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Magnetic rings are widely used in automotive, home appliances, and consumer electronics. Due to the materials used, processing techniques, and other factors, there will be top cracks, internal cracks, adhesion, and other defects on individual magnetic rings during the manufacturing process. To find such defects, the most sophisticated YOLOv5 target identification algorithm is frequently utilized. However, it has problems such as high computation, sluggish detection, and a large model size. This work suggests an enhanced lightweight YOLOv5 (MR-YOLO) approach for the identification of magnetic ring surface defects to address these issues. To decrease the floating-point operation (FLOP) in the feature channel fusion process and enhance the performance of feature expression, the YOLOv5 neck network was added to the Mobilenetv3 module. To improve the robustness of the algorithm, a Mosaic data enhancement technique was applied. Moreover, in order to increase the network’s interest in minor defects, the SE attention module is inserted into the backbone network to replace the SPPF module with substantially more calculations. Finally, to further increase the new network’s accuracy and training speed, we substituted the original CIoU-Ioss for SIoU-Loss. According to the test, the FLOP and Params of the modified network model decreased by 59.4% and 47.9%, respectively; the reasoning speed increased by 16.6%, the model’s size decreased by 48.1%, and the mAP only lost by 0.3%. The effectiveness and superiority of this method are proved by an analysis and comparison of examples.

When halogen lamps are used as the excitation source in long pulse thermography (LPT) of composite materials, the non-uniform surface temperature distribution will appear in the thermographic image recorded by the infrared camera and so the performance of the LPT technology will be affected severely. To address this issue, a simple method is proposed in this work to enhance the defect detectability for the LPT technique. In this method, the locations of the pixels in the non-defective area are firstly determined by an empirical rule, and then the thermal data of these non-defective areas is selected for estimating the non-uniform surface temperature distribution by a second order polynomial curve-fitting model. Finally, the thermal image sequences after non-uniform background removal are analyzed using thermographic signal reconstruction coefficients to enhance the quality of thermal images for damage detection further. To assess the performance of the method proposed in this paper, the defect detection experiment for the carbon fiber reinforced plastic (CFRP) using this method is carried out. Experimental results show that this method proposed in this paper can effectively remove the temperature non-uniformity, and it can reveal 11 out of 12 defects on the CFRP sample with a high signal-to-noise ratio of 9.09, where all the defects with aspect ratio ≥ 2.5 can be detected, and 1 illegible defect with aspect ratio of 1.67 can also be detected.

Tokamak is an important device for controlled nuclear fusion research. The plasma electronic density control system (PEDCS) is an important system for controlling the Tokamak discharge process, which should be of high stability, rapidity, and accuracy. Gas seeding systems are widely used in many Tokamak devices to achieve plasma electronic density control. According to the mechanism model analysis for the plasma electronic density object, an adapted single neuron proportion integration differentiation (PID) control algorithm with the radial basis function (RBF) neural network tuning is studied. The principle and the implementation of the intelligent control algorithm are described in detail in this paper. The intelligent controller enables the system to optimize the PID parameters online according to the density state in the discharge process. The experimental results show that the adapted algorithm achieves a good control effect and also improves the control performance. The proposed method provides a useful reference for Tokamak devices and other similar control systems.

Given the complex characteristics of permanent magnet linear synchronous motors and the external interference encountered during their operation, controlled precision and efficiency are necessary. In this paper, the mechanism of biological endocrine hormone regulation is analyzed, and an intelligent controller based on the obtained neuroendocrine algorithm is implemented in the control system of a permanent magnet linear synchronous motor. The controller mainly includes a hypothalamic regulation module, a single-neuron proportion integration differential module, and an ultrashort feedback module. It is designed and referenced to the long feedback, short feedback, and ultrashort feedback loop mechanisms of neuroendocrine hormone regulation and abides by the principle of human neuroendocrine hormone regulation. The antagonistic hormone regulation module achieves rapid and stable elimination of errors through the fusion of enhanced regulation with regulation inhibition, and the single-neuron proportion integration differential module enhances the adaptive and self-learning capabilities of the control system. The proposed control is successfully used in a permanent magnet linear synchronous motor, and the experimental results show that the controller presents many advantages, such as fast dynamic responses, strong online adjustment ability, and good running stability in the control system, all of which improve the robustness of the control system.

