scholarly journals Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhelin Huang ◽  
Fangfang Yang
Keyword(s):  
Time Series ◽  
Lithium Battery ◽  
Lithium Ion ◽  
Exponential Model ◽  
Frequency Resolution ◽  
Series Data ◽  
Discharge Data ◽  
Time Frequency ◽  
Complex Exponential ◽  
Charge Discharge

Accurate prediction of battery quality using early-cycle data is critical for battery, especially lithium battery in microgrid networks. To effectively predict the lifetime of lithium-ion batteries, a time series classification method is proposed that classifies batteries into high-lifetime and low-lifetime groups using features extracted from early-cycle charge-discharge data. The proposed method is based on a smooth localized complex exponential model that can extract battery features from time-frequency maps and self-adaptively select the time-frequency resolution to maximize the discrepancy of data from the two groups. A smooth localized complex exponential periodogram is then calculated to obtain the time-frequency decomposition of the whole time series data for further classification. The experimental results show that, by using battery features extracted from the first 128 charge-discharge processes, the proposed method can accurately classify batteries into high-lifetime and low-lifetime groups, with classification accuracy and specificity as high as 95.12% and 92.5%, respectively.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

LamaH: Large-sample Data for Hydrology in Central Europe

2021 ◽  
Author(s):  
Christoph Klingler ◽  
Mathew Herrnegger ◽  
Frederik Kratzert ◽  
Karsten Schulz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Vegetation Indices ◽  
River Network ◽  
Series Data ◽  
High Temporal Resolution ◽  
Stream Length ◽  
Discharge Data ◽  
Large Sample ◽  
Access To Data

<p>Open large-sample datasets are important for various reasons: i) they enable large-sample analyses, ii) they democratize access to data, iii) they enable large-sample comparative studies and foster reproducibility, and iv) they are a key driver for recent developments of machine-learning based modelling approaches.</p><p>Recently, various large-sample datasets have been released (e.g. different country-specific CAMELS datasets), however, all of them contain only data of individual catchments distributed across entire countries and not connected river networks.</p><p>Here, we present LamaH, a new dataset covering all of Austria and the foreign upstream areas of the Danube, spanning a total of 170.000 km² in 9 different countries with discharge observations for 882 gauges. The dataset also includes 15 different meteorological time series, derived from ERA5-Land, for two different basin delineations: First, corresponding to the entire upstream area of a particular gauge, and second, corresponding only to the area between a particular gauge and its upstream gauges. The time series data for both, meteorological and discharge data, is included in hourly and daily resolution and covers a period of over 35 years (with some exceptions in discharge data for a couple of gauges).</p><p>Sticking closely to the CAMELS datasets, LamaH also contains more than 60 catchment attributes, derived for both types of basin delineations. The attributes include climatic, hydrological and vegetation indices, land cover information, as well as soil, geological and topographical properties. Additionally, the runoff gauges are classified by over 20 different attributes, including information about human impact and indicators for data quality and completeness. Lastly, LamaH also contains attributes for the river network itself, like gauge topology, stream length and the slope between two sequential gauges.</p><p>Given the scope of LamaH, we hope that this dataset will serve as a solid database for further investigations in various tasks of hydrology. The extent of data combined with the interconnected river network and the high temporal resolution of the time series might reveal deeper insights into water transfer and storage with appropriate methods of modelling.</p>

Modeling and forecasting multifractal wavelet scale: Western market versus eastern market

2018 ◽  
Vol 29 (11) ◽  
pp. 1850109 ◽  
Author(s):  
Emrah Oral ◽  
Gazanfer Unal
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Price Change ◽  
Wavelet Coherence ◽  
Series Data ◽  
Primary Study ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Time Frequency

This leading primary study is about modeling multifractal wavelet scale time series data using multiple wavelet coherence (MWC), continuous wavelet transform (CWT) and multifractal detrended fluctuation analysis (MFDFA) and forecasting with vector autoregressive fractionally integrated moving average (VARFIMA) model. The data is acquired from Yahoo Finances!, which is composed of 1671 daily stock market of eastern (NIKKEI, TAIEX, KOPSI) and western (SP500, FTSE, DAX) markets. Once the co-movement dependencies on time-frequency space are determined with MWC, the coherent data is extracted out of raw data at a certain scale by using CWT. The multifractal behavior of the extracted series is verified by MFDFA and its local Hurst exponents have been calculated obtaining root mean square of residuals at each scale. This inter-calculated fluctuation function time series has been re-scaled and used to estimate the process with VARFIMA model and forecasted accordingly. The results have shown that the direction of price change is determined without difficulty and the efficiency of forecasting has been substantially increased using highly correlated multifractal wavelet scale time series data.

Pendugaan Data Runtun Waktu Debit Aliran Sungai Cikeruh dengan Metode Thomas-Fiering

2021 ◽  
Vol 1 (1) ◽  
pp. 13-20
Author(s):  
Meiske Shabrina Pesik ◽  
Didi Suhaedi ◽  
M. Yusuf Fajar
Keyword(s):  
Time Series ◽  
River Flow ◽  
Time Series Data ◽  
Seasonal Pattern ◽  
Series Data ◽  
Discharge Data ◽  
Flow Discharge ◽  
Mean Error ◽  
The People ◽  
Monthly Period

