scholarly journals Data-Driven Object Vehicle Estimation by Radar Accuracy Modeling with Weighted Interpolation

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2317
Author(s):  
Woo Young Choi ◽  
Jin Ho Yang ◽  
Chung Choo Chung
Keyword(s):  
Measurement Uncertainty ◽  
Relative Velocity ◽  
Tracking Error ◽  
Estimation Method ◽  
Position Error ◽  
Data Driven ◽  
Weighted Interpolation ◽  
Design Data ◽  
Feasible Set ◽  
Estimation Scheme

For accurate object vehicle estimation using radar, there are two fundamental problems: measurement uncertainties in calculating an object’s position with a virtual polygon box and latency due to commercial radar tracking algorithms. We present a data-driven object vehicle estimation scheme to solve measurement uncertainty and latency problems in radar systems. A radar accuracy model and latency coordination are proposed to reduce the tracking error. We first design data-driven radar accuracy models to improve the accuracy of estimation determined by the object vehicle’s position. The proposed model solves the measurement uncertainty problem within a feasible set for error covariance. The latency coordination is developed by analyzing the position error according to the relative velocity. The position error by latency is stored in a feasible set for relative velocity, and the solution is calculated from the given relative velocity. Removing the measurement uncertainty and latency of the radar system allows for a weighted interpolation to be applied to estimate the position of the object vehicle. Our method is tested by a scenario-based estimation experiment to validate the usefulness of the proposed data-driven object vehicle estimation scheme. We confirm that the proposed estimation method produces improved performance over the conventional radar estimation and previous methods.

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

scholarly journals High-Resolution ISAR Imaging and Autofocusing via 2D-ADMM-Net

2021 ◽  
Vol 13 (12) ◽  
pp. 2326
Author(s):  
Xiaoyong Li ◽  
Xueru Bai ◽  
Feng Zhou
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Phase Error ◽  
Random Phase ◽  
Back Propagation ◽  
Estimation Method ◽  
Data Driven ◽  
Reconstruction Performance ◽  
Imaging Results ◽  
Isar Imaging

A deep-learning architecture, dubbed as the 2D-ADMM-Net (2D-ADN), is proposed in this article. It provides effective high-resolution 2D inverse synthetic aperture radar (ISAR) imaging under scenarios of low SNRs and incomplete data, by combining model-based sparse reconstruction and data-driven deep learning. Firstly, mapping from ISAR images to their corresponding echoes in the wavenumber domain is derived. Then, a 2D alternating direction method of multipliers (ADMM) is unrolled and generalized to a deep network, where all adjustable parameters in the reconstruction layers, nonlinear transform layers, and multiplier update layers are learned by an end-to-end training through back-propagation. Since the optimal parameters of each layer are learned separately, 2D-ADN exhibits more representation flexibility and preferable reconstruction performance than model-driven methods. Simultaneously, it is able to better facilitate ISAR imaging with limited training samples than data-driven methods owing to its simple structure and small number of adjustable parameters. Additionally, benefiting from the good performance of 2D-ADN, a random phase error estimation method is proposed, through which well-focused imaging can be acquired. It is demonstrated by experiments that although trained by only a few simulated images, the 2D-ADN shows good adaptability to measured data and favorable imaging results with a clear background can be obtained in a short time.

scholarly journals Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 99 ◽  
Author(s):  
Yueqi Gu ◽  
Orhun Aydin ◽  
Jacqueline Sosa
Keyword(s):  
Los Angeles ◽  
A Priori ◽  
Safety Data ◽  
Spatial Optimization ◽  
Optimal Number ◽  
Multidisciplinary Optimization ◽  
Data Driven ◽  
Fault System ◽  
Design Data ◽  
The Impact

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

Hybrid Model-Based and Data-Driven Diagnostic Algorithm for Gas Turbine Engines

2020 ◽  
Author(s):  
Amare Fentaye ◽  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Mikael Stenfelt ◽  
Konstantinos Kyprianidis
Keyword(s):  
High Pressure ◽  
Measurement Uncertainty ◽  
Gas Turbine ◽  
Diagnostic Algorithm ◽  
Data Driven ◽  
Intermediate Pressure ◽  
Path Component ◽  
Fault Diagnostic ◽  
Component Faults ◽  
Pressure Compressor

