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Rolling bearings in production practice usually serve in a healthy state. Some fault state labels are scarce or even no labels, resulting in unbalanced data categories. Meanwhile, frequent working condition switching results in significant differences in data distribution among working conditions, and labeled data in some working states cannot be fully utilized. To deal with the challenge of low fault identification accuracy caused by these practical factors, this paper proposed a novel adversarial unsupervised subdomain adaption multi-channel deep convolutional network (ASMDCN). Firstly, a parallel three-channel depth feature extraction module is built, and a multi-scale convolution kernel is used to fully extract the rich features of vibration signals under various working conditions. Secondly, a novel loss function is designed to adequately consider the classification difficulty of samples and the degree of class imbalance. Finally, the adversarial training strategy is used to force the feature extractor to extract the domain invariant features, and the Local Maximum Mean discrepancy (LMMD) is used to align the global and related subdomains of the source and target domains. The experimental results show that the designed feature extraction can fully extract the domain-invariant features of the rolling bearings under different working conditions. Under the proposed objective function optimization, the network model can fully align the features of multi-source and single-target domain under unbalanced data and has strong generalization performance.

This paper studies a class of quasi-cyclic LDPC (QC-LDPC) codes, i.e., Tanner (3, 23)-regular QC-LDPC codes of code length $23p$ with $p$ being a prime and $p \equiv 1 (\mathrm {mod} 69)$ . We first analyze the cycle structure of Tanner (3, 23)-regular QC-LDPC codes, and divide their cycles of lengths 4, 6, 8, and 10 into five equivalent types. We propose the sufficient and necessary condition for the existence of these five types of cycles, i.e., the polynomial equations in a 69th unit root of the prime field $\mathbb {F}_{p}$ . We check the existence of solutions for such polynomial equations by using the Euclidean division algorithm and obtain the candidate girth values of Tanner (3, 23)-regular QC-LDPC codes. We summarize the results and determine the girth distribution of Tanner (3, 23)-regular QC-LDPC codes.

With the advancement of the wood processing industry, the demand for the detection of surface defects in wood has become increasingly urgent. The application of automated production technology has enhanced the efficiency and precision of wood processing, which can significantly impact product quality and competitiveness. However, current methods for detecting surface defects in wood suffer from issues such as low detection accuracy, high computational complexity, and poor real-time performance. In response to these challenges, this paper proposes a high-precision, lightweight, real-time wood surface defect detection method based on YOLO(GBCD-YOLO) model. Firstly, the Ghost Bottleneck is introduced to improve the computational efficiency and inference speed of deep neural networks. Furthermore, the BiFormer is incorporated in the neck to enhance the performance of natural language processing tasks. Simultaneously, CARAFE is utilized as an upsampling replacement to enhance perceptual and capture abilities for details. In addition, the Dynamic Head is introduced to enhance the method’s flexibility and generalization ability, and the loss function is replaced with complete intersection over union (CIoU). The proposed method was evaluated using an optimized dataset and the YOLOv5s model was chosen as the baseline. The experimental results show that compared with the original YOLOv5s, the mAP (0.5) has been improved by 13.45%, reaching 88.72%. The mAP (0.5:0.95) increased by 11.95%, and FPS increased by 6.25%. In addition, the parameter of the improved model has been reduced by 15.49%. These results indicate that the proposed GBCD-YOLO improves the real-time detection performance of wood surface defects.

The increasing integration of distributed household photovoltaics (PVs) and electric vehicles (EVs) may further aggravate voltage violations and unbalance of low-voltage distribution networks (LVDNs). DC distribution networks can increase the accommodation of PVs and EVs and mitigate mutilple power quality problems by the flexible power regulation capability of voltage source converters. This paper proposes schemes to establish hybrid AC/DC LVDNs considering the conversion of the existing three-phase four-wire low-voltage AC systems to DC operation. The characteristics and DC conversion constraints of typical LVDNs are analyzed. In addition, converter configurations for typical LVDNs are proposed based on the three-phase four-wire characteristics and quantitative analysis of various DC configurations. Moreover, an optimal planning method of hybrid AC/DC LVDNs is proposed, which is modeled as a bi-level programming model considering the annual investments and three-phase unbalance. Simulations are conducted to verify the effectiveness of the proposed optimal planning method. Simulation results show that the proposed optimal planning method can increase the integration of PVs while simultaneously reducing issues related to voltage violation and unbalance.

