scholarly journals A Fast and Accurate Guessing Entropy Estimation Algorithm for Full-key Recovery

2020 ◽  
pp. 26-48
Author(s):  
Ziyue Zhang ◽  
A. Adam Ding ◽  
Yunsi Fei
Keyword(s):  
Experimental Data ◽  
Computational Cost ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Estimation Methods ◽  
Security Evaluation ◽  
Side Channel ◽  
Data Sets ◽  
Key Recovery ◽  
Entropy Estimation

Guessing entropy (GE) is a widely adopted metric that measures the average computational cost needed for a successful side-channel analysis (SCA). However, with current estimation methods where the evaluator has to average the correct key rank over many independent side-channel leakage measurement sets, full-key GE estimation is impractical due to its prohibitive computing requirement. A recent estimation method based on posterior probabilities, although scalable, is not accurate.We propose a new guessing entropy estimation algorithm (GEEA) based on theoretical distributions of the ranking score vectors. By discovering the relationship of GE with pairwise success rates and utilizing it, GEEA uses a sum of many univariate Gaussian probabilities instead of multi-variate Gaussian probabilities, significantly improving the computation efficiency.We show that GEEA is more accurate and efficient than all current GE estimations. To the best of our knowledge, it is the only practical full-key GE evaluation on given experimental data sets which the evaluator has access to. Moreover, it can accurately predict the GE for larger sizes than the experimental data sets, providing comprehensive security evaluation.

Effect of Load-Dependent Friction on the Estimation of Rack Force in Electric Power-Assisted Steering System

2019 ◽  
Vol 141 (11) ◽  
Author(s):  
Yijun Li ◽  
Taehyun Shim ◽  
Dexin Wang ◽  
Timothy Offerle
Keyword(s):  
Electric Power ◽  
A Priori ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Steering System ◽  
Estimation Methods ◽  
Current Steering ◽  
Force Estimation ◽  
Operation Conditions ◽  
Friction Estimation

The rack force is valuable information for a vehicle dynamics control system, as it relates closely to the road conditions and steering feel. Since there is no direct measurement of rack force in current steering systems, various rack force estimation methods have been proposed to obtain the rack force information. In order to get an accurate rack force estimate, it is important to have knowledge of the steering system friction. However, it is hard to have an accurate value of friction, as it is subject to variation due to operation conditions and material wear. Especially for the widely used column-assisted electric power steering (C-EPAS) system, the load-dependent characteristic of its worm gear friction has a significant effect on rack force estimation. In this paper, a rack force estimation method using a Kalman filter and a load-dependent friction estimation algorithm is introduced, and the effect of C-EPAS friction on rack force estimator performance is investigated. Unlike other rack force estimation methods, which assume that friction is known a priori, the proposed system uses a load-dependent friction estimation algorithm to determine accurate friction information in the steering system, and then a rack force is estimated using the relationship between steering torque and angle. The effectiveness of this proposed method is verified by carsim/simulink cosimulation.

scholarly journals A Fast Motion Parameters Estimation Method Based on Cross-Correlation of Adjacent Echoes for Wideband LFM Radars

2017 ◽  
Author(s):  
Yixiong Zhang ◽  
Rujia Hong ◽  
Cheng-Fu Yang ◽  
Yunjian Zhang ◽  
Zhenmiao Deng ◽  
...  
Keyword(s):  
Cross Correlation ◽  
Computational Cost ◽  
Estimation Method ◽  
Doppler Frequency ◽  
Parameters Estimation ◽  
Estimation Methods ◽  
Radar Systems ◽  
Isar Imaging ◽  
Motion Parameters ◽  
Wideband Radar

