• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.31-36
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• 2008

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the enviromuent. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.920-924
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• 2005

This paper presents adaptive learning data of evolvable neural networks (ENNs) for time series prediction of nonlinear dynamic systems. ENNs are a special class of neural networks that adopt the concept of biological evolution as a mechanism of adaptation or learning. ENNs can adapt to an environment as well as changes in the environment. ENNs used in this paper are L-system and DNA coding based ENNs. The ENNs adopt the evolution of simultaneous network architecture and weights using indirect encoding. In general just previous data are used for training the predictor that predicts future data. However the characteristics of data and appropriate size of learning data are usually unknown. Therefore we propose adaptive change of learning data size to predict the future data effectively. In order to verify the effectiveness of our scheme, we apply it to chaotic time series predictions of Mackey-Glass data.

• 한국정보디스플레이학회:학술대회논문집
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• 2009.10a
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• pp.1099-1102
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• 2009

Conventional stereoscopic (3D) displays using binocular parallax generate unnatural conflicts between convergence and accommodation. Those conflicts can affect the ability to fuse binocular images and may cause visual fatigue. This study examined time-series changes in visual fatigue and depth sensation while viewing stereoscopic images with changing parallax. We examined the physiological changes, including the subjective symptoms of visual fatigue, when viewing five parallax conditions. The time-series results suggest that 2D and 3D images produce significantly different types of visual fatigue over the range of binocular disparity.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.04a
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• pp.487-492
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• 1999

The purpose of this study to find the mixture of concrete for Top-Down method. Throughout test of elapsed time change and L-type flow, it was proposed basic performance and level of top-down method concrete. When change as to elapsed time is considered, so series of F10 added 10% S.P satisfied slump and demanded flow (60$\pm$5cm), and L-type slump, L-type flow satisfied liquidity, it can be considered basic mixture of designed actual frame later.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of Environmental Impact Assessment
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• v.12 no.1
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• pp.45-54
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• 2003

The purpose of this paper is to examine the trend on the change of the cherry blossom flowering time due to the temperature change by selecting regions that have long periods of cherry blossom flowering time data as cases. With the flowering time data, the distribution of cherry blossom flowering time, time series change and change rate of cherry blossom flowering time were analyzed. Also, the correlation between the cherry blossom flowering time and the temperature was analyzed. The flowering of cherry blossom is earlier in metropolitan areas, and in the east coastal region than the west coastal region. The trend on the change of the cherry blossom flowering time is very similar to change the temperature. The change rate of the cherry blossom flowering time is rising up in the whole regions under study, and is relatively high in metropolitan areas. Especially, the cherry blossom flowering time festinated greatly in Pohang that is one of the heavily industrialized cities. From the analysis of correlation analysis between cherry blossom flowering time and temperature elements, the cherry blossom flowering time is highly related with mean temperature of March, which the month is just before the beginning of flowering.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1379-1391
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• 2020

UAV in the agricultural application are capable of collecting ultra-high resolution image. It is possible to obtain timeliness images for phenological phases of the crop. However, the UAV uses a variety of sensors and multi-temporal images according to the environment. Therefore, it is essential to use normalized image data for time series image application for crop monitoring. This study analyzed the variability of UAV reflectance and vegetation index according to Aviation Image Making Environment to utilize the UAV multispectral image for agricultural monitoring time series. The variability of the reflectance according to environmental factors such as altitude, direction, time, and cloud was very large, ranging from 8% to 11%, but the vegetation index variability was stable, ranging from 1% to 5%. This phenomenon is believed to have various causes such as the characteristics of the UAV multispectral sensor and the normalization of the post-processing program. In order to utilize the time series of unmanned aerial vehicles, it is recommended to use the same ratio function as the vegetation index, and it is recommended to minimize the variability of time series images by setting the same time, altitude and direction as possible.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.3
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• pp.237-243
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• 1992

The large value of the snubber capacitor is needed to protect the devices in high voltage converters using series connected power semiconductors. But that results in more losses and longer commutation time. So, new technique of series connection is required, which can minimize the value of snubber capacitor and also promote the reliability of high voltage converters. We study on the switching characteristics of series connected power semiconductors and then propose a novel switching algorithm for series-connection which is able to implement not only the dynamic voltage balancing in spite of the differerce of switching characteristics, but the minimization of the value of snubber capacitor, through the change of the value of snubber capacitor by Miller effect. Finally, we illustrate the validity of this synchronization by computer simulation and experimental results.

• Journal of Digital Contents Society
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• v.19 no.9
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• pp.1727-1737
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• 2018

In this paper, we propose a system to detect spatial structures in land cover maps and to detect time-series spatial structure changes. At first, the proposed system detects patches in a certain area at different times and calculates their measures to analyse spatial structure patterns of the area. Then the system conducts patch mapping among the detected time-series patches and decides 6 types of patch changes such as keeping, creating, disappearing, splitting, merging, and changing in a mixed way. Also, the system stores the patch-based spatial structure patterns of time-series land cover maps in binary form to extract changes. This demonstrated that the proposed change detection system can be used as a basis for planning the reconstruction of the urban spatial structure by measuring the degree of urban sprawl.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.