The rudder system is extensively used in aerospace, ships, missiles and other safety demanding areas. Therefore, it is paramount to ensure that the performance of the system is optimal. Rudder system testing equipment is a special tool used to diagnose its failure. Traditional ones can only artificially analyze the massive and complex tested data. Due to the low-test degree of automation, the performance of such testing tools is limited. Aiming to address this shortcoming, we developed a new rudder system testing equipment with four independent loading platforms and intelligent data analysis systems. It sufficiently shortens the installation and commission time of pneumatic actuators and the processing time of the testing data which largely improves its performance and accuracy. Given the imbalanced nature of the data an adaptive sampling algorithm considering informative instances (ASCIN) leveraging the Support Vector Machine (SVM) is proposed to process the originally collected data. The optimal parameters in SVM and ASCIN are searched by Whale Optimization Algorithm (WOA). Experiments are designed to assess the performance of ASCIN in comparison with existing approaches in the area of imbalanced data learning. The results show that the algorithm developed in this study has higher performance relative to traditional approaches. The application of these intelligent algorithms in fault detection and location of rudder system overcomes the limitation of traditional testing equipment and provides a new concept for future research into more intelligent one.

Fiber Optic Gyroscope (FOG) is a new type of rotation sensor that plays an important role in navigation and guidance system. As the sense element of FOG, the fiber coil (FOC) performance directly affects the FOG measurement precision. Many factors influence the quality of FOC, ideal structure and constant small tension control in the winding process were key factors. In order to prevent FOC edge defect, we proposed piecewise variable speed winding method to make regular FOC structure more accessible. In view of the shortage of the common control method used in FOC winding, we optimized fuzzy PID by introducing domain adjusting factors and designed a new tension controller based on mechanical analysis. The optimization controller can adaptively adjust the control factors to ideal settings and ensure constant tension throughout all variable winding speeds stages even in the present of complex external disturbance. Comparative experiments shown that the new controller has a higher accuracy and better robustness than commonly used controller.

Blind deblurring of single image is a challenging image restoration problem. Recent various image priors have been successfully explored to solve this ill-posed problem. In this paper, based on the non-local self-similarity, we propose a novel method for blind image deblurring, which can simultaneously capture the intrinsic structure correlation and spatial sparsity of an image. Specifically, we use the hyper-Laplace prior to model the structure information of non-local similar patches, and embed it into the low-rank model as a smooth term of the energy equation. Since the established energy function is non-convex, an effective iterative optimization scheme is designed to effectively implement the proposed algorithm. In addition, we evaluate the proposed method for non-uniform deblurring problem. Extensive experimental results on both synthetic and real-world images show that the proposed method performs competitively against the state-of-the-art methods.

Time-domain induced polarization (TDIP) measurement is seriously affected by power line interference and other field noise. Moreover, existing TDIP instruments generally output only the apparent chargeability, without providing complete secondary field information. To increase the robustness of TDIP method against interference and obtain more detailed secondary field information, an improved dataprocessing algorithm is proposed here. This method includes an efficient digital notch filter which can effectively eliminate all the main components of the power line interference. Hardware model of this filter was constructed and Vhsic Hardware Description Language code for it was generated using Digital Signal Processor Builder. In addition, a time-location method was proposed to extract secondary field information in case of unexpected data loss or failure of the synchronous technologies. Finally, the validity and accuracy of the method and the notch filter were verified by using the Cole-Cole model implemented by SIMULINK software. Moreover, indoor and field tests confirmed the application effect of the algorithm in the fieldwork.

Electric servo system (ESS) is a servo mechanism in a control system of an aircraft, a ship, etc., which controls efficiency and directly affects the energy consumption and the dynamic characteristics of the system. However, the control performance of the ESS is affected by uncertainties such as friction, clearance, and component aging. In order to improve the control performance of the ESS, a control technology combining particle swarm optimization (PSO) and finite time servo system control (FTSSC) was introduced into ESS. In fact, it is difficult to know the uncertain physical parameters of the real ESS. In this paper, the genetic algorithm (GA) was introduced into PSO and the inertia weight was improved, which increased the parameter optimization precision and convergence speed. A new feedback controller is proposed to improve response speed and reduce errors by using FTSSC theory. The performance of the controller based on PSO identification algorithm was verified by co-simulation experiments based on Automatic Dynamic Analysis of Mechanical Systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). Meanwhile, the proposed strategy was validated on the servo test platform in the laboratory. Compared with the existing control strategy, the control error was reduced by 75% and the steady-state accuracy was increased by at least 50%.

Existing sparse decomposition techniques often rely on over-complete dictionaries and extensive prior knowledge, leading to slow computation and low efficiency. This paper introduces a novel sparse decomposition method, Adaptive Evolutionary Atomic Sparse Decomposition (AEA-SD). A general atom (g-atom) is designed to accommodate various typical signals, accompanied by an adaptive atom-based automatic target signal localization algorithm. To accelerate convergence, a fast sparse decomposition method based on Bat algorithm is developed, which eliminates the reliance on redundant dictionaries. The performance of AEA-SD is verified by using magnetotelluric (MT) data. Experimental results demonstrate that AEA-SD reduces CPU usage by over 30% compared to traditional algorithms and enhances signal extraction accuracy by at least 58%, 46%, and 58% in low, middle, and high frequency bands, respectively. Furthermore, the method’s adaptability makes it applicable to diverse scenarios requiring the removal of complex background noise from sensor signals.