• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1201-1210
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• 2019

Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1172-1183
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• 2012

Aure's tonality was considered as the sound metrics for the expression of the tonality of gear whine sound in a previous research. It was failed to use the Aure's tonality as a sound metric for the tonal impression. Thus Aures's tonality, was developed for tonal impression in previous research. However, this metric did not express well the tonality of gear whine sound since the whine sound is a non-stationary signal with frequency modulation and amplitude modulation. In this study, the new method for the tonality evaluation for a non-stationary signal is presented. It is developed based on the prominence ratio, tonality impression function, and lower threshold level. It improves the accuracy and reliability of the sound quality index being used for the sound quality evaluation of the axle-gear whine sound.

• Journal of Broadcast Engineering
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• v.16 no.1
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• pp.178-181
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• 2011

With the development of mobile devices and intelligent surveillance system loaded with pan/tilt cameras, object tracking with non-stationary cameras has become a topic with increasing importancy. Since it is difficult to model a background image in a non-stationary camera environment, colors and texture are the most important features in the tracking algorithm. However, colors in the background similar to those in the target arise instability in the tracking. Recently, we proposed a robust color based tracking algorithm that uses an area weighted centroid shift. In this letter, we update the model such that it becomes more stable against background colors. The proposed algorithm also incorporates time filtering by adding an additional energy term to the energy functional.

• Earthquakes and Structures
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• v.18 no.3
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• pp.297-312
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• 2020

The KiK-net by NIED is a vertical array measurement system. In the database of KiK-net, singular pulse waves were observed in the seismic record at the borehole of TTRH02 during the mainshock (the magnitude of Japan Meteorological Agency (M_J) 7.3, M_W 6.8) and aftershock (M_j 4.2) of Tottori-ken Seibu earthquake in 2000. Singular pulse waves were also detected in the seismic records at the borehole of IWTH25 during the Iwate-Miyagi Nairiku earthquake in 2008 (M_J 7.2, M_W 6.9). These pulse waves are investigated by using the wave shape analysis methods, e.g., the non-stationary Fourier spectra and the double integrated displacement profiles. Two types of vibration modes are discriminated as the occurrence mechanism of the singular pulse waves. One corresponds to the reversal points in the displacement profile with the amplitude from 10^-4 m to 10^-1 m, which is mainly related to the fault activity and the amplification pass including the mechanical amplification (collision) of the seismograph in the casing pipe. The other is the cyclic pulse waves in the interval of reversal points, which is estimated as the backlash of the seismograph itself with the amplitude from 10^-5 m to 10^-4 m.

• Earthquakes and Structures
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• v.18 no.4
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• pp.519-526
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• 2020

Non-stationary random seismic response and reliability of multi-degree of freedom hysteretic structure system are studied based on the cumulative damage failure mechanism. First, dynamic Eqs. of multi-degree of freedom hysteretic structure system under earthquake action are established. Secondly, the random seismic response of a multi-degree freedom hysteretic structure system is investigated by the combination of virtual excitation and precise integration. Finally, according to the damage state level of structural, the different damage state probability of high-rise frame structure is calculated based on the boundary value of the cumulative damage index in the seismic intensity earthquake area. The results show that under the same earthquake intensity and the same floor quality and stiffness, the lower the floor is, the greater the damage probability of the building structure is; if the structural floor stiffness changes abruptly, the weak layer will be formed, and the cumulative damage probability will be the largest, and the reliability index will be relatively small. Meanwhile, with the increase of fortification intensity, the reliability of three-level structure fortification is also significantly reduced. This method can solve the problem of non-stationary random seismic response and reliability of high-rise buildings, and it has high efficiency and practicability. It is instructive for structural performance design and estimating the age of the structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6C
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• pp.615-623
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• 2003

This paper presents effective quality-of-service renegotiating schemes for streaming video. The conventional network supporting quality-of-service generally allows a negotiation at call setup. However, it is not efficient for the video application since the compressed video traffic is statistically non-stationary. Thus, we consider the network supporting quality-of-service renegotiations during the data transmission, and study effective quality-of-service renegotiating schemes for streaming video. Simple token bucket model, whose parameters are token filling rate and token bucket size, is adopted for the video traffic model. The renegotiating time instants and the parameters are determined by analyzing the statistical information of compressed video traffic. In this paper, two renegotiating approaches, i.e. fixed renegotiating interval case and variable renegotiating interval case, are examined. Finally, the experimental results are provided to show the performance of the proposed schemes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.3
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• pp.163-172
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• 2016

Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.12
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• pp.850-856
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• 2007

In a supercooled or capsule type ice storage system, aqueous solution (or water) may have trouble with non-uniform dissolution though the system contributes to the simplicity of system and ecological improvement. The non-uniform dissolution increases the instability of the system because it may cause an ice blockage in pipe or cooling part. In order to observe the supercooled state, a cooling experiment was performed with pressurization to an ethylene glycol(EG) 3 mass% solution in stationary state. Also, the effect of the pressurization from 101 to 505 kPa to the dissolution of supercooled aqueous solution was measured with the dissolution time of the supercooled aqueous solution at a fixed cooling rate of brine. At results, the dissolution of supercooled point decreased as the pressure of the aqueous solution in the vessel increased. Moreover, the dissolution point increased as the heat flux for cooling increased.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.157-164
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• 2005

The conventional adaptive detectors can not maintain sufficient detection performance at the presence of non-stationary clutter with unknown characteristics. This is caused by the lack of a priori information about clutter parameters changing over radar coordinates. To solve this problem, it is necessary to use clutter classifiers which have functions, such as the selection of the applied algorithm and its parameters extraction according to clutter conditions. In this paper, we describe the implementation of a clutter environment classifier for adaptive processing. In the environment classifier implemented on Visual C++, the extraction of the parameters and selection of processing algorithm for the adaptive processing unit are possible, and the result of algorithms can be verified at each stage.