Adaptive Identification and Application of Flow Mapping and Inverse Flow Mapping for Electrohydraulic Valves

2021 ◽  
Author(s):  
Jianbin Liu ◽  
André Sitte ◽  
Jürgen Weber
Keyword(s):  
Power System ◽  
Flow Characteristic ◽  
Performance Model ◽  
Identification Performance ◽  
Flow Mapping ◽  
Adaptive Identification ◽  
Identification Methods ◽  
Uncertain Structure ◽  
Electrohydraulic Valve ◽  
Model Structures

Good estimation of flow mapping (FM) and inverse flow mapping (IFM) for electrohydraulic valves are important in automation of fluid power system. The purpose of this paper is to propose adaptive identification methods based on LSM, BPNN, RBFNN, GRNN, LSSVM and RLSM to estimate the uncertain structure and parameters in flow mapping and inverse flow mapping for electrohydraulic valves. In order to reduce the complexity and improve the identification performance, model structures derived from new algorithm are introduced. The above identification methods are applied to map the flow characteristic of an electrohydraulic valve. With the help of novel simulation architecture via OPC UA, the accuracy and efficiency of these algorithms could be verified. Some issues like invertibility of flow mapping are discussed. At last, places and suggestions to apply these methods are made.

Personal Identification and Authentication Based on Keystroke Dynamics in Japanese Long-Text Input

2011 ◽  
pp. 212-231 ◽  
Author(s):  
Toshiharu Samura ◽  
Haruhiko Nishimura
Keyword(s):  
Experimental Study ◽  
Hybrid Model ◽  
Euclidean Distance ◽  
Personal Identification ◽  
Skill Level ◽  
Identification Performance ◽  
Keystroke Dynamics ◽  
Typing Skill ◽  
Identification Methods ◽  
Text Input

We have investigated several characteristics of keystroke dynamics in Japanese long-text input. We performed experiments with 189 participants, classified into three groups according to the number of letters they could type in five minutes. In this experimental study, we extracted feature indices from the keystroke timing for each alphabet letter and for each two-letter combination composed of a consonant and vowel in Japanese text. Taking into account two identification methods using Weighted Euclidean Distance (WED) and Array Disorder (AD), we proposed a hybrid model for identifying individuals on the basis of keystroke data in Japanese long-text input. By evaluating the identification performance of individuals in the three groups, the effectiveness of the method was found to correspond to the typing skill level of the group.

scholarly journals Structural Dynamic Load and Parameter Identification Based on Dummy Measurements of Displacement

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ting Wang ◽  
Zhimin Wan ◽  
Weiguang Zheng ◽  
Shuilong He
Keyword(s):  
Parameter Identification ◽  
Structural Dynamics ◽  
Information Integration ◽  
Integration Method ◽  
Covariance Matrices ◽  
Least Square ◽  
Structural Dynamic ◽  
Identification Methods ◽  
Uncertain Structure ◽  
Previous Identification

Structural load and parameter identification are the essential contents in the field of structural dynamics, and some studies pay attention to the coupled recognition of uncertain structure parameters as well as unacquainted loads. Gillijns and De Moor presented the Kalman-type filter, which was used in the work for coupled identification as the unabbreviated form of GDF. However, it has been demonstrated that it is unstable for the extended GDF (EGDF) method, drifting in the identified unacquainted loads as well as displacement, just like most previous identification methods based on the least-square algorithm. In order to deal with this unstable issue, this paper applied the dummy measurements of displacements on a position level and modifies the EGDF algorithm using the information integration method about the accelerated measurements with dummy measurements. Numerical example of a truss is used for validating the applicability of the method in the work, and an influence of covariance matrices in dummy displacements is also considered.

Proteoform characterization based on top-down mass spectrometry

2020 ◽  
Author(s):  
Jiancheng Zhong ◽  
Yusui Sun ◽  
Minzhu Xie ◽  
Wei Peng ◽  
Chushu Zhang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Medical Profession ◽  
Practical Importance ◽  
Scoring Methods ◽  
Identification Performance ◽  
Top Down ◽  
Identification Methods ◽  
Current State ◽  
Speed Up

Abstract Proteins are dominant executors of living processes. Compared to genetic variations, changes in the molecular structure and state of a protein (i.e. proteoforms) are more directly related to pathological changes in diseases. Characterizing proteoforms involves identifying and locating primary structure alterations (PSAs) in proteoforms, which is of practical importance for the advancement of the medical profession. With the development of mass spectrometry (MS) technology, the characterization of proteoforms based on top-down MS technology has become possible. This type of method is relatively new and faces many challenges. Since the proteoform identification is the most important process in characterizing proteoforms, we comprehensively review the existing proteoform identification methods in this study. Before identifying proteoforms, the spectra need to be preprocessed, and protein sequence databases can be filtered to speed up the identification. Therefore, we also summarize some popular deconvolution algorithms, various filtering algorithms for improving the proteoform identification performance and various scoring methods for localizing proteoforms. Moreover, commonly used methods were evaluated and compared in this review. We believe our review could help researchers better understand the current state of the development in this field and design new efficient algorithms for the proteoform characterization.

scholarly journals Validating Performance Models for Hybrid Power Plant Control Assessment

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4330 ◽  
Author(s):  
Lennart Petersen ◽  
Florin Iov ◽  
German Claudio Tarnowski ◽  
Vahan Gevorgian ◽  
Przemyslaw Koralewicz ◽  
...  
Keyword(s):  
Power Plant ◽  
Power System ◽  
System Integration ◽  
Reactive Power ◽  
Field Measurements ◽  
Voltage Regulation ◽  
Performance Model ◽  
Performance Models ◽  
Hybrid Power ◽  
Hybrid Power Plant

The need for simple, but accurate performance models of wind turbine generators (WTGs), photovoltaic (PV) plants, and battery energy storage systems (BESS) for various hybrid power plant (HPP) studies motivates the present work. Particularly, the development and verification stage of HPP controls requires reduced-order models to minimize the complexity and computation effort of simulation platforms. In this paper, such models are proposed, and the most essential parts of the models are validated through field measurements. The models target power system integration studies involving active and reactive power, as well as frequency and voltage regulation where detailed models, as proposed in the standards, can be cumbersome. Field measurements of two Vestas WTGs, one 1-MW PV plant, and one 1-MW/1-MWh BESS are used for model validation. The results show that the WTG and PV performance models correctly estimate the power generation variability according to fluctuations in wind speed and solar irradiance. The BESS performance model provides satisfactory results related to grid-forming control performance and estimation of state-of-charge. The presented validation work enables using the proposed performance models for power system studies and HPP control design in all model-based design stages, that is, preliminary analysis, design, verification, and validation with a high level of confidence.

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