Noise Reduction Method for Time Series Data of plant. Growth with Adjustment of Cut off Frequency of Low Pass Filter.

Off the coast of the Boso Peninsula, there is a triple junction of the Pacific Plate, the Philippine Sea Plate, and the North American Plate and the Boso Peninsula is one of the seismically active areas in Japan. There are also epicenter areas such as the 1703 Genroku Kanto Earthquake (M8.2), the 1923 Taisho Kanto Earthquake (M7.9), and the Boso Slow Slip which occurs every 6 years, which are geologically interesting places. To estimate the subsurface resistivity structure of the whole Boso area, Magnetotelluric (MT) survey with 41 sites (inter-sites distance of 7 km) has been conducted in 2014-2016, using U43 (12 sites, 1 Hz sampling ; Tierra Technica) and MTU-5, 5A, net (41 sites, 15, 150, and 2400 Hz sampling; Phoenix Geophysics). However, the Boso area is greatly affected by leak current from DC-driven trains, factories, and power lines, so the observed data are contaminated by artificial noises. When we tried to apply the conventional noise reduction method (e.g., remote reference (Gamble et al., 1979) and BIRRP (Chave and Thomson, 2004)) in frequency domain, the obtained MT sounding curve was not ideal. In particular, the phase between the periods of 20 and 400 sec was close to 0 degrees. It suggests that the method used is insufficient to reduce the near-field effect for the Boso data. Thus, we developed a new noise reduction method using MSSA (Multi-channel Singular Spectrum Analysis) as a pre-processing method in time domain.The procedure is as follows;(1) Decompose 6 component data (Hx, Hy, Ex, Ey, Hxr and Hyr: H and E means magnetic and electric field, respectively, x and y indicates NS and EW component, and r denotes the reference field observed at a quiet station) using MSSA into 6&#215;M principal components (PCs). &#160;Here, M shows the window length of MSSA.(2) Check contribution and periods of each PC and eliminate the PCs which are corresponding to the longer periods of variation. That is &#8220;detrend&#8221; of the original data.(3) Apply the second MSSA to the detrended time series data to separate signals and noises shorter than 400 sec.(4) Calculating correlation coefficients between H and Hr and between E and Hr for each PC and select the PCs with higher correlation to reconstruct time series data to make MT analysis.Then, we perform MT analysis by BIRRP to estimate apparent resistivity,As a result, the coherences of H-Hr, and E-Hr were improved and the MT sounding curve became smoother than those results by the conventional noise reduction methods. This indicated that the effectiveness of the proposed noise reduction. However, further investigation in different periods and sites will be required.

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CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-1-2021-31-2021 ◽

2021 ◽

Vol V-1-2021 ◽

pp. 31-38

Author(s):

M. Arnold ◽

M. Hoyer ◽

S. Keller

Keyword(s):

Time Series ◽

Event Detection ◽

Time Series Data ◽

Radar Data ◽

Data Driven ◽

Series Data ◽

Pass Filter ◽

Low Pass Filter ◽

Feature Based ◽

Bridge Crossing

Abstract. This study focuses on detecting vehicle crossings (events) with ground-based interferometric radar (GBR) time series data recorded at bridges in the course of critical infrastructure monitoring. To address the challenging event detection and time series classification task, we rely on a deep learning (DL) architecture. The GBR-displacement data originates from real-world measurements at two German bridges under normal traffic conditions. As preprocessing, we only apply a low-pass filter. We develop and evaluate a one-dimensional convolutional neural network (CNN) to achieve a solely data-driven event detection. As a baseline machine learning approach, we use a Random Forest (RF) with a selected feature-based input. Both models’ performance is evaluated on two datasets by focusing on identifying events and pure bridge oscillations. Generally, the event classification results are promising, and the CNN outperforms the RF with an overall accuracy of 94.7% on the test subset. By relying on an entirely unknown second dataset, we focus on the models’ performances regarding the distinction between events and decays. On this dataset, the CNN meets this challenge successfully, while the feature-based RF classifies the majority of non-event decays as events. To sum up, the presented results reveal the potential of a data-driven DL approach concerning the detection of bridge crossing events in GBR-based displacement time series data. Based on such an event detection, a prospective assessment of bridge conditions seems feasible as an extension to previous structural health monitoring approaches.

