An innovative approach based on meta-learning for real-time modal fault diagnosis with small sample learning

The actual multimodal process data usually exhibit non-linear time correlation and non-Gaussian distribution accompanied by new modes. Existing fault diagnosis methods have difficulty adapting to the complex nature of new modalities and are unable to train models based on small samples. Therefore, this paper proposes a new modal fault diagnosis method based on meta-learning (ML) and neural architecture search (NAS), MetaNAS. Specifically, the best performing network model of the existing modal is first automatically obtained using NAS, and then, the fault diagnosis model design is learned from the NAS of the existing model using ML. Finally, when generating new modalities, the gradient is updated based on the learned design experience, i.e., new modal fault diagnosis models are quickly generated under small sample conditions. The effectiveness and feasibility of the proposed method are fully verified by the numerical system and simulation experiments of the Tennessee Eastman (TE) chemical process

Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images

To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches

DFANet: Denoising Frequency Attention Network for Building Footprint Extraction in Very-High-Resolution Remote Sensing Images

With the rapid development of very-high-resolution (VHR) remote-sensing technology, automatic identification and extraction of building footprints are significant for tracking urban development and evolution. Nevertheless, while VHR can more accurately characterize the details of buildings, it also inevitably enhances the background interference and noise information, which degrades the fine-grained detection of building footprints. In order to tackle the above issues, the attention mechanism is intensively exploited to provide a feasible solution. The attention mechanism is a computational intelligence technique inspired by the biological vision system capable of rapidly and automatically catching critical information. On the basis of the a priori frequency difference of different ground objects, we propose the denoising frequency attention network (DFANet) for building footprint extraction in VHR images. Specifically, we design the denoising frequency attention module and pyramid pooling module, which are embedded into the encoder–decoder network architecture. The denoising frequency attention module enables efficient filtering of high-frequency noises in the feature maps and enhancement of the frequency information related to buildings. In addition, the pyramid pooling module is leveraged to strengthen the adaptability and robustness of buildings at different scales. Experimental results of two commonly used real datasets demonstrate the effectiveness and superiority of the proposed method; the visualization and analysis also prove the critical role of the proposal

Triple Change Detection Network via Joint Multi-frequency and Full-scale Swin-Transformer for Remote Sensing Images

Xue D, Lei T, Yang S, et al. Triple Change Detection Network via Joint Multi-frequency and Full-scale Swin-Transformer for Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2023:1-1.Although deep learning-based change detection (CD) methods achieve great success in remote sensing images, they still suffer from two main challenges. First, popular Convolutional Neural Networks (CNNs) are weak in extracting discriminated features focusing on changed regions, since most methods ignore the multi-frequency components of bi-temporal images. Second, although existing CD methods employ the Transformer structure to capture long-range dependency for global feature representation, it is difficult for them to simultaneously take into account the long-range dependency of changed objects at various scales. To address the above issues, we propose a triple change detection network (TCD-Net) via joint multi-frequency and full-scale Swin-Transformer. The proposed TCD-Net has two main advantages. First, we propose a multi-frequency channel attention (MFCA) module to boost the ability of modeling the channel correlation, which can compensate for the problem of insufficient feature representation caused by only performing global average pooling (GAP). Furthermore, a joint multi-frequency difference feature enhancement (JM-DFE) guiding block is proposed to improve the boundary quality and the position awareness of truly changed objects, which can effectively extract channel features of multi-frequency information and thus improve the discriminative ability of features. Second, unlike Siamese-based structures, we propose a full-scale Swin-Transformer (FST) module as the third branch to model and aggregate the long-range dependency of multi-scale changed objects, which can alleviate the missed detections of small objects and achieve more compact changed regions effectively. Experiments on three public CD datasets exhibit that the proposed TCD-Net achieves better CD accuracy with smaller model complexity than state-of-the-art methods. The code is publicly available at https://github.com/RSCD-mz/TCD-Net

Research on internal external collaborative optimization strategy for multi microgrids interconnection system

Microgrid is one of the most effective solutions to integrate renewable generation into power system. However, microgrids also have some limitations, such as increasing renewable energy accommodation, promoting multi-energy complementarity, and improving energy efficiency. Through autonomous management and energy interaction among microgrids. The establishment of multi microgrids provides ideas for solving the above problems. Multi microgrids has obvious advantages in promoting renewable energy accommodation, improving the economy and energy utilization of power grid operation, and reducing the impact on the distribution network. Therefore, from the perspective of energy scheduling, this paper proposes a internal external collaborative optimization strategy for multi microgrids. Firstly, a cogeneration microgrid model based on graph theory is established, which can not only represent the energy transmission process inside the microgrid, but also the energy interaction process between microgrids. Secondly, a microgrid interconnection pipeline transmission model based on the loss function is established. This model can not only reflect the energy transmission state in the pipeline, but also facilitate the linear solution of the optimization model. Finally, by setting new decision variables and constraints, the linear solution of the internal external collaborative optimization of the multi microgrids is realized, and its rationality and feasibility are verified by a case

