Phase change materials (PCMs) for heat energy storage have received an extensive attention in recent years. For heat energy storage application, a new type of polyurea (PU) microencapsulated phase ...change materials (MicroPCMs) were prepared by interfacial polycondensation method with isophorone diisocyanate (IPDI) and ethylene diamine (EDA) as shell monomers and paraffin as core material. The influences of monomer mass ratio, emulsifier type, emulsifier dosage and emulsifying stirring speed on MicroPCMs were investigated systemically. The morphology, chemical composition and particle size distribution of the MicroPCMs were characterized by using scanning electron microscope (SEM), fourier transform infrared (FT-IR) spectrum and laser particle size analyzer respectively. The results show that the MicroPCMs prepared under the optimal conditions have spherical shape and an average diameter of 2.42μm. The results of differential scanning calorimeter (DSC) analysis show that the phase latent heat of the MicroPCMs is 92.5J/g with 44.5% core content. The results of anti-osmosis test confirm that the MicroPCMs with the core/shell ratio of 75/25 have good compactness and stability. The microencapsulation technology developed is expected to be used in air conditioning, heating and other fields.
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•Polyurea/paraffin microencapsulated phase change materials were prepared.•Isophorone diisocyanate and ethylene diamine were used as shell monomers.•The phase latent heat of the MicroPCMs is 92.5J/g with 44.5% core content.•The MicroPCMs with the core/shell ratio of 75/25 have good compactness and stability.
In ocean remote sensing missions, recognizing an underwater acoustic target is a crucial technology for conducting marine biological surveys, ocean explorations, and other scientific activities that ...take place in water. The complex acoustic propagation characteristics present significant challenges for the recognition of underwater acoustic targets (UATR). Methods such as extracting the DEMON spectrum of a signal and inputting it into an artificial neural network for recognition, and fusing the multidimensional features of a signal for recognition, have been proposed. However, there is still room for improvement in terms of noise immunity, improved computational performance, and reduced reliance on specialized knowledge. In this article, we propose the Residual Attentional Convolutional Neural Network (RACNN), a convolutional neural network that quickly and accurately recognize the type of ship-radiated noise. This network is capable of extracting internal features of Mel Frequency Cepstral Coefficients (MFCC) of the underwater ship-radiated noise. Experimental results demonstrate that the proposed model achieves an overall accuracy of 99.34% on the ShipsEar dataset, surpassing conventional recognition methods and other deep learning models.
► Effects of particle size and pH value on the hydrophilicity of graphene oxide are investigated with measuring the water contact angle. ► The water contact angle of different graphene oxides ...decreases from 61.8° to 11.6°. ► The hydrophilicity of graphene oxide is sensitive to particle size and pH value.
Graphene-based material has attracted extensive attention from both experimental and theoretical scientific communities due to its extraordinary properties. As a derivative of graphene, graphene oxide has also become an attractive material and been investigated widely in many areas since the ease of synthesizing graphene oxide and its solution processability. In this paper, we prepared graphene oxide by the modified Hummers method. The hydrophilicity of graphene oxide with different particle sizes and pH values was characterized with water contact angle. And we find the water contact angle of the different graphene oxides decreases from 61.8° to 11.6°, which indicates graphene oxide has the excellent hydrophilicity. The X-ray photoelectron spectroscopy, zeta potential and dynamic light scattering measurements were taken to study the chemical state of elements and the performances of graphene oxide in this experiment. The results show the hydrophilicity of graphene oxide is sensitive to particle size and pH value, which result in the variations of the ionizable groups of graphene oxide. Our work provides a simple ways to control the hydrophilicity of graphene oxide by adjusting particle size and pH value.