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Detection and diagnosis of dynamics in time series data: Theory of noise reduction

10.1063/1.45298 ◽

1994 ◽

Author(s):

Robert Cawley ◽

Guan-Hson Hsu ◽

Liming W. Salvino

Keyword(s):

Time Series ◽

Noise Reduction ◽

Time Series Data ◽

Series Data ◽

Detection And Diagnosis

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Noise reduction in chaotic time series data

Pramana ◽

10.1007/bf02827599 ◽

1999 ◽

Vol 52 (1) ◽

pp. 25-37 ◽

Cited By ~ 1

Author(s):

A. Bhowal ◽

T. K. Roy

Keyword(s):

Time Series ◽

Noise Reduction ◽

Time Series Data ◽

Chaotic Time Series ◽

Series Data

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Estimation of Causal Relationship Between Multiple Time-Series Data and Events Case Study of Plant Growth

Advanced Science Letters ◽

10.1166/asl.2015.5799 ◽

2015 ◽

Vol 21 (3) ◽

pp. 537-540

Author(s):

Takahiro Kawamura ◽

Akihiko Ohsuga

Keyword(s):

Time Series ◽

Plant Growth ◽

Causal Relationship ◽

Time Series Data ◽

Series Data ◽

Multiple Time ◽

Multiple Time Series

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Noise Reduction Analysis of Radar Rainfall Using Chaotic Dynamics and Filtering Techniques

Advances in Meteorology ◽

10.1155/2014/517571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Soojun Kim ◽

Huiseong Noh ◽

Narae Kang ◽

Keonhaeng Lee ◽

Yonsoo Kim ◽

...

Keyword(s):

Noise Reduction ◽

Correlation Coefficient ◽

Standard Error ◽

Rainfall Series ◽

Radar Rainfall ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass ◽

Filtering Technique ◽

Filtering Techniques

The aim of this study is to evaluate the filtering techniques which can remove the noise involved in the time series. For this, Logistic series which is chaotic series and radar rainfall series are used for the evaluation of low-pass filter (LF) and Kalman filter (KF). The noise is added to Logistic series by considering noise level and the noise added series is filtered by LF and KF for the noise reduction. The analysis for the evaluation of LF and KF techniques is performed by the correlation coefficient, standard error, the attractor, and the BDS statistic from chaos theory. The analysis result for Logistic series clearly showed that KF is better tool than LF for removing the noise. Also, we used the radar rainfall series for evaluating the noise reduction capabilities of LF and KF. In this case, it was difficult to distinguish which filtering technique is better way for noise reduction when the typical statistics such as correlation coefficient and standard error were used. However, when the attractor and the BDS statistic were used for evaluating LF and KF, we could clearly identify that KF is better than LF.

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Spatio-temporal prediction of suspended sediment concentration in the coastal zone using an artificial neural network and a numerical model

Journal of Hydroinformatics ◽

10.2166/hydro.2012.123 ◽

2012 ◽

Vol 14 (3) ◽

pp. 574-584 ◽

Cited By ~ 2

Author(s):

B. Bhattacharya ◽

T. van Kessel ◽

D. P. Solomatine

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Numerical Model ◽

Series Data ◽

Ann Model ◽

Pass Filter ◽

Low Pass Filter ◽

Temporal Prediction ◽

Artificial Neural

A problem of predicting suspended particulate matter (SPM) concentration on the basis of wind and wave measurements and estimates of bed shear stress done by a numerical model is considered. Data at a location at 10 km offshore from Noordwijk in the Dutch coastal area is used. The time series data have been filtered with a low pass filter to remove short-term fluctuations due to noise and tides and the resulting time series have been used to build an artificial neural network (ANN) model. The accuracy of the ANN model during both storm and calm periods was found to be high. The possibilities to apply the trained ANN model at other locations, where the model is assisted by the correctors based on the ratio of long-term average SPM values for the considered location to that for Noordwijk (for which the model was trained), have been investigated. These experiments demonstrated that the ANN model's accuracy at the other locations was acceptable, which shows the potential of the considered approach.

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