A New High Accurate Estimation Method for Evaluating the Daily Solar Energy by Nested Percentiles Algorithm

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.

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Robust Directional Angle Estimation of Underwater Acoustic Sources Using a Marine Vehicle

Sensors ◽

10.3390/s18093062 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3062 ◽

Cited By ~ 3

Author(s):

Jinwoo Choi ◽

Jeonghong Park ◽

Yoongeon Lee ◽

Jongdae Jung ◽

Hyun-Taek Choi

Keyword(s):

Source Localization ◽

Angular Displacement ◽

Estimation Method ◽

Time Delay Estimation ◽

Accurate Estimation ◽

Acoustic Source ◽

Angle Estimation ◽

Underwater Acoustic ◽

Acoustic Sources ◽

Marine Vehicle

Acoustic source localization is used in many underwater applications. Acquiring an accurate directional angle for an acoustic source is crucial for source localization. To achieve this purpose, this paper presents a method for directional angle estimation of underwater acoustic sources using a marine vehicle. It is assumed that the vehicle is equipped with two hydrophones and that the acoustic source transmits a specific signal repeatedly. The proposed method provides a probabilistic model for time delay estimation. The probability is recursively updated by prediction and update steps. The prediction step performs a probability transition using the angular displacement of the marine vehicle. The predicted probability is updated using a generalized cross correlation function with a verification process using entropy measurement. The proposed method can provide a reliable and accurate estimation of the directional angles of underwater acoustic sources. Experimental results demonstrate good performance of the proposed probabilistic directional angle estimation method in both an inland water environment and a harbor environment.

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Noise masking method based on an effective ratio mask estimation in Gammatone channels

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2018.7 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 1

Author(s):

Feng Bao ◽

Waleed H. Abdulla

Keyword(s):

Signal To Noise Ratio ◽

Estimation Method ◽

Wiener Filter ◽

Power Spectra ◽

Auditory Scene Analysis ◽

Accurate Estimation ◽

Noise Power ◽

Noise Masking ◽

Time Frequency ◽

Mask Estimation

In computational auditory scene analysis, the accurate estimation of binary mask or ratio mask plays a key role in noise masking. An inaccurate estimation often leads to some artifacts and temporal discontinuity in the synthesized speech. To overcome this problem, we propose a new ratio mask estimation method in terms of Wiener filtering in each Gammatone channel. In the reconstruction of Wiener filter, we utilize the relationship of the speech and noise power spectra in each Gammatone channel to build the objective function for the convex optimization of speech power. To improve the accuracy of estimation, the estimated ratio mask is further modified based on its adjacent time–frequency units, and then smoothed by interpolating with the estimated binary masks. The objective tests including the signal-to-noise ratio improvement, spectral distortion and intelligibility, and subjective listening test demonstrate the superiority of the proposed method compared with the reference methods.

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A Study on Sustainable Consumption of Fuel—An Estimation Method of Aircraft

Energies ◽

10.3390/en14227559 ◽

2021 ◽

Vol 14 (22) ◽

pp. 7559

Author(s):

Lisha Li ◽

Shuming Yuan ◽

Yue Teng ◽

Jing Shao

Keyword(s):

Neural Network ◽

Fuel Consumption ◽

Influencing Factors ◽

Carbon Emission ◽

Estimation Method ◽

Civil Aviation ◽

Accurate Estimation ◽

Low Carbon ◽

Fuel Price ◽

Aircraft Fuel

Though the development of China’s civil aviation and the improvement of control ability have strengthened the safety operation and support ability effectively, the airlines are under the pressure of operation costs due to the increase of aircraft fuel price. With the development of optimization controlling methods in flight management systems, it becomes increasingly challenging to cut down flight fuel consumption by control the flight status of the aircraft. Therefore, the airlines both at home and abroad mainly rely on the accurate estimation of aircraft fuel to reduce fuel consumption, and further reduce its carbon emission. The airlines have to take various potential factors into consideration and load more fuel to cope with possible negative situation during the flight. Therefore, the fuel for emergency use is called PBCF (Performance-Based Contingency Fuel). The existing PBCF forecasting method used by China Airlines is not accurate, which fails to take into account various influencing factors. This paper aims to find a method that could predict PBCF more accurately than the existing methods for China Airlines.This paper takes China Eastern Airlines as an example. The experimental data of flight fuel of China Eastern Airlines Co, Ltd. were collected to find out the relevant parameters affecting the fuel consumption, which is followed by the establishment of the LSTM neural network through the parameters and collected data. Finally, through the established neural network model, the PBCF addition required by the airline with different influencing factors is output. It can be seen from the results that the all the four models are available for the accurate prediction of fuel consumption. The amount of data of A319 is much larger than that of A320 and A330, which leads to higher accuracy of the model trained by A319. The study contributes to the calculation methods in the fuel-saving project, and helps the practitioners to learn about a particular fuel calculation method. The study brought insights for practitioners to achieve the goal of low carbon emission and further contributed to their progress towards circular economy.

