In training environments, the proposed policy utilizing a repulsion function and limited visual field achieved a success rate of 938%; this rate decreased to 856% in environments with numerous UAVs, 912% in high-obstacle environments, and 822% in environments with dynamic obstacles, according to extensive simulations. Moreover, the findings suggest that the proposed machine-learning approaches outperform conventional methods in complex, congested settings.
Employing adaptive neural networks (NNs), this article investigates the event-triggered containment control of nonlinear multiagent systems (MASs). For nonlinear MASs characterized by unknown nonlinear dynamics, immeasurable states, and quantized input signals, neural networks are selected for modeling unknown agents, and an NN state observer is subsequently developed, utilizing the intermittent output signal. Later, an innovative event-based mechanism, including the communication paths between sensor and controller, and between controller and actuator, was established. An output-feedback containment control scheme, employing an adaptive neural network and event-triggered communication, is designed. Leveraging adaptive backstepping control and first-order filter design principles, quantized input signals are represented as the sum of two bounded nonlinear functions. The controlled system has been shown to be semi-globally uniformly ultimately bounded (SGUUB), with followers residing entirely within the convex region enclosed by the leaders. A simulation is presented to verify the performance of the developed neural network containment system.
Leveraging a substantial collection of remote devices, federated learning (FL), a decentralized machine learning method, trains a joint model with the aid of dispersed training data. A major obstacle to achieving strong distributed learning performance in a federated learning network is the inherent system heterogeneity, arising from two factors: 1) the diverse computational capabilities of participating devices, and 2) the non-identical distribution of training data across the network. Past efforts on the complex FL issue, including FedProx, lack a clear definition, making it an open research problem. The system-heterogeneity issue within federated learning is addressed in this work, along with the proposal of a novel algorithm, federated local gradient approximation (FedLGA), designed to reconcile divergent local model updates using gradient approximation. FedLGA uses an alternate Hessian estimation method for this, adding only linear complexity to the aggregator's computational load. FedLGA, as we theoretically prove, delivers convergence rates on non-i.i.d. data when the device heterogeneity ratio is considered. Non-convex optimization with distributed federated learning exhibits a time complexity of O([(1+)/ENT] + 1/T) for complete device participation, and O([(1+)E/TK] + 1/T) for partial participation. E signifies epochs, T signifies total communication rounds, N signifies total devices and K signifies devices per round. Across numerous datasets, comprehensive experiments confirm FedLGA's effectiveness in dealing with the system heterogeneity issue, demonstrably outperforming existing federated learning methods. Compared to FedAvg, FedLGA's performance on the CIFAR-10 dataset exhibits an improvement in peak test accuracy, rising from 60.91% to 64.44%.
The safe deployment of multiple robots within an obstacle-dense and intricate environment is the central concern of this work. For the safe relocation of a group of velocity- and input-constrained robots between designated areas, a sophisticated formation navigation method capable of preventing collisions is critical. External disturbances and constrained dynamics create a challenging environment for safe formation navigation. A method based on a novel robust control barrier function is proposed, enabling collision avoidance under globally bounded control inputs. A nominal velocity and input-constrained formation navigation controller, utilizing relative position information from a predefined-time convergent observer, is first designed. Consequently, novel and sturdy safety barrier conditions are established to prevent collisions. Ultimately, a locally-defined quadratic optimization-based safe formation navigation controller is presented for each robotic unit. The proposed controller's effectiveness is clearly shown through simulation examples and comparison with existing research.
