• Title, Summary, Keyword: Vector Auto Regressive

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Identification of dynamic characteristics of structures using vector backward auto-regressive model

  • Hung, Chen-Far;Ko, Wen-Jiunn;Peng, Yen-Tun
    • Structural Engineering and Mechanics
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    • v.15 no.3
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    • pp.299-314
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    • 2003
  • This investigation presents an efficient method for identifying modal characteristics from the measured displacement, velocity and acceleration signals of multiple channels on structural systems. A Vector Backward Auto-Regressive model (VBAR) that describes the relationship between the output information in different time steps is used to establish a backward state equation. Generally, the accuracy of the identified dynamic characteristics can be improved by increasing the order of the Auto-Regressive model (AR) in cases of measurement of data under noisy circumstances. However, a higher-order AR model also induces more numerical modes, only some of which are the system modes. The proposed VBAR model provides a clear characteristic boundary to separate the system modes from the spurious modes. A numerical example of a lumped-mass model with three DOFs was established to verify the applicability and effectiveness of the proposed method. Finally, an offshore platform model was experimentally employed as an application case to confirm the proposed VBAR method can be applied to real-world structures.

Feature Extraction based on Auto Regressive Modeling and an Premature Contraction Arrhythmia Classification using Support Vector Machine (Auto Regressive모델링 기반의 특징점 추출과 Support Vector Machine을 통한 조기수축 부정맥 분류)

  • Cho, Ik-sung;Kwon, Hyeog-soong;Kim, Joo-man;Kim, Seon-jong
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.2
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    • pp.117-126
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    • 2019
  • Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

Prediction of Hydrogen Masers' Behaviors Against UTCr with R

  • Lee, Ho Seong;Kwon, Taeg Yong;Lee, Young Kyu;Yang, Sung-hoon;Yu, Dai-Hyuk
    • Journal of Positioning, Navigation, and Timing
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    • v.9 no.2
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    • pp.89-98
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    • 2020
  • Prediction of clock behaviors is necessary to generate very high stable system time which is essential for a satellite navigation system. For the purpose, we applied the Auto-Regressive Integrated Moving Average (ARIMA) model to the prediction of two hydrogen masers' behaviors with respect to the rapid Coordinated Universal Time (UTCr). Using the packaged programming language R, we made an analysis and prediction of time series data of [UTCr - clocks]. The maximum variation width of the residuals which were obtained by the difference between the predicted and measured values, was 6.2 ns for 106 days. This variation width was just one-sixth of [UTCr-UTC (KRIS)] published by the BIPM for the same period. Since the two hydrogen masers were found to be strongly correlated, we applied the Vector Auto-Regressive Moving Average (VARMA) model for more accurate prediction. The result showed that the prediction accuarcy was improved by two times for one hydrogen maser.

The Relationship Study for Major Petrochemical Complexes and Liquid Cargo Ports by the Granger and Toda-Yamamoto Causality Test (Granger 및 Toda-Yamamoto 인과 검정을 통한 주요 석유화학단지와 액체화물 항만들의 관계성 연구)

  • Lee, Gwamg-Un;Shin, Chang-Hoon
    • Journal of Navigation and Port Research
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    • v.43 no.6
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    • pp.469-474
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    • 2019
  • One of the world's major resources is crude oil, the most fundamental part of the industry. There is no place that does not use crude oil. Petroleum refining products and chemical production industrial products are produced through nearby petrochemical complexes and ports after importing crude oil. There would be a possible relationship among the petrochemical complexes and nearby regional ports working with liquid cargoes. To confirm these relations, Ulsan Port, Daesan Port, and Yeosu Gwangyang Port were selected for this study. A Vector Auto Regressive model using time series data was applied. A Unit Root Test was performed. The relationship was confirmed through the Granger and Toda Yamamoto Causality Test.

