Simulating blood accumulation with improved smoothed particle hydrodynamics in surgical simulation system.

Background: Blood accumulation often occurs during bleeding in surgery. Simulating the blood accumulation in surgical simulation system not only enhances the realism and immersion of surgical training, but also helps researchers better understand the physical properties of blood flow.

Methods: To realistically simulate the blood accumulation during the bleeding, this paper proposes a novel kernel function with non-negative second derivatives to improve the SPH method.

A new model of electrosurgical tissue damage for neurosurgery simulation.

Background: Bipolar hemostasis electrocoagulation is a fundamental procedure in neurosurgery. A precise electrocoagulation model is essential to enable realistic visual feedback in virtual neurosurgical simulation. However, existing models lack an accurate description of the heat damage and irreversible tissue deformation caused by electrocoagulation, thus diminishing the visual realism.

A fully autonomous robotic ultrasound system for thyroid scanning.

The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a fully autonomous robotic ultrasound system, which is able to scan thyroid regions without human assistance and identify malignant nod- ules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets.

Adaptive Fixed-Time Control for High-Order Stochastic Nonlinear Time-Delay Systems: An Improved Lyapunov-Krasovskii Function.

In this article, the adaptive tracking control problem is considered for high-order stochastic nonlinear time-delay systems in fixed-time. Being different from existing results, an improved Lyapunov-Krasovskii function is designed, which can not only compensate for the time-delay term but also remove the obstacle from the high-order term. Due to the introduction of the Lyapunov-Krasovskii function into the total Lyapunov function, it makes it difficult to stabilize the controlled system within a fixed-time interval.

Simulation of soft tissue deformation under physiological motion based on complementary dynamic method.

Background And Objective: Physiological motions have a significant impact on soft tissue deformation and accuracy of surgical procedures, which is essential for realistic surgical simulation. While existing studies offer accurate simulation of soft tissue deformation, integrating physiological motions into deformation models of soft tissue remains a challenging task.

Methods: This paper introduces a novel deformation model, based on complementary dynamics, to animate soft tissue deformation under physiological motion.

Design and Psychophysical Evaluation of a Novel Wearable Upper-Arm Tactile Display Device.

A novel wearable upper arm tactile display device, which can simultaneously provide three types of tactile stimuli (i.e., squeezing, stretching, and vibration) is presented.

Modeling fibrous soft tissue dissection with elastic-plastic deformation for simulation of brain tumor removal.

Background And Objective: Realistic modeling the dissection of brain tissue is of key importance for simulation of brain tumor removal in virtual neurosurgery systems. However, existing methods are unable to characterize inelastic behaviors of brain tissue, such as plastic deformation and dissection evolution, making it ineffective in simulating brain tumor removal procedures.

Methods: In this paper, a model of fibrous soft tissue dissection for the simulation of brain tumor removal is proposed.

Command filter-based adaptive fuzzy decentralized control for stochastic nonlinear large-scale systems with saturation constraints and unknown disturbance.

In this article, the problem of decentralized fuzzy adaptive control is addressed for a class of stochastic interconnected nonlinear large-scale systems including saturation and unknown disturbance. Fuzzy logic systems (FLSs) are used to estimate packaged nonlinear uncertainties. The command filter technique is presented to eliminate the "explosion of complexity" obstacle associated with the backstepping procedures and the corresponding error compensation mechanism is constructed to alleviate the effect of the errors generated by command filters.

Fast Finite-Time Control for Nonaffine Stochastic Nonlinear Systems Against Multiple Actuator Constraints via Output Feedback.

This research addresses the finite-time control problem for nonaffine stochastic nonlinear systems with actuator faults and input saturation. Specifically, a new finite-time control scheme is constructed based on the adaptive backstepping framework, with the usage of a state observer and taking advantage of the universal approximation capability of the fuzzy-logic system (FLS). The novelty of this work is that it considers the output feedback problem of a completely nonaffine stochastic system and incorporates the idea of the dynamic surface control (DSC) design.

Neural-Network-Based Adaptive Fixed-Time Control for Nonlinear Multiagent Non-Affine Systems.

In this research, the adaptive neural network consensus control problem is addressed for a class of non-affine multiagent systems (MASs) with actuator faults and stochastic disturbances. To overcome difficulties associated with actuator faults and uncertain functions of the designed MAS, a neural network fault-tolerant control scheme is developed. Moreover, an adaptive backstepping controller is developed to solve the non-affine appearance in multiagent stochastic non-affine systems using the mean value theorem.

Classification of Imbalanced Electrocardiosignal Data using Convolutional Neural Network.

Background And Objective: In the application of wearable heart-monitors, it is of great significance to analyze electrocardiogram (ECG) signals for anomaly detection. ECG arrhythmia classification remains an open problem in that it cannot easily recognize data from minority classes due to the imbalanced dataset and particular characteristic of the time series signal. In this study, a novel method is presented as a possible solution to imbalanced classification problems.

Input-to-State Stability for Time-Delay Systems With Large Delays.

In this article, we consider the input-to-state stability (ISS) problem for a class of time-delay systems with intermittent large delays, which may cause the invalidation of traditional delay-dependent stability criteria. The topic of this article features that it proposes a novel kind of stability criterion for time-delay systems, which is delay dependent if the time delay is smaller than a prescribed allowable size. While if the time delay is larger than the allowable size, the ISS can be preserved as well provided that the large-delay periods satisfy the kind of duration condition.