Abstract Data-driven algorithms require large and comprehensive training samples in order to provide reliable diagnostic solutions. However, in many gas turbine applications, it is hard to find fault data due to proprietary and liability issues. Operational data samples obtained from end-users through collaboration projects do not represent fault conditions sufficiently and are not labeled either. Conversely, model-based methods have some accuracy deficiencies due to measurement uncertainty and model smearing effects when the number of gas path components to be assessed is large. The present paper integrates physics-based and data-driven approaches aiming to overcome this limitation. In the proposed method, an adaptive gas path analysis (AGPA) is used to correct measurement data against the ambient condition variations and normalize. Fault signatures drawn from the AGPA are used to assess the health status of the case engine through a Bayesian network (BN) based fault diagnostic algorithm. The performance of the proposed technique is evaluated based on five different gas path component faults of a three-shaft turbofan engine, namely intermediate-pressure compressor fouling (IPCF), high-pressure compressor fouling (HPCF), high-pressure turbine erosion (HPTE), intermediate-pressure turbine erosion (IPTE), and low-pressure turbine erosion (LPTE). Robustness of the method under measurement uncertainty has also been tested using noise-contaminated data. Moreover, the fault diagnostic effectiveness of the BN algorithm on different number and type of measurements is also examined based on three different sensor groups. The test results verify the effectiveness of the proposed method to diagnose single gas path component faults correctly even under a significant noise level and different instrumentation suites. This enables to accommodate measurement suite inconsistencies between engines of the same type. The proposed method can further be used to support the gas turbine maintenance decision-making process when coupled with overall Engine Health Management (EHM) systems.

Speed Estimation Method for Sensor Less Control of AC Motor Drives Based on Stator Field Orientation

2011 ◽  
Vol 130-134 ◽  
pp. 1361-1364
Author(s):  
Shou Qiang Chen ◽  
Fan Li ◽  
Yu Long
Keyword(s):  
Direct Torque Control ◽  
Estimation Method ◽  
Orientation Angle ◽  
Torque Control ◽  
Speed Estimation ◽  
Field Orientation ◽  
Orientation Control ◽  
Ac Motor ◽  
Field Orientation Control ◽  
Estimation Scheme

Based on stator-field-orientation control combining with both direct torque control (DTC) and rotor-field-orientation control a speed estimation scheme is presented in this paper. Two key problems are discussed in detail. First is to use phase operation to decide stator-field-orientation angle θs and then the discrete-tracking-differential (DTD) technique is employed to high quality stator current. The experiments show that accurate speed estimation can be obtained by the method.

scholarly journals Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4772 ◽  
Author(s):  
Kaizhi Liang ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Zhenpo Wang ◽  
Shangfeng Jiang
Keyword(s):  
Electric Vehicles ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Gradient Boosting ◽  
Estimation Model ◽  
Ohmic Resistance ◽  
Extreme Gradient Boosting ◽  
Battery Packs ◽  
Mean Square Errors

Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

A data-driven approach for trajectory-based aircraft operation with controlled time of arrival and along-track wind effects

2020 ◽  
Vol 42 (12) ◽  
pp. 2166-2177
Author(s):  
Gaoyang Jiang ◽  
Zhongsheng Hou
Keyword(s):  
Control Problem ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Time Of Arrival ◽  
Multiple Time ◽  
Data Driven Approach ◽  
Along Track

Trajectory-based aircraft operation and control is one of the hot issues in air traffic management. However, the accurate mechanism modeling of aircraft is tough work, and the operation data have not been effectively utilized in many studies. So, in this work, we apply the model-free adaptive iterative learning control method to address the time-of-arrival control problem in trajectory-based aircraft operation. This problem is first formulated into a trajectory tracking problem with along-track wind disturbance. Through rigorous analysis, it is shown that this method, combined with point-to-point iterative learning control (ILC) strategy, can effectively deal with the arrival time control problem with multiple time constraints. Then, the terminal ILC strategy is applied, aiming to resolve the same problem with a time constraint at the end point. Compared with the PID (Proportional Integral Derivative) type ILC, the proposed method improves control performance by 11.15% in root mean square of tracking error and 9.32% in integral time absolute error. The sensitivity and flexibility of the data-driven approach is further verified through numerical simulations.

