Technological Trends of Soft Error Estimation Based on Accurate Estimation Method

Fast and accurate estimation of soft error rate in VLSI circuits is an essential step in a soft error tolerant ASIC design. In order to have a cost effective protection against radiation effects in combinational logics, an accurate and fast method for identification of most susceptive gates and paths is needed. In this paper, an efficient, fast and accurate method for soft error propagation probability (SEPP) estimation is presented and its performance is evaluated. This method takes into account all three masking factors in multi cycles. It also considers multiple event transients as a new challenge in soft error tolerant VLSI circuit design. Compared with Monte Carlo (MC) simulation-based fault injection method, our SEPP estimation method has a high level of accuracy (with less than 2% difference) while offering 1000× speedup as compared with MC-based simulation.

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Chip-level soft error estimation method

IEEE Transactions on Device and Materials Reliability ◽

10.1109/tdmr.2005.858334 ◽

2005 ◽

Vol 5 (3) ◽

pp. 365-381 ◽

Cited By ~ 78

Author(s):

H.T. Nguyen ◽

Y. Yagil ◽

N. Seifert ◽

M. Reitsma

Keyword(s):

Error Estimation ◽

Estimation Method ◽

Soft Error ◽

Error Estimation Method

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

Advances in Mechanical Engineering ◽

10.1177/16878140211027735 ◽

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.

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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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DQ-Frame Admittance Estimation of Three-Phase Converters with a Wide Range of Operating Points

10.36227/techrxiv.14229071.v1 ◽

2021 ◽

Author(s):

Hong Gong ◽

xiongfei wang ◽

Dongsheng Yang

Keyword(s):

Error Estimation ◽

Closed Loop ◽

Estimation Method ◽

Experimental Results ◽

Multidimensional Interpolation ◽

Three Phase ◽

Wide Range ◽

Linearized Model ◽

Operating Points ◽

Error Estimation Method

The <i>dq</i>-frame admittance of closed-loop controlled three-phase converters is a linearized model that is dependent on the operating points of the system. Yet, it is impractical to measure the converter admittance at all operating points. This paper, thus, proposes an approach to estimating the <i>dq</i>-frame admittance of three-phase converters at a wide range of operating points. The method applies multidimensional interpolation to a given set of admittance data, which is measured from the pre-defined operating points. The accuracy of interpolation is then evaluated by using the posterior error estimation method. The number of pre-defined operating points is next adjusted to find a good compromise between the accuracy and efficiency of the approach. Simulations and experimental results verify the effectiveness of the approach.<div><br></div>

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

Asian Journal of Scientific Research ◽

10.3923/ajsr.2019.480.487 ◽

2019 ◽

Vol 12 (4) ◽

pp. 480-487

Author(s):

Mohammed Mohammed E ◽

Doaa Abd El-Shafi Abd El-Rah

Keyword(s):

Solar Energy ◽

Estimation Method ◽

Accurate Estimation

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An Array Error Estimation Method for Constellation SAR Systems

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2010.2046878 ◽

2010 ◽

Vol 7 (4) ◽

pp. 731-735 ◽

Cited By ~ 18

Author(s):

Aifei Liu ◽

Guisheng Liao ◽

Lun Ma ◽

Qing Xu

Keyword(s):

Error Estimation ◽

Estimation Method ◽

Error Estimation Method

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SAR Observation Error Estimation Based on Maximum Relative Projection Matching

International Journal of Antennas and Propagation ◽

10.1155/2020/3517834 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Y. Zhang ◽

B. P. Wang ◽

Y. Fang ◽

Z. X. Song

Keyword(s):

Error Estimation ◽

Estimation Method ◽

Estimation Methods ◽

Estimation Accuracy ◽

Reconstruction Method ◽

Observation Error ◽

Estimation Model ◽

Reconstruction Process ◽

Single Observation ◽

Imaging Results

The existing sparse imaging observation error estimation methods are to usually estimate the error of each observation position by substituting the error parameters into the iterative reconstruction process, which has a huge calculation cost. In this paper, by analysing the relationship between imaging results of single-observation sampling data and error parameters, a SAR observation error estimation method based on maximum relative projection matching is proposed. First, the method estimates the precise position parameters of the reference position by the sparse reconstruction method of joint error parameters. Second, a relative error estimation model is constructed based on the maximum correlation of base-space projection. Finally, the accurate error parameters are estimated by the Broyden–Fletcher–Goldfarb–Shanno method. Simulation and measured data of microwave anechoic chambers show that, compared to the existing methods, the proposed method has higher estimation accuracy, lower noise sensitivity, and higher computational efficiency.

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