• Journal of Korea Multimedia Society
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• v.10 no.5
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• pp.559-568
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• 2007

Anchor-person scene detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper proposes an efficient algorithm extracting anchor ranges that exist in sports news video for unit structuring of sports news. To detect anchor person scenes, first, anchor person candidate scene is decided by DCT coefficients and motion vector information in the MPEG4 compressed video. Then, from the candidate anchor scenes, image processing method is utilized to classify the news video into anchor-person scenes and non-anchor(sports) scenes. The proposed scheme achieves a mean precision and recall of 98% in the anchor-person scenes detection experiment.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.595-606
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• 2011

This paper proposes a robust watermarking scheme for GIS digital map by using the geometric properties of polyline and polygon, which are the fundamental components in vector data structure. In the proposed scheme, we calculate the lengths and the perimeters of all polylines and polygons in a map and cluster them to a number of groups. Then we embed the binary watermark by changing the mean of lengths and perimeters in an embedding group. For improving the safety and robustness, we permute the binary watermark through PRNS(pseudo-random number sequence) processing and embed it repeatedly in a model. Experimental results verified that our scheme has a good invisibility, safety and robustness to various geometric attacks and also our scheme needs not the original map in the extracting process of watermark.

• Wind and Structures
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• v.29 no.5
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• pp.313-321
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• 2019

The wind blowing at high velocity in an open storage yard leads to wind erosion and loss of material. Fence structures can be constructed around the periphery of the storage yard to reduce the erosion. The fence will cause turbulence and recirculation behind it which can be utilized to reduce the wind erosion and loss of material. A properly designed fence system will produce lesser turbulence and longer shelter effect. This paper aims to show the applicability of Support Vector Machine (SVM) to predict the recirculation length. A SVM model was built, trained and tested using the experimental data gathered from the literature. The newly developed model is compared with numerical turbulence model, in particular, modified $k-{\varepsilon}$ model along with the experimental results. From the results, it was observed that the SVM model has a better capability in predicting the recirculation length. The SVM model was able to predict the recirculation length at a lesser time as compared to modified $k-{\varepsilon}$ model. All the results are analyzed in terms of statistical measures, such as root mean square error, correlation coefficient, and scatter index. These examinations demonstrate that SVM has a strong potential as a feasible tool for predicting recirculation length.

• Korean Journal of Optics and Photonics
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• v.14 no.6
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• pp.583-587
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• 2003

In this paper, the optimal performance of optical second-order polarization mode dispersion (PMD) compensation has been investigated theoretically in terms of minimization of the root-mean-square (RMS) pulse broadening. The optimal compensation vector in feedforward-type second-order PMD compensation has been derived, and the RMS pulse broadening factor after the optimal second-order PMD compensation has been analytically calculated. The calculated result has been compared with the previously reported simulation result where numerically optimized feedback scheme was adopted. They are in good agreement, which verifies the validity of the derivation. The investigation in this work will form the basis for the implementation of the feed-forward-type second-order PMD compensation.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.1
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• pp.75-85
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• 2021

Evaluating the quantitative damage to rocks through acoustic emission (AE) has become a research focus. Most studies mainly used one or two AE parameters to evaluate the degree of damage, but several AE parameters have been rarely used. In this study, several data-driven models were employed to reflect the combined features of AE parameters. Through uniaxial compression tests, we obtained mechanical and AE-signal data for five granite specimens. The maximum amplitude, hits, counts, rise time, absolute energy, and initiation frequency expressed as the cumulative value were selected as input parameters. The result showed that gradient boosting (GB) was the best model among the support vector regression methods. When GB was applied to the testing data, the root-mean-square error and R between the predicted and actual values were 0.96 and 0.077, respectively. A parameter analysis was performed to capture the parameter significance. The result showed that cumulative absolute energy was the main parameter for damage prediction. Thus, AE has practical applicability in predicting rock damage without conducting mechanical tests. Based on the results, this study will be useful for monitoring the near-field rock mass of nuclear waste repository.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• Geomechanics and Engineering
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• v.36 no.4
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• pp.381-390
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• 2024

