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This article discusses the complex approach to managing atherosclerotic cardiovascular disease (ASCVD), focusing on person-centred care (PCC) to align treatment strategies with individual patient narratives, values, and preferences. It identifies significant challenges in management of ASCVD, such as the necessity for multidisciplinary strategies and the need for enhanced patient care, particularly given the coexistence of ASCVD with other cardiometabolic risk factors. The paper points out existing practice gaps, including limited patient-provider information sharing and decision making, and considers the role of technology in personalizing care and improving outcomes. Strategies such as electronic health records, telehealth platforms, and motivational interviewing are examined for their potential to boost patient engagement and adherence to treatment. In addition, the article discusses systemic issues such as health care provider burnout and the importance of creating customized care plans for patients with multiple health conditions. The integration of varied approaches, including the involvement of community pharmacists and health coaches, is suggested as important for effective management of ASCVD. This review highlights the need for an innovative, holistic strategy for management of ASCVD and advocates for a transformative shift toward PCC that integrates individual, community, and system-level interventions to enhance patient engagement, therapy adherence, and overall outcomes.

While robotic tactile sensors have been developed to help robots to perceive and interact effectively with their surrounding environment by mimicking the structure and function of human skin, most of them overlook the role of near-contact behavior and data structure modeling in robotic perception, which limits robotic exploration capabilities. To address this problem, this article presents a novel proximity-tactile fingertip (PT-TIP) sensor, and a new multireceptive graph convolutional network (MR-GCN) that seamlessly integrates near-contact behavior and tactile perception in rich sensory data. Moreover, MR-GCN utilizes two graph structures, including topology graph and affinity graph, to capture temporal and spatial connections and differences among sensing units on PT-TIP, and it learns a robust feature representation from different receptive fields with attention mechanisms. The performance of MR-GCN was evaluated in two common robotic tasks, namely, object recognition and grasp stability detection, and the results show that the presented method outperforms state-of-the-art work in both tasks.

To address model-based event-triggered fixed-time consensus of nonlinear multi-agent systems with both fixed topology and switching topologies, a novel model-based event-triggered protocol is presented. In the proposed protocol, agents in multi-agent systems update controllers by utilizing estimated state values in the triggered intervals. And for reducing the burdens of bandwidth further, the model-based event-triggered protocol is extended to self-triggered protocol which does not need continuous communication in triggered intervals. It is proven that Zeno behavior is excluded. Our main contributions are giving a novel distributed model-based event-triggered protocol for fixed-time consensus and extending it into a self-triggered protocol. Finally, a couple of simulation examples are provided to verify the effectiveness of the proposed protocols.

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. L’Hopital’s rule is used to determine the boundedness of the Lyapunov–Krasovskii function, and the fixed-time boundedness of the integral functions can be inferred. By utilizing the fixed-time Lyapunov stability theorem, it is proved that the controlled system is semi-globally practical fixed-time stable (SGPFS), all the closed-loop signals (CLSs) are bounded within the fixed-time interval, and the tracking error converges into a small region around zero. The validity of the designed scheme is substantiated via simulation results.

Despite the availability of guide dogs and sticks, visually impaired people still have difficulties in traveling and sometimes may encounter dangers. This article presents a novel head-mounted haptic navigation device, for which vibrotactile stimulation is employed at different locations of the head helmet. A set of different navigation instructions is provided through coding the sequential stimuli to the visually impaired people, assisting them in their daily activities. Experiments show that the frontal and temporal regions of the head are more sensitive to vibrotactile stimuli than the top, and the difference thresholds of vibration duration for different parts of the head were derived. The subject’s accuracy and reaction time to navigation direction cues were tested in both indoor and outdoor environments. The results showed that the overall navigation accuracy was around 80%, and the response time was around 1.5 s.

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. Being different from the existing control methods, the developed adaptive fixed-time control approach can ensure that the outputs of all followers track the reference signal synchronously in the fixed time, and all signals of the controlled system are semi-globally uniformly fixed-time stable. The simulation results confirm that the presented control strategy is effective in achieving control goals.