Image registration is an important basis of image processing, which is of great significance in image mosaicking, target recognition, and change detection. Aiming at the automatic registration ...problem of multi-angle optical images for ground scenes, a registration method combining point features and line features to register images is proposed. Firstly, the LSD (Line Segment Detector) algorithm is used to extract line features of images. The obtained line segments whose length are less than a given threshold are eliminated by a visual significant algorithm. Then, an affine transform model obtained by estimating a Gaussian mixture model (GMM) is applied to the image to be matched. Lastly, Harris point features are utilized in fine matching to overcome shortages of methods based on line features. In experiments, the proposed algorithm is compared with popular feature-based registration algorithms. The results indicate that the proposed algorithm in this work has obvious advantages in terms of registration accuracy and reliability for optical images acquired at different angles.
An accurate inversion of the fraction of absorbed photosynthetically active radiation (FPAR) based on remote sensing data is particularly important for understanding global climate change. At ...present, there are relatively few studies focusing on the inversion of FPAR using Chinese autonomous satellites. This work intends to investigate the inversion of the FPAR obtained from the FengYun-3C (FY-3C) data of domestic satellites by using the PROSAIL model and the look-up table (LUT) algorithm for different vegetation types from various places in China. After analyzing the applicability of existing models using FY-3C data and MOD09GA data, an inversion strategy for FY-3C data is implemented. This strategy is applied to areas with various types of vegetation, such as grasslands, croplands, shrubs, broadleaf forests, and needleleaf forests, and produces FPAR products, which are cross-validated against the FPAR products from the Moderate Resolution Imaging Spectro Radiometer (MODIS), Geoland Version 1 (GEOV1), and Global Land Surface Satellite (GLASS). Accordingly, the results show that the FPAR retrieved from the FY-3C data has good spatial and temporal consistency and correlation with the three FPAR products. However, this technique does not favor all types of vegetation equally; the FY-FPAR is relatively more suitable for the inversion of grasslands and croplands during the lush period than for others. Therefore, the inversion strategy provides the potential to generate large-area and long-term sequence FPAR products from FY-3C data.
Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor ...performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability.
The detection of small objects in Unmanned Aerial Vehicle (UAV) images, which contain a large number of objects with extremely small pixels, has been a major challenge over the last few decades. ...Owing to the lack of sufficiently detailed representation of object features, conventional detectors based on deep learning are unsatisfactory when detecting small objects. Meanwhile, the flight altitude and the shooting angle of the UAV always changing, which leads to an uneven dispersion of the multiple objects and a varying density of small objects. In response to the issues mentioned above, a novel lightweight Cross-layer Triple-branch Parallel Fusion Network (CTPFNet) is proposed to improve the real-time detection accuracy of small objects under UAV images. Firstly, a novel downsampling structure called the Inverse Residual Pooling Cascade (IRPC) module is proposed to obtain richer feature information about small objects. Secondly, we construct an improved global feature extraction structure Efficient Layer Aggregation Networks-Transformer (ELAN-Trans) to enhance the association between global features. Then, we design the Hybrid Dilated Depth Separable-Spatial Pyramid Pooling-Fast (HDDS-SPPF) to capture more contextual information by using the dilated convolution operations. To enhance the cross-scale transfer fusion of low-level original features containing more detailed localization information and deep-level deep features containing more semantic information, we propose a CTPF module embedded into the neck region for the secondary feature reuse of adjacent feature maps with varying resolutions. Extensive experiments on the public VisDrone2021-DET dataset show that the proposed model achieves significant performance gains with fewer parameters.
Remote sensing image change detection method plays a great role in land cover research, disaster assessment, medical diagnosis, video surveillance, and other fields, so it has attracted wide ...attention. Based on a small sample dataset from SZTAKI AirChange Benchmark Set, in order to solve the problem that the deep learning network needs a large number of samples, this work first uses nongenerative sample augmentation method and generative sample augmentation method based on deep convolutional generative adversarial networks, and then, constructs a remote sensing image change detection model based on an improved DeepLabv3+ network. This model can realize end-to-end training and prediction of remote sensing image change detection with subpixel convolution. Finally, Landsat 8, Google Earth, and Onera satellite change detection datasets are used to verify the generalization performance of this network. The experimental results show that the improved network accuracy is 95.1% and the generalization performance is acceptable.
Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, ...which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability.
With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the ...UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.
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