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Existing algorithms based on remote sensing for landslide displacement estimation, such as C2C, C2M, DOD, and M3C2, are sensitive to errors generated in data processing, and further improving their accuracy is difficult. To address this issue, given that redundant observations may occur in landslide monitoring, we proposed a robust estimation method of landslide displacement from multitemporal unmanned aerial vehicle (UAV) photogrammetry-derived point clouds. The proposed method first establishes the relevant graph to manage the trajectory of error propagation for landslide displacement estimation for all possible paths. Two modules, namely intra- and inter-estimates, are explored to reduce the impact of outliers and high surface roughness in point clouds derived by UAV photogrammetry. Individually, the intraestimate operation is used to calculate landslide displacement between two temporal point clouds by robust estimation considering outlier constraint, and the interestimate operation is used to obtain the optimal calculation of landslide displacement by minimizing a given objective function using IGG robust estimation proposed by the Institute of Geodesy and Geophysics at the Chinese Academy of Sciences. Experimental results show that the proposed method is significantly superior to conventional methods, such as C2C, C2M, and M3C2, with an accuracy improvement of at least 8%.

Existing convolutional neural network (CNN) based methods usually tend to ignore the contextual information for citrus tree canopy segmentation. Although popular transformer models are helpful in extracting global semantic information, they ignore the edge details between citrus tree canopies and the background. To address these issues, we propose a parallel fusion neural network considering both local and global semantic information for citrus tree canopy segmentation from 3-D data, which are derived by unmanned aerial vehicle (UAV) mapping. In the feature extraction stage, a parallel architecture, concatenated by EfficientNet-V2 and CSwin transformer, is used to extract local and global information of citrus trees. In the feature fusion stage, we design a coordinate attention-based fusion module to retain the contextual information and local edge details of citrus tree canopies. Additionally, to exaggerate the exclusivity between tree canopies and complex backgrounds, 3-D data incorporating RGB imagery and canopy height model derived by UAV photogrammetry are generated for citrus tree canopy segmentation. Experimental results indicate that the proposed method performs considerably better than methods based only on CNN or transformer models and is superior to state-of-the-art methods (e.g., the highest mIoU score of 93.46%).

The core loss calculation is essential for the design and performance evaluation of high-frequency transformers. It is related to the dimensions, temperature rises performance, and operating efficiency of transformer designs. Some scholars have examined the correlation between the characteristics of the core and losses, such as the core air gap length and the core structure; some investigations have also analyzed the effects of harmonics, distortion flux, and other factors on core losses. However, in reality, the core operating environment of high-frequency transformers is very complex, and achieving the required accuracy of loss model calculations is difficult. This paper discussed the insufficiency of the classical Bertotti loss separation model in estimating core loss under non-sinusoidal excitation of high-frequency transformers. Based on the derivation and modification of the classical loss model, the magnetic performance test platform of the nanocrystalline core material was established. In order to obtain a higher-order correction calculation method for the core loss of high-frequency transformers under trapezoidal wave excitation, the experimentally measured loss curves of nanocrystalline materials were further corrected. Finally, the accuracy of the correction model was verified by changing the loss results by varying the trapezoidal wave rise time constant.

Travel online reviews is important experience related information for understanding an inherent personality trait, novelty seeking (NS), which influences tourism motivation and the choice of tourism destinations. Manual classification of these reviews is challenging due to their high volume and unstructured nature. This paper aims to develop a classification framework and deep learning model to overcome these limitations. A multi-dimensional classification framework was created for NS personality trait that includes four dimensions synthesized from prior literature: relaxation seeking, experience seeking, arousal seeking and boredom alleviation. Based on 30 000 reviews from TripAdvisor we propose a deep learning model using Bidirectional Encoder Representations from Transformers (BERT)- Bidirectional Gated Recurrent Unit (BiGRU) to recognize NS automatically from the reviews. The classifier based on BERT- BiGRU and NS multi-dimensional scales achieved precision and F1 scores of 93.4% and 93.3% respectively, showing that NS personality trait can be relatively accurately recognized. This study also demonstrates that the classifier based on multi-dimensional NS scales can produce satisfactory results using the deep learning model. The findings also indicate that the BERT- BiGRU model achieves the best effect compared to the same kind of deep learning models. Moreover, it proves that personality traits can be automatically identified from travel reviews based on computational techniques. For practical purposes, this study provides a comprehensive classification framework for NS, which can be used in marketing and recommendation systems operating in the tourism industry.

