• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.254-260
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• 2019

Recently, the importance of signal processing has become gradually significant, as the frequency of video media increases in various fields. However, numerous kinds of noise generated in the transmission and reception processes can possibly affect the signal information, and the noise removal is for that reason essential as a preprocessing step. In this paper, we propose an algorithm to remove the mixed noise which is composed of impulse noise and AWGN. This algorithm is used for image restoration by noise judgment for efficient noise removal in a complex noise environment, and the noise is removed by considering spatial characteristics and pixel variations. Simulation results show that unlike existing methods, the algorithm has excellent noise cancellation characteristics by minimizing both noise effects and consequently eliminating the mixed noise; for objective judgment, we compared and analyzed the data using PSNR and profile.

• Smart Media Journal
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• v.9 no.4
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• pp.118-125
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• 2020

This paper is conducted for automatically recommending and providing information services desired by users on websites of local governments and public institutions with vast amounts of information, In this system, we defined a method of collecting data based on the SiiRU CMS system that collects and preprocesses data, and a study that provides curation services (contents and menus) to users through a collaborative filtering algorithm based on machine learning. Also, the data used in the paper is conducted based on about 1 million data collected in 2019. The analyzed data can provide important information that cannot be easily accessed by providing a cloud tag service or recommended menu for users to conveniently view, and the environment configuration that can realize this service to local governments and public institutions is also provided.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.47-54
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• 2022

Recently, with the development of deep learning technology, research on recognizing human behavior is in progress. In this paper, a study was conducted to recognize risky behaviors that may occur in a single-person household environment using deep learning technology. Due to the nature of single-person households, personal privacy protection is necessary. In this paper, we recognize human dangerous behavior in privacy protection video with Gaussian blur filters for privacy protection of individuals. The dangerous behavior recognition method uses the YOLOv5 model to detect and preprocess human object from video, and then uses it as an input value for the behavior recognition model to recognize dangerous behavior. The experiments used ResNet3D, I3D, and SlowFast models, and the experimental results show that the SlowFast model achieved the highest accuracy of 95.7% in privacy-protected video. Through this, it is possible to recognize human dangerous behavior in a single-person household environment while protecting individual privacy.

• Journal of IKEEE
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• v.27 no.4
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• pp.472-476
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• 2023

Fundamental frequency/pitch period are major characteristics of speech signals. They are used in many speech applications like speech coding, speech recognition, speaker identification, and so on. In this paper, some of inflection points are used to estimate the pitch which is the inverse of the fundamental frequency. The inflection points are defined as points where local maxima, local minima or the slope changes occur. The speech signal is preprocessed to remove unnecessary inflection points due to the high frequency components using a low pass filter. Only the inflection points from local maxima are used to get the pitch period. While the existing pitch estimation methods process speech signals in blockwise, the proposed method detects the inflection points in sample and produces the pitch period/fundamental frequency estimates along the time. Computer simulation shows the usefulness of the proposed method as a fundamental frequency estimator.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).

• Journal of Korean Society of Forest Science
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• v.109 no.1
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• pp.41-49
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• 2020

In this study, the effect of seed pre-treatments and pellet coating materials to enhance the efficiency of large-scale propagation of Aster koraiensis seeds were investigated. Seeds were immersed in water for one day, and only those that sank were used for pre-treatment to use filled seeds. Pre-treatments were divided into hormone treatments, with gibberellic acid (GA₃; 200 and 500 ppm) and 24-epibrassinolide (10^-6, 10^-7, and 10^-8M), and priming with potassium nitrate (100 mM of KNO₃). To produce pellet-coated seeds, pellet materials (DTCS or DTK) were applied to control (unprimed) and primed seeds with binders (PVA or CMC). The maximum germination percent (GP) of seeds before pellet coating was 65% (with the priming treatment), and there was no difference in the GP of seeds among hormone treatments. For seeds sown in a growth chamber on filter paper, GP was 41% for control (unprimed/uncoated) seeds, 65% for uncoated primed seeds, 71% for DTCS/PVA-pellet-coated seeds, and 42% for DTK/CMC-pellet-coated seeds. Seeds that were primed first and then pellet-coated showed greatly improved GP, mean germination time (MGT), and germination rate than seeds that were only pellet-coated. For seeds sown in commercial soil in a greenhouse, control seeds had a GP of 27%, whereas primed seeds had the highest GP (58%), and their MGT and GT were 9.4 days and 7.0%·day, respectively. In addition, DTK/PVA-pellet-coated seeds (40%) also had a GP higher than the control (27%), and their MGT was 15-27 days. For seeds sown in sandy-loam soil in a greenhouse, unprimed-pellet-coated seeds and primed-pellet-coated seeds both had GPs ranged of 39%, which were lower than that of control seeds. In general, the seeds that were pellet-coated with DTK had a higher GP than those pellet-coated with DTCS. Furthermore, the MGT of unprimed-pellet-coated seeds was 15.0-19.8 days, which was longer than the MGT of primed-pellet-coated seeds. These results suggest that priming enhances seedling emergence of Aster koraiensis seeds. Moreover, when priming is combined with pellet coating, DTK is a more suitable pellet material than DTCS, and PVA and CMC are equally suitable adhesives.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.2
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• pp.87-95
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• 2016