In wideband radar systems, the performance of motion parameters estimation can significantly affect the performance of object detection and the quality of inverse synthetic aperture radar (ISAR) imaging. Although the traditional motion parameters estimation methods can reduce the range migration (RM) and Doppler frequency migration (DFM) effects in ISAR imaging, the computational complexity is high. In this paper, we propose a new fast non-searching motion parameters estimation method based on cross-correlation of adjacent echoes (CCAE) for wideband LFM signals. A cross-correlation operation is carried out for two adjacent echo signals, then the motion parameters can be calculated by estimating the frequency of the correlation result. The proposed CCAE method can be applied directly to the stretching system, which is commonly adopted in wideband radar systems. Simulational results demonstrate that the new method can achieve better estimation performances, with much lower computational cost, compared with existing methods. The experimental results on real radar data sets are also evaluated to verify the effectiveness and superiority of the proposed method compared to the state-of-the-art existing methods.

Different inference approaches for the estimators of the sushila distribution

2021 ◽  
Vol 16 (4) ◽  
pp. 251-260
Author(s):  
Marcos Vinicius de Oliveira Peres ◽  
Ricardo Puziol de Oliveira ◽  
Edson Zangiacomi Martinez ◽  
Jorge Alberto Achcar
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Bayesian Method ◽  
Weighted Least Squares ◽  
Computational Cost ◽  
Estimation Method ◽  
Computation Time ◽  
Ordinary Least Squares ◽  
Likelihood Method ◽  
Estimation Methods

In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.

A Review of Research on the State-of-Charge Estimation Methods for Electric Vehicle Battery

2019 ◽  
Vol 13 ◽  
Author(s):  
Mingyue Zhang ◽  
Xiaobin Fan ◽  
Jing Gan ◽  
Zeng Song ◽  
Bin Zhao
Keyword(s):  
Control Theory ◽  
Electric Vehicle ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Of Charge ◽  
Estimation Methods ◽  
Advantages And Disadvantages ◽  
Electric Vehicle Battery ◽  
State Of Charge Estimation

Background: Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time change and non-linear properties, which are extremely complex systems. Therefore, accurate estimating the state of charge is a challenging thing. Objective: The study aims to report the latest progress in the studies of the state-of-charge estimation methods for electric vehicle battery. Methods: This paper reviews various representative patents and papers related to the state of charge estimation methods for electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional estimation algorithm based on the battery experiment, the estimation algorithm based on modern control theory and other estimation algorithm based on the innovative ideas, especially focusing on the algorithms based on control theory. Results: The advantages and disadvantages, current and future developments of the state-of-charge estimation methods are finally provided and discussed. Conclusion: Each kind of state of charge estimation method has its own characteristics, suitable for different occasions. At present, algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish rich database, improve hardware technology, put up with more perfect battery model, and give full play to the advantages of each algorithm.

scholarly journals Validation of the Axial Thrust Estimation Method for Radial Turbomachines

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Jonna Tiainen ◽  
Ahti Jaatinen-Värri ◽  
Aki Grönman ◽  
Petri Sallinen ◽  
Juha Honkatukia ◽  
...  
Keyword(s):  
Experimental Data ◽  
Gas Turbines ◽  
Estimation Method ◽  
Estimation Methods ◽  
Design Parameters ◽  
Preliminary Design ◽  
Axial Thrust ◽  
Safety Margins ◽  
Public Data ◽  
Radial Pump

The fast preliminary design and safe operation of turbomachines require a simple and accurate prediction of axial thrust. An underestimation of these forces may result in undersized bearings that can easily overload and suffer damage. While large safety margins are used in bearing design to avoid overloading, this leads to costly oversizing. In this study, the accuracy of currently available axial thrust estimation methods is analyzed by comparing them to each other and to theoretical pressure distribution, numerical simulations, and new experimental data. Available methods tend to underestimate the maximum axial thrust and require data that are unavailable during the preliminary design of turbomachines. This paper presents a new, simple axial thrust estimation method that requires only a few preliminary design parameters as the input data and combines the advantages of previously published methods, resulting in a more accurate axial thrust estimation. The method is validated against previously public data from a radial pump and new experimental data from a centrifugal compressor, the latter measured at Lappeenranta-Lahti University of Technology LUT, Finland, and two gas turbines measured at Aurelia Turbines Oy, Finland. The maximum deviation between the estimated axial thrust using the hybrid method and the measured one is less than 13%, while the other methods deviate by tens of percent.

