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Rheological soft tissue models play an important role in designing control methods for modern teleoperation systems. In the meanwhile, these models are also essential for creating a realistic virtual environment for surgical training. The implementation of model-based control in teleoperation has been a frequently discussed topic in the past decades, offering solutions for the loss of stability caused by time delay, which is one of the major issues in long-distance force control. In this paper, mass-spring-damper soft tissue models are investigated, showing that the widely used linear models do not represent realistic behavior under surgical manipulations. A novel, nonlinear model is proposed, where mechanical parameters are estimated using curve fitting methods. Theoretical reaction force curves are estimated using the proposed model, and the results are verified using measurement results from uniaxial indentation. The model is extended with force estimation by nonuniform surface deformation, where the surface deformation function is approximated according to visual data. Results show that using the proposed nonlinear model, a good estimation of reaction force can be achieved within the range of 0-4 mm, provided that the tissue deformation shape function is appropriately approximated. [ABSTRACT FROM AUTHOR]

A 50th birthday is an important milestone in the life of any individual and certainly in the development of collectives and organizations. January 2021 is such a milestone in the life of the IEEE Transactions of Systems, Man, and Cybernetics, which had its very first issue published in January 1971. Our 50th Anniversary Issue celebrates this remarkable achievement by introducing 20 survey articles from early pioneers of our field, as well as works by leading scientists conducting research at the cutting edge of systems science, with a focus on human aspects, cybernetics, with crucial societal impact. Our goal is to provide a vista of the remarkable developments in the past, giving a snapshot of the present state of our field, and indicating possible avenues for future progress in our rapidly changing research discipline. [ABSTRACT FROM AUTHOR]

We study Sobolev spaces on the n -dimensional unit ball B n ( 1 ) endowed with a parameter-depending Finsler metric F a , a ∈ [ 0 , 1 ] , which interpolates between the Klein metric ( a = 0 ) and Funk metric ( a = 1 ) , respectively. We show that the standard Sobolev space defined on the Finsler manifold ( B n ( 1 ) , F a ) is a vector space if and only if a ∈ [ 0 , 1 ) . Furthermore, by exploiting variational arguments, we provide non-existence and existence results for sublinear elliptic problems on ( B n ( 1 ) , F a ) involving the Finsler–Laplace operator whenever a ∈ [ 0 , 1 ) . [ABSTRACT FROM AUTHOR]

Abstract: A problem of hardware implementation of fuzzy rule-based systems with reconfigurable parametric families of operations is discussed. The methods of generation of parametric families of digital fuzzy conjunctors and t-norms by means of monotone sum and ordinal sum of basic t-norms are studied in the paper. The paper considers several methods of generation of such operations utilizing only the simplest operations, such as comparison, min, max, bounded sum and bounded difference, providing efficient hardware implementation of different reconfigurable parametric families of digital fuzzy conjunctors in one modular structure. [Copyright &y& Elsevier]

Abstract: This paper offers a comprehensive study of information aggregation in intelligent systems prompted by common engineering interest. After a motivating introduction we consider aggregation functions and their fundamental properties as a basis for further development. Four main classes of aggregation functions are identified, and important subclasses are described and characterized as prototypes. For practical purposes, we outline two procedures to identify aggregation function that fits best to empirical data. Finally, we briefly recall some applications of aggregation functions in decision making, utility theory, fuzzy inference systems, multisensor data fusion, image processing, and their hardware implementation. [Copyright &y& Elsevier]

Abstract: We enlarge the class of fuzzy transforms (F-transforms) by considering different types of fuzzy partitions. New types of F-transforms are constructed based on B-splines, Shepard kernels, Bernstein basis polynomials and Favard–Szász–Mirakjan type operators. We study approximation properties of F-transforms. Particular cases of the classical Korovkin''s theorems are presented, together with new error estimates for different types of fuzzy transforms. It turns out that F-transforms can be studied in the framework of classical approximation theory. [Copyright &y& Elsevier]

Hydraulic differential electric servo cylinders are strongly nonlinear, coupled multivariable electromechanical tools applicable for driving, e.g., manipulators. Since they have cons siderable advantages in comparison with electric drives, it would he desirable to extend their application to dynamic continuous path (CP) control, too. From this point of view these systems have the following specialties: a) the most important phenomena influencing their behavior as, e.g., warming up of the sliding surfaces, are determined by local effects, and cannot be controlled globally; b) the friction forces show discontinuous variation at the zero transition of the piston's velocity that is a locally nonlinearizable nonlinearity. A common proportional, integral, and derivative (ND) controller may generate a noise-like acceleration signal due to feeding back the effects of such fluctuations. Warming up of the working fluid during operation influences these friction properties, too. Dynamic interaction between the system and its environment neither measured nor modeled by the controller is another agent influencing the system's observable behavior. For this purpose a special controller was elaborated. It implements certain adaptivity to compensate the effects of the inaccurate model and the unknown external disturbances, and also contains a nonconventional noise filtering technique to reduce the effect of the fluctuating friction forces. In the paper the control method is described, and then its capabilities are illustrated via simulation results. [ABSTRACT FROM AUTHOR]