This study focuses on reachability problems in differential games. An improved level set (LS) method for computing reachable tubes (RTs) is proposed in this article. The RT is described as a sub-LS of a value function, which is the viscosity solution of a Hamilton–Jacobi (HJ) equation with running cost. We generalize the concept of RTs and propose a new class of RTs, which are referred to as cost-limited one. In particular, a performance index can be specified for the system, and A set of initial states of the system’s evolutions that can reach the target set before the performance index grows to a given allowable cost is referred to as a cost-limited RT (CRT). Such an RT can be obtained by specifying the corresponding running cost function for the HJ equation. Different nonzero sub-LSs of the viscosity solution of the HJ equation at a certain time point can be used to characterize the CRTs with different allowable costs (or the RTs with different time horizons), thus reducing the storage space consumption. The validity and accuracy of the suggested technique are demonstrated via some examples.

For applications such as autonomous vehicles, instance segmentation of captured images about surrounding environments is critical, and in the meantime, these types of tasks require high accuracy as well. However, most existing models do not consider the extra computation caused by complex architectures and the poor ability of overly lightweight structures to capture semantic information. To enable real-time instance segmentation including semantic segmentation and object detection, a new framework based on an encoder–decoder architecture is proposed, which differs from existing work in two aspects: 1) it adopts two branches to generate a prototype and semantic masks and predicts instance class confidence, bounding box and the coefficients of prototype masks and 2) it adds a spatial attention module (SAM) and the Laplacian operator into a loss function for the segmentation branch to refine boundaries. The instance masks are then produced by combining the generated segmentation mask and mask coefficients. An expansion crop training strategy is employed to accelerate training and detection speed and alleviate bad results due to convolutional translation invariance. Extensive experiments are carried out on available public datasets, including Cityscapes Urban and COCO 2017. Results show that the proposed model achieves 38.9 AP on the Cityscapes test set and 46.8 AP on the COCO test set with eight categories of instances. Compared with other state-of-the-art methods for real-time applications, our method reaches 29.0 FPS with improved accuracy.

This research focuses on the issue of adaptive fuzzy fault-tolerant position tracking for permanent magnet synchronous motors (PMSMs) subject to finite-time prescribed performance. An improved finite-time prescribed performance control strategy, in which unknown nonlinear functions can be tackled via fuzzy logic systems (FLSs), is presented via incorporating the approach of prescribed performance control with the technique of command filter. In addition, the command filtered method is utilized to conquer the ‘explosion of complexity’ emerged in the classic backstepping method and the error compensation mechanism is adopted to diminish the error generated by filtering process. Further, the impact of actuator failures is dealt with based on fault-tolerant control. It is proven that the designed controllers not only assure the semi-global boundedness of all the controlled system signals, but also make the output tracking error is preserved in a specified prescribed performance within a finite-time interval. Finally, simulation results are supplied to display the significance and potential of the proposed control technique.

Fractional-order chaotic systems show great potential in secure communications because of their unique properties. In order to improve their encryption and anti-attack capabilities, a new compounding mechanism is developed for fractional-order multidrive and response chaotic systems. Rather than using simple addition, which is often employed in existing work, the compounding is based on multiplication, making the system topology much more complex and difficult to predict. A synchronization method is thus developed for the compounded system using a projective approach and the synchronization error is able to converge to zero within the range defined by a prescribed performance function. In addition, the developed synchronization controller represents a general form, and it can be easily transformed into other methods by choosing different design parameters. Simulation results and comparative analysis are also carried out between the proposed and prior control techniques to illustrate the effectiveness of the presented scheme.

