Abstract:Infrared cameras are suitable for complex environments, and the use of infrared images to detect black-flying UAV targets has important application value. Aiming at the problems such as small size of UAV target, few pixels in the image, weak texture detail information, and the difficulty of the algorithm to effectively extract the infrared UAV target features resulting in low detection accuracy, this paper proposes a target detection algorithm with multi-scale learning. By constructing a multi-scale feature fusion structure in the neck network of the model, introducing a multi-scale feature learning module, cascading the features of the deep network and the shallow network, acquiring the features of the target at multiple scales, enriching the semantic and feature information of the feature map, the algorithm significantly improves the accuracy of the detection of the target of small UAVs. The SIoU is used instead of the CIoU loss function in the training process, which minimizes the loss of the network model in the training process and improves the regression accuracy. The experimental results show that compared with other infrared small targets and mainstream detection algorithms, the method proposed in this paper can effectively improve the detection accuracy of UAV targets, and can meet the detection accuracy requirements for detecting UAV targets in practical applications.

Abstract:As a representative of the next generation of gyroscopes , atomic gyroscopes have become a focal point of research in the field of high-precision inertial navigation and has garnered significant attention due to its theoretically ultra-high precision, exceptional long-term stability, and immense potential for miniaturization and integration. Among them, the atom interferometric gyroscope, as a type of atomic gyroscope, had attracted widespread interest in the field of inertial navigation.. The development of atomic interferometric gyroscopes was systematically reviewed. Beginning with fundamental principles, it elaborates on critical technical components including atomic source preparation, interferometric loop construction, and phase resolution. Through rigorous analysis, the paper establishes intrinsic correlations between core performance parameters such as sensitivity and ultimate accuracy while elucidating their mutual constraint mechanisms. Furthermore, it reveals the physical origins of engineering bottlenecks including limited data update rates and narrow dynamic ranges.?Finally, the article outlines future development directions and trends for atomic interference gyroscopes, emphasizing the need for in-depth research in several areas: solving external interference issues to improve accuracy, improving chip processing technology for miniaturization and integration, enhancing combined inertial sensors, increasing data update rates, and exploring ways to expand dynamic range.

Abstract:A modified physical vapor deposition method for the controllable growth of α-In2Se3 was proposed, and the broad-spectrum response performance of three thicknesses of α-In2Se3 nanosheets in the visible to near-infrared wavelength range was systematically studied. The results indicate that the thickness of α-In2Se3 nanosheets can significantly regulate the photoelectric performance, and the photoresponsivity and specific detection rate increase with increasing thickness. In addition, it was found that the α-In2Se3 with a thickness of 32.8 nm exhibited a photocurrent anisotropy ratio (dichroic ratio) of 4 at 635 nm, indicating good polarization-sensitive detection functionality. In summary, the two-dimensional α-In2Se3 prepared by the physical vapor deposition method demonstrates a wide visible-infrared spectral response and good polarization detection ability, making it an ideal candidate material for two-dimensional multifunctional optoelectronic devices.

Abstract:Due to the low damping characteristic of the superconducting electrodynamic suspension (EDS) system which may cause suspension instability problem, a six degree-of-freedom dynamic model for the high speed maglev sled system is established, and it is found that its suspension damping would become negative when the traveling speed exceeds 23.6 m/s, resulting in suspension instability to the maglev sled. The vertical damping ratio of the EDS system is calculated using the least square fitting method, and the relationship between the vertical damping and the speed is then obtained. To stablilize the suspension system, a distributed dynamic vibration absorbers (DVA) scheme is proposed, and the effects of the DVA parameters on the suspension stability are investigated; to minimize the influence of the DVA scheme on the acceleration performance of the maglev sled, the feasibility of applying low mass (≤ 1kg) DVAs to the maglev sled is disccussed; further, the vibration suppression effect of this scheme is investigated considering the vertical misalignments of guideway girders, which shows that the proposed scheme can significantly increase the damping and stability of the suspension system, and it also well suppresses the vibration of the sled-body caused by misalignments of the ground suspension coils. This scheme provides a useful reference for the design of the suspension system of the maglev sled system, the vaccum tube high speed maglev train, etc.

Abstract:In view of the insufficient mining of implicit associative relationships and the problem of neglecting the temporal evolution factor, we propose a domain-specific social events detection method via temporal evolution feature mining. Our model divides data into slices by time, and takes duplicate event records from different sources into account which reflects the importance of events, and constructs entity interaction graph, which reduces the impact of database error. Multi-relational graph convolutional network is improved to update graph structure information of historical evolution sequence by interaction relationships. Attention mechanism is used to learn core features to obtain global embedding of sequence units. Implicit association is mined sufficiently. Based on recurrent neural network, temporal evolution features are extracted to obtain the global embedding and the temporal evolution factor is mined effectively. Experiment results show that our method can be applied to detection task, which is better than existing methods.

