Notes

Analysis along with new confirmation in the qualities involving asymmetric multi-stage water guiding body piezoelectric pump.
Identification of an Brand new Mutation inside RSK2, the particular Gene regarding Coffin-Lowry Malady (CLS), in Two Linked People along with Slight and Atypical Phenotypes.
Various employment methods lead to different contribution proportions inside a web-based examine, but also in similar complying.
This article investigates the problem of event-triggered secure path tracking control of autonomous ground vehicles (AGVs) under deception attacks. Mezigdomide To relieve the burden of the shareable vehicle communication network and to improve the tracking performance in the presence of deception attacks, a learning-based event-triggered mechanism (ETM) is proposed. Different from existing ETMs, the triggering threshold of the proposed mechanism can be dynamically adjusted with conditions of the latest vehicle state. Each vehicle in this study is deemed as an agent, under which a novel control strategy is developed for these autonomous agents with deception attacks. With the assistance of Lyapunov stability theory, sufficient conditions are obtained to guarantee the stability and stabilization of the overall system. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed theoretical results.This article investigates the adaptive learning control problem for a class of nonlinear autonomous underwater vehicles (AUVs) with unknown uncertainties. The unknown nonlinear functions in the AUVs are approximated by radial basis function neural networks (RBFNNs), in which the weight updating laws are designed via gradient descent algorithm. The proposed gradient descent-based control scheme guarantees the semiglobal uniform ultimate boundedness (SUUB) of the system and the fast convergence of the weight updating laws. In order to reduce the computational burden during the backstepping control design process, the command-filter-based design technique is incorporated into the adaptive learning control strategy. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.For full-state constrained nonlinear systems with input saturation, this article studies the output-feedback tracking control under the condition that the states and external disturbances are both unmeasurable. A novel composite observer consisting of state observer and disturbance observer is designed to deal with the unmeasurable states and disturbances simultaneously. Distinct from the related literature, an auxiliary system with approximate coordinate transformation is used to attenuate the effects generated by input saturation. Then, using radial basis function neural networks (RBF NNs) and the barrier Lyapunov function (BLF), an opportune backstepping design procedure is given with employing the dynamic surface control (DSC) to avoid the problem of ``explosion of complexity.'' Based on the given design procedure, an output-feedback controller is constructed and guarantees all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Mezigdomide It is shown that the tracking error is regulated by the saturated input error and design parameters without the violation of the state constraints. Mezigdomide Finally, a simulation example of a robot arm is given to demonstrate the effectiveness of the proposed controller.With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents to gradually approach the final collective objective by continuously learning from the environment based on their individual observations. In this regard, independent reinforcement learning (IRL) is often deployed in multiagent collaboration to alleviate the problem of a nonstationary learning environment. However, behavioral strategies of intelligent agents in IRL can be formulated only upon their local individual observations of the global environment, and appropriate communication mechanisms must be introduced to reduce their behavioral localities. In this article, we address the problem of communication between intelligent agents in IRL by jointly adopting mechanisms with two different scales. For the large scale, we introduce the stigmergy mechanism as an indirect communication bridge between independent learning agents, and carefully design a mathematical method to indicate the impact of digital pheromone. For the small scale, we propose a conflict-avoidance mechanism between adjacent agents by implementing an additionally embedded neural network to provide more opportunities for participants with higher action priorities. In addition, we present a federal training method to effectively optimize the neural network of each agent in a decentralized manner. Finally, we establish a simulation scenario in which a number of mobile agents in a certain area move automatically to form a specified target shape. Extensive simulations demonstrate the effectiveness of our proposed method.Deep encoder-decoders are the model of choice for pixel-level estimation due to their redundant deep architectures. Yet they still suffer from the vanishing supervision information issue that affects convergence because of their overly deep architectures. link2 In this work, we propose and theoretically derive an enhanced deep supervision (EDS) method which improves on conventional deep supervision (DS) by incorporating variance minimization into the optimization. A new structure variance loss is introduced to build a bridge between deep encoder-decoders and variance minimization, and provides a new way to minimize the variance by forcing different intermediate decoding outputs (paths) to reach an agreement. We also design a focal weighting strategy to effectively combine multiple losses in a scale-balanced way, so that the supervision information is sufficiently enforced throughout the encoder-decoders. To evaluate the proposed method on the pixel-level estimation task, a novel multipath residual encoder is proposed and extensive experiments are conducted on four challenging density estimation and crowd counting benchmarks. link2 The experimental results demonstrate the superiority of our EDS over other paradigms, and improved estimation performance is reported using our deeply