Point cloud completion aims to predict the missing part for an incomplete 3-D shape. Existing point cloud completion methods based on deep learning complete the point cloud by extracting global features from the incomplete point cloud. However, such methods cannot generate a uniformly distributed point cloud and the accurate structure details of the object. To solve the problem, a novel method for completing point clouds is proposed in this article. Our approach is a two-step strategy. First, to predict the sparse point cloud with uniform density, the sequence generation completion (SGC) method is proposed. By numbering the subspace obtained from the spatial subdivision, the point cloud is represented with a sequence of numbers and the point cloud completion problem is turned into a sequence generation problem. Second, to obtain the dense point cloud and generate the accurate structural details of point clouds, we propose a resolution scaling network (RSN). This network takes local resolution as input and increases the weight of low-resolution regions by learning to preserve the comprehensive structural information of the sparse point cloud, which is crucial to generate dense point cloud. The comprehensive experiments on several public datasets demonstrate the effectiveness of our method. Source code and pretrained models will be available at github.com/Pikachu-NCU/Sequence-Generate-Completion-Method.

To mitigate the data transmission burden, an new memory event-trigger scheme is developed for the load frequency control (LFC) of multi-area networked power systems with the integration of various new energy sources. $H_{\infty }$ stability criteria under less conservative conditions are established by utilizing an improved Lyapunov stability theory and a second-order Bessel-Legendre (B-L) inequality for the LFC power system using the presented memory event-trigger scheme. Simulation on a two-area LFC system and IEEE-39 bus station test system are performed to validate the proposed method.

This brief focuses on adaptive neural fixed-time tracking control problem for the multi-input and multi-output (MIMO) nonlinear systems with dynamic uncertainty. In the controller design process, the difficulties are to overcome the multiple singularity problems. The hyperbolic tangent function and the piecewise function are invoked to deal with the singularity problems that occur in the process of realizing the fixed-time stability of the dynamic signal and designing the control signal, respectively. By mean of fixed-time stability theorem, the presented controller can ensure the boundedness of all closed-loop signals (CLS) and the ideal tracking performance in a period of fixed-time. Simulation results test the feasibility of the presented algorithm.

The multi-dimensional force/torque decoupling and calibration is extremely crucial to increase the accuracy of the Wheel Force Transducer/Sensor (WFT). A novel interpretable nonlinear decoupling and calibration approach to WFT is presented. A physical interpretable prime-error framework is developed such that the linear prime part accounts for most force-voltage responses while the nonlinear error part accounts for the gross error deviation. The conventional least-square decoupling is improved with the delicate nonlinear error modeling using a polynomial base module and a hyperbolic activation function. The developed framework is proved to be mathematically solvable and physically feasible by a two-step calibration scheme. A two-axis WFT is tested and compared with the proposed interpretable nonlinear decoupling model (IND), the least-square-based method (LSM), and the error-based neural network model (eNN). Results demonstrate that the proposed IND provides an accurate, practical, and effective scheme for modeling and calibrating WFTs and maintains a good balance among accuracy, generalization ability, and computational efficiency for real applications.

Emergency events such as fire, flood and COVID-19 occurred in urban rail transit (URT) usually triggered chain effect and evolved into huge disaster. This kind of chain with complexity and uncertainty evolution brought great challenges to the safety management of the system. Thus, the coupling effects of emergencies and then its relationship with the chain evolution is necessary to analyze emphatically. A Graph Evaluation and Review Technique Simulation (GERTS) evolution network is firstly constructed to describe the coupling effect and chain evolution of emergencies. Then, considering the internal and external influencing factors of the emergency chain, a dynamic evolution model of the emergency chain based on Coupled Map Lattice (CML) is proposed. This paper takes fire chain of URT as an example to simulate the evolution process of emergency chain, and analyze the impact of different coupling effects and various influencing factors on the evolution of emergency chain. The results of numerical simulation show that the AND-coupling can significantly inhibit the evolution of emergency events, while the OR-coupling and CO-coupling can expand the impact scope of emergency events. In addition, the evolution speed of emergency events can be controlled by increasing the coupling action time and improving the URT repair ability. When an emergency event occurs, the analysis of coupling effect and the accompanied chain evolution will help managers to make scientific judgment on the development trend of the emergency events and make targeted emergency defense measures.

