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Remote sensing object detection (RSOD) plays a crucial role in resource utilization, geological disaster risk assessment and urban planning. Deep learning-based object-detection algorithms have proven effective in remote sensing image studies. However, accurate detection of objects with small size, dense distribution and complex object arrangement remains a significant challenge in the remote sensing field. To address this, a refined and efficient object-detection algorithm (RE-YOLO) has been proposed in this paper for remote sensing images. Initially, a refined and efficient module (REM) was designed to balance computational complexity and feature-extraction capabilities, which serves as a key component of the RE_CSP block. RE_CSP block efficiently extracts multi-scale information, overcoming challenges posed by complex backgrounds. Moreover, the spatial extracted attention module (SEAM) has been proposed in the bottleneck of backbone to promote representative feature learning and enhance the semantic information capture. In addition, a three-branch path aggregation network (TBPAN) has been constructed as the neck network, which facilitates comprehensive fusion of shallow positional information and deep semantic information across different channels, enabling the network with a robust ability to capture contextual information. Extensive experiments conducted on two large-scale remote sensing datasets, DOTA-v1.0 and SCERL, demonstrate that the proposed RE-YOLO outperforms state-of-the-art other object-detection approaches and exhibits a significant improvement in generalization ability. [ABSTRACT FROM AUTHOR]

BackgroundTraditional X-ray fluorescence spectrum analysis has the limitations of poor accuracy of the characteristic peak counting rate and shadow peak.PurposeThis study aims to propose a long and short term memory (LSTM) neural network model based on deep learning for the loss correction of the characteristic peak count rate and shadow peak.MethodsFirstly, a LSTM neural network model based on deep learning was proposed to estimate accurately the amplitudes of nuclear pulse signals by learning samples. Then, a convolutional neural network (CNN) with unique convolutional kernel structure was introduced to deal with the challenges of large sample size of the nuclear pulse signal and the low training efficiency of the model by extracting the sample features layer by layer, thereby effectively reducing the number of samples and the complexity of model training. Finally, a series of offline nuclear pulse sequences of powdered iron ore samples were used to generate the dataset required for model training. Among the 64 000 entries in this dataset, 44 800 were used as training sets, 12 800 were used as validation sets, and the remaining 6 400 were used as testing sets.ResultsThe trained CNN-LSTM model saves considerable training time, overcomes the defects of local convergence of traditional methods, and accurately estimates the parameters of input pulse under different degrees of distortion. Results show that the accuracy rate of the training and verification sets is greater than 99%. An analysis of the count repair results reveals that the average value of the correction ratio of the three shadow peaks, that is, the correction ratio of the depth learning model trained in this study to the count loss derived from the distorted pulses, is 91.52%.ConclusionsThe CNN-LSTM model can effectively correct the shadow peaks derived from the amplitude loss of distorted pulses and improve the accuracy of the characteristic peak count rate in X-ray fluorescence spectra. The model is shown to have high application value for the field of X-ray fluorescence spectroscopy.

Correctly and quickly measuring the attitude of aircraft is an important prerequisite for achieving precise docking missions for manned spacecraft. Traditional attitude measurement requires splicing three attitude parameters. This paper proposes an improved optimization algorithm that is applied to attitude measurement with electromagnetic wave structure vectors as reference, achieving synchronous, overall, and rapid measurement of attitude parameters. Firstly, a guidance vector in established based on the changing relationship between multiple electromagnetic vector sensors and received signals, the attitude rotation m the form of an exponential function in the guidance vector in expanded by Taylor approximation. Then an equal-step traversal algorithm combined with Newton method in used to quickly search the spectral peak of the space spectral function of multiple navigation signals, and the attitude measurement of the aircraft in completed. Simulation results show that the attitude measurement accuracy of this method is high and the error can be controlled at about 0. 01°, which improves the efficiency of attitude measurement. This method also has good anti-interference ability and has good application value. [ABSTRACT FROM AUTHOR]

The precise thermal control of aerial cameras is crucial for the acquisition of high-resolution imagery, and an accurate temperature prediction is essential to achieve this. This paper presents a methodology for modifying thermal network models to improve the accuracy of temperature prediction for aerial cameras. Seven types of thermal parameters are extracted from the thermal network model, and a thermally sensitive analysis identifies eleven key parameters to streamline the processing time. Departing from traditional methods that rely on steady-state data, this study conducts transient thermal tests and leverages polynomial fitting to facilitate thorough parameter modification. To ensure data reliability, the Monte-Carlo algorithm is employed to explore the parameter spaces of key parameters, analyzing temperature errors. Subsequently, the Least-Squares method is utilized to obtain optimal estimates of the key parameter values. As a result, the updated model demonstrates significantly improved accuracy in temperature predictions, achieving a reduction in the maximum absolute error between the predicted and experimental results from 22 °C to 4 °C, and a lowering of the relative error from 33.8% to 6.1%. The proposed modification method validates its effectiveness in modeling and enhancing the precision of thermal network models for aerial cameras. [ABSTRACT FROM AUTHOR]

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera's unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks play an important role in underwater communication systems. Communication through collaborative communication is an effective way to solve critical problems in underwater communication systems. Underwater sensors are often deployed in spaces that overlap with those of marine mammals, which can adversely affect them. For this reason, in this paper, a marine mammal conflict avoidance method that can be dynamically adjusted according to the channel idle time duration and sensor node demand is designed, and the derivation of the maximum occupancy time duration is performed. Meanwhile, in addition, combining the potential of reinforcement learning in adaptive management, efficient resource optimization, and solving complex problems, this study also proposes a reinforcement learning-based relay-assisted spectrum switching method (R2S), which aims to achieve a reasonable allocation of spectrum resources in relay collaborative communication systems. The experimental results show that the method proposed in this study can effectively reduce the disturbance to marine mammals while performing well in terms of conflict probability, interruption probability, and quality of service. [ABSTRACT FROM AUTHOR]

Currently, a set of scientific, systematical quality competitiveness evaluation system are lacked in our country, which is applied to aviation equipment, including aircraft, engines and missiles repair factory.The system engineering theory and process analysis method are used to build the quality competitiveness evaluation index system in the aspects of quality system ability, quality support ability, quality operation ability and quality performance ability, which covers the quality competitiveness evaluation index system with 19 second-level and 60 third-level indexes.At the same time, an evaluation method based on the G1 expert combination of multiple relevant weights is proposed to comprehensively integrate the intention of decision makers or experts to form a set of standardized weight coefficients.According to the evaluation criteria, the quality competitiveness of aeronautic equipment repair factory is evaluated by experts using the scoring and fuzzy evaluation, and is further analyzed.The results show that the index system is rational, the evaluation method is feasible, and the system is of a certain guiding and referential significance for the aeronautic equipment repair factory to improve its quality competitiveness.

Cloud operation is a new operational mode brought by cloud computing technology in the military field. In the future aviation and space information confrontation, cloud operation will have the invisible power application and the combination of virtual and real combat operations. In this paper the construction requirements of aviation and space information equipment system are analyzed. Based on the concept of cloud operation, aviation and space information equipment system is constructed. The enlightenment of the cloud operation concept to the construction of aviation and space information equipment system is discussed. The winning mechanism of cloud operation, equipment integration capability, reliability and safety, and overall coordination etc., are studied. The research deepens the understanding of the requirements and implementation methods of the aviation and space information equipment system construction, and is of certain reference significance for the construction and application of the aerospace battlefield information equipment system.