Xiaodong Han

The remaining useful life (RUL) prediction of rolling bearings is crucial for optimizing maintenance schedules, reducing downtime, and extending machinery lifespan. However, existing multi-channel feature fusion methods do not fully capture the correlations between channels and time points in multi-dimensional sensor data. To address the above problems, this paper proposes a multi-channel feature fusion algorithm based on a hybrid attention mechanism and temporal convolutional networks (TCNs), called MCHA-TFCN. The model employs a dual-channel hybrid attention mechanism, integrating self-attention and channel attention to extract spatiotemporal features from multi-channel inputs. It uses causal dilated convolutions in TCNs to capture long-term dependencies and incorporates enhanced residual structures for global feature fusion, effectively extracting high-level spatiotemporal degradation information. The experimental results on the PHM2012 dataset show that MCHA-TFCN achieves excellent performance, with an average Root-Mean-Square Error (RMSE) of 0.091, significantly outperforming existing methods like the DANN and CNN-LSTM.

A broadband cascode distributed low-noise amplifier (CDLNA) with a wide operating temperature range is proposed in this work. A series of diodes are used as bias networks to achieve temperature compensation. To enhance the gain level in the passband, the negative resistance network is employed to offset drain transmission line losses. An attenuator based on reverse-biased diodes and resistors is designed to improve stability across the wideband. The circuit is implemented using a 0.15- $\mu $ m GaAs pHEMT process with a chip area of 2.6 $\times$ 1.55 mm $^{2}$ . Test results show that the CDLNA achieves an average gain of 17.5 dB, a linearity index OP1 dB of 14.0 dBm, a minimum noise figure (NF) of 1.2 dB, and an operating frequency range of 0.3–28 GHz. The operating temperature range is $-$ 55 $^{\circ}$ C to 85 $^{\circ}$ C, within which the gain variation is less than 742 ppm/ $^{\circ}$ C, and the NF variation is less than 2667 ppm/ $^{\circ}$ C

Aiming for efficient coordination mechanisms for the distributed weighted set cover problem, we study from ordinal potential game theoretic learning and propose a Nash equilibrium selection algorithm (NESA). An ordinal potential game model is established, where the local utility function is designed by incorporating a greedy heuristic. To distinguish Nash equilibria of different global fitness, we further classify them into the inferior Nash equilibrium (INE) and the superior Nash equilibrium (SNE), and show that the optimal solution must be an SNE. High-quality SNE solutions are obtained by assigning each player a local stochastic rule based on its category and a finite memory. By demonstrating the existence of a finite improvement path from each INE to an SNE, we prove finite-time convergence of the NESA. Numerical experiments are carried out and comparisons against representative methods are presented, which demonstrate the effectiveness as well as the superiority of our methodology to the state-of-the-art.

The increasing number of satellites for specific space tasks makes it difficult for traditional satellite task planning that relies on ground station planning and on-board execution to fully exploit the overall effectiveness of satellites. Meanwhile, the complex and changeable environment in space also poses challenges to the management of multi-satellite systems (MSS). To address the above issues, this paper formulates a mixed integer optimization problem to solve the autonomous task planning for MSS. First, we constructed a multi-agent-based on-board autonomous management and multi-satellite collaboration architecture. Based on this architecture, we propose a hybrid genetic algorithm with simulated annealing (H-GASA) to solve the multi-satellite cooperative autonomous task planning (MSCATP). With the H-GASA, a heuristic task scheduling scheme was developed to deal with possible task conflicts in MSCATP. Finally, a simulation scenario was established to validate our proposed H-GASA, which exhibits a superior performance in terms of computational power and success rate compared to existing algorithms.

With the increasing number of satellites in orbit, traditional scheduling methods can no longer satisfy the increasing data demands of users. The timeliness of remote sensing images with large data volumes is poor in the backhaul process through low-earth-orbit (LEO) satellite networks. To address the above problems, we propose an edge-computing load-balancing method for LEO satellite networks based on the maximum flow of virtual links. First, the minimum rectangle composed of computing nodes is determined by the source and destination nodes of the transmission task under the configuration of the 2D-Torus topology of LEO satellite networks. Second, edge computing virtual links are established between computing nodes and users. Third, the Ford-Fulkerson algorithm is used to obtain the maximum flow of the topology with virtual links. Finally, a strategy is generated for computing and transmission resource allocation. The simulation results show that the proposed method can optimize the total capacity of the multi-node information backhaul in the remote sensing scenario of LEO satellite networks. The effectiveness of the proposed algorithm is verified in several special scenarios.