The consensus problem of the multi-agent systems with uncertainty and denial-of-service attack is studied. The interval observer control method is utilized to reduced the influence of unknown external disturbances. By introducing sliding mode term, the observation error of the system is released and the robustness is improved. Using the coordination theory, the control algorithm based on the sliding mode interval observer drive the uncertain system to achieve the coordinated consensus. In order to make the multi-agent systems achieve asymptotically consensus under denial-of-service attack, an attack time security protocol based on persistent dwell time scheme is proposed. Finally, the simulation results are given to demonstrate the validity and superiority of the proposed method.

Brain–machine fusion, also known as hybrid intelligence or brain–computer interface (BCI), is considered one of the most promising technologies of the 21st century. Its potential impact spans a wide range of disciplines, including cognitive science, information science, artificial intelligence, biology, neuroscience, and engineering. The research in this field aims to seamlessly integrate biological intelligence (i.e., the human brain) with machine intelligence (computers or robots) to create a new, powerful form of hybrid intelligence that far surpasses the limitations of current biological and machine intelligence systems. Brain–machine fusion not only signifies the convergence of cutting-edge science and technology but also heralds a new era in which the way humans interact with machines undergoes a profound transformation. The research in this field delves deep into the understanding of human thought processes and cognition, as well as the creation of novel sensory and motor channels to facilitate more natural and intuitive interactions. The scope of brain–machine fusion research extends beyond mere information exchange, encompassing the integration of emotions and motivations. Understanding and interpreting a user’s emotional state and motivations are crucial for optimizing the performance of fusion systems, aiding in better meeting user needs and providing more personalized experiences. A key objective is enhancing a user’s operational capacity in handling complex tasks. This can encompass highly intricate decision making, problem solving, and task execution, with broad applications in fields, such as healthcare, military, industry, and entertainment. Furthermore, brain–machine fusion necessitates the development of cognitive interaction models that can adapt actively to a user’s cognitive characteristics and integrate with machine learning algorithms to achieve personalized adaptability in intelligent systems, thereby enhancing the level of interaction between the user and the system.

This brief focuses on the issue of fuzzy finite-time position tracking control for single-link flexible-joint robotic systems subject to multiple actuator constraints. At first, fuzzy logic systems are invoked to estimate completely unknown nonlinear functions, which can appropriately overcome heavy calculations. Next, the inherent computational complexity problem is eliminated via adopting command filter technology and the correlative error compensation mechanism is exploited to mitigate the influence of the errors brought by the filter. Further, the developed controller not only assures the semi-global finite-time stable of the controlled system, but also makes the tracking error enter a small region around the origin within fast finite time. The significance and potential of the presented control technique can be testified through simulation results.

In order to enable intelligent robots to recognize unknown objects as accurately as human beings, object perception research is of great significance in service and industrial robot application scenarios. However, object perception using spectral measurements under few-shot learning usually leads to a poor result because of inadequate training samples. To overcome this problem, this work proposes a novel few-shot learning with coupled dictionary learning (FSL-CDL) framework. First, a hybrid feature fusion method is developed to extract the multiple dimension-reduced features of original spectral measurements to build the hybrid features. Then, based on the hybrid features, a multitask coupled learning method is developed to effectively recognize unknown objects under few-shot learning. In this method, two coupling patterns, i.e., interspectroscopy coupling and intraspectroscopy coupling, effectively bridge the gap between two spectral measurements. Finally, the proposed FSL-CDL is compared with other advanced algorithms on the SMM50 dataset, and reaches 97.5% and 98.4% recognition accuracy under one-shot and five-shot learning, respectively, which are better than other algorithms. Besides, FSL-CDL can be extended to other perception tasks which contains multiple heterogeneous measurements.

Graphical Abstract

This paper focuses on the problem of distributed fault detection and leader-following output consensus for heterogeneous multiagent systems subject to deception attacks. During the information exchange and dissemination, a malicious attacker can make full use of specialized computer technology and launch stochastic deception attacks against some vulnerable agents over the network. The attack signals in actual operation tend to be energy-constrained, and Bernoulli distribution can be used to describe the random features. Taking the attack information into account, the distributed fault detection observer and the dynamic consensus compensator are designed in two separate steps. In order to reduce unnecessary information transmission, a dynamic event-triggered mechanism with output-dependent threshold is introduced to the adjustment of consensus protocol. According to Lyapunov stability theory and linear matrix inequality (LMI) techniques, sufficient conditions are derived for developing the model gains of the observer and the compensator. Finally, a simulation example of RLC circuit systems is provided to illustrate the effectiveness of the obtained theoretical results.

