Civilizational transition in the focus of interdisciplinary methodology. Review on the book: Vasilenko L.A. and Meshcheryakova N.N. (2021) Sociology of Digital Society: monograph, TPU Publishing House, Tomsk, Russia (In Russian)

The research paper presents a review of the monograph devoted to the methodological and theoretical aspects of the digital societysociology. L.A. Vasilenko and N.N. Meshcheryakova introduce the digital society conceptas a new independent stage in the existence of a post-industrial society and a transitional stage to a postdigital one. The concept is constituted through the description of the digital society essential features, which qualitatively distinguish it on the general line of civilizational development. The authors submit that classical sociology, with its spectrum of theories, methods and techniques, is not enough to understand the hybrid reality, partially virtualized. The monograph lays the theoretical and methodological foundations of sociology of digital society. Its content is aimed at summing up the study resultsof the digital society as a special stage of the information society and the formation of the author’s vision of the way to solve problems. Reviews of the most significant studies have been generated in the text. A multidisciplinary approach to the review and to the presentation of one’s own positions and developments has been presented. The authors go far beyond the subject of sociological science itself, and form a three-dimensional picture of digital society as a whole. Digital society is considered from the perspective of a civilizational approach. The authors consistently concentrate their attention on the issue of the evolution of the information society formation, then move on to the main aspect of the author’s concept. They are deploying arguments in favor of proving that the digital society is the next stage in the information society development. An original scenario approach to the empirical data interpretation on the digitalisation process has been implemented. When organising the study and formulating conclusions, positive and negative consequences and scenarios of evolution are highlighted. The book is of considerable interest to sociologists, philosophers, specialists in the field of social management

Bayesian calibration for multiple source regression model

In large variety of practical applications, using information from different sources or different kind of data is a reasonable demand. The problem of studying multiple source data can be represented as a multi-task learning problem, and then the information from one source can help to study the information from the other source by extracting a shared common structure. From the other hand, parameter evaluations obtained from various sources can be confused and conflicting. This paper proposes a Bayesian based approach to calibrate data obtained from different sources and to solve nonlinear regression problem in the presence of heteroscedastisity of the multiple-source model. An efficient algorithm is developed for implementation. Using analytical and simulation studies, it is shown that the proposed Bayesian calibration improves the convergence rate of the algorithm and precision of the model. The theoretical results are supported by a synthetic example, and a real-world problem, namely, modeling unsteady pitching moment coefficient of aircraft, for which a recurrent neural network is constructed

Two-layer adaptive augmentation for incremental backstepping flight control of transport aircraft in uncertain conditions

Presence of uncertainties caused by unforeseen malfunctions in actuation system or changes in aircraft behaviour could lead to aircraft loss-of-control during flight. The paper presents Two-Layer Adaptive augmentation for Incremental Backstepping (TLA-IBKS) control algorithm designed for a large transport aircraft. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of control effectiveness. The proposed technique is capable to detect possible failures for an overactuated system. At the first layer, the system performs monitoring of a combined effectiveness and detects possible failures via an innovation process. If a problem is detected the algorithm initiates the second-layer algorithm for adaptation of effectiveness of individual control effectors. Filippov generalization for nonlinear differential equations with discontinuous right-hand sides is utilized to develop Lyapunov based tuning function adaptive law for the second layer adaptation and to prove uniform asymptotic stability of the resultant closed-loop system. Conducted simulation manifests that if the input-affine property of the IBKS is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the proposed TLA-IBKS algorithm demonstrates improved stability and tracking performance

Two-layer on-line parameter estimation for adaptive incremental backstepping flight control for a transport aircraft in uncertain conditions

Presence of uncertainties caused by unforeseen malfunctions of the actuator or changes in aircraft behavior could lead to aircraft loss of control during flight. The paper presents two-layer parameter estimation procedure augmenting Incremental Backstepping (IBKS) control algorithm designed for a large transport aircraft. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of the control effectiveness. The proposed identification technique is capable to detect possible problems such as a failure or presence of unknown actuator dynamics even in case of redundancy of control actuation. At the first layer, the system performs monitoring of possible failures. If a problem in one of the control direction is detected the algorithm initiates the second-layer identification determining the individual effectiveness of the each control surface involved in this control direction. Analysis revealed a high robustness of the IBKS to actuator failures. However, in severe conditions with a combination of multiple failures and presence of unmodelled actuator dynamics IBKS could lost stability. Meanwhile, proposed control derivative estimation procedure augmenting the IBKS control helps to sustain stability