In the Tokamak discharge experiment, obtaining the largest possible null field region is a necessary condition for the smooth breakdown of the plasma, and adjusting the poloidal field coil current is key to achieving a better null field region. This paper, based on the Sino-Thai Tokamak cooperation project Thailand Tokamak-1 (TT-1) device, employs an exponentially decreasing Particle Swarm Optimization (PSO) algorithm to optimize the poloidal field coil current to create the desired null field region in the vacuum chamber area. First, a calculation model for the mutual inductance coefficient and the null field region is established according to the characteristics and magnetic structure of the TT-1 device, enabling the calculation of the null field region. Then, an optimization model for the poloidal field coil current is established, aiming to create a sufficiently large null field region (less than 10 Gauss) to facilitate breakdown. The optimization is carried out using both a typical linearly decreasing PSO algorithm and an improved PSO algorithm to determine the optimal poloidal field coil current. Compared to the unmodified PSO algorithm, the improved PSO algorithm reduces the root mean square error by 31.80%. The results show that the improved PSO algorithm is more suitable for the optimization of the poloidal field coil, has stronger optimization capabilities, and can effectively create the desired null field region, providing an important reference for the smooth breakdown of plasma in the TT-1 device.

— In Tokamak experiments, the MDSPlus database is commonly used for data management. It collects and stores data from each subsystem. However, the complexity and high coupling of the system make it vulnerable to failure. If one module malfunctions, it can cause the entire system to collapse, hindering future maintenance and expansion. To address this issue, this paper proposes a data acquisition and management system based on the PCI-1706 multi-channel synchronous data acquisition cards and the MDSPlus database, along with a data Scope program. The data acquisition system employs a multi-process design to achieve synchronization among multiple channels by using an external clock and trigger signal. Meanwhile, the data management system utilizes MDSPlus, and a client Scope program is developed to enable remote data access and analysis. The reflection memory card is employed to establish a network for data interaction between the server and the acquisition machine, enhancing storage speed. Experimental tests have confirmed the multi-card and multi-channel synchronous acquisition functions of the data acquisition system, as well as the remote data access capability of the Scope program. This system exhibits advantages such as low cost, simple structure, stable operation, and easy maintenance. It proves to be effective in Tokamak device experiments and can also serve as a reference for other data management systems.

A hydrogen cyanide laser interferometer is mostly used to measure the plasma electron density in many Tokamak devices. The real-time calculation system of the plasma electron density based on a field-programmable gate array is proposed in this work. An Altera EP4CE30F23C8 FPGA chip is selected as the master chip, and an AD9238 chip of 10 MSps is employed for analog-to-digital conversion. The FPGA-based adapted Fast Fourier Transform and the proposed processing algorithm are designed to obtain the plasma electron density. The calculated density is stored in the secure digital card and can also be transmitted to the plasma control system via Ethernet. The experimental results show that the proposed system can effectively obtain the plasma density. The maximum error range is from − 1 to 1 degree and the time resolution is 0.025 ms which is better than that of the convention method 0.1 ms. Meanwhile, this system is highly flexible and reduces design costs to meet the demands of Tokamak devices.

The heat flux loaded on the divertor is an important issue in tokamaks. At present, the heat flux on the divertor is calculated from the divertor surface temperature measured by the infrared (IR) camera. However, the movement of the field of view and the geometrical distortion are present in the IR images. Small movements occur with machine shaking during normal discharges, while large ones often arise at disruptions or the rapid variations in the magnetic field. As a result, it is difficult to obtain the real temperature evolution on the specified divertor surface from the IR images. In this paper, the scale invariant feature transform descriptors and K -means clustering have been applied for vibration correction on the IR videos in EAST. The projective transformation is used to correct the geometrical distortion in the IR images. The experimental results show that the proposed methods are efficient in compensating for image translations brought about by vibrations and for perspective distortions in the images, thereby allowing assignment of the IR emission-derived surface temperatures to the correct locations on the divertor surface.

The controlling of plasma shape and position are essential to the success of Tokamak discharge. A real-time image acquisition system was designed to obtain plasma radiation image during the discharge processes in the Experimental Advanced Superconducting Tokamak (EAST) device. The hardware structure and software design of this visible camera system are introduced in detail. According to the general structure of EAST and the layout of the observation window, spatial location of the discharging plasma in the image was measured. An improved Sobel edge detection algorithm using iterative threshold was proposed to detect plasma boundary. EAST discharge results show that the proposed method acquired plasma position and boundary with high accuracy, which is of great significance for better plasma control.

Disruptions are the most dangerous instabilities in tokamak plasma. During plasma disruption, the large amounts of energy will be deposited on Plasma Facing Components (PFCs) which is a damaging threat for the divertor target and the first wall materials. Therefore, studying the characteristic of heat deposition on the first wall is very significant. The Infrared (IR) camera is an effective tool to measure the surface temperature profile on the first wall on the Experimental Advanced Superconducting Tokamak (EAST). With a finite difference method, the heat flux arrived to the divertor can be calculated from the surface temperature. However, the surface layer on the divertor has a great influence on the calculation of the heat flux on the divertor. The numerical method for solving heat conduction for semi-infinite model is given in this paper. And the thermal resistance of surface layers is considered in this numerical method. In addition, the distribution of heat flux on the divertor during disruption is also shown.