Strong and ultrastrong coupling between intersubband transitions in quantum wells and cavity photons have been realized in mid-infrared and terahertz spectral regions. However, most previous works employed a large number of quantum wells on rigid substrates to achieve coupling strengths reaching the strong or ultrastrong coupling regime. In this work, we experimentally demonstrate ultrastrong coupling between the intersubband transition in a single quantum well and the resonant mode of photonic nanocavity at room temperature. We also observe strong coupling between the nanocavity resonance and the second-order intersubband transition in a single quantum well. Furthermore, we implement for the first time such intersubband cavity polariton systems on soft and flexible substrates and demonstrate that bending of the single quantum well does not significantly affect the characteristics of the cavity polaritons. This work paves the way to broaden the range of potential applications of intersubband cavity polaritons including soft and wearable photonics.

In order to help robots understand and perceive an object's properties during noncontact robot-object interaction, this article proposes a deeply supervised subspace learning method. In contrast to previous work, it takes the advantages of low noise and fast response of noncontact sensors and extracts novel contactless feature information to retrieve cross-modal information, so as to estimate and infer material properties of known as well as unknown objects. Specifically, a depth-supervised subspace cross-modal material retrieval model is trained to learn a common low-dimensional feature representation to capture the clustering structure among different modal features of the same class of objects. Meanwhile, all of unknown objects are accurately perceived by an energy-based model, which forces an unlabeled novel object's features to be mapped beyond the common low-dimensional features. The experimental results show that our approach is effective in comparison with other advanced methods.

Most existing virtual assembly technologies cannot effectively unify the operating and feedback areas in both visualization and haptics with a large mobile workspace. Therefore, it is difficult to ensure high interactive naturalness and operational efficiency in a large assembly space. To address these problems, we propose a natural mobile interactive virtual assembly method with bare hand manipulation and unrestricted force feedback (MIVAM-BHM-UFF), covering a mobile interaction method, an unrestricted force feedback (UFF) method, and a bare hand manipulation (BHM) method. The mobile interaction method is developed to automatically track and capture the operator’s gestures in a moving way, expanding the interactive space. The UFF method is designed to provide a noncontact electromagnetic feedback force, eliminating the limitation of the traditional force feedback device and improving the interactive naturalness. A BHM method is proposed to improve interactive efficiency by unifying the operational area and feedback area. To verify the effectiveness of MIVAM-BHM-UFF, five assembly experiments were carried out. Experimental results indicate that compared with four assembly techniques, MIVAM-BHM-UFF shows better user experiences in the virtual assembly processes.

Reliable extrinsic calibration is critical to fuse camera and inertial measurement unit (IMU), which are usually used in stereo visual-inertial odometry (VIO), however, it is difficult to obtain extrinsic parameters in practice. This article proposes a stereo VIO with the capability of calibrating the unknown extrinsic parameters online. The initial values of IMU-camera and camera–camera transformations are estimated during the initial process, which fully leverages commonly observed features between stereo cameras. The pose and velocity are estimated and the extrinsic parameters are jointly refined. In addition, the accelerometer and gyroscope biases, and gravity direction are taken into account. The proposed VIO scheme has been demonstrated to be effective without prior knowledge of extrinsic parameters.

