• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.1-12
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• 1996

We propose a new approach to classifying a time series data into one of the autoregressive moving-average (ARMA) models. It is bases on two pattern recognition concepts for solving time series identification. The one is an extended sample autocorrelation function (ESACF). The other is a neural network-driven decision tree classifier(NNDTC) in which two pattern recognition techniques are tightly coupled : neural network and decision tree classfier. NNDTc consists of a set of nodes at which neural network-driven decision making is made whether the connecting subtrees should be pruned or not. Therefore, time series identification problem can be stated as solving a set of local decisions at nodes. The decision values of the nodes are provided by neural network functions attached to the corresponding nodes. Experimental results with a set of test data and real time series data show that the proposed approach can efficiently identify the time seires patterns with high precision compared to the previous approaches.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.294-297
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• 1996

This paper proposes the method to recognize of time series data based on the chaotic feature extraction. Features extract from time series data using the chaotic time series data analysis and the pattern recognition process is using a neural network classifier. In experiment, EEG(electroencephalograph) signals are extracted features by correlation dimension and Lyapunov experiments, and these features are classified by multilayer perceptron neural networks. Proposed chaotic feature extraction enhances recognition results from chaotic time series data.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.952-954
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• 2003

In this paper, we present the different characteristics of NDVI fluctuation pattern between irrigated and non-irrigated area in Suphanburi province, in Central Thailand. For non-irrigated rice cultivation area, there is a strong correlation between NDVI fluctuation and peak rainfall, while there is a lower correlation with irrigated area. In this study, the 'peak detector' classifier was developed to identify the area of non-irrigated and irrigated cropping and its cropping intensity (number of crops per year). This classifier was created based on cropping characteristics such as number of crops, time or planting period of each crop and its relationship with the peak of rainfall. The classified result showed good accuracy in identification irrigated and nonirrigated rice cultivation areas.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.477-478
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• 2007

This paper proposes a new concept of support vector machine (SVM) based voltage stability classifier using time-series phasor data. The classifier, based on a linear SVM, can provide very effective signals for identification of long-term voltage stability. In addition, the SVM output is applicable as an voltage stability indicator when an amount of corrective controls are performed just to make the system reach around at the maximum deliverable point.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.36-39
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• 2006

This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurement data. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of PMU for monitoring power system dynamics. The methodology for fast monitoring of the system is initiated locally which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper briefly describes the mathematical background of SVM, and explains the procedure for fast classification of voltage stability using the SVM algorithm. To illustrate the effectiveness of the classifier, this paper includes numerical examples with a 11-bus test system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.2
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• pp.130-135
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• 2004

This paper presents a new design methods of the short-term load forecasting system (STLFS) using the data mining. The structure of the proposed STLFS is divided into two parts: the Takagi-Sugeno (T-S) fuzzy model-based classifier and predictor The proposed classifier is composed of the Gaussian fuzzy sets in the premise part and the linearized Bayesian classifier in the consequent part. The related parameters of the classifier are easily obtained from the statistic information of the training set. The proposed predictor takes form of the convex combination of the linear time series predictors for each inputs. The problem of estimating the consequent parameters is formulated by the convex optimization problem, which is to minimize the norm distance between the real load and the output of the linear time series estimator. The problem of estimating the premise parameters is to find the parameter value minimizing the error between the real load and the overall output. Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.58-65
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• 1997

A new automatic classification method with high and stable accuracy for time-series image data is presented in this paper. This method is based on prior condition that a classified map of the target area already exists, or at least one of the time-series image data had been classified. The classified map is used as a reference map to specify training areas of classification categories. The new automatic classification method consists of five steps, i.e., extraction of training data using reference map, detection of changed pixels based upon the homogeneity of training data, clustering of changed pixels, reconstruction of training data, and classification as like maximum likelihood classifier. In order to evaluate the performance of this method qualitatively, four time-series Landsat TM image data were classified by using this method and a conventional method which needs a skilled operator. As a results, we could get classified maps with high reliability and fast throughput, without a skilled operator.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.1-9
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• 2023

The problem of classifying a non-line-of-sight (NLOS) signal in a multipath channel is important to improve global navigation satellite system (GNSS) positioning accuracy in urban areas. Conventional deep learning-based NLOS signal classifiers use GNSS satellite measurements such as the carrier-to-noise-density ratio (CN_0), pseudorange, and elevation angle as inputs. However, there is a computational inefficiency with use of these measurements and the NLOS signal features expressed by the measurements are limited. In this paper, we propose a Convolutional Neural Network (CNN)-based NLOS signal classifier that receives successive Auto-correlation function (ACF) outputs according to a time-series, which is the most primitive output of GNSS signal processing. We compared the proposed classifier to other DL-based NLOS signal classifiers such as a multi-layer perceptron (MLP) and Gated Recurrent Unit (GRU) to show the superiority of the proposed classifier. The results show the proposed classifier does not require the navigation data extraction stage to classify the NLOS signals, and it has been verified that it has the best detection performance among all compared classifiers, with an accuracy of up to 97%.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.1
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• pp.69-74
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• 1998

In order to monitor the tool wear in milling operation, cutting force is measured as the tool wear increased. The digital signal processing methods are used to detect the tool wear . As AR parameter extract the feature of tool wear , it can be used as input parameter of pattern classifier. The FFT monitor the tool wear exactly , but it can not do real time signal processing. The band energy method can be used to real time monitoring of tool wear ,but int can degrade the exact monitoring.