scholarly journals Parameter Estimation and Applications of the Weibull Distribution for Strength Data of Glass Fiber

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Wu ◽  
Hanyang Xie ◽  
Jyun-You Chiang ◽  
Gang Peng ◽  
Yan Qin
Keyword(s):  
Parameter Estimation ◽  
Weibull Distribution ◽  
Glass Fiber ◽  
Fiber Strength ◽  
Estimation Method ◽  
Real Data ◽  
Estimation Methods ◽  
Data Sets ◽  
Strength Data ◽  
Parameter Estimation Method

Glass fiber is a good substitute for metal materials. However, in the process of manufacturing, it is necessary to carry out sampling inspection on its tensile strength to infer its quality. According to previous literatures, the strength data can be well fitted by the Weibull distribution, while the poor parameter estimation method cannot obtain reliable analysis results. Therefore, a new parameter estimation method is proposed. Based on the simulation results, it is found that the proposed parameter estimation method outperforms the other competitors to obtain reliable estimates of the Weibull parameters. Finally, the proposed parameter estimation method is applied to two real data sets of glass fiber strength for illustration. The results of data analysis show that our proposed parameter estimation method is more suitable for these data sets than other estimation methods.

scholarly journals Damage Detection and Isolation from Limited Experimental Data Using Simple Simulations and Knowledge Transfer

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 80
Author(s):  
Asif Khan ◽  
Jun-Sik Kim ◽  
Heung Soo Kim
Keyword(s):  
Experimental Data ◽  
Deep Learning ◽  
Learning Strategy ◽  
Computational Cost ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Actual System ◽  
Disk System ◽  
Health States ◽  
Class Invariant

A simulation model can provide insight into the characteristic behaviors of different health states of an actual system; however, such a simulation cannot account for all complexities in the system. This work proposes a transfer learning strategy that employs simple computer simulations for fault diagnosis in an actual system. A simple shaft-disk system was used to generate a substantial set of source data for three health states of a rotor system, and that data was used to train, validate, and test a customized deep neural network. The deep learning model, pretrained on simulation data, was used as a domain and class invariant generalized feature extractor, and the extracted features were processed with traditional machine learning algorithms. The experimental data sets of an RK4 rotor kit and a machinery fault simulator (MFS) were employed to assess the effectiveness of the proposed approach. The proposed method was also validated by comparing its performance with the pre-existing deep learning models of GoogleNet, VGG16, ResNet18, AlexNet, and SqueezeNet in terms of feature extraction, generalizability, computational cost, and size and parameters of the networks.

scholarly journals Assessing the collective utility of multiple analyses on clinical alcohol use disorder data

2019 ◽  
Vol 26 (10) ◽  
pp. 1046-1055
Author(s):  
Erich Kummerfeld ◽  
Alexander Rix ◽  
Justin J Anker ◽  
Matt G Kushner
Keyword(s):  
Factor Analysis ◽  
Alcohol Use ◽  
Causal Inference ◽  
Latent Variable ◽  
Alcohol Use Disorder ◽  
Estimation Method ◽  
Estimation Methods ◽  
Data Sets ◽  
Learning Methods ◽  
Graph Learning