Potentially, fractional-order derivatives can optimize the functioning of backpropagation (BP) neural networks. The convergence of fractional-order gradient learning methods to true extreme points is, as demonstrated by several studies, potentially not guaranteed. Convergence to the precise extreme point is ensured through the truncation and modification of fractional-order derivatives. However, the true convergence capability of the algorithm is fundamentally tied to the assumption that the algorithm converges, a condition that compromises its practical feasibility. This article details the design of a novel truncated fractional-order backpropagation neural network (TFO-BPNN) and a novel hybrid version, the HTFO-BPNN, to resolve the preceding issue. narcissistic pathology In order to mitigate overfitting, a squared regularization term is appended to the fractional-order backpropagation neural network. Furthermore, a novel dual cross-entropy cost function is introduced and utilized as the loss function for the two separate neural networks. The penalty parameter modulates the influence of the penalty term, thereby mitigating the gradient vanishing issue. From a convergence perspective, the capability of the two proposed neural networks to converge is initially shown. Subsequently, a theoretical examination of convergence toward the actual extreme point is conducted. Finally, the simulation data convincingly illustrates the feasibility, high accuracy, and adaptable generalization performance of the introduced neural networks. Studies comparing the suggested neural networks with relevant methods reinforce the conclusion that TFO-BPNN and HTFO-BPNN offer superior performance.
Visuo-haptic illusions, another name for pseudo-haptic techniques, are based on the user's more prominent visual senses and how it impacts the perception of haptics. A perceptual threshold acts as a boundary for these illusions, forcing a separation between their virtual and physical representations. Pseudo-haptic methods have been instrumental in the study of haptic properties, including those related to weight, shape, and size. We examine the perceptual thresholds of pseudo-stiffness in a virtual reality grasping experiment within this paper. A study of 15 users evaluated the potential and extent of compliance induction on a non-compressible tangible object. The observed results highlight that (1) inducing compliance in solid physical objects is achievable and (2) pseudo-haptic approaches can successfully simulate stiffness levels exceeding 24 N/cm (k = 24 N/cm), replicating the feel of objects from the flexibility of gummy bears and raisins to the firmness of solid objects. Pseudo-stiffness efficiency gains are facilitated by the scale of the objects, but a primary correlation exists with the input force from the user. selleck Our findings, when viewed comprehensively, offer unique potential for simplifying the design of future haptic interfaces, and expanding the capabilities of passive VR props in terms of haptics.
To precisely locate a crowd, one must determine the position of each person's head. The variable distances of pedestrians relative to the camera result in a substantial disparity in the scales of objects within an image, termed the intrinsic scale shift. A key issue in crowd localization is the ubiquity of intrinsic scale shift, which renders scale distributions within crowd scenes chaotic. The paper investigates access methods to manage the chaotic scale distribution caused by inherent scale shifts. We propose Gaussian Mixture Scope (GMS) for the regularization of the 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. To counteract the disarray among sub-distributions, an alignment is then introduced. Despite the effectiveness of GMS in smoothing the data distribution, it separates the harder samples from the training set, leading to overfitting. We maintain that the impediment in the process of transferring latent knowledge exploited by GMS from data to model is to blame. Therefore, the role of a Scoped Teacher, bridging the gap in knowledge transfer, is proposed. Consistency regularization is further introduced to effect knowledge transformation. Consequently, further restrictions are implemented on Scoped Teacher to ensure consistent features between teacher and student interfaces. The superiority of our proposed GMS and Scoped Teacher method is supported by extensive experiments performed on four mainstream crowd localization datasets. Comparing our crowd locators to existing methods, our work showcases the best possible F1-measure across a four-dataset evaluation.
Emotional and physiological signal capture is fundamental to designing Human-Computer Interaction (HCI) systems that successfully integrate with the human emotional experience. Nonetheless, the issue of efficiently prompting emotional responses in subjects involved in EEG-based emotional research remains a challenge. bioinspired design To investigate the effectiveness of olfactory cues in modulating video-evoked emotions, we developed a novel experimental framework. The presentation of odors during different phases of the video stimuli allowed for the creation of four distinct categories: olfactory-enhanced videos, where odors were introduced during the initial or later stages (OVEP/OVLP), and traditional videos, where no odors were presented (TVEP/TVLP), or where odors were introduced during the initial or final stages (TVEP/TVLP). The differential entropy (DE) feature, in conjunction with four classifiers, was used to assess emotion recognition performance.