Turbidity is a key indicator to the user that the 'Discolored Water' phenomenon known to be caused by corrosion of the pipeline in the water supply system. 'Discolored Water' is defined as a state with a turbidity of the degree to which the user visually be able to recognize water. Therefore, this study used data mining techniques in order to estimate turbidity changes in water supply system. Decision tree analysis was applied in data mining techniques to develop estimation models for turbidity changes in the water supply system. The pH and residual chlorine dataset was used as variables of the turbidity estimation model. As a result, the case of applying both variables(pH and residual chlorine) were shown more reasonable estimation results than models only using each variable. However, the estimation model developed in this study were shown to have underestimated predictions for the peak observed values. To overcome this disadvantage, a high-pass filter method was introduced as a pretreatment of estimation model. Modified model using high-pass filter method showed more exactly predictions for the peak observed values as well as improved prediction performance than the conventional model.

• Geophysics and Geophysical Exploration
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• v.14 no.4
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• pp.289-297
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• 2011

Chirp sub-bottom profilers (SBP) data are comparatively higher-resolution data than other seismic data and it's raw signal can be used as a final section after conducting basic filtering. However, Chirp SBP signal has possibility to include various noise in high-frequency band and to provide the distorted image for the complex geological structure in time domain. This study aims at the goal to establish the workflow of Chirp SBP data processing for enhanced image and to analyze the proper parameters for the domestic continental shelf. After pre-processing, we include the dynamic S/N filtering to eliminate the high-frequency component noise, the dip scan stack to enhance the continuity of reflection events and finally the post-stack depth migration to correct the distorted structure on the time domain sections. We demonstrated our workflow on the data acquired by domestically widely used equipments and then we could obtain the improved seismic sections of depth domain. This workflow seems to provide the proper seismic section to interpretation when applied to data processing of Chirp SBP that are largely used for domestic acquisition.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.93-102
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• 1996

For an atomatic generation of DEM(Digital Elevation Model) by computer, it is a time-consumed work to determine adquate matches from stereo images. Correlation and evenly distributed area-based method is generally used for matching operation. In this paper, we propose a new approach that computes matches efficiantly by changing the size of mask window and search area according to the given terrain information. For image segmentation, at first edge-preserving smoothing filter is used for preprocessing, and then region growing algorithm is applied for the filterd images. The segmented regions are classifed into mountain, plain and water area by using MRF(Markov Random Filed) model. Maching is composed of predicting parallex and fine matching. Predicted parallex determines the location of search area in fine matching stage. The size of search area and mask window is determined by terrain information for each pixel. The execution time of matching is reduced by lessening the size of search area in the case of plain and water. For the experiments, four images which are covered $10km{\times}10km(1024{\times}1024\;pixel)$ of Taejeon-Kumsan in each are studied. The result of this study shows that the computing time of the proposed method using terrain information for matching operation can be reduced from 25% to 35%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.3C
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• pp.374-386
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• 2004

The mammogram provides the way to observe detailed internal organization of breasts to radiologists for the early detection. This paper is mainly focused on efficiently detecting the Microcalcification's Region Of Interest(ROI)s. Breast cancers can be caused from either microcalcifications or masses. Microcalcifications are appeared in a digital mammogram as tiny dots that have a little higher gray levels than their surrounding pixels. We can roughly determine the area which possibly contain microcalifications. In general, it is very challenging to find all the microcalcifications in a digital mammogram, because they are similar to some tissue parts of a breast. To efficiently detect microcalcifications ROI, we used four sequential processes; preprocessing for breast area detection, modified multilevel thresholding, ROI selection using simple thresholding filters and final ROI selection with two stages of neural networks. The filtering process with boundary conditions removes easily-distinguishable tissues while keeping all microcalcifications so that it cleans the thresholded mammogram images and speeds up the later processing by the average of 86％. The first neural network shows the average of 96.66％ recognition rate. The second neural network performs better by showing the average recognition rate 98.26％. By removing all tissues while keeping microcalcifications as much as possible, the next parts of a CAD system for detecting breast cancers can become much simpler.