• The Journal of the Acoustical Society of Korea
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• v.25 no.2E
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• pp.69-73
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• 2006

In this paper, we propose a robust music audio fingerprinting system for automatic music retrieval. The fingerprint feature is extracted from the long-term dynamic modulation spectrum (LDMS) estimation in the perceptual compressed domain. The major advantage of this feature is its significant robustness against severe background noise from the street and cars. Further the fast searching is performed by looking up hash table with 32-bit hash values. The hash value bits are quantized from the logarithmic scale modulation frequency coefficients. Experiments illustrate that the LDMS fingerprint has advantages of high scalability, robustness and small fingerprint size. Moreover, the performance is improved remarkably under the severe recording-noise conditions compared with other power spectrum-based robust fingerprints.

• Journal of the Korea Concrete Institute
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• v.12 no.5
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• pp.153-164
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• 2000

Numerical studies were carried out to investigate long-term behavior of flat plates, subjected to combined in-plane compressive and transverse loads. For the numerical studies, a computer program of nonlinear finite element analysis was developed. It can address creep and shrinkage as weel as geometrical and material nonlinearity, and also it can address various load combinations and loading sequences of transverse load, in-plane compressive load and time. This numerical method was verified by comparison with the existing experiments. Parametric studies were performed to investigate the strength variations of flat plates with four parameters; 1) loading sequence of floor load, compressive load and time 2) uniaxial and biaxial compression 3) the ratio of dead to live load 4) span length. Through the numerical studies, the behavioral characteristics of the flat plates and the governing load combinations were examined. These results will be used to develop a design procedure for the long-term behavior of flat plates in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.409-410
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• 2022

This paper proposes a fusion model based on Long-Short Term Memory networks (LSTM) and CNN deep learning methods, and applied to multi-category news datasets, and achieved good results. Experiments show that the fusion model based on deep learning has greatly improved the precision and accuracy of text sentiment classification. This method will become an important way to optimize the model and improve the performance of the model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.248-255
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• 2006

Among a variety of influencing components, time-variant seepage and long-term underground motion are important to understand the abnormal behavior of tunnels. Excessiveness of these two components could be the direct cause of severe damage on tunnels. however, it is not easy to quantify the effect of these on the behavior of tunnels. These parameters can be estimated by using inverse methods once the appropriate relationship between inputs and results are clarified. Various inverse methods or parameter estimation techniques such as artificial neural network and least square method can be used depending on the characteristics of given problems. Numerical analyses, experiments, or monitoring results are frequently used to prepare a set of inputs and results to establish the back analysis models. In this study, a back analysis method has been developed to estimate geotechnically hard-to-known parameters such as permeability of tunnel filter, underground water table, long-term rock mass load, size of damaged zone associated with seepage and long-term underground motion. The artificial neural network technique is adopted and the numerical models developed in the firstpart are used to prepare a set of data for learning process. Tunnel behavior especially the displacements of the lining has been exclusively investigated for the back analysis.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Herbal Formula Science
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• v.19 no.1
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• pp.183-194
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• 2011

Objectives : To estimate the shelf-life by long-term storage test of Pyungwi-san. Methods : Experiments were conducted to evaluate the stability such as the selected physicochemical, heavy metal, microbilogical experiment under an acceleration test and long-term storage test of Pyungwi-san in different storage under room temperature, refrigeration and freezing. Futhermore, HPLC analysis was performed for the determinations of glycyrrhizin in the Pyungwi-san on an Inertsil ODS-3 column(250 mm ${\times}$ 4.6 mm, 5 um) using solvent 35% acetonitrile include 0.05% phosphoric acid at 254 nm. The flow rate was 1.0 mL/min. Results : The significant change was not showed in pH, heavy metal, microbiological, identification test and quantitative analysis based on acceleration test and long-term storage test. Retention time of glycyrrhizin in HPLC chromatogram was about 16.065 min and calibration curve showed good linearity($R^2$ = 0.9999). The contents of glycyrrhizin in acceleration test and long-term storage test were 0.068~0.076 mg/mL and 0.066~0.077 mg/mL, respectively. Shelf-lifes of room temperature, refrigeration and freezing by long-term storage test were predicted 41, 24 and 34 months, respectively. Conclusions : The suggested shelf-life would be helpful on the storage and distribution of herbal medicine.