Although in-orbit anomaly detection is extremely important to ensure spacecraft safety, the complex spatial-temporal correlation and sparsity of anomalies in the data pose significant challenges. This study proposes the new multi-task learning-based time series anomaly detection (MTAD) method, which captures the spatial-temporal correlation of the data to learn the generalized normal patterns and hence facilitates anomaly detection. First, four proxy tasks are implemented for feature extraction through joint learning: (1) Long short-term memory-based data prediction; (2) autoencoder-based latent representation learning and data reconstruction; (3) variational autoencoder-based latent representation learning and data reconstruction; and (4) joint latent representation-based data prediction. Proxy Tasks 1 and 4 capture the temporal correlation of the data by fusing the latent space, whereas Tasks 2 and 3 fully capture the spatial correlation of the data. The isolation forest algorithm then detects anomalies from the extracted features. Application to a real spacecraft dataset reveals the superiority of our method over existing techniques, and further ablation testing for each task proves the effectiveness of fusing multiple tasks. The proposed MTAD method demonstrates promising potential for effective in-orbit anomaly detection for spacecraft.

Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.

In many space missions, spacecraft are required to have the ability to avoid various obstacles and finally reach the target point. In this paper, the path planning of spacecraft attitude maneuver under boundary constraints and pointing constraints is studied. The boundary constraints and orientation constraints are constructed as finite functions of path evaluation. From the point of view of optimal time and shortest path, the constrained attitude maneuver problem is reduced to optimal time and path solving problem. To address this problem, a metaheuristic maneuver path planning method is proposed (cross-mutation grey wolf algorithm (CMGWO)). In the CMGWO method, we use angular velocity and control torque coding to model attitude maneuver, which increases the difficulty of solving the problem. In order to deal with this problem, the grey wolf algorithm is used for mutation and evolution, so as to reduce the difficulty of solving the problem and shorten the convergence time. Finally, simulation analysis is carried out under different conditions, and the feasibility and effectiveness of the method are verified by numerical simulation.

The continuous development of space missions has put forward requirements for high performance, high reliability, intelligence, effective integration, miniaturization, and quick turn around productization of the electronic system of satellites. The complexity of satellites has continued to increase, and the focus of satellite competition has shifted from the launch of success shifts to communication capacity, performance indicators, degree of flexibility, and continuous service capabilities. So, the importance of onboard avionics system is becoming increasingly prominent. In the future, the advanced avionics system integrates most of the platform’s electronic equipment. The design level of the system largely determines the performance of the satellite platform. This chapter focuses on the application requirements of the new generation of intelligent avionics system for future communication satellites and adopts an “open” architecture of “centralized management, distributed measurement and drive, and software and hardware ‘modular’ design” to build a universal, standardized, and scalable intelligent avionics system.

Superpixels group perceptually similar pixels into homogeneous sub-regions that act as meaningful features for advanced tasks. However, there is still a contradiction between color homogeneity and shape regularity in existing algorithms, which hinders their performance in further processing. In this work, a novel Contour Optimized Non-Iterative Clustering (CONIC) method is presented. It incorporates contour prior into the non-iterative clustering framework, aiming to provide a balanced trade-off between segmentation accuracy and visual uniformity. After the conventional grid sampling initialization, a regional inter-seed correlation is first established by the joint color-spatial-contour distance. It then guides a global redistribution of all seeds to modify the number and positions iteratively. This is done to avoid clustering falling into the local optimum and achieve the exact number of user-expectation. During the clustering process, an improved feature distance is elaborated to measure the color similarity that considers contour constraint and prevents the boundary pixels from being wrongly assigned. Consequently, superpixels acquire better visual quality and their boundaries are more consistent with the object contours. Experimental results show that CONIC performs as well as or even better than the state-of-the-art superpixel segmentation algorithms, in terms of both efficiency and segmentation effects.