supervised encoder-decoder.In this article, we study the leader-following practical attitude consensus problem of a group of multiple uncertain rigid spacecraft systems over jointly connected networks by a distributed event-triggered control law. We first establish a lemma that allows the problem to be converted to a distributed practical stabilization problem of a well-defined uncertain dynamical system. Then, we combine the adaptive distributed observer technique and the adaptive control technique to design an event-triggered adaptive control law and an event-triggered mechanism to solve our problem. The effectiveness of our design is illustrated by a numerical example.Benefit from avoiding the utilization of labeled samples, which are usually insufficient in the real world, unsupervised learning has been regarded as a speedy and powerful strategy on clustering tasks. However, clustering directly from primal data sets leads to high computational cost, which limits its application on large-scale and high-dimensional problems. link2 link3 Recently, anchor-based theories are proposed to partly mitigate this problem and field naturally sparse affinity matrix, while it is still a challenge to get excellent performance along with high efficiency. To dispose of this issue, we first presented a fast semisupervised framework (FSSF) combined with a balanced K-means-based hierarchical K-means (BKHK) method and the bipartite graph theory. Thereafter, we proposed a fast self-supervised clustering method involved in this crucial semisupervised framework, in which all labels are inferred from a constructed bipartite graph with exactly k connected components. The proposed method remarkably accelerates the general semisupervised learning through the anchor and consists of four significant parts 1) obtaining the anchor set as interim through BKHK algorithm; 2) constructing the bipartite graph; 3) solving the self-supervised problem to construct a typical probability model with FSSF; and 4) selecting the most representative points regarding anchors from BKHK as an interim and conducting label propagation. The experimental results on toy examples and benchmark data sets have demonstrated that the proposed method outperforms other approaches.Deep neural network-based systems are now state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from noise or adversarial examples) are often enough to change network-based decisions, which was recently shown to cause an autonomous vehicle to swerve into another lane. link3 In light of these dangers, numerous algorithms have been developed as defensive mechanisms from these adversarial inputs, some of which provide formal robustness guarantees or certificates. link3 This work leverages research on certified adversarial robustness to develop an online certifiably robust for deep reinforcement learning algorithms. The proposed defense computes guaranteed lower bounds on state-action values during execution to identify and choose a robust action under a worst case deviation in input space due to possible adversaries or noise. Moreover, the resulting policy comes with a certificate of solution quality, even though the true state and optimal action are unknown to the certifier due to the perturbations. The approach is demonstrated on a deep Q-network (DQN) policy and is shown to increase robustness to noise and adversaries in pedestrian collision avoidance scenarios, a classic control task, and Atari Pong. This article extends our prior work with new performance guarantees, extensions to other reinforcement learning algorithms, expanded results aggregated across more scenarios, an extension into scenarios with adversarial behavior, comparisons with a more computationally expensive method, and visualizations that provide intuition about the robustness algorithm.This article is concerned with the H∞ state estimation problem for a class of bidirectional associative memory (BAM) neural networks with binary mode switching, where the distributed delays are included in the leakage terms. A couple of stochastic variables taking values of 1 or 0 are introduced to characterize the switching behavior between the redundant models of the BAM neural network, and a general type of neuron activation function (i.e., the sector-bounded nonlinearity) is considered. In order to prevent the data transmissions from collisions, a periodic scheduling protocol (i.e., round-robin protocol) is adopted to orchestrate the transmission order of sensors. The purpose of this work is to develop a full-order estimator such that the error dynamics of the state estimation is exponentially mean-square stable and the H∞ performance requirement of the output estimation error is also achieved. Sufficient conditions are established to ensure the existence of the required estimator by constructing a mode-dependent Lyapunov-Krasovskii functional. Then, the desired estimator parameters are obtained by solving a set of matrix inequalities. Finally, a numerical example is provided to show the effectiveness of the proposed estimator design method.We study the distribution of successor states in Boolean networks (BNs). The state vector y is called a successor of x if y = F(x) holds, where x,y ∊ 0,1n are state vectors and F is an ordered set of Boolean functions describing the state transitions. This problem is motivated by analyzing how information propagates via hidden layers in Boolean threshold networks (discrete model of neural networks) and is kept or lost during time evolution in BNs. In this article, we measure the distribution via entropy and study how entropy changes via the transition from x to y, assuming that x is given uniformly at random. We focus on BNs consisting of exclusive OR (XOR) functions, canalyzing functions, and threshold functions. As a main result, we show that there exists a BN consisting of d-ary XOR functions, which preserves the entropy if d is odd and n > d, whereas there does not exist such a BN if d is even. We also show that there exists a specific BN consisting of d-ary threshold functions, which preserves the entropy if n mod d = 0.
Website: https://www.selleckchem.com/products/cc-92480.html