Connectionist artificial intelligence (AI) can power many critical tasks for connected and autonomous vehicles (CAVs). However, connectionist AI lacks interpretability and usually needs large amount of data for learning. A Markov logic network (MLN), which combines first-order logic (FOL) with statistical learning, learns weighted FOL formulas for inference. MLNs can incorporate domain expert knowledge in the form of FOL formulas to achieve data-efficient learning and transparent decision process. In this paper, we propose a hybrid driving decision-making system, which integrates a MLN module and a deep Q-network (DQN) for enhanced driving safety. The MLN module evaluates the safety of ranked actions from DQN to reduce potential collisions. A collective MLN (Co-MLN) learning algorithm is proposed and it enables CAVs collectively learn a global MLN model for safe state transitions, given distributed small amount of noisy data. A hybrid DQN-MLN learning algorithm is also developed for CAVs to collectively learn to drive in new driving environments. Simulations performed using a highway driving simulator show that the proposed Co-MLN algorithm is highly data-efficient and the learned hybrid driving system can effectively reduce collisions. In addition, the learned MLN module provides transparency for safety-critical driving decisions.

Vision-based localization and mapping can be easily affected by unstable feature tracking and illumination variations. To address these problems, we propose a point-based stereo visual odometry (VO) system with image brightness adjustment and feature tracking prediction. The system incorporates two threads that run in parallel: front-end and back-end. The front-end thread performs brightness adjustment, feature tracking, and motion estimation between frames. When the brightness of image changes significantly, a cumulative gray-scale histogram is used to estimate the exposure of the camera and adjust the brightness of the image. Additionally, a constant acceleration motion model and stereo geometric constraint are used to predict the location of feature points in the target image, providing a reliable initial guess for the Lucas–Kanade (LK) optical flow tracker. In order to improve the accuracy and reduce computational complexity, the back-end performs a sliding window bundle adjustment (BA) to achieve optimal camera poses and landmark positions. Experiments on publicly available datasets indicate that the proposed scheme has a better performance than state-of-the-art stereo VO.

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. By using the Lyapunov stability theory, all the signals of the controlled system can be semiglobal finite-time stable in probability (SGFSP) while the system is imposed with multiple actuator constraints. In the meantime, the problem of “complexity explosion” is avoided. Two simulation examples are given to demonstrate the validity of the presented strategy.

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. Different from existing results on similar topics, we present the main result based on a unified Lyapunov–Krasovskii function (LKF). In this way, the frequency restriction can be removed and the analysis complexity can be simplified. A numerical example is provided to verify the proposed results.

Engineered destruction of target proteins by recruitment to the cell’s degradation machinery has emerged as a promising strategy in drug discovery. The majority of molecules that facilitate targeted degradation do so via a select number of ubiquitin ligases, restricting this therapeutic approach to tissue types that express the requisite ligase. Here, we describe a new strategy of targeted protein degradation through direct substrate recruitment to the 26S proteasome. The proteolytic complex is essential and abundantly expressed in all cells; however, proteasomal ligands remain scarce. We identify potent peptidic macrocycles that bind directly to the 26S proteasome subunit PSMD2, with a 2.5-Å-resolution cryo-electron microscopy complex structure revealing a binding site near the 26S pore. Conjugation of this macrocycle to a potent BRD4 ligand enabled generation of chimeric molecules that effectively degrade BRD4 in cells, thus demonstrating that degradation via direct proteasomal recruitment is a viable strategy for targeted protein degradation.

Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiogram (ECG) signals to enhance detection performance under noise conditions. To address the issues, this article proposes a new type of the deep learning (DL) framework based on EEG and ECG called the product fuzzy convolutional network (PFCN). It should be noted that this article first investigates how to fuse EEG and ECG signals to deal with driving fatigue detection under noise conditions in both simulated and real-field driving environments. Specifically, the PFCN includes three subnetworks. The first uses a fuzzy neural network (FNN) with feedback and a product layer, effectively capturing the particularity and temporal variation of high-dimensional EEG signals and reducing the time-space complexity. The second subnetwork uses a 1-D convolution to convert the ECG data into feature sequences, providing high accuracy and low computational complexity in ECG data classification. The third subnetwork proposes a fusion-separation mechanism to effectively fuse the extracted ECG and EEG features, suppressing the noise interference and ensuring higher detection accuracy. To evaluate the performance of PFCN, a series of experiments has been set up in both simulated and real-field driving environments. The results indicate that the proposed PFCN model has better robustness and detection accuracy compared with several mainstream fatigue detection models.