Competition between Online Stock Message Boards in Predictive Power: Focused on Multiple Online Stock Message Boards

  • Kim, Hyun Mo;Park, Jae Hong
    • Asia pacific journal of information systems
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    • v.26 no.4
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    • pp.526-541
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    • 2016
  • This research aims to examine the predictive power of multiple online stock message boards, namely, NAVER Finance and PAXNET, which are the most popular stock message boards in South Korea, in stock market activities. If predictive power exists, we then compare the predictive power of multiple online stock message boards. To accomplish the research purpose, we constructed a panel data set with close price, volatility, Spell out acronyms at first mention.PER, and number of posts in 40 companies in three months, and conducted a panel vector auto-regression analysis. The analysis results showed that the number of posts could predict stock market activities. In NAVER Finance, previous number of posts positively influenced volatility on the day. In PAXNET, previous number of posts positively influenced close price, volatility, and PER on the day. Second, we confirmed a difference in the prediction power for stock market activities between multiple online stock message boards. This research is limited by the fact that it only considered 40 companies and three stock market activities. Nevertheless, we found correlation between online stock message board and stock market activities and provided practical implications. We suggest that investors need to focus on specific online message boards to find interesting stock market activities.

A comparative study of models for molten carbonate fuel cell (MCFC) processes

  • Kim, Tae Young;Kim, Beom Seok;Park, Tae Chang;Yeo, Yeong Koo
    • Korean Journal of Chemical Engineering
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    • v.34 no.7
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    • pp.1952-1960
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    • 2017
  • The necessity of this work arose from the need for identification of a comprehensive plant model that can be used in the model-based control of the MCFC plant. Various models for molten carbonate fuel cell (MCFC) processes are presented and evaluated in this paper. Both a rigorous model based on mass and energy balances and implicit models based on operation data were investigated and analyzed. In particular, auto-regressive moving average (ARMA) model, least-squares support vector machine (LSSVM) model, artificial neural network (ANN) model and partial least squares (PLS) model for a MCFC system were developed based on input-output operating data. Among these models, the ARMA model showed the best agreement with plant operation data.

A Study on the Seoul Apartment Jeonse Price after the Global Financial Crisis in 2008 in the Frame of Vecter Auto Regressive Model(VAR) (VAR분석을 활용한 금융위기 이후 서울 아파트 전세가격 변화)

  • Kim, Hyun-woo;Lee, Du-Heon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.16 no.9
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    • pp.6315-6324
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    • 2015
  • This study analyses the effects of household finances on rental price of apartment in Seoul which play a major role in real estate policy. We estimate VAR models using time series data. Economy variables such as sales price of apartment in Seoul, consumer price index, hiring rate, real GNI and loan amount of housing mortgage, which relate to household finances and influence the rental price of apartment, are used for estimation. The main findings are as follows. In the short term, the rental price of apartment is impacted by economy variables. Specifically, Relative contributions of variation in rental price of apartment through structural shock of economy variables are most influenced by their own. However, in the long term, household variables are more influential to the rental price of apartment. These results are expected to contribute to establish housing price stabilization policies through understanding the relationship between economy variables and rental price of apartment.

Hybrid CSA optimization with seasonal RVR in traffic flow forecasting

  • Shen, Zhangguo;Wang, Wanliang;Shen, Qing;Li, Zechao
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.10
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    • pp.4887-4907
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    • 2017
  • Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

Automated data interpretation for practical bridge identification

  • Zhang, J.;Moon, F.L.;Sato, T.
    • Structural Engineering and Mechanics
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    • v.46 no.3
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    • pp.433-445
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    • 2013
  • Vibration-based structural identification has become an important tool for structural health monitoring and safety evaluation. However, various kinds of uncertainties (e.g., observation noise) involved in the field test data obstruct automation system identification for accurate and fast structural safety evaluation. A practical way including a data preprocessing procedure and a vector backward auto-regressive (VBAR) method has been investigated for practical bridge identification. The data preprocessing procedure serves to improve the data quality, which consists of multi-level uncertainty mitigation techniques. The VBAR method provides a determinative way to automatically distinguish structural modes from extraneous modes arising from uncertainty. Ambient test data of a cantilever beam is investigated to demonstrate how the proposed method automatically interprets vibration data for structural modal estimation. Especially, structural identification of a truss bridge using field test data is also performed to study the effectiveness of the proposed method for real bridge identification.