Traditional chicken disease diagnosis methods rely on manual observation and experiential judgment, which are inefficient and susceptible to subjective factors. This paper aims to construct a chicken disease diagnostic model with deep learning algorithm for enhancing the accuracy and efficiency. Firstly, this paper proposes a chicken disease diagnostic model based on YOLOv8 algorithm. An SE-attention mechanism is designed for YOLOv8 structure to improve the detection accuracy. The SE-Attention based YOLOv8 detection model can identify and classify diseases by analyzing the feces images of chickens. Experiments on chicken disease diagnosis are performed to validate the proposed model's feasibility and effectiveness. Ablation study is constructed to validate the advantages of the SE-attention mechanism.

Abstract Synthetic images facilitate stereo matching. However, synthetic images may suffer from image distortion, domain bias, and stereo mismatch, which would significantly restrict the widespread use of stereo matching models in the real world. The first goal in this paper is to synthesize real‐looking images for minimizing the domain bias between the synthesized and real images. For this purpose, sharpened disparity maps are produced from a mono real image. Then, stereo image pairs are synthesized using these imperfect disparity maps and the single real image in the proposed pipeline. Although the synthesized images are as realistic as possible, the domain styles of the synthesized images are always very different from the real images. Thus, the second goal is to enhance the domain generalization ability of the stereo matching network. For that, the feature extraction layer is replaced with a teacher–student model. Then, a constraint of binocular contrast features is imposed on the output of the model. When tested on the KITTI, ETH3D, and Middlebury datasets, the accuracy of the method outperforms traditional methods by at least 30%. Experiments demonstrate that the approaches are general and can be conveniently embedded into existing stereo networks.

A robust model predictive control (RMPC) framework is proposed for nonlinear cyber-physical systems (CPSs) with input saturation and energy-bounded disturbance in this article. The coexistence of Lipschitz nonlinearity and actuator saturation remains a challenging problem in controller design. In our article, a major concern is to address the incorporation of bipartite nonlinear characteristics and exogenous disturbance under RMPC scheme. Via the saturation relaxation and Lipschitz condition, the conversion of a tractable linear matrix inequalitie-constrained problem is proposed in a less conservative way. For the purpose of enhancing the robustness and disturbance rejection, the proposed RMPC framework is associated with mixed$H_{2}$/$H_{\infty }$requirements. In particular, the algorithm is then cast into minimizing the upper bound of infinite-horizon cost function, and it is updated online for the linear control law. The computational feasibility is proved recursively, which is the key point in the practical implementation. Also, both the closed-loop stability and performance of CPS are derived. Eventually, the laboratory tank and reactor–separator process are presented to verify and illustrate the effectiveness of the proposed RMPC with mixed$H_{2}$/$H_{\infty }$performance.

Abstract This paper uses a fuzzy set scheme to present an adaptive fractional differential algorithm for image edge enhancement and texture preservation. In the proposed algorithm, an image's membership function and area feature are used to calculate the fuzzy set of images. The function of adaptive fractional differential order (FAFDO) can be constructed by making the linear transformation of the fuzzy set. Then, the fuzzy adaptive fractional differential mask (FAFDM) is obtained by substituting the FAFDO into the fractional differential mask. Finally, the image edge and texture are enhanced and preserved by applying airspace filtering of the FAFDM convolution. The experimental results show that, compared to fractional differential or fuzzy set‐based image enhancement algorithms, the proposed algorithm can adaptively enhance the image edge and preserve the image texture by analysing the fuzziness of the image itself.

This article develops a robust packetized predictive control framework to deal with the quantized-feedback control problem of networked systems subject to Markovian packet dropouts and input saturation. In the proposed framework, the Markov chain model of packet dropout is established from the link of the controller to the actuator. To deal with the quantized measurements, a robust packetized predictive control method is presented with a quantized-feedback law. The problem of unreliable transmission is addressed by proposing a packet dropout compensation strategy with a forgetting factor. An augmented Markovian jump system model is established to take the packet dropouts into account. The synthesis of packetized predictive control is then developed by minimizing a worst case cost function with respect to the model uncertainties. The recursive feasibility of the proposed controller design problem and the mean-square stability of the closed-loop systems are proved, respectively. The proposed packetized predictive control method is demonstrated by simulating a four-tank process system.

The application of cyber-physical system (CPS) and edge computing in smart logistics systems has greatly improved the work efficiency. This article mainly focuses on the planning and scheduling problems in the CPS-based multiple automatic guided vehicles logistics system. A virtual network map with uneven time constraints is first proposed, which realizes the control and interaction of actual work in the control layer. Then, an improved A* path planning algorithm based on the characteristics of the path network model is implemented. A novel traffic control method is added to the planning process to achieve time-sensitive and proactive scheduling and collision avoidance. Results of the numerical experiments reveal that the proposed CPS-based heuristic method could be effectively applied to the multiple automatic guided vehicles logistics system to further improve system security as well as work efficiency.

This paper proposes an adaptive explicit synchronization framework to address the cooperative control for heterogeneous uncertain dynamical networks under switching communication topologies. The main contribution is to develop an adaptive explicit synchronization algorithm, in which the synchronization state can be completely tracked by each agent in real time rather than only be measured after the synchronization process of all agents is over. By introducing appropriate assumptions, a class of adaptive explicit synchronization protocols is designed by using a combination of the virtual leader's states, the neighboring agents' relative information, distributed feedback gain, and distributed average weighted parameters. It is proved in the sense of Lyapunov that, if the dwell time is larger than a positive threshold, the cooperative control problem for the closed-loop heterogeneous uncertain dynamical networks under switching of strongly-connected communication topologies can be solved by the proposed adaptive explicit synchronization algorithm. Furthermore, by assuming that the topology is frequently strongly-connected, it shows that intermittent adaptive explicit synchronization can be achieved with well-designed control parameters. Two examples are presented to demonstrate the effectiveness of the proposed theory.