Based on extensive simulations, the proposed policy, incorporating a repulsion function and a limited visual field, demonstrates a 938% success rate in training environments, dropping to 856% in environments with a high density of UAVs, 912% in environments with a high number of obstacles, and 822% in environments with dynamic obstacles. Moreover, the findings suggest that the proposed machine-learning approaches outperform conventional methods in complex, congested settings.
This article focuses on the adaptive neural network (NN) event-triggered approach to containment control in a class of nonlinear multiagent systems (MASs). In light of the unknown nonlinear dynamics, immeasurable states, and quantized input signals within the analyzed nonlinear MASs, neural networks are selected to model unknown agents, and an NN-based state observer is designed using the discontinuous output signal. Afterwards, an innovative, event-driven mechanism, involving sensor-to-controller and controller-to-actuator channels, was put into place. An adaptive neural network event-triggered output-feedback containment control scheme is proposed, which leverages adaptive backstepping control and first-order filter design techniques. The scheme dissects quantized input signals into the sum of two bounded nonlinear functions. It has been established that the controlled system satisfies semi-global uniform ultimate boundedness (SGUUB) conditions, and the followers' trajectories are constrained to the convex hull spanned by the leaders. As a final step, a simulation instance serves to confirm the effectiveness of the presented neural network confinement control approach.
Federated learning (FL), a decentralized machine-learning system, utilizes many remote devices to create a joint model, utilizing the distributed training data across those devices. The achievement of robust distributed learning in a federated learning network encounters a substantial hurdle in the form of system heterogeneity, which arises from two core aspects: 1) the differences in computational power among devices, and 2) the non-uniform distribution of data across the network's members. Earlier explorations of the diverse FL issue, like FedProx, are deficient in formalization, leaving this an open question. This paper introduces the concept of system-heterogeneous federated learning and proposes a new algorithm, federated local gradient approximation (FedLGA), to resolve the divergence among locally updated models via gradient approximation techniques. FedLGA's approach to achieving this involves an alternative Hessian estimation method, requiring only an added linear computational burden on the aggregator. Our theoretical findings confirm that FedLGA demonstrates convergence rates on non-i.i.d. datasets, even with a device-heterogeneous ratio influencing the model Considering distributed federated learning for non-convex optimization problems, the complexity for full device participation is O([(1+)/ENT] + 1/T), and O([(1+)E/TK] + 1/T) for partial participation. The parameters used are: E (local epochs), T (communication rounds), N (total devices), and K (devices per round). Extensive experimentation across diverse datasets demonstrates FedLGA's ability to effectively manage system heterogeneity, surpassing existing federated learning approaches. FedLGA’s application to the CIFAR-10 dataset shows a stronger performance than FedAvg, with a noticeable improvement in the peak testing accuracy from 60.91% to 64.44%.
In the present study, we address the secure deployment of multiple robots navigating a challenging environment filled with obstacles. To facilitate the secure movement of a team of robots operating under velocity and input constraints, a robust navigation method that prevents collisions within a formation is necessary. The problem of safe formation navigation is compounded by the interaction of constrained dynamics and disruptive external forces. A novel robust control barrier function-based method is presented for enabling collision avoidance, constrained by globally bounded control input. Starting with the design of a formation navigation controller, incorporating nominal velocity and input constraints, only relative position information from a pre-defined convergent observer was utilized. Thereafter, new and substantial safety barrier conditions are derived, ensuring collision avoidance. To conclude, a robot-specific safe formation navigation controller, founded on local quadratic optimization, is introduced. To effectively illustrate the proposed controller's performance, simulation examples and comparisons with existing results are included.