• Biomedical Science Letters
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• v.15 no.3
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• pp.217-227
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• 2009

The present study was set to evaluate the effect of pomegranate extracts on improvement of the menopausal syndromes such as face flushing in ovariectomized rats by carrying out short- and long-term experiments. Pomegranate extracts used to feed rats were prepared from the pulp part which does not contain the rid of the pomegranate, and were dissolved in propylene glycol. From the short-term (16 days) experiment, it was clear that when the 25, 250, 1,250 mg/kg/day concentrations of pomegranate extracts were orally fed to ovariectomized rats, the body temperature of the rats in all the 3 groups were decreased with statistical significance compared to other control groups which were fed with propylene glycol only. Especially, the body temperature decreased by $2.7^{\circ}C$ compared to control groups even when the pomegranate extracts were fed at the low concentration of 25 mg/kg/day implying the usefulness of pomegranate extracts in improving face flushing troubles. In addition, the body weight of the groups fed with pomegranate extracts also decreased when compared to groups fed with only propylene glycol, and the results were also statistically significant. In case of the estradiol level in the blood of rats, the levels were somewhat higher in the groups fed with pomegranate extracts than the control groups, even though the difference was not statistically significant. As found from the results of the short-term experiment, in long-term experiment, the groups fed with pomegranate extracts showed statistically significant decrease in the body temperature and the body weight, whereas the increase of the estradiol levels in blood in each groups were statistically insignificant. During the short- and long-term experiments, no sign of toxicity was found in rats fed with pomegranate extracts indicating no toxic side effects of the pomegranate extracts when orally fed. The concentrations of pomegranate extracts 25, 250, 1250 mg/kg/day treated to ovariectomized rats in this study can be estimated to be 1.5, 15, and 75 g/day when treated to women whose body weight is 60 kg which is average for women with menopausal syndromes. Since even the 75 g/day of high concentration of pomegranate extracts did not show any toxicity in short- and long-term experiments, taking 1.5 g/day concentration of pomegranate extracts would be safe dose for not causing any side effects. Therefore, it can be concluded from the results of this study that taking 1.5 g/day of pomegranate extracts for certain period time will improve the menopausal syndromes including face flushing.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.523-523
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• 2000

We have developed a method to build time series prediction models by Genetic Programming (GP). Our proposed CP includes two new techniques. One is the parameter optimization algorithm, and the other is the new mutation operator. In this paper, the sunspot prediction experiment by our proposed CP was performed. The sunspot prediction is good benchmark, because many researchers have predicted them with various kinds of models. We make three experiments. The first is to compare our proposed method with the conventional methods. The second is to investigate about the relation between a model-building period and prediction precision. In the first and the second experiments, the long-term data of annual sunspots are used. The third is to try the prediction using monthly sunspots. The annual sunspots are a mean of the monthly sunspots. The behaviors of the monthly sunspot cycles in tile annual sunspot data become invisible. In the long-term data of the monthly sunspots, the behavior appears and is complicated. We estimate that the monthly sunspot prediction is more difficult than the annual sunspot prediction. The usefulness of our method in time series prediction is verified by these experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.393-398
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• 2007

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.394-398
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• 2016

Numerical analysis was conducted using Delft3D developed by Deltares in Netherlands to predict long-term river bed changes in Saemangeum Area. Tidal flow, discharge through the drainage gates and river bed changes in numerical model was verified by comparing to the results of field observation and hydraulic experiments. We calculated long-term river bed changes in Saemangeum area for 10 years from 2031 to 2040 after completion of development in Saemangeum. It is shown that 70 cm and 139 cm of accumulation occur in estuaries of Dongjin River and Mankyong River, respectively. Variation of flood level was also investigated considering long-term river bed changes. There was no change in estuary of Dongjin River but maximum flood level in estuary of Mankyong River increased 81 cm.