scholarly journals Verification for ATS Method of New Sterilizing-Value-Estimation-Method

2021 ◽  
Vol 22 (3) ◽  
pp. 77-86
Author(s):  
Isamu MUKAI
Keyword(s):  
Estimation Method ◽  
Value Estimation

scholarly journals An Initial Value Estimation Method for the Kalman and Extended Kalman Filters in Underground Metal Detection

2019 ◽  
Vol 9 (19) ◽  
pp. 4113 ◽  
Author(s):  
Yadong Wan ◽  
Zhen Wang ◽  
Peng Wang ◽  
Zhiyang Liu ◽  
Na Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Detection Methods ◽  
Polarizability Tensor ◽  
Filter Method ◽  
Excellent Performance ◽  
Initial Value ◽  
Initial State ◽  
Metal Detection ◽  
Value Estimation

As an underground metal detection technology, the electromagnetic induction (EMI) method is widely used in many cases. Therefore, the EMI detection algorithms with excellent performance are worth studying. One of the EMI detection methods in the underground metal detection is the filter method, which first obtains the secondary magnetic field data and then uses the Kalman filter (KF) and the extended Kalman filter (EKF) to estimate the parameters of metal targets. However, the traditional KF methods used in the underground metal detection have an unsatisfactory performance of the convergence as the algorithms are given a random or a fixed initial value. Here, an initial state estimation algorithm for the underground metal detection is proposed. The initial state of the target’s horizontal position is estimated by the first order central moments of the secondary field strength map. In addition, the initial state of the target’s depth is estimated by the full width at half maximum (FWHM) method. In addition, the initial state of the magnetic polarizability tensor is estimated by the least squares method. Then, these initial states are used as the initial values for KF and EKF. Finally, the position, posture and polarizability of the target are recursively calculated. A simulation platform for the underground metal detection is built in this paper. The simulation results show that the initial value estimation method proposed for the filtering algorithm has an excellent performance in the underground metal detection.

scholarly journals Proper b-value Estimation Method in Rock Acoustic Emission Testing

2020 ◽  
Author(s):  
Xiling Liu ◽  
MengSi Han ◽  
Wei He ◽  
Xibing Li ◽  
Daolong Chen
Keyword(s):  
Acoustic Emission ◽  
Estimation Method ◽  
B Value ◽  
Emission Testing ◽  
Value Estimation ◽  
Acoustic Emission Testing

scholarly journals Microarray missing values imputation methods: Critical analysis review

2009 ◽  
Vol 6 (2) ◽  
pp. 165-190 ◽  
Author(s):  
Mou'ath Hourani ◽  
Emary El
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Missing Values ◽  
Gene Array ◽  
Estimation Method ◽  
Least Square ◽  
Support Vector ◽  
Expression Data ◽  
Imputation Methods ◽  
Value Estimation

Gene expression data often contain missing expression values. For the purpose of conducting an effective clustering analysis and since many algorithms for gene expression data analysis require a complete matrix of gene array values, choosing the most effective missing value estimation method is necessary. In this paper, the most commonly used imputation methods from literature are critically reviewed and analyzed to explain the proper use, weakness and point the observations on each published method. From the conducted analysis, we conclude that the Local Least Square (LLS) and Support Vector Regression (SVR) algorithms have achieved the best performances. SVR can be considered as a complement algorithm for LLS especially when applied to noisy data. However, both algorithms suffer from some deficiencies presented in choosing the value of Number of Selected Genes (K) and the appropriate kernel function. To overcome these drawbacks, the need for new method that automatically chooses the parameters of the function and it also has an appropriate computational complexity is imperative.

Research on data fusion method of multi-source complex system

2021 ◽  
Author(s):  
Yuxiang Cai
Keyword(s):  
Data Fusion ◽  
Estimation Method ◽  
Fuzzy Neural ◽  
Accurate Perception ◽  
Value Estimation ◽  
Null Value ◽  
Neural Network Algorithm ◽  
Basic Technology ◽  
Power And Energy ◽  
The Internet Of Things

Multi source fusion of data collected by various sensors to realize accurate perception is the key basic technology of the Internet of things. At present, there are many problems in the fusion of various kinds of data collected by sensors, such as more noise and more null values. In this paper, the fuzzy neural network algorithm is proposed to establish the model, combined with the Delphi method and the null value estimation method based on the prediction value to construct the data fusion system. This method has rich application scenarios in the construction of IOT system in the field of power and energy.

The Seed Hull Maturity Index as an Estimator of Yield and Value of Virginia-type Peanuts1

1982 ◽  
Vol 9 (1) ◽  
pp. 27-30
Author(s):  
Harold E. Pattee ◽  
Francis G. Giesbrecht ◽  
James W. Dickens ◽  
Johnny C. Wynne ◽  
James H. Young ◽  
...  
Keyword(s):  
Low Cost ◽  
Maximum Yield ◽  
Estimation Method ◽  
Maturity Index ◽  
Estimation Equations ◽  
Value Estimation ◽  
Term Basis ◽  
Seed Hull ◽  
Relationship Of

Abstract The Seed Hull Maturity Index (SHMI) is a low cost maturity estimation method which has been shown to be correlated to yield and value per hectare using short term studies. To test the relationship of SHMI to yield and value on a long term basis, an equation was developed for deriving SHMI from 9 years of market grade information. Comparison of observed and derived SHMI values produced an R of 0.93. Among the cultivars used only Florigiant, NC6, and NC7 are either major commercial cultivars or cultivars being evaulated commercially. The data from this study confirmed that SHMI optimum values must be determined for each cultivar of interest. SHMI was shown to best estimate value per hectare. The value estimation equations for Florigiant and NC6 are given. The SHMI at which maximum value occurs is 3.0 for Florigiant and 3.1 for NC6. The SHMI at which maximum yield occurs is 2.7 for both cultivars.

Preliminary Study on Risk-Informed LBLOCA of CPR1000

2017 ◽  
Author(s):  
Jianyang Song ◽  
Jingquan Liu ◽  
Ting Wang ◽  
Pingping Liu ◽  
Zhikang Lin
Keyword(s):  
Nuclear Power ◽  
Safety Assessment ◽  
Power Plants ◽  
Estimation Method ◽  
Lessons Learned ◽  
Load Spectrum ◽  
Analysis Methodology ◽  
Assessment Approach ◽  
Value Estimation ◽  
Probabilistic Sequences

The safety assessment approach of nuclear power plants (NPPs) has been evolved with the technological progress and the lessons learned from the major events. Recently, the risk-informed analysis methodology combined probabilistic safety assessment (PSA) and traditional deterministic methodology has been a hot topic. Following the Risk-Informed Analysis Methodology, the PCT margin of CPR1000 Nuclear Power Plant was re-evaluated in this paper. In the PSA analysis, 162 probabilistic sequences had been identified after LBLOCA occurs. Then 18 probabilistically significant sequences were selected for the deterministic methodology analysis with deterministic realistic method (DRM) for CPR1000 NPP. With calculated PCT of each dominant sequence, a load spectrum of PCT for LBLOCA was generated. Then the risk-informed PCT margin can be evaluated by two different methods, namely the expecting value estimation method and the sequence probability coverage method. In conclusion, it was found that the PCT margin evaluated by the Risk-Informed Analysis Methodology can be greater than that of the deterministic DRM methodology by 16∼34°C.

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