• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.123-138
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• 2023

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.177-181
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• 2021

With the recent advances in information and communication technology, online management of students' learning data has become the norm. Research on learning analysis that predicts the near future (in a few years) of students' careers using machine learning methods and state transition models has been widely conducted. It is important for educators to evaluate the comprehension stability of students to prevent a decrease in their comprehension rate and dropouts in the class. In this study, we measured the comprehension process of university students in different types of lectures. Herein, we report on the results of data analysis using time series and data statistics, and consider several educational approaches.

• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.39-49
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• 2017

As the Internet technology advances, data on the web is increasing sharply. Many research study about incremental learning for classifying effectively in data increasing. Web document contains the time-series data such as published date. If we reflect time-series data to classification, it will be an effective classification. In this study, we analyze the time-series variation of the words. We propose an efficient classification through dividing the dataset based on the analysis of time-series information. For experiment, we corrected 1 million online news articles including time-series information. We divide the dataset and classify the dataset using SVM and $Na{\ddot{i}}ve$ Bayes. In each model, we show that classification performance is increasing. Through this study, we showed that reflecting time-series information can improve the classification performance.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.149-163
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• 2023

Affected by external factors, errors in time series data collected by sensors are common. Using the traditional method of constraining the speed change rate to clean the errors can get good performance. However, they are only limited to the data of stable changing speed because of fixed constraint rules. Actually, data with uneven changing speed is common in practice. To solve this problem, an online cleaning algorithm for time series data based on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changes periodically, we use the extreme learning machine to learn the law of speed changes from past data and predict the speed ranges that change over time to detect the data. In order to realize online data repair, a dual-window mechanism is proposed to transform the global optimal into the local optimal, and the traditional minimum change principle and median theorem are applied in the selection of the repair strategy. Aiming at the problem that the repair method based on the minimum change principle cannot correct consecutive abnormal points, through quantitative analysis, it is believed that the repair strategy should be the boundary of the repair candidate set. The experimental results obtained on the dataset show that the method proposed in this paper can get a better repair effect.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.135-148
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• 1995

Neural Networks have been advocated as an alternative to statistical forecasting methods. However, the empirical evidences are not consistent. In the present experiments, multi-layered perceptron (MLP) are adopted as approximator to the time series generating processes. To prevent the MLP from being overfitted to the given time series, the information obtained from ARMA modeling is used to determine the architecture of MLP. The proposed approach was tested empirically using the subsamples of the 111 time series used in the first Markridakis Competition. The forecasting results were analyzed to find out the factors that affect the performance of MLP. The experimental results show that the proposed approach outperforms ARMA models in terms of fitting and forecasting accuracy. In addition, it is found that the use of deseasonalized data improves the forecasting accuracy of MLP.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.321-328
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• 2024

This paper designs a disease prediction algorithm to diagnose migraine among the types of diseases in advance by learning algorithms using machine learning-based time series analysis. This study utilizes patient data statistics, such as electroencephalogram activity, to design a prediction algorithm to determine the onset signals of migraine symptoms, so that patients can efficiently predict and manage their disease. The results of the study evaluate how accurate the proposed prediction algorithm is in predicting migraine and how quickly it can predict the onset of migraine for disease prevention purposes. In this paper, a machine learning algorithm is used to analyze time series of data indicators used for migraine identification. We designed an algorithm that can efficiently predict and manage patients' diseases by quickly determining the onset signaling symptoms of disease development using existing patient data as input. The experimental results show that the proposed prediction algorithm can accurately predict the occurrence of migraine using machine learning algorithms.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.969-971
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• 1999

Wavelet analysis is applying to many fields such as the time-frequency localization of a time series and a time varying data. In this paper, a statistical testing based Wavelet power spectrum analysis for the stationary Nino3 Sea Surface Temperature(SST) data was executed. Specially, the 95% confidence level for SST was effective in searching the periods of El-Nino using various wavelet basis functions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.357-372
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• 2016

Augustine volcano, located along the Aleutian Arc, is one of the most active volcanoes in Alaska and nearby islands, with seven eruptions occurring between 1812 and 2006. This study monitored the surface displacement before and after the most recent 2006 eruption. For analysis, we conducted a time-series analysis on data observed at the permanent GPS(Global Positioning System) observation stations in Augustine Island between 2005 and 2011. According to the surface displacement analysis results based on GPS data, the movement of the surface inflation at the average speed of 2.3 cm/year three months prior to the eruption has been clearly observed, with the post-eruption surface deflation at the speed of 1.6 cm/year. To compare surface displacements measurement by GPS observation, ENVISAT(Environmental satellite) radar satellite data were collected between 2003 and 2010 and processed the SBAS(Small Baseline Subset) method, one of the time-series analysis techniques using multiple InSAR(Interferometric Synthetic Aperture Radar) data sets. This result represents 0.97 correlation value between GPS and InSAR time-series surface displacements. This research has been completed precise surface deformation using GPS and time-series InSAR methods for a detection of precursor symptom on Augustine volcano.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.33-38
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• 2020

Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.579-599
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• 2014

The purpose of this study was to identify the trends and risk of bias of research using time series analysis on health and welfare in Korea and to suggest a direction for future health and welfare research. The database searches identified 6,543 papers. Following the process for screening and selecting, a total of 91 papers were included in the systematic review. There has been a steady increase in the number of articles using time series analysis from 1987 to 2013. Time series analysis was applied in medicine and health science journals. The main goals were explanation and description. Most of the subjects were heath status and utilization of healthcare services. The main model used in the time series analysis was ARIMA followed by time series regression. The data were gathered from various sources, including the national statistical office and government agencies. For assessing risk of bias, some studies were found to have inadequate sample sizes or showed no time series graphs and plots. These findings suggest greater widespread utilization of time series analysis in the field of health and welfare and to use the appropriate analysis methods and statistical procedures to obtain more reliable results to improve the quality of research.