AbstractObjectiveThe objective of this study was to assess the potential of combining graph learning methods with latent variable estimation methods for mining clinically useful information from observational clinical data sets.Materials and MethodsThe data set contained self-reported measures of psychopathology symptoms from a clinical sample receiving treatment for alcohol use disorder. We used the traditional graph learning methods: Graphical Least Absolute Shrinkage and Selection Operator, and Friedman's hill climbing algorithm; traditional latent variable estimation method factor analysis; recently developed graph learning method Greedy Fast Causal Inference; and recently developed latent variable estimation method Find One Factor Clusters. Methods were assessed qualitatively by the content of their findings.ResultsRecently developed graphical methods identified potential latent variables (ie, not represented in the model) influencing particular scores. Recently developed latent effect estimation methods identified plausible cross-score loadings that were not found with factor analysis. A graphical analysis of individual items identified a mistake in wording on 1 questionnaire and provided further evidence that certain scores are not reflective of indirectly measured common causes.Discussion and ConclusionOur findings suggest that a combination of Greedy Fast Causal Inference and Find One Factor Clusters can enhance the evidence-based information yield from psychopathological constructs and questionnaires. Traditional methods provided some of the same information but missed other important findings. These conclusions point the way toward more informative interrogations of existing and future data sets than are commonly employed at present.

scholarly journals A Robust DOA Estimator Based on Compressive Sensing for Coprime Array in the Presence of Miscalibrated Sensors

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3538 ◽  
Author(s):  
Jiaxun Kou ◽  
Ming Li ◽  
Chunlan Jiang
Keyword(s):  
Compressive Sensing ◽  
Degrees Of Freedom ◽  
Computational Cost ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Estimation Methods ◽  
Fine Grid ◽  
Mismatch Problem ◽  
Maximum Correntropy Criterion ◽  
Coprime Array

Coprime array with M + N sensors can achieve an increased degrees-of-freedom (DOF) of O ( M N ) for direction-of-arrival (DOA) estimation. Utilizing the compressive sensing (CS)-based DOA estimation methods, the increased DOF offered by the coprime array can be fully exploited. However, when some sensors in the array are miscalibrated, these DOA estimation methods suffer from degraded performance or even failed operation. Besides, the key to the success of CS-based DOA estimation is that every target falls on the predefined grid. Thus, a coarse grid may cause the mismatch problem, whereas a fine grid requires great computational cost. In this paper, a robust CS-based DOA estimation algorithm is proposed for coprime array with miscalibrated sensors. In the proposed algorithm, signals received by the miscalibrated sensors are viewed as outliers, and correntropy is introduced as the similarity measurement to distinguish these outliers. Incorporated with maximum correntropy criterion (MCC), an iterative sparse reconstruction-based algorithm is then developed to give the DOA estimation while mitigating the influence of the outliers. A multiresolution grid refinement strategy is also incorporated to reconcile the contradiction between computational cost and the mismatch problem. The numerical simulation results verify the effectiveness and robustness of the proposed method.

Comparative performance of species richness estimation methods

Parasitology ◽  
1998 ◽  
Vol 116 (4) ◽  
pp. 395-405 ◽  
Author(s):  
B. A. WALTHER ◽  
S. MORAND
Keyword(s):  
Species Richness ◽  
Estimation Method ◽  
Estimation Methods ◽  
Sampling Effort ◽  
Data Sets ◽  
Richness Estimation ◽  
First Order ◽  
Species Richness Estimation ◽  
Total Species Richness ◽  
Total Species

In most real-world contexts the sampling effort needed to attain an accurate estimate of total species richness is excessive. Therefore, methods to estimate total species richness from incomplete collections need to be developed and tested. Using real and computer-simulated parasite data sets, the performances of 9 species richness estimation methods were compared. For all data sets, each estimation method was used to calculate the projected species richness at increasing levels of sampling effort. The performance of each method was evaluated by calculating the bias and precision of its estimates against the known total species richness. Performance was evaluated with increasing sampling effort and across different model communities. For the real data sets, the Chao2 and first-order jackknife estimators performed best. For the simulated data sets, the first-order jackknife estimator performed best at low sampling effort but, with increasing sampling effort, the bootstrap estimator outperformed all other estimators. Estimator performance increased with increasing species richness, aggregation level of individuals among samples and overall population size. Overall, the Chao2 and the first-order jackknife estimation methods performed best and should be used to control for the confounding effects of sampling effort in studies of parasite species richness. Potential uses of and practical problems with species richness estimation methods are discussed.

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