Application of Real-time Monitoring Buoy Systems for Physical and Biogeochemical Parameters in the Coastal Ocean around the Korean Peninsula

We introduce technological achievements while developing real-time ocean monitoring buoy systems in the key coastal regions around the Korean peninsula, and highlight their potential contribution to oceanographic studies in the region. Major achievements are an integration of physical and biogeochemical sensors, real-time and two-way communication, sustainable maintenance with stable power supply and mooring design, and the two-way control of sensor and sampling strategies with high sampling rates (as often as every minute). The time-series data from two buoy systems deployed in the key coastal regions are given as examples to show their potential use in studying oceanographic issues, such as major current variations along the east coast of Korea, wind-driven episodic events including typhoon passages, and frequent changes due to internal wave passages. The real-time and high-frequency monitoring of biogeochemical properties of seawater together with physical parameters could be used for numerous oceanographic studies in the coastal region, i.e., air-sea gas exchange, harmful dinoflagellate bloom, interaction between physical and biogeochemical processes.

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KAJIAN DAMPAK PEMEKARAN WILAYAH TERHADAP KESENJANGAN EKONOMI ANTAR DAERAH PESISIR DI PROVINSI BENGKULU

Jurnal AGRISEP Kajian Masalah Sosial Ekonomi Pertanian dan Agribisnis ◽

10.31186/jagrisep.15.1.27-36 ◽

2016 ◽

Vol 15 (1) ◽

pp. 27-36

Author(s):

Nyanyu Neti Arianti ◽

Indra Cahyadinata

Keyword(s):

Time Series ◽

Time Series Data ◽

Coastal Region ◽

Series Data ◽

Coastal Regions ◽

Regional Division ◽

Economic Disparity ◽

The Impact

This research were conducted to analyze the impact of regional division to the economic disparity among the coastal regions in Bengkulu Province. The data used in this research were time series data of GRDP and population for the period of 1993 to 2000 (before division) and 2004 to 2011 (after divison) obtained from Statistic Office. The result of this research showed that the value of Index Williamson(IW) before regional division was 0,22 lower than the IW value after the regional division (0,43). That was meaned that after the regional division, economic disparity of the coastal region in Bengkulu Province higher than before the regional division. Keywords : regional divisions, economic disparity, coastal region

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Embedding-based real-time change point detection with application to activity segmentation in smart home time series data

Expert Systems with Applications ◽

10.1016/j.eswa.2021.115641 ◽

2021 ◽

pp. 115641

Author(s):

Unai Bermejo ◽

Aitor Almeida ◽

Aritz Bilbao ◽

Gorka Azkune

Keyword(s):

Time Series ◽

Real Time ◽

Change Point ◽

Smart Home ◽

Time Series Data ◽

Time Change ◽

Change Point Detection ◽

Series Data ◽

Point Detection

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Use of an ensemble Kalman filter for real-time inversion of leaf area index from MODIS time series data

2009 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2009.5417369 ◽

2009 ◽

Cited By ~ 2

Author(s):

Zhiqiang Xiao ◽

Shunlin Liang ◽

Jindi Wang ◽

Xiyan Wu

Keyword(s):

Time Series ◽

Kalman Filter ◽

Leaf Area Index ◽

Real Time ◽

Leaf Area ◽

Ensemble Kalman Filter ◽

Time Series Data ◽

Series Data ◽

Time Inversion ◽

Area Index

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A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions ◽

10.1115/dscc2020-3329 ◽

2020 ◽

Author(s):

Meenakshi Narayan ◽

Ann Majewicz Fey

Keyword(s):

Time Series ◽

Adaptive Control ◽

Real Time ◽

Time Series Data ◽

Mean Squared Error ◽

Force Sensor ◽

Sensor Data ◽

Series Data ◽

Model Free ◽

Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

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An End to End Real Time Architecture for Analyzing and Clustering Time Series Data: Case of an Energy Management System