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Optical Enhancement of Exoskeleton-Based Estimation of Glenohumeral Angles

Applied Bionics and Biomechanics ◽

10.1155/2016/5058171 ◽

2016 ◽

Vol 2016 ◽

pp. 1-20 ◽

Cited By ~ 4

Author(s):

Camilo Cortés ◽

Luis Unzueta ◽

Ana de los Reyes-Guzmán ◽

Oscar E. Ruiz ◽

Julián Flórez

Keyword(s):

Estimation Method ◽

Position Estimation ◽

Accurate Estimation ◽

Joint Angles ◽

Estimation Errors ◽

Marker Position ◽

Human Movements ◽

Posture Estimation ◽

Kinematic Models ◽

Method Accuracy

In Robot-Assisted Rehabilitation (RAR) the accurate estimation of the patient limb joint angles is critical for assessing therapy efficacy. In RAR, the use of classic motion capture systems (MOCAPs) (e.g., optical and electromagnetic) to estimate the Glenohumeral (GH) joint angles is hindered by the exoskeleton body, which causes occlusions and magnetic disturbances. Moreover, the exoskeleton posture does not accurately reflect limb posture, as their kinematic models differ. To address the said limitations in posture estimation, we propose installing the cameras of an optical marker-based MOCAP in the rehabilitation exoskeleton. Then, the GH joint angles are estimated by combining the estimated marker poses and exoskeleton Forward Kinematics. Such hybrid system prevents problems related to marker occlusions, reduced camera detection volume, and imprecise joint angle estimation due to the kinematic mismatch of the patient and exoskeleton models. This paper presents the formulation, simulation, and accuracy quantification of the proposed method with simulated human movements. In addition, a sensitivity analysis of the method accuracy to marker position estimation errors, due to system calibration errors and marker drifts, has been carried out. The results show that, even with significant errors in the marker position estimation, method accuracy is adequate for RAR.

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A New Friction Estimation Approach for Precision Positioner

First International Conference on Integration and Commercialization of Micro and Nanosystems, Parts A and B ◽

10.1115/mnc2007-21535 ◽

2007 ◽

Author(s):

Wen Wang ◽

Xinxin Li ◽

Zichen Chen

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Taylor Series ◽

Estimation Method ◽

Estimation Accuracy ◽

Accurate Estimation ◽

Low Velocity ◽

Friction Estimation ◽

Simulation Results ◽

Long Stroke

Precision positioner has been significantly developed as the rapid growth of MEMS and IC industries. As for short-stroke position, the loss of friction can be avoided by using flexible hinges. Long-stroke postioner, however, in which moved-to-be mass always stands on the guide-way part, a main source of friction, makes friction unavoidable. Friction estimation is based on certain filters, such as Extended Kalman filter (EKF). However, estimation accuracy of Kalman filter, especially at low-velocity movement, is not very well. To solve this problem, the paper proposes an estimation method based on DD2 to make an accurate estimation. And the result shows this method is promising in real-time friction estimation. After background introduction, in section 2, the relation of EKF and Taylor series and EKF implementation are reviewed and its limitations are noted as well. A briefly introduction to DD2 is given in Section 3 and friction estimation case comparing the simulation results of DD2 estimation with that of EKF described in Section 4, respectively. At last, conclusions are summarized.