On distribution of migratory fry of pink salmon in the stream of a small Sakhalin river

Distribution of migratory fry of pink salmon Oncorhynchus gorbuscha in the stream of the Voznesenka River (south-eastern Sakhalin) is investigated and its high inhomogeneity is revealed. One of the reasons is the stream structure - the fry aggregate mainly in the main flow. Another reason could be the fry behavior - they instinctively try to form aggregations with visual contact, in particular in relatively light nights or in conditions of their high abundance (short distance between the individuals)

Fault detection, isolation and adaptive augmentation for incremental backstepping flight control

Uncertainties caused by unforeseen malfunctions of the actuator or changes in aircraft behavior could lead to aircraft loss of control during flight. The paper presents a Two-Layer Framework (TLF) augmenting Incremental Backstepping (IBKS) control algorithm designed for an aircraft. IBKS uses angular accelerations and current control deflections to reduce the dependency on the dynamics model. Nevertheless, knowledge of the control effectiveness is still required for proper tracking performance and stability guarantee and becomes essential in a case of failure. The proposed TLF is designed to detect possible problems such as a failure or presence of unknown actuator dynamics and to adapt the control effectiveness. At the first layer, the system performs detection and isolation of possible failures. After a problem being detected and isolated, the algorithm initiates the second-layer adaptation of the individual effectiveness of the failed control effector. For some critical scenarios, when the input-affine property of the IBKS is violated, e.g., for a combination of multiple failures, the IBKS could lose stability. Meanwhile, the proposed TLF-IBKS algorithm has improved tracking and stability performanc

Sparse online Gaussian process adaptation for incremental backstepping flight control

Presence of uncertainties caused by unforeseen malfunctions in actuation or measurement systems or changes in aircraft behaviour could lead to aircraft loss-of-control during flight. This paper considers sparse online Gaussian Processes (GP) adaptive augmentation for Incremental Backstepping (IBKS) flight control. IBKS uses angular accelerations and control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of the relationship between inner and outer loops and control effectiveness. Proposed indirect adaptation significantly reduces model dependency. Global uniform ultimate boundness is proved for the resultant GP adaptive IBKS. Conducted research shows that if the input-affine property is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the proposed sparse GP-based estimator provides fast online identification and the resultant controller demonstrates improved stability and tracking performance

Group design project in control engineering: Adapting to COVID-19 pandemic

Group Design Project (GDP) is a common education strategy in engineering. However, due to the COVID-19 pandemic, GDP cannot be fulfilled in a typical lab condition. The paper describes an example of delivering intensive hands-on, group project-based engineering course Autonomous Vehicle Dynamics and Control at Cranfield University. The project was designed to be implemented using modern simulation tools. As a result, students have not only obtained a better understanding of the engineering areas but also learned the usage of essential engineering and IT tools. The students obtained skillsets useful in modern engineering applications, where a simulation environment could improve the quality of the system before deployment and reduce a development cost

On-line learning and updating unmanned tracked vehicle dynamics

Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle mission

A global-local meta-modelling technique for model updating

The finite element model updating procedure of large or complex structures is challenging for engineering practitioners and researchers. Iterative methods, such as genetic algorithms and response surface models, have a high computational burden for these problems. This work introduces an enhanced version of the well-known Efficient Global Optimisation technique to address this issue. The enhanced method, refined Efficient Global Optimisation or rEGO, exploits a two-step refinement and selection technique to expand the global search capability of the original method to a global–local, or hybrid, search capability. rEGO is tested and validated on four optimisation test functions against the original methods and genetic algorithms with different settings. Good results in terms of precision and computational performance are achieved, so an application for model updating is sought. A penalty function for the finite element model updating is identified in residuals of the modified total modal assurance criterion. Finally, rEGO for finite element model updating is implemented on a hybrid, numerical and experimental, case study based on a well-known experimental dataset and on a higher dimension finite element model of a wing spar. Satisfactory results in terms of precision and computational performance are achieved when compared to the original methods and genetic algorithms, needing two orders of magnitude fewer evaluations and achieving comparable results in terms of precision.Engineering and Physical Sciences Research Council (EPSRC): 227762