In recent years, the toroidal dipoles have had a profound impact on several fields including electromagnetism. However, the on-demand design of toroidal metasurfaces is still a very time-consuming process. In this paper, a method of neural network simulating the nonlinear relationship between the structural parameters of metasurfaces and its multipole scattered powers is proposed based on a deep learning algorithm. The forward network can quickly predict the scattered powers from input structural parameters, which can achieve an accuracy comparable to the electromagnetic simulations. In addition, with the required scattering spectrum as input, the appropriate parameters of the structure could be automatically calculated and then output by the inverse network which can achieve a low mean square error of 0.074 in training set and 0.18 in the test set. Compared with the conventional design process, the proposed deep learning model can guide the design of the toroidal dipole metasurface faster and pave the way for the rapid development of toroidal metasurfaces.

With the fast development in the fields of photovoltaics and integrated circuits, the measurement of surface topographies and micro-scaled defects has attracted intensive interests. However, existing measurement methods are time-consuming and unsuitable for in-situ detection. Therefore, a fast topographic measurement method is developed based on the deflectometric microscope system. Microscopic deflectometry is an attractive tool due to its high sensibility to surface slopes and large dynamic range of measurement. For the quantitative reconstruction of surface topographies from slopes, an integration method is developed based on the minimum spanning tree, and the integration path is designed based on a sparse representation and a curl map. The robustness and accuracy of the integration method are validated by numerical simulation and experiments. This method can achieve a high measurement accuracy with a high lateral resolution and a depth of field of 90 μm, hereby improving the manufacturing efficiency and quality of opto-electronics functional devices.

Owing to the rapid development of unmanned aerial vehicle (UAV) technology and various photogrammetric software, UAV photogrammetry projects are becoming increasingly automated. However, marking ground control points (GCPs) in current UAV surveys still generally needs to be manually completed, which brings the problem of inefficiency and human error. Based on the characteristics of UAV photogrammetry, a novel type of circular coded target with its identification and decoding algorithm is proposed to realize an automatic and accurate approach for marking GCPs. UAV survey experiments validate the feasibility of the proposed method, which has comparative advantages in efficiency, robustness, and accuracy over traditional targets. Additionally, we conducted experiments to discuss the effects of projection size and viewing angle, number of coded bits, and environmental conditions on the proposed method. The results show that it can achieve robust identification and accurate positioning even under challenging conditions, and a smaller number of coded bits is recommended for better robustness.

Crop classification is one of the focused topics in remote sensing study nowadays. Optical imageries, although providing much information, are often contaminated with cloud or other weather effects while SAR imageries are more resilient to those. Temporal series data are often used to improve classification accuracy, especially in crop classification. This article investigates the usage of temporal series SAR imagery on crop classification in vast rural areas of China. The selected area of interest has a complicated, heavily mixed agriculture as well as lots of nonagricultural landcovers. Total ten classes are considered, six of them being crop types. A pixel-based classifier using subspace kth nearest neighbor (KNN) algorithm is applied to open source Sentinel-1 polarized SAR data. Discussion includes analyzing temporal features of the SAR observation, time domain smoothing, feature engineering, different classification algorithms, and selection of temporal series. The study results in an overall accuracy of 98.2% for the ten classes in fivefold cross validation, indicating a propitious application for agricultural monitoring using SAR data.

Quadcopter is an important way for the human to explore the physical world. The brain-computer interface (BCI) technology is used to control the quadcopter flight in order to help disabled persons communicate with the external world freely. In this study, a quadcopter control system using a hybrid BCI based on off-line optimization and enhanced human-machine interaction was designed to control the quadcopter flight in 3D physical space. The proposed system implemented the control of quadcopter moving up/down, forward/backward, left/right by six different SSVEP, and turning left/right by left-hand and right-hand motor imagery. Meanwhile, the optimization of the control system and the human-machine interaction enhancement improved practicability in real-time use. Five subjects participated in an on-line experiment to control the quadcopter flight in real-time. The average classification accuracy of EEG-based commands in the on-line experiment was 87.09±2.82% and information transfer rate (ITR) was 0.857±0.085 bits/min. The results demonstrated the feasibility of multidirectional control of quadcopter flight in 3D space by using hybrid BCI technology and revealed the practicality and operability of the hybrid BCI control system based on off-line optimization and human-machine interaction enhancement.

Reconstruction of complex geological surface is widely used in oil and gas exploration, geological modeling, geological structure analysis, and other fields. It is an important basis for data visualization and visual analysis in these fields. The complexity of geological structures, the inaccuracy and sparsity of seismic interpretation data, and the lack of tectonic morphological information can lead to uncertainty in geological surface reconstruction. The existing geological surface uncertainty characterization and uncertain reconstruction methods have a shortcoming in balancing the interpolation error of high-confidence samples and model structure risk. Based on support vector regression (SVR), a method with confidence constraints for uncertainty characterization and the modeling of geological surfaces is proposed in this article. The proposed method minimizes the structural risk by adding a regularization term representing the model complexity, integrates high-confidence samples, such as drilling data, based on confidence constraints, and utilizes well path points by assigning appropriate inequality constraints to the corresponding prediction points. The results based on a real-world fault data set show that the uncertainty envelopes and fault realizations generated by the proposed method are constrained by well observations and well paths, effectively reducing the uncertainty.

