• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.703-710
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• 2010

In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially correlation function. The main purpose of this paper is to prevent damage or crack from the fatigue loaded on an excavator in advance. Generally, the larger data, the longer processing time, and the amount of the data used in this paper are also large, especially every sampling period, 1600 data are gathered and calculated. So this paper focuses on minimizing the number of required sensors by using the correlation function. From the cross correlation function, similar pattern sensors are eliminated and dissimilar pattern sensors are considered, and from the auto correlation function, the overload can be detected. To prove the efficiency of the proposed overload detecting algorithm, this paper shows the computer simulation results.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.709-716
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• 2004

This paper describes an intelligent real-time monitoring system of a semiconductor processing equipment, which determines normal or not for a wafer in processing, using multiple time-series pattern recognition. The proposed system consists of three phases, initialization, learning and real-time prediction. The initialization phase sets the weights and tile effective steps for all parameters of a monitoring equipment. The learning phase clusters time series patterns, which are producted and fathered for processing wafers by the equipment, using LBG algorithm. Each pattern has an ACI which is measured by a tester at the end of a process The real-time prediction phase corresponds a time series entered by real-time with the clustered patterns using Dynamic Time Warping, and finds the best matched pattern. Then it calculates a predicted ACI from a combination of the ACI, the difference and the weights. Finally it determines Spec in or out for the wafer. The proposed system is tested on the data acquired from etching device. The results show that the error between the estimated ACI and the actual measurement ACI is remarkably reduced according to the number of learning increases.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.377-384
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• 2015

In this paper we propose a visualization tool for distortion-free time-series matching. Supporting distortion-free is a very important factor in time-series matching to get more accurate matching results. In this paper, we visualize the result of time-series matching, which removes various time-series distortions such as noise, offset translation, amplitude scaling, and linear trend by using moving average, normalization, linear detrending transformations, respectively. The proposed visualization tool works as a client-server model. The client sends a user-selected time-series, of which distortions are removed, to the server and visualizes the matching results. The server efficiently performs the distortion-free time-series matching on the multi-dimensional R*-tree index. By visualizing the matching result as five different charts, we can more easily and more intuitively understand the matching result.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.277-286
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• 2002

Genetic programming (GP) has been combined with quantum mechanical perturbation theory to make a new algorithm to construct mathematical models and perform predictions for chaotic time series from real world. Procedural similarities between time series modeling and perturbation theory to solve quantum mechanical wave equations are discussed, and the exemplary GP approach for implementing them is proposed. The approach is based on multiple populations and uses orthogonal functions for GP function set. GP is applied to original time series to get the first mathematical model. Numerical values of the model are subtracted from the original time series data to form a residual time series which is again subject to GP modeling procedure. The process is repeated until predetermined terminating conditions are met. The algorithm has been successfully applied to construct highly effective mathematical models for many real world chaotic time series. Comparisons with other methodologies and topics for further study are also introduced.

• IE interfaces
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• v.20 no.3
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.313-318
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• 2021

The advent of the Deep Learning has applied to many industrial and general applications having an impact on our lives these days. Certain type of machine learning model is needed to be designed for a specific problem of field. Recently, there are many instances to solve the various COVID-19 related problems using deep learning model. Therefore, in this paper, a deep learning model for predicting number of outpatients of a hospital in advance is suggested. The suggested deep learning model is designed by using the Keras in Jupyter Notebook. The prediction result is being analyzed with the real data in graph, as well as the loss rate with some validation data to verify either for the underfitting or the overfitting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.31-40
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• 2008

We generally use lower-dimensional transformations to convert high-dimensional sequences into low-dimensional points in similar sequence matching. These traditional transformations, however, show different characteristics in indexing performance by the type of time-series data. It means that the selection of lower-dimensional transformations makes a significant influence on the indexing performance in similar sequence matching. To solve this problem, in this paper we propose a hybrid approach that integrates multiple transformations and uses them in a single multidimensional index. We first propose a new notion of hybrid lower-dimensional transformation that exploits different lower-dimensional transformations for a sequence. We next define the hybrid distance to compute the distance between the transformed sequences. We then formally prove that the hybrid approach performs the similar sequence matching correctly. We also present the index building and the similar sequence matching algorithms that use the hybrid approach. Experimental results for various time-series data sets show that our hybrid approach outperforms the single transformation-based approach. These results indicate that the hybrid approach can be widely used for various time-series data with different characteristics.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.5
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• pp.375-384
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• 2013

Most of regions within the city of Mokpo, located on the southwest coast of the Korean Peninsula, are subjected to significant subsidence because about 70% of the city is land reclaimed from the sea (Kim et al., 2005). In this study, we aimed to estimate the rate of subsidence over Mokpo by using PALSAR L-band dataset from 2006 to 2010. Time series analysis was performed as well using GPS surveying data from 2010 to 2012. Results from these two independent datasets are then compared and analyzed over the common period of time. GPS data processing provides the results of seasonal variation on the surface, that is, via repeatedly rising and falling in association with the periodic cycle. Therefore, a time series analysis was performed to calculate the rate of ground subsidence. The deformation rates calculated for the same point are 3.89cm/yr and 2.65cm/yr from the GPS data and SAR data, respectively. SAR and GPS data processing results show a very similar pattern in terms of magnitude of annual subsidence. Thus, if the two datasets are integrated together, new modeling on ground subsidence is feasible. Lastly, subsidence was detected in a landfill area in the city of Mokpo, which has been continuously occurring through 2012.