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Fast battery capacity estimation using convolutional neural networks

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331220966425 ◽

2020 ◽

pp. 014233122096642 ◽

Cited By ~ 1

Author(s):

Yihuan Li ◽

Kang Li ◽

Xuan Liu ◽

Li Zhang

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Smart Grids ◽

Estimation Method ◽

Lithium Ion ◽

Accurate Estimation ◽

Capacity Estimation ◽

Starting Point ◽

Battery Capacity ◽

Mean Square Errors

Lithium-ion batteries have been widely used in electric vehicles, smart grids and many other applications as energy storage devices, for which the aging assessment is crucial to guarantee their safe and reliable operation. The battery capacity is a popular indicator for assessing the battery aging, however, its accurate estimation is challenging due to a range of time-varying situation-dependent internal and external factors. Traditional simplified models and machine learning tools are difficult to capture these characteristics. As a class of deep neural networks, the convolutional neural network (CNN) is powerful to capture hidden information from a huge amount of input data, making it an ideal tool for battery capacity estimation. This paper proposes a CNN-based battery capacity estimation method, which can accurately estimate the battery capacity using limited available measurements, without resorting to other offline information. Further, the proposed method only requires partial charging segment of voltage, current and temperature curves, making it possible to achieve fast online health monitoring. The partial charging curves have a fixed length of 225 consecutive points and a flexible starting point, thereby short-term charging data of the battery charged from any initial state-of-charge can be used to produce accurate capacity estimation. To employ CNN for capacity estimation using partial charging curves is however not trivial, this paper presents a comprehensive approach covering time series-to-image transformation, data segmentation, and CNN configuration. The CNN-based method is applied to two battery degradation datasets and achieves root mean square errors (RMSEs) of less than 0.0279 Ah (2.54%) and 0.0217 Ah (2.93% ), respectively, outperforming existing machine learning methods.

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Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model

Mathematical Problems in Engineering ◽

10.1155/2019/4952036 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Haiying Wang ◽

Xinping Wang ◽

Chao Wang ◽

Jian Xu

Keyword(s):

Neural Network ◽

Test Data ◽

Compression Test ◽

Clustering Algorithm ◽

Estimation Method ◽

Wavelet Neural Network ◽

Accurate Estimation ◽

Short Term ◽

Data Estimation ◽

Concrete Cube

Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c-means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis of the massive concrete cube specimen compression test data. Then, using an optimized error correction time series estimation method based on the wavelet neural network (WNN), a concrete cube specimen compressive strength test data estimation model was constructed. Taking the results of cluster analysis as data samples, the short-term accurate estimation of concrete quality was carried out. It was found that the mean absolute percentage error, e1, and the root mean square error, e2, for the samples were 6.03385% and 3.3682KN, indicating that the proposed method had higher estimation accuracy and was suitable for concrete compressive test data short-term quality estimations.

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Accurate Estimation Method of Arterial Wall Movement in Longitudinal Direction

2019 IEEE International Ultrasonics Symposium (IUS) ◽

10.1109/ultsym.2019.8925634 ◽

2019 ◽

Author(s):

Koji Iwamori ◽

Shohei Mori ◽

Mototaka Arakawa ◽

Hiroshi Kanai

Keyword(s):

Arterial Wall ◽

Estimation Method ◽

Longitudinal Direction ◽

Accurate Estimation ◽

Wall Movement

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The use of benthic macroinvertebrate families for river quality assessment in Lithuania

Open Life Sciences ◽

10.2478/s11535-012-0052-1 ◽

2012 ◽

Vol 7 (4) ◽

pp. 741-758 ◽

Cited By ~ 1

Author(s):

Giedrė Višinskienė ◽

Rasa Bernotienė

Keyword(s):

Quality Assessment ◽

Ecological Status ◽

Estimation Method ◽

Environmental Parameters ◽

Biotic Index ◽

Benthic Macroinvertebrate ◽

Accurate Estimation ◽

Macroinvertebrate Communities ◽

River Quality ◽

Lithuanian Rivers

AbstractThe aim of this study was to evaluate the influence of environmental factors on the distribution of macroinvertebrate taxa in different sized lowland Lithuanian rivers. A secondary aim was to assess ecological river quality and to determine the most suitable biotic index. A final aim was to determine the most appropriate macroinvertebrate families for river quality assessment in Lithuania. Species composition and quantitative characteristics of benthic macroinvertebrate communities have been investigated using standard kick-sampling method by a standard hand net in 24 different river sites in spring. Physical and chemical environmental parameters were measured in the same study site as the macroinvertebrate sampling. A total of 186 taxa representing 66 families or higher taxonomic ranks of benthic macroinvertebrates have been identified. Water temperature and current velocity influenced the highest number of ivestigated families. Seven of the most tolerant and eleven of the most sensitive macroinvertebrate taxa for hydrochemical parameters related with organic pollution were determined. The DSFI method was founded to be the best index for assessment of ecological status for Lithuanian rivers until more accurate estimation method will be created.

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