An increase in the performance of backpropagation (BP) neural networks may stem from the implementation of fractional-order derivatives. Several studies have reported that fractional-order gradient learning methods' convergence to actual extreme points might be problematic. To ensure convergence to the true extreme point, fractional-order derivatives are truncated and modified. However, the algorithm's true convergence capability hinges on its inherent convergence, a factor that restricts its real-world applicability. This article introduces a novel truncated fractional-order backpropagation neural network (TFO-BPNN) and a novel hybrid TFO-BPNN (HTFO-BPNN) for tackling the aforementioned issue. medial superior temporal A squared regularization term is strategically introduced into the fractional-order backpropagation neural network framework to minimize overfitting. In the second place, a novel dual cross-entropy cost function is suggested and implemented as the loss function for the two neural networks. The penalty parameter facilitates adjustment of the penalty term's contribution, thus reducing the gradient vanishing effect. The convergence capabilities of the two proposed neural networks are initially demonstrated with respect to convergence. The theoretical analysis extends to a deeper examination of the convergence to the actual extreme point. Ultimately, the simulation's outcomes effectively portray the applicability, high accuracy, and robust generalization properties of the designed neural networks. Comparative evaluations of the suggested neural networks alongside comparable methods further bolster the prominence of TFO-BPNN and HTFO-BPNN.
Pseudo-haptic techniques, or visuo-haptic illusions, deliberately exploit the user's visual acuity to distort their sense of touch. The illusions, owing to a perceptual threshold, are confined to a particular level of perception, failing to fully encapsulate virtual and physical engagements. Studies of haptic properties, such as weight, shape, and size, have extensively utilized pseudo-haptic methodologies. Estimating perceptual thresholds for pseudo-stiffness in virtual reality grasping is the focus of this paper. A user study (n = 15) was undertaken to evaluate the potential for and level of compliance achievable with a non-compressible tangible object. Our findings demonstrate that (1) a rigid, physical object can be influenced into complying and (2) pseudo-haptic methods can replicate stiffness exceeding 24 N/cm (k = 24 N/cm), a range encompassing materials like gummy bears and raisins, extending up to rigid solids. The efficiency of pseudo-stiffness is amplified by the size of the objects, although it is primarily influenced by the applied force from the user. β-lactam antibiotic By combining our results, we discover fresh opportunities to streamline the creation of future haptic interfaces and to expand the tactile capabilities of passive VR props within virtual reality.
Estimating the precise head location of each individual in a crowd is the core of crowd localization. Due to the varying distances of pedestrians from the camera, significant discrepancies in the sizes of objects within a single image arise, defining the intrinsic scale shift. The ubiquity of intrinsic scale shift in crowd scenes, causing chaotic scale distributions, makes it a primary concern in accurate crowd localization. The paper concentrates on access to resolve the problems of scale distribution volatility resulting from inherent scale shifts. Gaussian Mixture Scope (GMS) is proposed as a method to regularize this chaotic scale distribution. Applying a Gaussian mixture distribution, the GMS dynamically adapts to variations in scale distributions, and further breaks down the mixture model into sub-normal distributions for the purpose of regulating the chaotic elements within. A regularization mechanism, in the form of an alignment, is subsequently introduced to manage the inherent chaos within sub-distributions. Although GMS effectively regularizes the data distribution, its impact on the training set's difficult instances results in overfitting. We argue that the impediment of transferring the latent knowledge exploited by GMS from data to the model accounts for the blame. Subsequently, a Scoped Teacher, embodying the role of a translator in the knowledge transition process, is introduced. In addition, consistency regularization is implemented to facilitate the transformation of knowledge. In order to accomplish this, additional limitations are imposed on Scoped Teacher to maintain consistent features for teachers and students. By implementing GMS and Scoped Teacher on four mainstream crowd localization datasets, our extensive experiments showcased the superiority of our methodology. Our work significantly outperforms existing crowd locators, attaining the best F1-measure across all four datasets.
The collection of emotional and physiological signals is indispensable for designing Human-Computer Interaction (HCI) systems that can acknowledge and react to human emotions. Despite progress, inducing subjects' emotions in EEG experiments related to emotion remains a difficult task. Selleck UK 5099 In this experimental investigation, a novel method was established to evaluate how odor presentation dynamically impacts video-induced emotions. This approach defined four stimulus categories: odor-enhanced videos with odors introduced during the initial or subsequent stages (OVEP/OVLP), and traditional videos with either no odors or odors presented early or late (TVEP/TVLP). In order to ascertain the proficiency of emotion recognition, the differential entropy (DE) feature was used in conjunction with four classifiers.