A gas migration law study of a large-scale 3D physical similarity simulation with an adaptive Kalman filter algorithm

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuyu Hao ◽  
Shugang Li ◽  
Tianjun Zhang
Keyword(s):  
Kalman Filter ◽  
Large Scale ◽  
Gas Migration ◽  
Adaptive Kalman Filter ◽  
Physical Similarity ◽  
Content Type ◽  
Gas Concentration ◽  
Rock Formation ◽  
Concentration Estimation ◽  
Migration Law

Purpose In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law. Design/methodology/approach First, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend. Findings This approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach. Originality/value For the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism.

An improved INS/PDR/UWB integrated positioning method for indoor foot-mounted pedestrians

Sensor Review ◽  
2019 ◽  
Vol 39 (3) ◽  
pp. 318-331 ◽  
Author(s):  
Qigao Fan ◽  
Jie Jia ◽  
Peng Pan ◽  
Hai Zhang ◽  
Yan Sun
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Integrated System ◽  
Positioning System ◽  
Adaptive Kalman Filter ◽  
Content Type ◽  
Positioning Method ◽  
Accuracy And Stability

Purpose The purpose of this paper is to relate to the real-time navigation and tracking of pedestrians in a closed environment. To restrain accumulated error of low-cost microelectromechanical system inertial navigation system and adapt to the real-time navigation of pedestrians at different speeds, the authors proposed an improved inertial navigation system (INS)/pedestrian dead reckoning (PDR)/ultra wideband (UWB) integrated positioning method for indoor foot-mounted pedestrians. Design/methodology/approach This paper proposes a self-adaptive integrated positioning algorithm that can recognize multi-gait and realize a high accurate pedestrian multi-gait indoor positioning. First, the corresponding gait method is used to detect different gaits of pedestrians at different velocities; second, the INS/PDR/UWB integrated system is used to get the positioning information. Thus, the INS/UWB integrated system is used when the pedestrian moves at normal speed; the PDR/UWB integrated system is used when the pedestrian moves at rapid speed. Finally, the adaptive Kalman filter correction method is adopted to modify system errors and improve the positioning performance of integrated system. Findings The algorithm presented in this paper improves performance of indoor pedestrian integrated positioning system from three aspects: in the view of different pedestrian gaits at different speeds, the zero velocity detection and stride frequency detection are adopted on the integrated positioning system. Further, the accuracy of inertial positioning systems can be improved; the attitude fusion filter is used to obtain the optimal quaternion and improve the accuracy of INS positioning system and PDR positioning system; because of the errors of adaptive integrated positioning system, the adaptive filter is proposed to correct errors and improve integrated positioning accuracy and stability. The adaptive filtering algorithm can effectively restrain the divergence problem caused by outliers. Compared to the KF algorithm, AKF algorithm can better improve the fault tolerance and precision of integrated positioning system. Originality/value The INS/PDR/UWB integrated system is built to track pedestrian position and attitude. Finally, an adaptive Kalman filter is used to improve the accuracy and stability of integrated positioning system.

Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 708-715
Author(s):  
Xiaobin Xu ◽  
Minzhou Luo ◽  
Zhiying Tan ◽  
Min Zhang ◽  
Hao Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Adaptive Kalman Filter ◽  
Swarm Optimization ◽  
Content Type ◽  
Kalman Filter Algorithm

Purpose This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm. Design/methodology/approach A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function. Findings The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement. Originality/value The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

Fuzzy adaptive Kalman filter for the drive system with an elastic coupling

2013 ◽  
Vol 62 (2) ◽  
pp. 251-265 ◽  
Author(s):  
Piotr J. Serkies ◽  
Krzysztof Szabat
Keyword(s):  
Kalman Filter ◽  
Control Structure ◽  
Experimental Tests ◽  
Drive System ◽  
Adaptive Kalman Filter ◽  
State Matrix ◽  
State Controller ◽  
Kalman Gain ◽  
Robust Adaptive ◽  
Fuzzy Adaptive

Abstract In the paper issues related to the design of a robust adaptive fuzzy estimator for a drive system with a flexible joint is presented. The proposed estimator ensures variable Kalman gain (based on the Mahalanobis distance) as well as the estimation of the system parameters (based on the fuzzy system). The obtained value of the time constant of the load machine is used to change the values in the system state matrix and to retune the parameters of the state controller. The proposed control structure (fuzzy Kalman filter and adaptive state controller) is investigated in simulation and experimental tests.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

