• Smart Structures and Systems
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• v.11 no.1
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• pp.123-134
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• 2013

Identifying structural modal parameters, especially those modes within high frequency range, from ambient data is still a challenging problem due to various kinds of uncertainty involved in vibration measurements. A procedure applying an ensemble empirical mode decomposition (EEMD) method is proposed for accurate and robust structural modal identification. In the proposed method, the EEMD process is first implemented to decompose the original ambient data to a set of intrinsic mode functions (IMFs), which are zero-mean time series with energy in narrow frequency bands. Subsequently, a Sub-PolyMAX method is performed in narrow frequency bands by using IMFs as primary data for structural modal identification. The merit of the proposed method is that it performs structural identification in narrow frequency bands (take IMFs as primary data), unlike the traditional method in the whole frequency space (take original measurements as primary data), thus it produces more accurate identification results. A numerical example and a multiple-span continuous steel bridge have been investigated to verify the effectiveness of the proposed method.

• Language and Information
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• v.10 no.1
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• pp.21-46
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• 2006

This study proposes a Word Sense Disambiguation (WSD) algorithm, based on concept learning with special emphasis on statistically meaningful lowest frequency words. Previous works on WSD typically make use of frequency of collocation and its probability. Such probability based WSD approaches tend to ignore the lowest frequency words which could be meaningful in the context. In this paper, we show an algorithm to extract and make use of the meaningful lowest frequency words in WSD. Learning method is adopted from the Find-Specific algorithm of Mitchell (1997), according to which the search proceeds from the specific predefined hypothetical spaces to the general ones. In our model, this algorithm is used to find contexts with the most specific classifiers and then moves to the more general ones. We build up small seed data and apply those data to the relatively large test data. Following the algorithm in Yarowsky (1995), the classified test data are exhaustively included in the seed data, thus expanding the seed data. However, this might result in lots of noise in the seed data. Thus we introduce the 'maximum a posterior hypothesis' based on the Bayes' assumption to validate the noise status of the new seed data. We use the Naive Bayes Classifier and prove that the application of Find-Specific algorithm enhances the correctness of WSD.

• The Journal of Information Systems
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• v.27 no.3
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• pp.123-140
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• 2018

Purpose The purpose of this study is to explore the optimal trading frequency which is useful for stock price prediction by using deep learning for charting image data. We also want to identify the appropriate time for accurate forecasting of stock price when performing pattern analysis. Design/methodology/approach In order to find the optimal trading frequency patterns and forecast timings, this study is performed as follows. First, stock price data is collected using OpenAPI provided by Daishin Securities, and candle chart images are created by data frequency and forecasting time. Second, the patterns are generated by the charting images and the learning is performed using the CNN. Finally, we find the optimal trading frequency patterns and forecasting timings. Findings According to the experiment results, this study confirmed that when the 10 minute frequency data is judged to be a decline pattern at previous 1 tick, the accuracy of predicting the market frequency pattern at which the market decreasing is 76%, which is determined by the optimal frequency pattern. In addition, we confirmed that forecasting of the sales frequency pattern at previous 1 tick shows higher accuracy than previous 2 tick and 3 tick.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.35-42
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• 2002

Frequency responses for most of the local seismic sensors in Korea have been roughly checked by mutual comparison of Fourier spectra of seismic records from accelerometer and seismometer, both of which are installed at the same location. Especially, because the frequency content of the seismic energy is usually above 1 Hz for local earthquakes, the reliability of low frequency response could have not been evaluated. Fortunately a recent large earthquake, Ms=7.2 on 02/06/29 containing dominant low frequency energy makes it possible to check the low frequency response of the seismic sensors, especially EpiSensor and JC-V100. Considering two types of sensor pairs, (STS-2 and EpiSensor, JC-V100 and EpiSensor), the low frequency response of EpiSensor is confirmed first by comparison with STS-2 which has proved low frequency response. Second, reliable low frequency limit of instrumentally corrected seismic data from JC-V100 data is estimated to be about 0.03 Hz by comparison with EpiSensor data.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.60-66
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• 2014

In this study, the overseas failure frequency data of the high-pressure gas pipeline were investigated to apply QRA of high-pressure gas pipeline. The typical overseas failure frequency data of high-pressure gas pipeline are DOT of United States, EGIG of Europe, and UKOPA of United Kingdom (UK). Comparative analysis of these data was shown that EGIG data was suitable for the situation in Korea. In order to apply QRA of high-pressure gas pipeline, non-linear regression analysis using the failure frequency data in the report of EGIG 8th was performed. In the future, intensive researches are required for the external interference because about 50% of the failure frequency of all incidents is the external interference, and for combining of domestic and overseas data.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.321-326
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• 2015

In this paper, we proposed an algorithm which could transmit reliable data between smart devices by using inaudible high frequency of audible frequency range and cyclic redundancy check method. The proposed method uses 18 kHz~22 kHz as high frequency which inner speaker of smart device can make a sound in audible frequency range (20 Hz~22 kHz). To increase transmission quantity of data, we send mixed various frequencies at high frequency range 1 (18.0 kHz~21.2 kHz). At the same time, to increase accuracy of transmission data, we send some mixed frequencies at high frequency range 2 (21.2 kHz~22.0 kHz) as checksum. We did experiments about data transmission between smart devices by using the proposed method to confirm data transmission speed and accuracy of the proposed method. From the experiments, we showed that the proposed method could transmit 32 bits data in 235 ms, the transmission success rate was 99.47%, and error detection by using cyclic redundancy check was 0.53%. Therefore, the proposed method will be a useful for wireless transmission technology between smart devices.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.28-35
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• 2010

This paper presents new Adaptive Linear Combination Structure (ADALINE) for tracking/estimating voltage-current phasor and frequency of power system. To estimate the phasors and frequency from sampled data, the algorithm assumes that orthogonal coefficients and speed of angular frequency of power system are unknown parameters. With adequate sampled data, the estimation problem can be considered as a linear weighted least squares (LMS) problem. In addition to determining the phasors (orthogonal coefficients), the procedure estimates the power system frequency. The main algorithm is verified through a computer simulation and data from field. The proposed algorithm is tested with transient and dynamic behaviors during power swing, a step change of frequency upon islanding of small generators and disconnection of load. The algorithm shows a very high accuracy, robustness, fast response time and adaptive performance over a wide range of frequency, from 10 to 2000 Hz.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1197-1220
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• 2014

Accurate actuator tracking is critical to achieve reliable real-time hybrid simulation results for earthquake engineering research. The frequency-domain evaluation approach provides an innovative way for more quantitative post-simulation evaluation of actuator tracking errors compared with existing time domain based techniques. Utilizing the Fast Fourier Transform the approach analyzes the actuator error in terms of amplitude and phrase errors. Existing application of the approach requires using the complete length of the experimental data. To improve the computational efficiency, two techniques including data decimation and frequency decimation are analyzed to reduce the amount of data involved in the frequency-domain evaluation. The presented study aims to enhance the computational efficiency of the approach in order to utilize it for future on-line actuator tracking evaluation. Both computational simulation and laboratory experimental results are analyzed and recommendations on the two decimation factors are provided based on the findings from this study.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.81-86
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• 2004

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.