• Journal of Computing Science and Engineering
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• 제5권4호
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• International Journal of Contents
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• 제10권1호
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• pp.47-53
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• 2014

The paper considers the challenges involved in measuring the similarities between time series, such as time shifts and the mixture of frequencies. To improve recognition accuracy, we investigate an improved linear dynamical system for discovering prominent features by exploiting the evolving dynamics and correlations in a time series, as the quality of unsupervised pattern recognition relies strongly on the extracted features. The proposed approach yields a set of compact extracted features that boosts the accuracy and reliability of clustering for time series data. Experimental evaluations are carried out on time series applications from the scientific, socio-economic, and business domains. The results show that our method exhibits improved clustering performance compared to conventional methods. In addition, the computation time of the proposed approach increases linearly with the length of the time series.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제19권1호
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• pp.7-21
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• 2012

The fuzzy time series is introduced by Song and Chissom([8]) to construct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully reflect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect the fluctuation of historical data and to improve the forecasting accuracy of fuzzy time series. Using the well-known enrollment, the proposed method is discussed and the forecasting accuracy is evaluated. Empirical studies show that the proposed method in forecasting accuracy is superior to existing methods and it fully reflects the fluctuation of historical data.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.429-432
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• 1999

Vegetation cover classification is examined based on a time series NOAA/AVHRR data. Time series data analysis methods including Fourier transform, Auto-Regressive (AR) model and temporal signature similarity matching are developed to extract phenological features of vegetation from a time series NDVI data from NOAA/AVHRR and to classify vegetation types. In the Fourier transform method, typical three spectral components expressing the phenological features of vegetation are selected for classification, and also in the AR model method AR coefficients are selected. In the temporal signature similarity matching method a new index evaluating the similarity of temporal pattern of the NDVI is introduced for classification.

• 한국콘텐츠학회논문지
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• 제15권6호
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• pp.399-410
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• 2015

본 연구의 목적은 사회서비스 수요를 구성하는 이용자 수와 제공기관 수를 예측 할 수 있도록 시계열 모형을 활용하여 각각의 예측 값을 구성하고, 실제 관측된 값과의 차이를 확인하여 사회서비스분야에서 시계열 예측모형의 타당성을 검증하는 연구이다. 분석 자료는 한국보건복지정보개발원에서 발간한 사회서비스 제공기관 공급실태분석에서 제시된 발달재활서비스 이용 현황을 연구 목적에 따라 가공하여 이차 분석하였다. 분석결과 이용자 수는 ARIMA(1,1,0) 모형이, 제공기관 수는 ARIMA(0,1,1) 모형이 최적의 예측모형으로 제시되었다. 예측모형에 의한 예측 값은 관측 값과의 어느 정도 차이는 있었지만, 관측값은 예측값의 최대값과 최소값의 범위에 놓여 있었다. 따라서 사회서비스의 이용자를 활용한 수요예측과 제공기관을 활용한 공급예측의 모형구축에 대한 타당성은 가능할 수 있음을 확인할 수 있었다.

• 정보처리학회논문지D
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• 제11D권3호
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• pp.709-716
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• 2004

본 논문에서는 다중 시계열 패턴인식 사용하여 생산장치의 상태자료부터 공정결과를 예측하여 정상 또는 비정상을 판정하는 지능형 감시시스템에 관하여 기술한다. 제안하는 감시스템은 초기화, 학습 그리고 인식의 세 단계로 구성된다. 초기화 단계에서는 감시대상의 생산장치가 가지는 인사들 각각의 가중치와 각 인자들이 가지는 시계열 자료 중에서 학습과 인식에 유효단계를 설정한다. 학습단계에서는 LBG알고리즘을 사용하여 이 생산장치에 의하여 생성되고 수집된 패턴들을 군집화 한다. 각 패턴은 시계열 형태의 자료와 처리 완료 후 계측기에 의하여 측정된 ACI로 구성된다. 인식단계에서는 DTW를 사용하여 실시간으로 입력된 패턴과 군집화된 패턴들 사이의 대응을 수행하여 가장 잘 정합되는 패턴을 찾는다. 다음은 이 패턴이 가지는 ACI, 차 그리고 가중치들의 조합으로 예측된 ACI 값을 산출한다. 최종적으로 예측된 ACI가 정상으로 수용할 수 있는 값 범위에 없는지 여부를 결정한다. 제안하는 시스템의 성능평가를 위하여 식각장치로부터 획득된 자료를 대상으로 실험하였다. 실험결과에서는 학습횟수가 증가함에 따라 예측 ACI값과 실측ACI값 사이의 오차가 현저히 감소함을 볼 수 있다

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.329-332
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• 2007

Annual vegetation growth patterns are determined by the intrinsic phenological characteristics of each land cover types. So, if typical growth patterns of each land cover types are well-estimated, and a NDVI time-series data of a certain area is compared to those estimated patterns, we can implement more advanced analyses such as a land surface-type classification or a land surface type change detection. In this study, we utilized Terra MODIS NDVI 250m data and compressed full annual NDVI time series data into several indices using the Harmonic Analysis of Time Series(HANTS) algorithm which extracts the most significant frequencies expected to be presented in the original NDVI time-series data. Then, we found these frequencies patterns, described by amplitude and phase data, were significantly different from each other according to vegetation types and these could be used for land cover classification. However, in spite of the capabilities of the HANTS algorithm for detecting and interpolating cloud-contaminated NDVI values, some distorted NDVI pixels of June, July and August, as well as the long rainy season in Korea, are not properly corrected. In particular, in the case of two or three successive NDVI time-series data, which are severely affected by clouds, the HANTS algorithm outputted wrong results.

• 한국환경복원기술학회지
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• 제14권3호
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• pp.1-14
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• 2011

In order to determine temporal changes of the urban landscape, interdependence and interaction among geo-spatial objects can be analyzed using GIS analytic methods. In this study, to investigate changes in the landscape structure of the Namyangju area, the size and shape of landscape patches, and the distance between the patches were analyzed with the Spatial Autocorrelation Method. In addition, both global and local spatial autocorrelation analyses were conducted. The results of global Moran's I revealed that both patch size and shape index transformed to a more dispersed pattern over time. Next, the local Moran's I of patch size in all time series determined that almost all patches were of a high-low pattern. Meanwhile, the local Moran's I of the shape index was found to have changed from a high-high pattern to a high-low pattern in time series. Finally, as time passes, the number of hot spot patches about size and shape index had been decreased according to the results of hot spot analysis. These changes appeared around the development projects in the study area. From the results of this study, degradation of landscape patches in Namyangju were ascertained and their specific areas were delineated. Such results can be used as useful data in selecting areas for conservation and for preparing plans and strategies in environmental restoration.

• Smart Structures and Systems
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• 제29권1호
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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.

• Journal of Information Processing Systems
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• 제15권6호
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• pp.1392-1405
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• 2019

The cathode voltage of aluminum electrolytic cell is relatively stable under normal conditions and fluctuates greatly when it has an anomaly. In order to detect the abnormal range of cathode voltage, an anomaly detection algorithm based on sliding window was proposed. The algorithm combines the time series segmentation linear representation method and the k-nearest neighbor local anomaly detection algorithm, which is more efficient than the direct detection of the original sequence. The algorithm first segments the cathode voltage time series, then calculates the length, the slope, and the mean of each line segment pattern, and maps them into a set of spatial objects. And then the local anomaly detection algorithm is used to detect abnormal patterns according to the local anomaly factor and the pattern length. The experimental results showed that the algorithm can effectively detect the abnormal range of cathode voltage.