This paper proposes a collaborative optimization design for a kind of centralized networked control system based on jitter. After the analysis of the network delay and jitter on the performance of the Train Networked Control System (TNCS) based on the MVB (Multifunction Vehicle Bus) network, the proposed strategy modifies the media allocating model of MVB directly related to the performance of the control system. Under the premise of ensuring the stability of the control system, and taking into account the impact of transmission jitter on the dynamic performance of the closed-loop control, this collaborative design method can minimize the network resource occupancy rate of the subsystem. Thus, it can overcome schedule failure in the traditional algorithm that excessively occupies network resources in order to reduce jitter. Finally, the authors present an algorithm based on EDA to find the optimal solution of the proposed strategy and illustrate the effectiveness of the strategy through numerical simulation and experimental tests.

Aiming at the difficulty of defect detection caused by the low contrast between defects such as scratches, deformation and foreign bodies on the surface of parts and the background, and the defects are greatly affected by the surrounding light, an accurate recognition method of low contrast defects based on improved YOLOv7 is proposed. A fusion Mosaic and MixUP online data enhancement method is proposed to expand the training sample data. The GAM attention module is added to the backbone network to enhance the feature extraction ability of low contrast defects, and SIoU loss function is used to focus on the accuracy of the model to accelerate the convergence speed of the model, and the fast suspected defect location is realized based on multi-camera. After focusing on the suspected defect position, the defect features are enhanced and accurately identified by rotating the 6RSS mechanism. Experiments show that the SIoU-YOLOv7-GAM algorithm shows better performance than the original YOLOv7 algorithm, and the average accuracy and recall rate are increased by 2.92 % and 5.02 %, respectively. The proposed multi-camera focusing detection method has a high recognition accuracy for low-contrast defects on the surface, and can eliminate the problem of defect error recognition to achieve accurate detection of low-contrast defects on the surface of parts.

From the perspective of collaborative design of control and scheduling, a software-defined networked collaborative control system (SD-NCCS) is established. On this basis, the system performance of SD-NCCS is analyzed and the average sensitivities of reference are used in utility function to evaluate the effects of network-induced delays. Moreover, the network pricing mechanism and game theory are introduced and a dynamic bandwidth resource allocation model of the overall control system for optimal performance is obtained. Thus, the problem of the network resource allocation of the SD-NCCS has been converted to be the problem of solving the Nash equilibrium point under the non-cooperative game model. Furthermore, the Nash equilibrium solution under this frame is obtained using the estimation distributed algorithm (EDA). Finally, a simple example is included to illustrate the performance of our scheme.

Titanium (Ti)-based metallic biomaterials (MBs) are traditionally employed as mechanical supports and constraints in clinical practice, owing to their superb comprehensive mechanical properties, great corrosion resistance, and good biocompatibility. Recently, Ti-based MBs have emerged as promising candidates for antitumor applications. These developments focus on the functionalization of Ti-based MBs to inhibit tumor propagation and recurrence. This work systematically examines the antitumor approaches of Ti-based MBs and categorizes them into physical and chemical approaches. Physical strategies, such as the photothermal and photocatalytic techniques, are usually related to material-specific properties. Chemical approaches often employ controlled local drug delivery (LDD) systems. Ti-based LDD systems enable the targeted release of chemotherapeutics, metal ions, or immunomodulatory agents at tumor sites. This review highlights the efficacy of these surface-functionalized Ti-based MBs against diverse tumors. Additionally, the challenges and prospects of antitumor Ti-based MBs are also discussed.

Aiming at the ineffective tracking problem of photovoltaic tracking system caused by cloud shading, an efficient and high-precision cloud trajectory prediction method is proposed. Firstly, the transmission method is used to filter the associated cloud type factors to reduce the computational complexity, and a GP-DCGAN gradient penalty deep convolution generation adversarial network is proposed to realize the generalization of image data. Secondly, by combining the alpha-IoU loss function with CA and CBAM attention mechanisms, an αCCB-YOLOv5s model is proposed to achieve accurate identification of clouds. Finally, based on the αCCB-YOLOv5s network, the DeepSORT module is added to realize multi-target tracking. Using a series of historical location information fed back by the tracking process, the Kalman filter algorithm is used to correct and predict the moving trajectory of the tracking cloud. The experimental results show that the detection categories of cloud type factors are reduced by 50% and the computational complexity is reduced by transmission method. At the same time, compared with the original YOLOv5s algorithm in cloud detection, the precision P of αCCB-YOLOv5s model is increased by 4.56%, the recall rate R increased by 5.48%, the mean average precision mAP increased by 4.4%, and the target detection performance is significantly improved. In addition, αCCB-YOLOv5s is used as a detector in the prediction model. The mean absolute error (MAE) and root mean square error (RMSE) are significantly reduced, and the prediction accuracy is greatly improved, which can lay a foundation for the follow-up intelligent photovoltaic tracking strategy research.

This letter presents a multi-stage solution to the calibration-target-assisted multistatic localization problem based on time delay (TD) measurements, in the presence of unknown receiver clock drifts and offsets caused by clock imperfections. In the first stage, the common clock offset is mitigated by differencing the direct and indirect-path TD measurements from the calibration target, providing a weighted least squares (WLS) estimate of the clock drift. In the second stage, the previously obtained drift estimate is used along with the direct-path measurements to produce the closed-form estimate of the transformed clock offset. In the third stage, localization is achieved by solving a constrained WLS problem constructed using these estimates together with the indirect-path TD measurements related to the unknown target. Simulations confirm that, in the small-noise/large-receiver regime, the resulting target position estimate approaches the Cramèr–Rao lower bound (CRLB) corresponding to the joint estimator that utilizes the complete measurement set.