The dialectics of authoring expansive learning: tracing the long tail of a Change Laboratory

2016 ◽  
Vol 28 (4) ◽  
pp. 245-262 ◽  
Author(s):  
Annalisa Sannino ◽  
Yrjö Engeström ◽  
Johanna Lahikainen
Keyword(s):  
Large Scale ◽  
Scale Transformation ◽  
Dialectical Tensions ◽  
Content Type ◽  
Organization Model ◽  
Expansive Learning ◽  
Conceptual Argument ◽  
University Library ◽  
Dialectical Process ◽  
Cultural Historical Activity

Purpose The paper aims to examine organizational authoring understood as a longitudinal, material and dialectical process of transformation efforts. The following questions are asked: To which extent can a Change Laboratory intervention help practitioners author their own learning? Are the authored outcomes of a Change Laboratory intervention futile if a workplace subsequently undergoes large-scale organizational transformations? Does the expansive learning authored in a Change Laboratory intervention survive large-scale organizational transformations, and if so, why does it survive and how? Design/methodology/approach The paper develops a conceptual argument based on cultural–historical activity theory. The conceptual argument is grounded in the examination of a case of eight years of change efforts in a university library, including a Change Laboratory (CL) intervention. Follow-up interview data are used to discuss and illuminate our argument in relation to the three research questions. Findings The idea of knotworking constructed in the CL process became a “germ cell” that generates novel solutions in the library activity. A large-scale transformation from the local organization model developed in the CL process to the organization model of the entire university library was not experienced as a loss. The dialectical tension between the local and global models became a source of movement driven by the emerging expansive object. Practitioners are modeling their own collective future competences, expanding them both in socio-spatial scope and interactive depth. Originality/value The article offers an expanded view of authorship, calling attention to material changes and practical change actions. The dialectical tensions identified serve as heuristic guidelines for future studies and interventions.

scholarly journals A new adaptive Kalman filter for navigation systems of carrier-based aircraft

2021 ◽  
Author(s):  
Lifei Zhang ◽  
Shaoping Wang ◽  
Maria Sergeevna Selezneva ◽  
Konstantin Avenirovich Neysipin
Keyword(s):  
Kalman Filter ◽  
Navigation Systems ◽  
Adaptive Kalman Filter

Dynamic output feedback control for nonlinear large-scale interconnected systems

2020 ◽  
Vol 39 (4) ◽  
pp. 801-821
Author(s):  
Ezzeddine Touti ◽  
Ali Sghaier Tlili ◽  
Muhannad Almutiry
Keyword(s):  
Decentralized Control ◽  
Output Feedback ◽  
Large Scale ◽  
Optimization Problem ◽  
Lyapunov Theory ◽  
Interconnected Systems ◽  
Dynamic Output Feedback ◽  
Content Type ◽  
Dynamic Output ◽  
Control Scheme

Purpose This paper aims to focus on the design of a decentralized observation and control method for a class of large-scale systems characterized by nonlinear interconnected functions that are assumed to be uncertain but quadratically bounded. Design/methodology/approach Sufficient conditions, under which the designed control scheme can achieve the asymptotic stabilization of the augmented system, are developed within the Lyapunov theory in the framework of linear matrix inequalities (LMIs). Findings The derived LMIs are formulated under the form of an optimization problem whose resolution allows the concurrent computation of the decentralized control and observation gains and the maximization of the nonlinearity coverage tolerated by the system without becoming unstable. The reliable performances of the designed control scheme, compared to a distinguished decentralized guaranteed cost control strategy issued from the literature, are demonstrated by numerical simulations on an extensive application of a three-generator infinite bus power system. Originality/value The developed optimization problem subject to LMI constraints is efficiently solved by a one-step procedure to analyze the asymptotic stability and to synthesize all the control and observation parameters. Therefore, such a procedure enables to cope with the conservatism and suboptimal solutions procreated by optimization problems based on iterative algorithms with multi-step procedures usually used in the problem of dynamic output feedback decentralized control of nonlinear interconnected systems.

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