• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.240-240
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• 2000

This paper presents two algorithms based on the concept of the frequency domain adaptive filter(FDAF). First the frequency domain recursive least squares(FRLS) algorithm with the overlap-save filtering technique is introduced. This minimizes the sum of exponentially weighted square errors in the frequency domain. To eliminate discrepancies between the linear convolution and the circular convolution, the overlap-save method is utilized. Second, the sliding method of data blocks is studied Co overcome processing delays and complexity roads of the FRLS algorithm. The size of the extended data block is twice as long as the filter tap length. It is possible to slide the data block variously by the adjustable hopping index. By selecting the hopping index appropriately, we can take a trade-off between the convergence rate and the computational complexity. When the input signal is highly correlated and the length of the target FIR filter is huge, the FRLS algorithm based on the block processing technique has good performances in the convergence rate and the computational complexity.

• Smart Structures and Systems
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• v.31 no.2
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• pp.171-181
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• 2023

Using the most up-to-date system identification methods in both time and frequency domains, the dynamic monitoring data from the reinforced concrete Egebaekvej Bridge near Holte, Denmark, is examined in this investigation. The bridge was erected in the 1960s and was still standing during test campaign before demolishing. The ARTeMIS Modal was adopted to derive the modal parameters from ambient vibration data. Several Operational Modal Analysis (OMA) approaches were applied, including Enhanced Frequency Domain Decomposition (EFDD), Curve-fit Frequency Domain Decomposition (CFDD), and Frequency Domain Decomposition (FDD). Afterward, Principal Component (SSI-PC), Unweighted Principal Component (SSI-UPC) Stochastic Subspace Identification methods were utilized. Danish engineering consulting company, COWI with the allowance of the bridge contractor BARSLUND, allow the researcher for this experimental test to demonstrate the impact of OMA applications.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.333-343
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• 2022

For water resource management, the design flood is calculated using the flood frequency analysis technique and the rainfall runoff model. The method by design flood frequency analysis calculates the stochastic design flood by directly analyzing the actual discharge data and is theoretically evaluated as the most accurate method. Actual discharge data frequency analysis of the measured flow was limited due to data limitations in the existing flood flow analysis. In this study, design flood frequency analysis was performed using the measured flow data stably secured through the water level-discharge relationship curve formula. For the frequency analysis of design flood, the parameters were calculated by applying the bayesian inference, and the uncertainty of flood volume by frequency was quantified. It was confirmed that the result of calculating the design flood was close to that calculated by the rainfall-runoff model by applying long-term rainfall data. It is judged that hydrological analysis can be done from various perspectives by using long-term actual flow data through hydrological survey.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1163-1170
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• 2015

Various computational methods for obtaining volatilities for financial time series are reviewed and compared with each other. We reviewed model based GARCH approach as well as the data based method which can essentially be regarded as a smoothing technique applied to the squared data. The method for high frequency data is focused to obtain the realized volatility. A hybrid method is suggested by combining the model based GARCH and the historical volatility which is a data based method. Korea stock prices are analysed to illustrate various computational methods for volatilities.

• Journal of dental hygiene science
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• v.23 no.4
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• pp.255-263
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• 2023

Background: This study used text mining techniques to determine public perceptions of dental fear, extracted keywords related to dental fear, identified the connection between the keywords, and categorized and visualized perceptions related to dental fear. Methods: Keywords in texts posted on Internet portal sites (NAVER and Google) between 1 January, 2000, and 31 December, 2022, were collected. The four stages of analysis were used to explore the keywords: frequency analysis, term frequency-inverse document frequency (TF-IDF), centrality analysis and co-occurrence analysis, and convergent correlations. Results: In the top ten keywords based on frequency analysis, the most frequently used keyword was 'treatment,' followed by 'fear,' 'dental implant,' 'conscious sedation,' 'pain,' 'dental fear,' 'comfort,' 'taking medication,' 'experience,' and 'tooth.' In the TF-IDF analysis, the top three keywords were dental implant, conscious sedation, and dental fear. The co-occurrence analysis was used to explore keywords that appear together and showed that 'fear and treatment' and 'treatment and pain' appeared the most frequently. Conclusion: Texts collected via unstructured big data were analyzed to identify general perceptions related to dental fear, and this study is valuable as a source data for understanding public perceptions of dental fear by grouping associated keywords. The results of this study will be helpful to understand dental fear and used as factors affecting oral health in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.225-236
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• 2008

