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This article investigates a Denial-of-Service (DoS) attack problem for nonlinear unknown discrete-time multiagent systems (MASs) to implement bipartite consensus tracking tasks with fixed and switching topologies. Firstly, an equivalent linearization data model of each agent is constructed using a pseudo partial derivative approach, where only one parameter needs to be estimated using input/output data of the controlled MASs. Meanwhile, the DoS attack behavior is described by a Bernoulli distribution process, and both cooperative and competitive relationships among agents are investigated. Moreover, an increment prediction compensator is designed to reduce the effect of DoS attacks. A data-based adaptive predictive bipartite consensus control algorithm is formulated. The corresponding theoretical analysis indicates that tracking errors of MASs with fixed and switching topologies converge to a small range around zero. Finally, several simulations and hardware tests further verify the proposed scheme's effectiveness. [ABSTRACT FROM AUTHOR]

Advanced intelligent control (AIC) is a rapidly evolving and complex field that poses significant challenges. By combining DLNet and the GJK algorithm, a two-level self-collision detection algorithm (DLGJK) is developed to solve real-time self-collision detection problems in a dual-manipulator system with fast-continuous and high-precision properties. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages in terms of the speed with which the roadmap is constructed, path planning and path length. "A Self-Collision Detection Algorithm of a Dual-Manipulator System Based on GJK and Deep Learning" [[15]] introduces AI technology into a control system based on the Gilbert-Johnson-Keerthi (GJK) algorithm. [Extracted from the article]

Deep neural networks have proven to be effective in solving computer vision and natural language processing problems. To fully leverage its power, manually designed network templates, i.e., Residual Networks, are introduced to deal with various vision and natural language tasks. These hand-crafted neural networks rely on a large number of parameters, which are both data-dependent and laborious. On the other hand, architectures suitable for specific tasks have also grown exponentially with their size and topology, which prohibits brute force search. To address these challenges, this paper proposes a quantum dynamic optimization algorithm to find the optimal structure for a candidate network using Quantum Dynamic Neural Architecture Search (QDNAS). Specifically, the proposed quantum dynamics optimization algorithm is used to search for meaningful architectures for vision tasks and dedicated rules to express and explore the search space. The proposed quantum dynamics optimization algorithm treats the iterative evolution process of the optimization over time as a quantum dynamic process. The tunneling effect and potential barrier estimation in quantum mechanics can effectively promote the evolution of the optimization algorithm to the global optimum. Extensive experiments on four benchmarks demonstrate the effectiveness of QDNAS, which is consistently better than all baseline methods in image classification tasks. Furthermore, an in-depth analysis is conducted on the searchable networks that provide inspiration for the design of other image classification networks. [ABSTRACT FROM AUTHOR]

The deterioration of numerous eye diseases is highly related to the fundus retinal structures, so the automatic retinal vessel segmentation serves as an essential stage for efficient detection of eye-related lesions in clinical practice. Segmentation methods based on encode-decode structures exhibit great potential in retinal vessel segmentation tasks, but have limited feature representation ability. In addition, they don't effectively consider the information at multiple scales when performing feature fusion, resulting in low fusion efficiency. In this paper, a newly model, named DEF-Net, is designed to segment retinal vessels automatically, which consists of a dual-encoder unit and a decoder unit. Fused with recurrent network and convolution network, a dual-encoder unit is proposed, which builds a convolutional network branch to extract detailed features and a recurrent network branch to accumulate contextual features, and it could obtain richer features compared to the single convolution network structure. Furthermore, to exploit the useful information at multiple scales, a multi-scale fusion block used for facilitating feature fusion efficiency is designed. Extensive experiments have been undertaken to demonstrate the segmentation performance of our proposed DEF-Net. [ABSTRACT FROM AUTHOR]

10.3390/machines9100216 5 Shen P., Wang Y.W., Chen Y., Fu P.Q., Zhou L.J., Liu L.J. Hydrodynamic Bearing Structural Design of Blood Pump Based on Axial Passive Suspension Stability Analysis of Magnetic-Hydrodynamic Hybrid Suspension System. Research and experiments on mechatronics systems, which are a synergistic integration of mechanical engineering, electronic control, and systems concepts, have contributed significantly to the design of systems, devices, processes, and products. Based on the analysis of the static moment safety protection of the human-machine linkage model, the motor driving force required in the rehabilitation process was calculated to achieve the purpose of rationalizing the rehabilitation movement of the patient's lower limb. In [[9]], the mechanism of the lower limb rehabilitation module was simplified and the geometric relationship of the human-machine linkage mechanism was deduced. [Extracted from the article]