Imaging of unmanned aerial vehicle easily suffer from haze, resulting in decline in the quality of required remote sensing images. The influence brings great challenges in later analysis and process. Although dark channel prior has acquired substantial achievements, some limitations, including imprecise estimation of atmospheric light, color distortion, and lower brightness of defogging image, still exist. In this article, to target these drawbacks, a novel defogging method for single image is proposed. First, a novel atmospheric scattering model is proposed to define the more accurate atmospheric light by introducing an adaptive variable strategy. Next, unlike traditional dark channel prior, a novel estimation method is presented by fusing dark and light channels to estimate more precise atmospheric light and transmittance. Then, we adopt the gray image corresponding to color image as a guidance image to refine the transmittance to further decrease the time complexity. Aiming at the region of low transmittance, a novel compensation function is created to improve the region of low transmittance and avoid color distortion. Moreover, a simple and effective calculation method is proposed to determine parameters in compensation function. Finally, the clear remote sensing image is established by an improved atmospheric scattering model. Extensive experiments on real-world datasets demonstrate that the proposed method outperforms several other state-of-the-art approaches both on subjective and objective quality evaluations.

Aiming at enhancing reactive power compensation (RPC) capability of the traditional single-phase bridgeless rectifier, a 2.5kVA circuit topology composed of a single-phase bridgeless rectifier and a third-order tuned LC series branch in parallel connection is proposed. The corresponding control strategy using the third harmonic injection pulse width modulation (THIPWM) technique is also presented, which enables the proposed single-phase bridgeless rectifier to operate under varied power factor with the near-sinusoidal current at the point of common coupling (PCC). The principle of the proposed topology is discussed in detail and reactive power compensation capability of the proposed single-phase bridgeless rectifier under a certain amount of active power to be transmitted to the load is studied. Simulation and experiment results are given to validate the feasibility of the proposed circuit topology and the correctness of the theoretical analysis.

With the rapid growth of Web services on the Internet, it becomes a great challenge for Web services discovery. Classifying Web services with similar functions is an effective method for service discovery and management. However, the functional description documents of Web services usually are short in their length, with sparse features and less information, which makes most topic models unable to model the short text well, consequently affecting the Web service classification. To solve this problem, a Web service classification method based on Wide & Bi-LSTM model is proposed in this paper. In this method, first, all the discrete features in the description documents of Web services are combined to perform the breadth prediction of Web service category by exploiting the wide learning model. Second, the word order and context information of the words in the description documents of Web services are mined by using the Bi-LSTM model to perform the depth prediction of the Web service category. Third, it uses the linear regression algorithm to integrate the breadth and depth prediction results of Web service categories as the final result of the service classification. Finally, compared with six Web service classification methods based on TF-IDF, LDA, WE-LDA, LSTM, Wide&Deep, and Bi-LSTM, respectively, the experimental results show that our approach achieves a better performance in the accuracy of Web service classification.

Cooperative vehicle infrastructure system (CVIS) is an essential part of the future intelligent transportation system (ITS). However, CVIS is uneasy to meet different QoS requirements of various information flows in the existing traffic environment. In fact, some real-time applications highly related to traffic safety early warning cannot be easily achieved or passengers' Internet accessing experience is always not very good. To overcome the above problem, this paper proposes a communication protocol named data networking for vehicle infrastructure cooperation (NDNVIC), which can respond to the vehicle's data query timely. In this paper, we utilize some ZigBee nodes as vehicle and RSU nodes and conduct several simulation experiments under a large-scale deployment with 150 RSU nodes to verify the effectiveness of NDNVIC. Compared with the traditional TCP/IP protocol, the response rate of NDNVIC is about four times, as fast as, that of TCP/IP.

Illumination-insensitive image representation is a great challenge in the computer vision field. Illumination variations considerably obstruct the effectiveness of image feature extraction. In this paper, we present a novel and generalized learning framework for illumination-insensitive image representation, which can learn the discriminative features through maximizing the inter-difference and minimizing intra-difference of the images with boosting. Particularly, we enhance the discriminative capacity of illumination-insensitive image representation in three aspects. First, we learn a subset of different synergistic Weber excitation patterns (SWEP) to generate the dominant SWEP (DSWEP) and DSWEP codebook for exploring optimal illumination-insensitive patterns. Second, a compact DSWEP (C-DSWEP) is learned with a boosted set of weight to generate C-DSWEP codebook. Discriminative learning is aimed at robustness and compactness. Third, the discriminative histogram learning model is established for encoding CDSEP to further improve the discriminative ability and reduce redundancy. The extensive experiments on CMUPIE, FERET, Yale B, Yale B ext., LFW, and PhoTex databases have highlighted the superiority and the robustness of our method compared with some other state-of-the-art methods.