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Re-identification technology for pedestrians and vehicles has been successfully applied in the field of intelligence analysis. However, there is a lack of research on re-identification technology for ship targets. In this paper, we propose a double-feature fusion-based maritime defogging re-identification network for intelligence analysis and supervision of ship targets. To reduce the impact of negative samples on features, we adopt a perspective-assisted adaptive query expansion method and a similarity-based feature fusion method. Furthermore, a defogging branch is embedded in the shallow layer of the re-identification branch. This branch utilizes weight sharing technology to extract fog-free features. The defogged image is then reconstructed using upsampling technology and the pyramid model, enhancing the recognition ability of the re-identification network in low-visibility scenarios. Finally, a pseudo-IOU based non-maximum suppression method is proposed to enhance the detection accuracy of ship targets. This method modifies the confidence of the detection frame. Experimental results demonstrate that the proposed method outperforms existing methods, and each module contributes to the network’s performance.

Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier using both labeled and pseudo-labeled samples. However, in data-scarce scenarios, reliance on labeled samples for initial classifier generation can degrade performance. Methods based on consistency regularization have shown promising results by encouraging consistent outputs for different semantic variations of the same sample obtained through diverse augmentation techniques. However, existing methods typically utilize only weak and strong augmentation variants, limiting information extraction. Therefore, a multi-augmentation contrastive semi-supervised learning method (MAC-SSL) is proposed. MAC-SSL introduces moderate augmentation, combining outputs from moderately and weakly augmented unlabeled images to generate pseudo-labels. Cross-entropy loss ensures consistency between strongly augmented image outputs and pseudo-labels. Furthermore, the MixUP is adopted to blend outputs from labeled and unlabeled images, enhancing consistency between re-augmented outputs and new pseudo-labels. The proposed method achieves a state-of-the-art performance (accuracy) through extensive experiments conducted on multiple datasets with varying numbers of labeled samples. Ablation studies further investigate each component's significance. [ABSTRACT FROM AUTHOR]

All‐solid‐state thin‐film lithium batteries (TFLBs) are the ideal wireless power sources for on‐chip micro/nanodevices due to the significant advantages of safety, portability, and integration. As the bottleneck for increasing the energy density of TFLBs, the key components of cathode, electrolyte, and anode are still underway to be improved. In this review, a brief history of TFLBs is first outlined by presenting several TFLB configurations. Based on the state‐of‐the‐art materials developed for lithium‐ion batteries (LIBs), the challenges and related strategies for the application of those potential electrode and electrolyte materials in TFLBs are discussed. Given the advanced manufacture and characterization techniques, the recent advances of TFLBs are reviewed for pursuing the high‐energy‐density and long‐term‐durability demands, which could guide the development of future TFLBs and analogous all‐solid‐state lithium batteries. [ABSTRACT FROM AUTHOR]

The accuracy of X-ray pulsar-based navigation is greatly affected by the Doppler effect caused by the spacecraft orbital motion and the systematic biases introduced by the pulsar directional error, spacecraft-borne clock error, etc. In this paper, an innovative navigation method simultaneously employing the pulse phase (PP), the difference of two neighbor PPs (DPP) and the Doppler frequency (DF) of X-ray pulsars as measurements is proposed to solve this problem. With the aid of the spacecraft orbital dynamics, a single pair of PP and DF relative to the spacecraft's state estimation error can be estimated by using the joint probability density function of the arrival photon timestamps as the likelihood function. The systematic biases involved to the PP is proved to be nearly invariant over two adjacent navigation periods and the major part of it is eliminated in the DPP; therefore, the DPP is also exploited as additional navigation measurement to weaken the impact of systematic biases on navigation accuracy. Results of photon-level simulations show that the navigation accuracy of the proposed method is remarkably better than that of the method only using PP, the method using both PP and DF and the method using both PP and DPP for Earth orbit. [ABSTRACT FROM AUTHOR]

Owing to their high energy density, lithium-oxygen batteries (LOBs) have been drawn great attention as one of the promising electrochemical energy sources. However, the sluggish kinetics of oxygen reduction/evolution reaction (ORR/OER) hamper the widespread application of LOBs. Herein, an elaborate designed catalysts which are constructed by FeN x moieties dispersed on the network-like hollow dodecahedral carbon and then decorated with Ru nanoparticles (FeN x -HDC@Ru). Since the homogeneously dispersed FeN x moieties could promote ORR performance, and the Ru nanoparticles could facilitate OER capability, the FeN x -HDC@Ru nanocomposites used as cathode catalysts can significantly improve LOBs performance. A lower discharge and charge overpotentials of 0.15 V and 0.78 V can be detected in the first cycle, respectively, and an excellent cycle performance of 90 cycles at 200 mA g -1 and 89 cycles at 500 mA g -1 can be demonstrated. Herein, the charge transfer kinetics has been enhanced with the internal network-like hollow structure and a low impedance Li 2 O 2 /catalysts contact interface could be earned by the constructed Ru nanoparticles, these factors would lead to an efficient acceleration to the formation and decomposition of Li 2 O 2 during discharge and charge process., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Inc. All rights reserved.)