Imbalanced problems are a significant challenge in data-driven diagnosis technology, as the data collected from the scenario of engineering are usually imbalanced. Imbalanced data pose a negative effect on classification algorithms and reduce the recognition performance of the minority samples. To address this challenge, ensemble classifiers have gained significant attention due to their superior performance compared with the individual classifiers. In this article, we provide improvements at both the data and algorithm levels using the stacking ensemble. At the data level, a data-space balanced partition (DSP) method based on undersampling is proposed, which recursively partitions the imbalanced data space into relatively balanced data subspaces. At the algorithm level, we propose a cost-sensitive stacking approach, which adjusts the weights of different categories to enhance the algorithm’s attention to the minority class. Based on these two improvements, a novel classification method for imbalanced problems, Data-Space balanced Partition based on Cost-sensitive Stacking learning (DPCStacking), is proposed. The proposed method is evaluated on 38 imbalanced datasets from KEEL. The experimental results demonstrate the effectiveness of the proposed improvements. Compared with the other six algorithms specifically designed to address imbalanced problems, the DPCStacking algorithm achieves the highest average accuracy (AAcc) and true positive rate (TPR) of 0.876 and 0.876, respectively.

While the sensors of current electroencephalography (EEG) wearables can be miniaturized such that they can be fit into an in-ear hearable device, the brain monitoring based on the state-of-art ear-EEG techniques has several drawbacks such as customization for one individual, time-consuming fabrication process, and short life cycle. To deal with this problem, a new method to design a sensor with one-channel dry electrode embedded in a generic substrate is proposed. The electrical characterizations were evaluated with 15 subjects and five EEG paradigms, in which ear-EEG and conventional scalp EEG were recorded simultaneously. Recordings from the generic dry-contact ear-EEG sensor were compared to conventional scalp EEG recordings. Reliable responses were extracted from the ear-EEG for different paradigms, albeit weaker in amplitude than from scalp EEG. The results demonstrate the feasibility of the designed generic ear-EEG sensor and its potential to achieve sustained, ease-of-use, cost-effective, and off-the-shelf ear-EEG recording use.

Most of the existing virtual acupuncture methods cannot allow operators to interact naturally in a large area. To address this problem, this article proposes a mobile natural human–robot interaction interface for virtual Chinese acupuncture teaching, including an automatic hand tracking method and an acupuncture interaction method combining vision and force feedback. The automatic hand tracking method can automatically track and capture the operator’s hand movements, allowing the operator to perform acupuncture in a large area. The acupuncture interaction method combining vision and force reproduces the real acupuncture operation mode. The proposed interaction method provides the operator with visual feedback and force feedback without limitation, which improves the naturalness and authenticity of the operation. Through a series of experiments, the proposed method was proven to be more natural and efficient in the virtual acupuncture teaching scene from the point view of interaction. The operators achieve a stronger sense of immersion using this method.

For the purpose of avoiding injury and realizing precise operations, the multilateral teleoperation system is the most efficient way to transport trace toxic or radioactive substances, to perform minimally invasive surgery, etc. It is essential to enhance the transparency of a multilateral teleoperation system including multiple masters and the single slave manipulator. However, there are few researchers focus on the allocation of the contact force of the single slave manipulator to different master manipulators. In this paper, we firstly introduce the concept of force translation for teleoperation systems consisting of dual hand master (left and right hands) manipulators and a single slave manipulator. Force translation reflects how the impedance on the single slave side is translated or allocated to contact forces on different master sides. To maintain the stability of the system and to improve transparency, we elucidate the mechanism of the force translation and analyze the relation among masters and the slave. Furthermore, the force translation mechanism is analyzed through numerical simulations and CHAI 3D virtual physical simulations. It is used to propose a multilateral impedance control, and the Lyapunov function is used to analyze the system stability. The results of numerical simulations and real robot experiments verify the effectiveness of the proposed control methods based on the proposed force translation mechanism.