Abstract. The Cikeruh River is a source of water for the people who live in the watershed area. The shift in land management has resulted in problems in the availability of water resources. As a policy to overcome this problem, an estimation of the flow rate of the Cikeruh river was carried out. Cikeruh river flow discharge data is observational data with a monthly period included in time series data or time series data. This data has a seasonal pattern so that the method that can be used to predict the discharge data is the Thomas-Fiering Method. To estimate the discharge data for 2018, the Cikeruh river flow discharge data were used every month from 2011 to 2017 as many as 84 historical data. Then after getting the results of the 2018 debit data estimation, the mean error value calculated using Thomas-Fiering was 0.0291. Abstrak. Sungai Cikeruh merupakan sumber air bagi masyarakat yang bermukim di wilayah daerah aliran sungai. Terjadinya pergeseran tata kelola lahan mengakibatkan permasalahan ketersediaan sumber daya air. Sebagai suatu kebijakan untuk mengatasi permasalahan tersebut maka dilakukan pendugaan debit aliran sungai Cikeruh. Data debit aliran sungai Cikeruh merupakan data pengamatan dengan periode bulanan yang termasuk dalam data time series atau data runtun waktu. Data ini memiliki pola  musiman sehingga metode yang dapat digunakan untuk membuat pendugaan data debit adalah Metode Thomas-Fiering. Untuk menduga data debit tahun 2018 digunakan data debit aliran sungai Cikeruh setiap bulannya dari tahun 2011 sampai 2017 sebanyak 84 data historis. Kemudian setelah mendapatkan hasil pendugaan data debit tahun 2018 didapatkan nilai Mean Error perhitungan menggunakan Thomas-Fiering adalah 0.0291.

scholarly journals Study on The Physical Characteristics and Hydrology of 15 Watershed in East Java

2013 ◽  
Vol 27 (2) ◽  
pp. 159
Author(s):  
Indarto Indarto
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Daily Rainfall ◽  
Study Data ◽  
Series Data ◽  
Simple Arithmetic ◽  
Discharge Data ◽  
Average Value ◽  
Physical Data ◽  
Areal Rainfall

The study demonstrated the application of statistical method to describe physical and hydro-meteorological characteristics by means of time series analysis.  Fifteen(15) watersheds in East Java were selected for this study. Data input for the analysis include: physical data, rainfall and discharge. Physical data of the watershed (topography, river network, land use, and soil type) are extracted from existing database and treated using GIS Software. Daily rainfall data were collected from existing pluviometers around the region. Daily discharge data were obtained from measurement station located at the outlet of each watershed. Areal Rainfall for each watershed was determined using average value of existing pluviometers around the watershed and determined using simple arithmetic method. These time series data are then imported to RAP (River Analysis Package).  Analysis on the RAP, include: general statistical, flow duration curve (FDC), and baseflow analysis. The result then presented in graphic and tables. Research shows that among the watersheds have different physical and hydrological characteristics.

Analysis of Experimental Shock and Impact Response Data of a Printed Wire Board

2003 ◽  
Author(s):  
Greg M. Heaslip ◽  
Jeff M. Punch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Test System ◽  
Impact Response ◽  
Series Data ◽  
Test Vehicle ◽  
Time Frequency ◽  
Response Data ◽  
Wavelet Techniques ◽  
The Impact

There is considerable reported evidence that a large percentage of portable electronics product failure is due to impact or shock during use. Failures of the external housing, internal electronic components, package-to-board interconnects, and liquid crystal display panels may occur as the result of dropping. For many orientations of drop, the Printed Wire Board (PWB) will flex significantly during the impact event and subsequent clattering. Reducing the curvature and acceleration of the PWB during impact is an integral part of the design strategy for such products. This paper investigates the response of a PWB subjected to drop and shock tests through a combination of an analytical model, explicit dynamic Finite Element Analysis (FEA), and experimentation. A test vehicle consisting of a double-sided copper clad laminate PWB, mounted as a double cantilever, is used as a basis for the investigation. A free fall drop-test system is used to represent the drop scenario, and a vibration/shock system is used to impart shocks to the test vehicle. Measurements from strain gages and accelerometers are recorded using a high-speed data acquisition system. Results from experimentation show the strain/time series data from which maximum strain, natural frequencies, and damping coefficient are extracted. These measurements are compared with theoretical calculations and FEA output for the various shock and impact profiles. The investigation illustrates the response of a PWB to various shock and impact scenarios through theory, numerical simulation, and experimentation. Wavelet techniques are used to analyse the time series data, and from the resultant time/frequency space, component frequencies are extracted. It is shown that wavelet techniques are a useful tool in the analysis of shock and impact response data.

scholarly journals Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression

2020 ◽  
Vol 10 (24) ◽  
pp. 8977
Author(s):  
Pangun Park ◽  
Mingyu Jung ◽  
Piergiuseppe Di Marco
Keyword(s):  
Time Series ◽  
Ridge Regression ◽  
Sliding Window ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Series Data ◽  
Degradation Model ◽  
Time Frequency ◽  
Model Based ◽  
Useful Life

Predicting the remaining useful life (RUL) of mechanical bearings is a challenging industrial task since RUL can differ even for the same equipment due to many uncertainties such as operating condition, model inaccuracy, and sensory noise in various industrial applications. This paper proposes the RUL prediction method combining analytical model-based and data-driven approaches to forecast when a failure will occur based on the time series data of bearings. Feature importance ranking and principal component analysis construct a reliable and predictable health indicator from various statistical time, frequency, and time–frequency domain features of the observed signal. The adaptive sliding window method then optimizes the parameters of the degradation model based on the ridge regression of the time series sequence with the sliding window. The proposed adaptive scheme provides significant performance improvement in terms of the RUL estimation accuracy and robustness against the possible errors of the degradation model compared to the traditional Bayesian approaches.

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