Long-wave infrared (LWIR) and visible (VIS) cameras can image information at different dimensions, but the way to calibrate these two types of cameras while registering and fusing the acquired images is difficult. We propose a calibration plate and a calibration method for thermal imaging and visible imaging to solve three problems: (1) the inability of the existing calibration plates to address LWIR and VIS cameras simultaneously; (2) severe heat interference in the calibration images of LWIR cameras; (3) difficulty in finding feature points for registration due to the different imaging spectra between thermal imaging and visible imaging. Simulation tests and error analysis show the error of outline central point computation is less than 0.1 pixel. Average errors of Euclidean distances from the margin outline scattered point sets of the closed circle and closed ellipse to the outline central points decrease by 10 and 9.9, respectively. The Mean Reprojection Error in the calibration of LWIR and VIS cameras are 0.1 and 0.227 pixels, respectively. Through image registration design and fusion experiments, the FMI_dct, MS-SSIM, Q_abf, SCD, and SSIM of the images fused after distortion correction are all higher than those of the images fused before distortion correction, with the highest increases being 4.6, 0.3, 3.1, 7.2, and 1.4. These results prove the effectiveness and feasibility of our method.

Although magnetic bearings are generally reliable, an important concern for machine applications is fault tolerance. The fault-tolerant operation of a magnetic bearing system can be realized through the identification and isolation of fault actuators and subsequent support reconstruction via residual actuators. In this study, a novel fault-diagnosis algorithm for electromagnetic actuators (EMAs) is proposed. The equivalent slope of the load current is defined as a fault-diagnosis threshold by theoretically analyzing the variation characteristics of the current in the modulation. The synchronous sampling of the current and its processing algorithm are designed. By combining these algorithms with the generalized bias current linearization theory reported previously, a fault-tolerant control (FTC) system for magnetic bearings is developed, and control of multi-position and multi-current loops is realized by using two digital signal processors (DSP). The FTC of the rotor motion and the fault-diagnosis of actuators are executed in parallel. The proposed control system is verified by the experiments, and experimental results demonstrate that the proposed system can cope with EMA fault in an extremely short time of less than 5 ms. Thus, the support can be reconstructed by employing the FTC to maintain rotor stability.

In this paper, the dynamics-based high-performance robot motion control technology has been mainly studied, and the overall structure is controlled via dynamics forward, given the nonlinearity, strong coupling and time-variability of robots. Considering the unavailability of precise robot model parameters and the uncertain disturbance in real operation, we put forward an active disturbance rejection control (ADRC) strategy based on dynamic feedforward, aiming to improve the control robustness and combining the simple structure, strong anti- disturbance ability, and no restriction from the control model of ADRC. Given the multi-joint coupling of robots, controlled decoupling is conducted by using dynamic characteristics. The ADRC cascade control structure and algorithm based on dynamic feedforward have been studied and the closed-loop stability of the system is investigated by analyzing the system dynamic linearization compensation and the anti-disturbance ability of the extended state observer. Experiments have shown the new strategy is more robust over uncertain disturbance than the conventional proportional-integral-derivative control strategy.

Background: Alzheimer’s disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. The therapy of AD depends heavily on an early diagnosis. In this study, our primary objective is to evaluate the classification framework, which combines graph theory and machine learning techniques for functional magnetic resonance imaging (fMRI), to distinguish AD, early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and healthy control (HC). Methods: A novel multi-feature selection method, incorporating the dual graph theoretical approach, is proposed for classification. This method utilizes three different feature selection methods after brain areas selection through graph-theory analyses in 96 subjects with brain parcellation by using the joint human connectome project multimodal parcellation (J-HCPMMP) of 180 areas per hemisphere. Results: The classification results show that the optimal features selected by the minimal redundancy maximal relevance (MRMR) based on support vector machine linear (SVM-linear) from graph measures for 36 areas of 360 areas. The classification accuracies for identifying HC vs. EMCI, HC vs. LMCI, HC vs. AD, EMCI vs. LMCI, LMCI vs. AD, and EMCI vs. AD, are 85.60%, 92.90%, 96.80%, 83.30%, 84.90% and 89.50%, respectively. Conclusion: The results indicate that the combination of graph measures and machine learning in fMRI connectivity analysis might be helpful for the diagnosis of AD, especially the use of local measures, which may better reflect functional changes in local brain regions because of cognitive impairment.

Three-dimensionally printed vascularized tissue, which is suitable for treating human cardiovascular diseases, should possess excellent biocompatibility, mechanical performance, and the structure of complex vascular networks. In this paper, we propose a method for fabricating vascularized tissue based on coaxial 3D bioprinting technology combined with the mold method. Sodium alginate (SA) solution was chosen as the bioink material, while the cross-linking agent was a calcium chloride (CaCl₂) solution. To obtain the optimal parameters for the fabrication of vascular scaffolds, we first formulated theoretical models of a coaxial jet and a vascular network. Subsequently, we conducted a simulation analysis to obtain preliminary process parameters. Based on the aforementioned research, experiments of vascular scaffold fabrication based on the coaxial jet model and experiments of vascular network fabrication were carried out. Finally, we optimized various parameters, such as the flow rate of internal and external solutions, bioink concentration, and cross-linking agent concentration. The performance tests showed that the fabricated vascular scaffolds had levels of satisfactory degradability, water absorption, and mechanical properties that meet the requirements for practical applications. Cellular experiments with stained samples demonstrated satisfactory proliferation of human umbilical vein endothelial cells (HUVECs) within the vascular scaffold over a seven-day period, observed under a fluorescent inverted microscope. The cells showed good biocompatibility with the vascular scaffold. The above results indicate that the fabricated vascular structure initially meet the requirements of vascular scaffolds.