The techniques to calculate the probability precipitation for the design of hydrological projects can be determined by the point frequency analysis and the regional frequency analysis. Probability precipitation usually calculated by point frequency analysis using rainfall data that is observed in rainfall observatory which is situated in the basin. Therefore, Probability precipitation through point frequency analysis need observed rainfall data for enough periods. But, lacking precipitation data can be calculated to wrong parameters. Consequently, the regional frequency analysis can supplement the lacking precipitation data. Therefore, the regional frequency analysis has weaknesses compared to point frequency analysis because of suppositions about probability distributions. In this paper, rainfall observatory in Korea did grouping by cluster analysis using position of timely precipitation observatory and characteristic time rainfall. Discordancy and heterogeneity measures verified the grouping precipitation observatory by the cluster analysis. So, there divided rainfall observatory in Korea to 6 areas, and the regional frequency analysis applies index-flood techniques and L-moment techniques. Also, the probability precipitation was calculated by the regional frequency analysis using variable kernel density function. At the results, the regional frequency analysis of the variable kernel function can utilize for decision difficulty of suitable probability distribution in other methods.

• Spatial Information Research
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• v.20 no.2
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• pp.137-153
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• 2012

Until now, many indexing methods that can reduce update cost have been proposed for managing massive moving objects. Because indexing methods for moving objects have to be updated periodically for managing moving objects that change their location data frequently. However these kinds indexing methods occur big load that exceed system capacity when the number of moving objects increase dramatically. In this paper, we propose the update frequency reducing method to combine MapReduce and existing indices. We use the update request grouping method for each moving object by using MapReduce. We decide to update by comparing the latest data and the oldest data in grouping data. We reduce update frequency by updating the latest data only. When update is delayed, for the data should not be lost and updated periodically, we store the data in a certain period of time in the hash table that keep previous update data. By the performance evaluation, we can prove that the proposed method reduces the update frequency by comparison with methods that are not applied the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2687-2692
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• 2010

There have been performed many researched for flood magnitude analysis, for example, the Flood-Duration-Frequency relations in the west. Because flood water stage data are more available rather than flood amount data at flood gauge stations of Korea, this study developed Flood water level-Duration-Frequency (Fwl-D-F) curves using rainfall Intensity-Duration-Frequency(I-D-F) curves for the quantitative flood risk assessment in urban watersheds. Fwl-D-F curve is made from water level data for 18 years at Joongrayng bridge station of Joongrayng River basin in Han River drainage area. Fwl-D-F curve can estimate the occurrence frequency for a certain flood elevation, which can be used for urban flood forecasting. It is expected that the flood elevation can be estimated from the forecasted rainfall data using both Fwl-D-F and I-D-F curves.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.5
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• pp.73-83
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• 2016

NAVER provides search frequency data of search terms via its DataLab service (http://datalab.naver.com/). Using this data, this paper examines the relation between the search frequency of firm's name and its future stock returns. Our results show that the search frequency of firm's name is a new investor attention measure, which is different from previously explored attention measures such as extreme returns, turnover, etc. Firms that go through higher search frequency this week tend to have higher returns in the next week. We do not find return reversal in the long run for the firms with higher search frequency. Furthermore, the extent to which search frequency affects stock returns becomes more pronounced following market-wide attention grabbing events. Our results indicate that search frequency incorporates information for future stock returns.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3A
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• pp.265-271
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• 2009

This paper proposes an efficient initial frequency estimator for Digital Video Broadcasting-Second Generation (DVB-S2). The initial frequency offset of the DVB-S2 is around ${\pm}5MHz$, which corresponds to 20% of the symbol rate at 25Msps. To estimate a large initial frequency offset, the algorithm which call provide a large estimation range is required. Through the analysis of the data-aided (DA) algorithms, we find that the Mengali and Moreli (M&M) algorithm can estimate a large initial frequency offset at low SNR. Since the existing frequency estimator based on M&M algorithm has a high hardware complexity, we propose the methods to reduce the hardware complexity of the initial frequency estimator. This can be achieved by reducing the number of autocorrelators and arctangents. The proposed architecture can reduce the hardware complexity about 64.5% compared to the existing frequency estimator and has been thoroughly verified on the Xilinx Virtex II FPGA board.