This paper proposes two novel approaches to estimate accurately mobile robot attitudes based on the fusion of low-cost accelerometers and gyroscopes. The first part of the paper demonstrates the use of a special test bench that both enables simulations of various dynamic behaviors of wheeled robots and measures their real attitude angles along with the raw sensor data. These measurements are applied in a simulation environment and we outline an offline optimization of Kalman filter parameters. The second part of the paper introduces a novel adaptive Kalman filter structure that modifies the noise covariance values according to the system dynamics. The instantaneous dynamics are characterized regarding the magnitudes of both the instantaneous vibration and the external acceleration. The proposed adaptive solution measures these magnitudes and utilizes fuzzy-logic to modify the filter parameters in real time. The results show that the adaptive filter improves the overall filter convergence by a remarkable 10.9 % over using the optimized Kalman filter, thereby demonstrating its efficacy as an accurate and robust attitude filter. The proposed filter performances are also benchmarked against other common methods indicating that the flexibility of the developed adaptive filter allowed it to compete and even outperform the benchmark filters. [ABSTRACT FROM AUTHOR]

Abstract: In the framework of the pseudo-analysis the classical space is generalized and there are proved important properties of introduced space. Three types of convergence of sequences of measurable functions are considered in this space. The inequalities for integrals based on pseudo-integral have been recently proposed as the Hölder, Minkowski and Markov inequalities, which are applied in observing relationship among introduced convergences. [Copyright &y& Elsevier]

Mamdani-type inference systems with trapezoidal-shaped fuzzy membership functions play a crucial role in a wide variety of engineering systems, including real-time control, transportation and logistics, network management, etc. The automatic identification or construction of such fuzzy systems input output data is one of the key problems in modeling. In the past years, the authors have investigated several different fuzzy t-norms, among others, algebraic and trigonometric ones, and the Hamacher product by substituting the standard 'min' t-norm operation, in order to achieve better model fitting. In the present paper, the focus is on examining the general parametric Hamacher t-norm, where the free parameter quite essentially influences the quality of modeling and the learning capability of the model identification system. Based on a wide scope of simulation experiments, a quasi-optimal interval for the value of the Hamacher operator is proposed. [ABSTRACT FROM AUTHOR]

Symbiotic autonomous systems (SAS) are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human–machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general-AI technologies that either function without human intervention or synergize humans and intelligent machines in coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviours. This paper explores the cognitive and mathematical foundations of SAS. The challenges to seamless human–machine interactions in a hybrid environment are addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, cognitive computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via autonomous knowledge learning systems that symbiotically work between humans and cognitive robots. This article is part of the theme issue 'Towards symbiotic autonomous systems'. [ABSTRACT FROM AUTHOR]

To commemorate the 50th anniversary of the IEEE Transactions on Systems, Man, and Cybernetics: Systems, this article examines and reports on its past to current topical coverage of systems science and engineering toward exploring the evolving focus of the research community. Results of a systematic bibliometric analysis are presented with associated conclusions, implications, and summary of topical areas. In addition, respective views regarding the current state of the field and where it is headed are offered by recent leaders of the IEEE Systems, Man, and Cybernetics Society, including its continued relevance and role in the advancement of systems technology. [ABSTRACT FROM AUTHOR]

The ever-increasing complexity of robot applications induces the need for methods to approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of tools to address this kind of problems. This survey presents a categorization of the major challenges in robotics that leverage DL technologies and introduces representative examples of successful solutions for the described problems. We also consider the question when and whether to use modular, monolithic models or end-to-end DL, in order to provide a guideline for the selection of the correct model structure and training strategy. By doing so, the current role and adaptability of different techniques at different hierarchical levels of a robot-application can be highlighted, thus providing a well-structured basis to assist future approaches. [ABSTRACT FROM AUTHOR]

Lower and upper bounds are deduced for some Jaeger integrals which involve the Bessel functions of the first and second kind. The upper bounds contain some elementary functions as well as incomplete gamma functions, while the lower bounds are expressed also in terms of incomplete gamma functions and are deduced via some known inequalities for Bessel functions of the first and second kinds. [ABSTRACT FROM AUTHOR]

Abstract: Cancer is one of the most destructive and lethal illnesses of the modern civilization. In the last decades, clinical cancer research shifted toward molecular targeted therapies which have limited side effects in comparison to conventional chemotherapy and radiation therapy. Antiangiogenic therapy is one of the most promising cancer treatment methods. The dynamical model for tumor growth under angiogenic stimulator/inhibitor control was posed by Hahnfeldt et al. in 1999; it was investigated and partly modified many times. In this paper, a modified version of the originally published model is used to describe a continuous infusion therapy. In order to generalize individualized therapies a robust control method is proposed using H ∞ methodology. Uncertainty weighting functions are determined based on the real pathophysiological case and simulations are performed on different tumor volumes to demonstrate the robustness of the proposed method. [Copyright &y& Elsevier]