Design and Implementation of High-speed DTW Accelerator for Time-series Data Classification

2021 ◽  
Vol 58 (3) ◽  
pp. 51-58
Author(s):  
Wonyoung Choi ◽  
Jaechan Cho ◽  
Yunho Jung
Keyword(s):  
Time Series ◽  
High Speed ◽  
Time Series Data ◽  
Data Classification ◽  
Series Data ◽  
Design And Implementation

scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

scholarly journals An Empirical Analysis of Energy Consumption, FDI and High Quality Development Based on Time Series Data of Zhejiang Province

2020 ◽  
Vol 17 (9) ◽  
pp. 3321
Author(s):  
Shaolong Zeng ◽  
Yiqun Liu ◽  
Junjie Ding ◽  
Danlu Xu
Keyword(s):  
Time Series ◽  
Economic Development ◽  
Energy Consumption ◽  
Open Economy ◽  
High Speed ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
High Quality ◽  
The Relationship

This paper aims to identify the relationship among energy consumption, FDI, and economic development in China from 1993 to 2017, taking Zhejiang as an example. FDI is the main factor of the rapid development of Zhejiang’s open economy, which promotes the development of the economy, but also leads to the growth in energy consumption. Based on the time series data of energy consumption, FDI inflow, and GDP in Zhejiang from 1993 to 2017, we choose the vector auto-regression (VAR) model and try to identify the relationship among energy consumption, FDI, and economic development. The results indicate that there is a long-run equilibrium relationship among them. The FDI inflow promotes energy consumption, and the energy consumption promotes FDI inflow in turn. FDI promotes economic growth indirectly through energy consumption. Therefore, improving the quality of FDI and energy efficiency has become an inevitable choice to achieve the transition of Zhejiang’s economy from high speed growth to high quality growth.

TSCache

2021 ◽  
Vol 14 (13) ◽  
pp. 3253-3266
Author(s):  
Jian Liu ◽  
Kefei Wang ◽  
Feng Chen
Keyword(s):  
Time Series ◽  
High Speed ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Efficient System ◽  
Indexing Structure ◽  
System Solution ◽  
Challenges And Opportunities ◽  
Cache System

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

The Design and Implementation of Multiple Square Wave Pulse Sequence Recorder

2011 ◽  
Vol 121-126 ◽  
pp. 1692-1696
Author(s):  
Xin Quan Jiao ◽  
Yong Xing Yao ◽  
Qing Meng
Keyword(s):  
Time Series ◽  
Data Exchange ◽  
Time Series Data ◽  
Pulse Sequence ◽  
Pulse Signal ◽  
Series Data ◽  
Signal Timing ◽  
Wave Pulse ◽  
Square Wave ◽  
Design And Implementation

For the need to test square wave pulse sequence, designed and implemented a recorder. Recorder using FPGA for judging square wave pulse’s edge and precise timing functions, using C8051F MCU to storage the results of time-series data and printing functions, FPGA and MCU using a custom protocol for data exchange and command transfer. After experimental verification, the recorder have 1ms accuracy to record 28 roads square wave pulse signal timing, and fixed-format print the results.

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