• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.570-573
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• 2021

오늘날 SNS를 사용하는 사람들이 증가함에 따라, 생성되는 데이터도 많아지고 종류도 매우 다양해졌다. 하지만 유익한 정보만 존재하는 것이 아니라, 부정적, 반사회적, 사행성 등의 부적절한 콘텐츠가 공존한다. 때문에 사용자에 따라 적절한 콘텐츠를 필터링 할 필요성이 증가하고 있다. 따라서 본 연구에서는 SNS Instagram을 대상으로 콘텐츠의 해시태그를 수집하여 데이터화 했다. 또한 k-평균 군집화 기법을 적용하여, 유사한 특성의 콘텐츠들을 군집화하고, 각 군집은 실루엣 계수(Silhouette Coefficient)와 키워드 다양성(Keyword Diversity)을 계산하여 콘텐츠의 적절성을 판단하였다.

• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.272-279
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• 2021

A pump-type eDNA filtering system that can control voltage and hydraulic pressure respectively has been developed, and applied a filter case that can filter out without damaging the filter. The filtering performance of the developed system was evaluated by comparing the eDNA concentration with the conventional vacuum-pressured filtering method at the catchment conduit intake reservoir. The developed system was divided into a voltage control (manual pump system) method and a pressure control (automatic pump system) method, and the pressure was measured during filtering and the pressure change of each system was compared. The voltage control method started with 65 [KPa] at the beginning of the filtering, and as the filtering time elapsed, the amount of filtrate accumulated in the filter increased, so the pressure gradually increased. As a result of controlling the pressure control method to maintain a constant pressure according to the designed algorithm, there was a difference in the width of the hydraulic pressure fluctuation during the filtering process according to the feedback time of the hydraulic pressure sensor, and it was confirmed that the pressure was converged to the target pressure. The filtering performance of the developed system was confirmed by measuring the eDNA concentration and comparing the voltage control method and the hydraulic control method with the control group. The voltage control method obtained similar results to the control group, but the hydraulic control method showed lower results than the control group. It is considered that the low eDNA concentration in the hydraulic control method is due to the large pressure deviation during filtering and maintaining a constant pressure during the filtering process. Therefore, rather than maintaining a constant pressure during filtering, it was confirmed that a voltage control method in which the pressure is gradually increased as the filtrate increases with the lapse of filtering time is suitable for collecting eDNA. As a result of comparing the average concentration of eDNA in lentic zone and lotic zone as a control group, it was found to be 96.2 [ng µL^-1] and 88.4 [ng µL^-1l], respectively. The result of comparing the average concentration of eDNA by the pump method was also high in the lentic zone sample as 90.7 [ng µL^-1] and 74.8 [ng µL^-1] in the lentic zone and the lotic zone, respectively. The high eDNA concentration in the lentic zone is thought to be due to the influence of microorganisms including the remaining eDNA.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.2
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• pp.71-76
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• 2016

Digital image processing is being utilized in various fields including medical industry, satellite photos, and factory automation image recognition. However, this kind of image data produces heat by an external cause in the course of being processed, transmitted, and stored. Most typical noises added in the images are AWGN and salt and pepper. MF, CWMF, and AWMF are methods used to restore images damaged by AWGN and the existing methods are likely to damage detailed information such as an edge. Therefore, this paper suggests an algorithm applying weight of average filter, average filter depending on pixel, and spatial weight filter based on edge size of local mask in an AWGN environment, in a different way. Also, this paper compares functions of existing methods by using PSNR to prove excellence of the suggested algorithm.

• Journal of Broadcast Engineering
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• v.2 no.2
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• pp.156-169
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• 1997

This paper proposes an image segmentation technique using watershed algorithm followed by region merging method. A gradient image is obtained by applying multiscale gradient algorithm to the image simplified by morphological filters. Since the watershed algorithm produces the oversegmented image. it is necessary to merge small segmented regions as wel]' as region having similar characteristics. For region merging. we utilize the merging criteria based on both the mean value of the pixels of each region and the edge intensities between regions obtained by the contour following process. Experimental results show that the proposed method produces meaningful image segmentation results.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.9-19
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• 2022

In this study, a systematic review was conducted to analyze the method of applying sEMG to evaluate the swallowing function of the elderly at each stage of swallowing, and to help objectively measure the swallowing stage of the older adults in clinical practice. From 2011 to 2021, 7 studies that met the selection criteria were selected using Pubmed, Scopus, and Web of Science (WoS). As a result of this study, the older adults and adults were divided into an experimental group and a control group and the swallowing phase was analyzed using sEMG only for the older adults. sEMG was used to evaluate swallowing in the oral and pharyngeal stages, and the sEMG attachment site was attached to the swallowing muscle involved in each stage. The collected sEMG data were filtered using a bandpass-filter and a notch-filter, and were analyzed using RMS, amplitude, and maximum spontaneous contraction. In this study, it was found that sEMG can be used as a tool to objectively and quantitatively evaluate the swallowing function in stages. Therefore, it is expected that this study will activate various studies that incorporate sEMG to evaluate the swallowing function in stages.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.192-198
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• 2018

Recently, the use of drone has been increasing rapidly in many ways. A drone can capture remote objects efficiently so it is suitable for surveillance and security systems. This paper discusses three methods for detecting moving vehicles using a drone. We compare three target detection methods using a background frame, preceding frames, or moving average frames. They are subtracted from a current frame. After the frame subtraction, morphological filters are applied to increase the detection rate and reduce the false alarm rate. In addition, the false alarm region is removed based on the true size of targets. In the experiments, three moving vehicles were captured by a drone, and the detection rate and the false alarm rate were obtained by three different methods and the results are compared.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.371-384
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• 2022

The fused LASSO signal approximator (FLSA) can be applied to find change points from the data having piecewise constant mean structure. It is well-known that the FLSA is inconsistent in change points detection. This inconsistency is due to a total-variation denoising penalty of the FLSA. ℓ₁ trend filter, one of the popular tools for finding an underlying trend from data, can be used to identify change points of piecewise linear trends. Since the ℓ₁ trend filter applies the sum of absolute values of slope differences, it can be inconsistent for change points recovery as the FLSA. However, there are few studies on the inconsistency of the ℓ₁ trend filtering. In this paper, we demonstrate the inconsistency of the ℓ₁ trend filtering with a numerical study.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.1-6
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• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.441-449
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• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.2
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• pp.106-111
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• 2009

Real-time video stabilization technology is used in correction for the camera vibrations of the hand-held camera by hand or fixed camera by external condition. This paper is about the counterplan to cope with the vibration of the movie generated by the large external cause relatively. we use the movie stabilization parameters with the phase correlation method based the DFT to get the displacements of the current frame to the reference frame. we use the kalman filter for the efficient and stable searching works on the phase correlation map and present the proper conditions for the real-time processing through the experiments. We propose the measure to evaluate the capability of the video stabilizer which is the standard deviation of the brightness of the center block. and compare the capability for the video sequences randomly shifted and the jittered video sequences obtained from camera.