The introduction of bio-based materials has been recommended in the geotechnical engineering field to reduce environmental pollutants such as heavy metals and greenhouse gases. However, bio-treated soil methods face limitations in field application due to short research periods and insufficient verification of engineering performance, especially when compared to conventional materials like cement. Therefore, this study aimed to develop a machine learning model for predicting the unconfined compressive strength, a representative soil property, of biopolymer-based soil treatment (BPST). Four machine learning algorithms were compared to determine a suitable model, including linear regression (LR), support vector regression (SVR), random forest (RF), and neural network (NN). Except for LR, the SVR, RF, and NN algorithms exhibited high predictive performance with an R² value of 0.98 or higher. The permutation feature importance technique was used to identify the main factors affecting the strength enhancement of BPST. The results indicated that the unconfined compressive strength of BPST is affected by mean particle size, followed by biopolymer content and water content. With a reliable prediction model, the proposed model can present guidelines prior to laboratory testing and field application, thereby saving a significant amount of time and money.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.286-293
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• 2010

There are two major conventional quantization algorithms: resolution-constrained quantization (RCQ) and entropy-constrained quantization (ECQ). Although RCQ works well for fixed transmission-rate, it produces the distortion outliers since the cell sizes are different. Compared with RCQ, ECQ has the constraints on the cell size but it produces the rate outliers. We propose the cell-size constrained vector quantization (CCVQ) that improves the generalized Lloyd algorithm (GLA). The CCVQ algorithm is able to make a soft-decision between RCQ and ECQ by using the flexible penalty measure according to the cell size. Although the proposed method increases the small amount of overall mean-distortion, it can reduce the distortion outliers.

• ETRI Journal
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• v.46 no.4
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• pp.708-715
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• 2024

We propose a 10-GHz 2 × 2 phased-array radio frequency (RF) receiver with an 8-bit linear phase and 15-dB gain control range using 65-nm complementary metal-oxide-semiconductor technology. An 8 × 8 phased-array receiver module is implemented using 16 2 × 2 RF phased-array integrated circuits. The receiver chip has four single-to-differential low-noise amplifier and gain-controlled phase-shifter (GCPS) channels, four channel combiners, and a 50-Ω driver. Using a novel complementary bias technique in a phase-shifting core circuit and an equivalent resistance-controlled resistor-inductor-capacitor load, the GCPS based on vector-sum structure increases the phase resolution with weighting-factor controllability, enabling the vector-sum phase-shifting circuit to require a low current and small area due to its small 1.2-V supply. The 2 × 2 phased-array RF receiver chip has a power gain of 21 dB per channel and a 5.7-dB maximum single-channel noise-figure gain. The chip shows 8-bit phase states with a 2.39° root mean-square (RMS) phase error and a 0.4-dB RMS gain error with a 15-dB gain control range for a 2.5° RMS phase error over the 10 to10.5-GHz band.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.3
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• pp.15-28
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• 2024

Wind forecast is one of the key meteorological factors required for safe aircraft takeoff and landing. In this study, we developed an artificial intelligence-based wind compensation method by learning the Korea Air Force Weather Research and Forecast (KAF-WRF) forecast data and the Airfield Meteorological Observation System (AMOS) data at five airports using Support Vector Machine (SVM). The SVM wind prediction models were composed of three types according to the learning period (30 days, 40 days, and 60 days) using seven KAF-WRF variables as training data, and the wind prediction performance at the five airports was evaluated using Root Mean Squared Errors (RMSE). According to the results, the SVM wind prediction model trained using U (east-west) and V (north-south) components performed approximately 18% better than the model trained using wind speed and wind direction. The wind correction of KAF-WRF with AMOS observations via SVM outperformed the conventional KAF-WRF wind predictions in eight out of ten cases, capturing abrupt changes in wind direction and speed with a 25% reduction in RMSE.