Fuzzy adaptive finite-time output feedback control of stochastic nonlinear systems.

An adaptive finite-time approach to the feedback control of stochastic nonlinear systems is presented. The fuzzy logic system (FLS) and a state observer are used to estimate the uncertain function and unmeasured state of the controlled system, respectively. A dynamic surface control (DSC) scheme is employed to deal with the "computational explosion" problem, which is inherent in traditional backstepping methods since the repetitive calculation of the derivatives of virtual control signals is avoided.

Finite-Time-Prescribed Performance-Based Adaptive Fuzzy Control for Strict-Feedback Nonlinear Systems With Dynamic Uncertainty and Actuator Faults.

In this article, finite-time-prescribed performance-based adaptive fuzzy control is considered for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. To deal with the difficulties associated with the actuator faults and external disturbance, an adaptive fuzzy fault-tolerant control strategy is introduced. Different from the existing controller design methods, a modified performance function, which is called the finite-time performance function (FTPF), is presented.

A morphing-Based 3D point cloud reconstruction framework for medical image processing.

Background And Objective: In the virtual surgery simulation system, the reconstruction of a highly precise soft tissue 3D model is an effective method to improve the user's visual telepresence. However, the traditional point cloud generation method based on subdivision and filling is unsatisfactory due to its low accuracy and slow speed.

Methods: To address this problem, we present a novel 3D point cloud reconstructing model based on Morphing.

A diffeomorphic unsupervised method for deformable soft tissue image registration.

Background And Objectives: The image registration methods for deformable soft tissues utilize nonlinear transformations to align a pair of images precisely. In some situations, when there is huge gray scale difference or large deformation between the images to be registered, the deformation field tends to fold at some local voxels, which will result in the breakdown of the one-to-one mapping between images and the reduction of invertibility of the deformation field. In order to address this issue, a novel registration approach based on unsupervised learning is presented for deformable soft tissue image registration.

Stabilization and Data-Rate Condition for Stability of Networked Control Systems With Denial-of-Service Attacks.

This article investigates the stabilization control and stabilizing data-rate condition problems for networked control systems, which transmit signals from the sensor to the controller over the communication network with denial-of-service (DoS) attacks. Considering a class of DoS attacks that only constrain its frequency and duration, we aim to explore the constraint condition for stabilization and minimum stabilizing data rate of the networked control systems. The framework consists of two main parts.

Cutting procedures with improved visual effects and haptic interaction for surgical simulation systems.

Background And Objectives: Surface rendering and physical models with constant parameters are often employed for cutting procedures in conventional surgical simulators. As a consequence, the internal structures of soft tissues cannot be rendered properly and haptic interaction is unrealistic. In order to improve both the visual and force feedback, a new volumetric geometric model is introduced.

A new volumetric geometric model for cutting procedures in surgical simulation.

Background And Objectives: Cutting procedures are the most common operations in surgical simulation. In order to provide realistic visual feedback with the details of the internal structures of soft tissue to the operator, a novel volumetric geometric model is presented for cutting procedures in surgical simulation.

Methods: A novel volumetric geometric model, which is based on volume rendering and the Bézier curve, is presented for cutting procedures.

Guaranteed Synchronization Performance Control of Nonlinear Time-Delay MIMO Multiagent Systems With Actuator Faults.

This paper addresses the synchronization control problem of leader-follower multiagent systems with each follower described by a class of high-order nonlinear multiple-input-multiple-output (MIMO) dynamics in the presence of time delays and actuator faults. A distributed synchronization scheme with guaranteed synchronization performance based on the radial basis function neural network (RBF NN) is introduced. We propose an augmented quadratic Lyapunov function by incorporating the lower bounds of control gain matrices and the actuator healthy indicator, and the problems caused by the unknown time-varying control gain matrices, actuator faults, and coupling terms among agents are solved.

Distributed Synchronization Control of Nonaffine Multiagent Systems With Guaranteed Performance.

This paper deals with the synchronization control problem in the leader-follower format of a class of high-order nonaffine nonlinear multiagent systems under a directed communication protocol. A novel adaptive neural distributed synchronization scheme with guaranteed performance is proposed. The main contribution lies in the fact that both nonaffine agent dynamics, which basically makes most existing agent dynamics as special cases, and guaranteed synchronization performance are taken into account.

Adaptive Neural Output-Feedback Decentralized Control for Large-Scale Nonlinear Systems With Stochastic Disturbances.

This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed.

Adaptive Fuzzy Finite-Time Control of Nonlinear Systems With Actuator Faults.

This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness.

Robust Fuzzy Adaptive Tracking Control for Nonaffine Stochastic Nonlinear Switching Systems.

This paper is concerned with the trajectory tracking control problem of a class of nonaffine stochastic nonlinear switched systems with the nonlower triangular form under arbitrary switching. Fuzzy systems are employed to tackle the problem from packaged unknown nonlinearities, and the backstepping and robust adaptive control techniques are applied to design the controller by adopting the structural characteristics of fuzzy systems and the common Lyapunov function approach. By using Lyapunov stability theory, the semiglobally uniformly ultimate boundness in the fourth-moment of all closed-loop signals is guaranteed, and the system output is ensured to converge to a small neighborhood of the given trajectory.

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form.