Carbon foam composites containing carbon nanotubes and graphene oxide as additives for enhanced mechanical, thermal, electrical and catalytic properties

Based on the versatile nature and applications of Carbon foam (CF), up to now many attempts were performed to improve the structure and properties of CF by incorporating various additives in the CF matrix. But these additives improved one property on the cost of another ones. Herein, we have synergistically incorporated multi-walled carbon nanotubes (MWCNTs) and graphene oxide (GO) with varying loadings as additives in the CF matrix via direct pyrolysis to achieve all the desired properties. Seven different types of CF composites including pure CF were prepared and their effect on the structure, mechanical, thermal, electrical and catalytic characteristics has been reported. The results revealed that after the inclusion of MWCNTs and GO contents, the microstructural performance of CF samples was amazingly improved. Additionally, it was observed that mechanical, thermal, electrical and catalytic behaviors of the CF samples were significantly enhances by the increase of nanohybrids. The compressive strength and Young's modulus reveals their optimum limits up to 19.3 and 57.4 MPa respectively on 2 wt.% MWCNTs-GO additive loadings. Similarly, the greatest thermal and electrical conductivities of 30.92 W/m. K and 27.4 × 103 S/m were showed by CF samples having 2 wt. % MWCNTs-GO loadings. Whereas, the decolorization activity of the CF and their nanocomposites were tested against methyl orange dye and it was observed that the sample with enhanced MWCNTs and GO have good decolorization activity and much sustainable than other samples. The 4% CF/MWCNTs-GO decolorized about 76% MO dye under exposure to UV light within 60 min. The decolorization of MO dye increases with increasing nanocomposite dosage and decreasing initial dye concentration

Investigation of High-Q Lithium Niobate-Based Double Ring Resonator Used in RF Signal Modulation

In recent years, millimeter-wave communication has played a crucial role in satellite communication, 5G, and even 6G applications. The millimeter-wave electro-optic modulator is capable of receiving and processing millimeter-wave signals effectively. However, the large attenuation of millimeter waves in the air remains a primary limiting factor for their future applications. Therefore, finding a waveguide structure with a high quality factor (Q-factor) is critical for millimeter-wave electro-optic modulators. This manuscript presents the demonstration of a double ring modulator made of lithium niobate with the specific goal of modulating an RF signal at approximately 35 GHz. By optimizing the microring structure, the double ring resonator with high Q-factor is studied to obtain high sensitivity modulation of the RF signal. This manuscript employs the transfer matrix method to investigate the operational principles of the double ring structure and conducts simulations to explore the influence of structural parameters on its performance. Through a comparison with the traditional single ring structure, it is observed that the Q-factor of the double ring modulator can reach 7.05 × 108, which is two orders of magnitude greater than that of the single ring structure. Meanwhile, the electro-optical tunability of the double ring modulator is 6 pm/V with a bandwidth of 2.4 pm, which only needs 0.4 V driving voltage. The high Q double ring structure proposed in this study has potential applications not only in the field of communication but also as a promising candidate for a variety of chemical and biomedical sensing applications

Recent advances in the structure and biomedical applications of nanodiamonds and their future perspectives

Nanodiamond (ND) is becoming the core center of interest for many researchers due to its immense utilization in different fields of science. The extraordinarily unique attributes provided by NDs include biocompatibility, easy production, chemical inertness, rich surface chemistry, small size, and fluorescence resistance. In biomedical sciences, all these characteristics uplift the standards of ND particles. Recent studies on the biomedical side have been found to strengthen its significance ever since ND tested positive for utilization in diagnostics and drug delivery. In addition, NDs have been proven beneficial in the manufacturing of biodegradable surgical instruments, scaffolds for tissue engineering, and drugs to eliminate resistant microbes and viruses. Genetic materials are also delivered to the center of the nucleus with the aid of ND particles. In this review, we have discussed in detail all the benefits of NDs, their applications in sensors, ND-blood interactions, blood-contacting, orthopedics, anti-cancer agents, antimicrobial agents, and drug/gene delivery. The importance and significance of the purification, synthesis, processing, and surface functionalization of NDs have been discussed. Recent advances in modern technologies in the field of biomedical and biological sciences also have been covered in this manuscript