LiDAR data by using filtering, and we reconstructed the DTM(Digital Terrain Model) using ground points data, buildings using nonground points data. After the reconstruction, we assessed the accuracy of the DTM and buildings. As a result of, DTM from LiDAR data were 0.16m and 0.59m in high raised apartments areas and low house areas respectively, and buildings were matched with the accuracy of a l/5,000 digital map.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.203-208
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• 2010

In this paper, we convert underwater acoustic signal made by sonar system into digital image. We propose the algorithm that detects target candidate and emphasizes information of target introducing image processing technique for the digital image. The process detecting underwater target estimates background noise in underwater acoustic signal changing irregularly, recomposes it. and eliminates background from original image. Therefore, it generates initial target group. Also, it generates weighted map through proceeding doppler information, ensures information for target candidate through filtering using weighted map for image eliminated background noise, and decides the target candidate area in the single frame. In this paper, we verified that proposed algorithm almost had eliminated the noise generated irregularly in underwater acoustic signal made by simulation, targets had been displayed more surely in the image of underwater acoustic signal through filtering and process of target detection.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.707-712
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• 2017

As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.

• The Journal of Engineering Research
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• v.2 no.1
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• pp.39-47
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• 1997

This paper presents a single board digital signal processing system which can perform two-dimensional (2-D) digital infinite impulse response (IIR) filtering in realtime. We have developed an architecture to provide not only the necessary computational power but also a balance of the system input/output and computational requirements. The architecture achieves large system throughput by using highly parallel processing at both the system and processor levels. It reduces system data communication requirements significantly by taking advantage of a custom-designed processor and by providing each processor with its own input and ouput channel. After system initialization, almost 100 percent of the time is used for data processing. Data transfers occur concurrently with data processing. The functional level simulation reveals that the system throughput can reach as high as one pixel per system cycle. With only 10MHz clock frequency system, it can implement up to fourth order 2-D IIR filters for video-rate data ($512\times512$ pixels per frame at 30 frames per second). If we increase the system frequency, the system can be used for the preprocessing and postprocessing of video signal of HDTV.