Abstract:The efficient online exploration of intelligent agents is important in reinforcement learning tasks, but still faces the problem of low utilization of interactive data with environment, or the need for additional tasks’ data. To solve this problem, an online exploration latent variable that obtained the characteristics of current task to assist the agents to behave was introduced. There was no need for additional multi-task data or additional environmental interaction steps in the current task. The exploring latent variable was updated in the learnable environment model, and the environment model underwent supervised updates based on the intelligent agent and real environment interaction data. The exploration in advance in the simulated environment model was assisted by the exploring latent variable, and thus the performance of agents in the real environment was improved. The performance in typical continuous control tasks was raised by about 30% in the experiments, which was of guiding significance for the single-task exploration and meta reinforcement learning research.

Abstract:Linear motor traction requires real-time and accurate feedback of rotor position and speed information for closed-loop control, in order to achieve functions such as constant speed cruise and fixed-point parking. However, electromagnetic sleds run in a long distance and non-contact way under the outdoor complex magnetic field environment, making their positioning and speed measuring technology unable to inherit from traditional wheel rail transportation and industrial machine tools. Presently, the main position and speed measuring methods that suit on maglev trains are faced with limitation from their basic principle or high cost. Aiming to solve the above problem, inspired by the structure of vernier calipers, this work proposes and designs a new position and speed measuring system. The principles of improved positioning accuracy, motion direction discrimination and position measurement are elucidated through theoretical analysis. proving the feasibility of the positioning and speed measuring scheme. The position prediction algorithm and Kalman filter algorithm are designed to further improve the accuracy and real-time performance. The corresponding hardware circuit and software program are designed and the positioning and speed measuring system is implemented. And, a synchronous belt guide rail verification platform is built to verify the designed system. Test results show that the system can achieve the designed positioning accuracy, and performs well in terms of real-time performance, accuracy, and engineering application value. Finally, the positioning and speed measuring system is applied to electromagnetic levitation propulsion platform.

Abstract:Fiber hybridization is one of the effective means to improve the toughness and ductility of fiber-reinforced polymer matrix composites, hence avoiding their catastrophic brittle failure. The interlaminar fracture toughness is an important factor affecting the mechanical behavior of fiber hybrid composites. In this paper, two kinds of epoxy resins with different toughness, 7901 and 9A16, were used as the matrix. Interlayer carbon/glass hybrid composites with different numbers of carbon fiber layers were designed and manufactured. The effects of mode Ⅱ interlaminar fracture toughness (GⅡC) on the failure mode and mechanical properties of carbon/glass hybrid composites were investigated through both theoretical and experimental investigation. The results showed that, the higher mode Ⅱ interlaminar fracture toughness was, the more the carbon layer tended to fail in fragmentation, which was beneficial for achieving pseudo-ductility. In addition, the GⅡC on the modulus and strength of hybrid composites was marginal

Abstract:To meet the requirements of lightweight and low error sensitivity in the optimization design of stiffened panels, the optimization design of stiffened panels is carried out considering the twist angle error of stringers. The finite element model of post-buckling instability of stiffened panels under axial compression was established, and the sensitivity of the load-carrying capacity to the twist angle error on stringers and the distribution characterisation of the torsional stringer was analyzed. At the same time, a sequential approximate optimization method based on surrogate model was proposed by using parallel sequential sampling strategy, and the lightweight design of stiffened panel was carried out under the influence of twist angle error of stringers. The optimal results show that, compared with the optimization design scheme without error influence, the optimization scheme considering the twist angle error of stringers has lower sensitivity to the twist angle error when the weight is reduced by more than 32%, which can effectively improve the reliability and engineering application value of the optimized structure.

Abstract:To address the challenges of intelligent diagnosis for open-circuit faults in power electronic inverters, such as the lack of actual fault samples and the issue of varying characteristic adaptability, a set of optimization methods was proposed from two key intelligent elements: data and algorithm, to support the practical applications of intelligent diagnosis for open-circuit faults in power electronic inverters. For the data element, a fault sample amplification method based on inverters′ characteristics was proposed, which finds out the minimum number of practical samples required for model training. For the algorithm element, an attention-enhanced method and a frequency points adaptive training method for the diagnosis model were proposed, which significantly improve model training effectiveness and diagnosis accuracy under wide-frequency inverter operation. The effectiveness of the proposed optimization methods for the intelligent elements was validated by experiments.