2018 6th International Renewable and Sustainable Energy Conference (IRSEC) ◽

10.1109/irsec.2018.8702839 ◽

2018 ◽

Cited By ~ 4

Author(s):

Hanaa Talei ◽

Mohamed Essaaidi ◽

Driss Benhaddou

Keyword(s):

Time Series ◽

Real Time ◽

Energy Management ◽

Management System ◽

Time Series Data ◽

Series Data ◽

Energy Management System ◽

End To End

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Study on Fractal Multistep Forecast for the Prediction of Driving Behavior

Journal of Advanced Transportation ◽

10.1155/2020/9150583 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Longhai Yang ◽

Hong Xu ◽

Xiqiao Zhang ◽

Shuai Li ◽

Wenchao Ji

Keyword(s):

Prediction Model ◽

Real Time ◽

Time Series Data ◽

Back Propagation ◽

Series Data ◽

Vehicle Speed ◽

Trajectory Data ◽

Time Data ◽

Characteristic Analysis ◽

Real Time Data

The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.

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A hybrid fuzzy quantum time series and linear programming model: Special application on TAIEX index dataset

Modern Physics Letters A ◽

10.1142/s0217732319502018 ◽

2019 ◽

Vol 34 (25) ◽

pp. 1950201 ◽

Cited By ~ 10

Author(s):

Pritpal Singh ◽

Gaurav Dhiman ◽

Sen Guo ◽

Ritika Maini ◽

Harsimran Kaur ◽

...

Keyword(s):

Time Series ◽

Linear Programming ◽

Real Time ◽

Time Series Data ◽

Programming Model ◽

Series Data ◽

Quantum Model ◽

Quantum Time ◽

Degree Of Membership ◽

Quantum Approach

The supremacy of quantum approach is able to provide the solutions which are not practically feasible on classical machines. This paper introduces a novel quantum model for time series data which depends on the appropriate length of intervals. In this study, the effects of these drawbacks are elaborately illustrated, and some significant measures to remove them are suggested, such as use of degree of membership along with mid-value of the interval. All these improvements signify the effective results in case of quantum time series, which are verified and validated with real-time datasets.

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Development of a real-time prediction model of driver behavior at intersections using kinematic time series data

Accident Analysis & Prevention ◽

10.1016/j.aap.2017.07.003 ◽

2017 ◽

Vol 106 ◽

pp. 428-436 ◽

Cited By ~ 3

Author(s):

Yaoyuan V. Tan ◽

Michael R. Elliott ◽

Carol A.C. Flannagan

Keyword(s):

Time Series ◽

Prediction Model ◽

Real Time ◽

Time Series Data ◽

Driver Behavior ◽

Series Data ◽

Time Prediction

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X-ray Flux and Spectral Variability of the TeV Blazars Mrk 421 and PKS 2155-304

Galaxies ◽

10.3390/galaxies8030064 ◽

2020 ◽

Vol 8 (3) ◽

pp. 64

Author(s):

Alok C. Gupta

Keyword(s):

Black Hole ◽

Time Series Data ◽

Series Data ◽

Physical Parameters ◽

Electromagnetic Spectrum ◽

Black Hole Mass ◽

Spectral Variability ◽

Massive Black Hole ◽

X Ray ◽

Northern And Southern Hemispheres

We reviewed X-ray flux and spectral variability properties studied to date by various X-ray satellites for Mrk 421 and PKS 2155-304, which are TeV emitting blazars. Mrk 421 and PKS 2155-304 are the most X-ray luminous blazars in the northern and southern hemispheres, respectively. Blazars show flux and spectral variabilities in the complete electromagnetic spectrum on diverse timescales ranging from a few minutes to hours, days, weeks, months and even several years. The flux and spectral variability on different timescales can be used to constrain the size of the emitting region, estimate the super massive black hole mass, find the dominant emission mechanism in the close vicinity of the super massive black hole, search for quasi-periodic oscillations in time series data and several other physical parameters of blazars. Flux and spectral variability is also a dominant tool to explain jet as well as disk emission from blazars at different epochs of observations.

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