scholarly journals Complexity Effective Sampling Frequency Offset Estimation Method for OFDM-Based HomePlug Green PHY Systems

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 544
Author(s):  
Yong-An Jung ◽  
Young-Hwan You
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Estimation Method ◽  
Frequency Offset ◽  
Sampling Frequency ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Pilot Symbols ◽  
Sampling Frequency Offset ◽  
Efficient Sampling ◽  
Estimation Scheme

The HomePlug Green PHY (HomePlug GP) specification provides an attractive solution to enable smart grid power line communication (PLC) applications by using robust orthogonal frequency division multiplexing (ROBO) mode. This paper proposes a computationally efficient sampling frequency offset (SFO) estimation technique in the HomePlug GP system without relying on pilot symbols. For this purpose, the proposed estimation scheme utilizes the redundant information contained within the repeat coding in the HomePlug GP ROBO mode, thus eliminating the need of dedicated pilots. Computer simulations are conducted to assess the performance of the proposed SFO estimation scheme and to compare it with the conventional decision-directed (DD) estimation schemes. Simulations indicate that the repeat coded ROBO signals are effectively used for the proposed estimation scheme, which provides an affordable estimation accuracy while reducing the complexity compared to the conventional DD estimation schemes.

Development of a Pilot Manufacturing Cyberinfrastructure With an Information Rich Mechanical CAD 3D Model Repository

2019 ◽  
Author(s):  
Akshay Bharadwaj ◽  
Yang Xu ◽  
Atin Angrish ◽  
Yong Chen ◽  
Binil Starly
Keyword(s):  
Product Design ◽  
Academic Research ◽  
Digital Design ◽  
Data Driven ◽  
Concept Design ◽  
Design Data ◽  
Meta Data ◽  
Pilot Implementation ◽  
Project Data ◽  
Manufacturing Data

Abstract Data driven advanced manufacturing research is dependent on access to large datasets made available from across the product lifecycle — from the concept design phase all the way down to end use and disposal. Despite such data being generated at a rapid pace, most product design data is archived in inaccessible silos. This is particularly acute in academic research laboratories and with data generated during product design and manufacturing courses. This project seeks to create an infrastructure that allow users (academia and the general public) to easily upload project data and related meta-data. Current manufacturing research must shift from siloed repositories of product manufacturing data to a federated, decentralized, open and inter-operable approach. In this regard, we build ‘FabWave’ a cyber-infrastructure tool designed to capture manufacturing data. In its first pilot implementation, we focused our attention to gathering information rich 3D Mechanical CAD data and related meta-data associated with them, with the intent to make it easier for users to upload and access product design data. We describe workflows that we have initially tested out within the two academic universities and under two different course structures. We have also developed automated workflows to gather license appropriate CAD assemblies from commercial repositories. Our intent is to create the only known largest available CAD model set within academia for enabling research in data-driven computational research in digital design, fabrication and quality control.

The model-free adaptive cross-coupled control for two-dimensional linear motor

2019 ◽  
Vol 42 (5) ◽  
pp. 1059-1069
Author(s):  
Baolin Zhang ◽  
Rongmin Cao ◽  
Zhongsheng Hou
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Error Estimation ◽  
Tracking Error ◽  
Estimation Method ◽  
Linear Motor ◽  
Experimental Comparison ◽  
Contour Error ◽  
Two Dimensional ◽  
Model Free

In order to improve the contour error accuracy of two-dimensional linear motor, an improved cross-coupled control (CCC) scheme combining real-time contour error estimation and model-free adaptive control (MFAC) is proposed. The real-time contour error estimation method is based on CCC theory and coordinate transformation idea. It can accurately determine the contour error point of regular contour and avoid the influence of tracking error on the contour error. At the same time, for the design of two-axis error controller, only the input and output data generated by two-dimensional linear motor in reciprocating motion are used to design a multiple input multiple output-model-free adaptive control (MIMO-MFAC) algorithm, this algorithm avoids the dependence on accurate mathematical model and reduces the control difficulty. The experimental comparison showed that the proposed method improves the system tracking accuracy and contour accuracy, and verifies the proposed method correctness and effectiveness.

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