Study on the Fast Search Planning Problem of Lost Targets for Maritime Emergency Response Based on an Improved Adaptive Immunogenetic Algorithm.

This study investigates the problem of rapid search planning for moving targets in maritime emergencies using an improved adaptive immune genetic algorithm. Given the complexity and uncertainty inherent in searching for moving targets in maritime emergency situations, a task planning method based on the improved adaptive immunogenetic algorithm (IAIGA) is proposed to enhance search efficiency and accuracy. This method utilizes a priori information to construct the potential regions of the target and the distribution probability within each region. It establishes a "prediction-scheduling" search strategy model, planning a rapid search task for disconnected targets based on overlapping probability through the IAIGA. By incorporating an immune mechanism, the algorithm enhances its global search capability and robustness. Additionally, the adaptive strategy enables dynamic adjustment of the algorithm's parameters to accommodate varying search scenarios. The experimental results demonstrate that the proposed IAIGA significantly outperforms traditional methods, providing higher search speeds and more accurate search results in the context of maritime emergency response. These findings offer effective technical support for maritime emergency operations.

Deployment of Constellation with Different Inclinations Using the Nodal Precession and Thrust.

Strategy selection is critical for constellation deployment missions, both in terms of energy consumption and time cost. The different effects of impulse thrust and continuous thrust on orbit elements lead to a different choice of strategy. With impulse thrust, constellation types are differentiated according to high and medium-low inclinations. Constellations with high inclination are deployed using a strategy that controls the inclination. Constellations with medium-low inclination are deployed using a strategy that controls the semi-long axis. With continuous thrust, constellations are classified according to high, medium, and low inclination. High inclination constellations are deployed with a strategy of controlling inclination. Medium inclination constellations are deployed with a strategy that controls the semi-long axis. Low inclination constellations are deployed with a strategy of directly applying continuous thrust.

An ISAR Image Component Recognition Method Based on Semantic Segmentation and Mask Matching.

The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks.

A Deep Learning-Based Satellite Target Recognition Method Using Radar Data.

A novel satellite target recognition method based on radar data partition and deep learning techniques is proposed in this paper. For the radar satellite recognition task, orbital altitude is introduced as a distinct and accessible feature to divide radar data. On this basis, we design a new distance metric for HRRPs called normalized angular distance divided by correlation coefficient (NADDCC), and a hierarchical clustering method based on this distance metric is applied to segment the radar observation angular domain. Using the above technology, the radar data partition is completed and multiple HRRP data clusters are obtained. To further mine the essential features in HRRPs, a GRU-SVM model is designed and firstly applied for radar HRRP target recognition. It consists of a multi-layer GRU neural network as a deep feature extractor and linear SVM as a classifier. By training, GRU neural network successfully extracts effective and highly distinguishable features of HRRPs, and feature visualization technology shows its advantages. Furthermore, the performance testing and comparison experiments also demonstrate that GRU neural network possesses better comprehensive performance for HRRP target recognition than LSTM neural network and conventional RNN, and the recognition performance of our method is almost better than that of other several common feature extraction methods or no data partition.

Advances in Spectroscopic Characters of Space Objects.

Spectroscopic characters of space objects are basic optical attributes which could stand for the material types of space objects. By comparing actual spectral characteristics with that obtained in the lab, the types of space materials can be identified, which is beneficial for the analysis of working states and compositions of space objects. Aiming at the problems of spectrum measurement and material information retrieval, the spectroscopic theory, retrieval methods and reddening effect of space objects spectrum are analyzed in this paper. The contributions on the spectrum from 350 to 2 500 nm from vibrational spectrum and electrical spectrum are investigated based on solid spectrum characters. Three methods commonly used in space objects material identification based on spectrum characters are studied, which are artificial neural network algorithm, particle swarm optimization algorithm and spectrum unmixing algorithm, and the features including spectrum reflectance and its derivative, center displacement are discussed. Reddening effect in spectrum measurement is studied, and it is shown that the reddening effect is related to the deoxidizing effect when some material access to space environments. The loose chemical bonds are formed due to the separation of oxygen and the combination of contaminations in space, which results in more absorptions of light energy and the higher slope of reflectance at longer wavelength that is named reddening effect. The reflectance of spectrum can be used to analyze material aging problems on the surface of satellite material under the continuous influences of harsh space environment, including chemical or physical changes, which are favorable for repairing existing satellite or launching new satellite.

Space Detumbling Robot Arm Deployment Path Planning Based on Bi-FMT* Algorithm.

In order to avoid damage to service satellites and targets during space missions and improve safety and reliability, it is necessary to study how to eliminate or reduce the rotation of targets. This paper focused on a space detumbling robot and studied the space detumbling robot dynamics and robot arm deployment path planning. Firstly, a certain space detumbling robot with a 'platform + manipulator + end effector' configuration is proposed. By considering the end effector as a translational joint, the entire space detumbling robot is equivalent to a link system containing six rotating joints and three translational joints, and the detailed derivation process of the kinematic and dynamic model is presented. Then, ADAMS and MATLAB were used to simulate the model, and the MATLAB results were compared with the ADAMS results to verify the correctness of the model. After that, the robot arm deployment problem was analyzed in detail from the aspects of problem description, constraint analysis and algorithm implementation. An algorithm of robot arm deployment path planning based on the Bi-FMT* algorithm is proposed, and the effectiveness of the algorithm is verified by simulation.

[Effects of Qichu Fujin Recipe on regeneration and repair of injured sciatic nerve in rats].

OBJECTIVE: To explore the effects of Qichu Fujin Recipe (QCFJR), a compound traditional Chinese medicine, in repairing sciatic nerve injury in rats. METHODS: A total of 60 male Sprague-Dawley (SD) rats were randomly divided into four groups: normal control group, untreated group, mecobalamin group and QCFJR group. Except the normal control group, sciatic nerve injury was induced by crushing of left sciatic nerve. Rats in the mecobalamin group were intragastrically administered with mecobalamin solutions 150 microg/(kg x d), and rats in the QCFJR group were intragastrically administered with QCFJR 35.2 g/(kg x d), while rats in the untreated group were intragastrically administered with normal saline (0.5 mL/d), once a day for 4 weeks respectively. Sciatic function index (SFI) was determined by walking tract analysis 1 week and 2 and 4 weeks after crushing. Five rats in each group were sacrificed for histological observation at the different time points. The remnant rate of gastrocnemius wet weight and the diameter of gastrocnemius cells were calculated. Expression of S-100 protein in the distal stump of injured nerve was observed by using immunohistochemical method. Distal injured sciatic nerves were determined with toluidine blue staining and observed under a light microscope 1 week and 2 and 4 weeks after operation. Diameter of axon and depth of myelin sheath were calculated by an image analysis system. Ultrastructure of nerve fibers was determined with uranyl acetate and lead citrate staining and observed under an electron microscope. RESULTS: Compared with the normal control group, SFI, remnant rate of gastrocnemius wet weight, diameter of gastrocnemius cells, expression of S-100, diameter of axon and depth of myelin sheath in the mecobalamin group and the QCFJR group were significantly decreased at different time points (P<0.05), superior to those of the untreated group (P<0.05, P<0.01). Except the remnant rate of gastrocnemius wet weight and the diameter of gastrocnemius cells, there were no significant differences in other indexes between the mecobalamin group and the QCFJR group 1 week and 2 and 4 weeks after crushing (P>0.05). CONCLUSION: QCFJR can stimulate the regeneration and repair of nerve fiber and delay the skeletal muscle atrophy after sciatic nerve injury in rats.