This study explores the consensus of multi-agent systems in the presence of ambient noise using intermittent Brownian noise stabilization. Firstly, the research provides a mathematical explanation of multi-agent systems and intermittent stochastic noise, and establishes a sufficient condition for multi-agent systems to achieve consensus using intermittent stochastic noise control input. Secondly, a consensus criterion is proposed for a class of multi-agent systems that are affected by ambient noise. Finally, the simulation results demonstrate that the intermittent stochastic noise stabilization technique can facilitate the establishment of consensus in multi-agent systems.

Accurately classifying tree species in urban forests remains a major challenge due to the limitations of existing methods. While hyperspectral imaging is capable of capturing detailed spectral information, it struggles with the issue of ‘similar reflectance curves for different species’. On the other hand, the intensity frequency curve also has the problem of ‘similar intensity frequency curves for different species’. To overcome these challenges, a new method is proposed in this study. The method combines UAV LiDAR intensity frequency features (IF) and hyperspectral features (HF) to accurately identify tree species, abbreviated as HICM. The results of the study demonstrate that HICM can accurately identify 16 common urban tree species with an overall accuracy (OA) of 92.0% and kappa of 91.2%. Compared with using only IF or HF for identification, HICM improves OA by 16.9% and 12%, and kappa by 21.6% and 10.1%, respectively. This shows that HF and IF have complementarity, and the combination of these two features effectively addresses the problems of “same species different frequency” and “same species different spectrum”.

NASA's (National Aeronautics and Space Administration) ICESat-2 with a Photon Counting LiDAR (Light Detection And Ranging) Sensor sensitively detects signal photons at high speed with an advanced detection system called the Advanced Topographic Laser Altimeter System (ATLAS). However, the sensor also extracts a large amount of background photon noise coming from the atmosphere, ground, sun, or other radiation. This condition is particularly evident in forest areas. This study proposes an automatic machine learning approach to utilize data for forestry applications to improve data availability compared to NASA's official product. Our method uses only a very limited number (10%) of sample points for training, ensuring operational efficiency and training accuracy. We conclude that the integrated learning performance generally outperforms single models, and the mean F1 score of all tests is approximately 0.9. The mean F1 score of the Stacked Ensembles model is 0.957 ahead of the other models. The top three variables used in training models are kNNdist5, kNNdist10, and h. These three variables could explain 51.6% of the components of the models. Over the regions tested, the proposed method could improve the proportion of signals correctly identified by 6.4%, 12.2%, 2.7%, 9.3%, and 1.4% in five datasets. The model performs better in low signal-to-noise (SNR) datasets less than 7.5. The method would be largely unaffected by differences in topography, noise distribution, and SNR. The classifiers could correct misclassified labels in ATL08 products and show good stability in different conditions.

Internal discharges in composite insulators often cause abnormal heating, interface failure and erosion of the core rod. However, the mechanism and the exact process of the degradation of HTV shed and FRP core rod caused by internal discharges are still unclear. In this work, abnormal heating insulator samples are taken from an AC 500 kV extra-high voltage (EHV) transmission line. Macro- and micro-scopic morphological observation, hardness tests, industrial CT tests and chemical property tests are applied on the samples. The cause of heating and the deterioration mechanism of each part of insulators is further explored. Cavities and air holes are found in the HTV skirts in this batch of insulators, which are generated during manufacturing. These cavities lead to the distortion of electrical field and become the origin of the discharges. Decomposition of polydimethylsiloxane leads to the hardening and chalking of the HTV shed. It is also found that the adhesive in the rod-housing interface fails during operation. Epoxy resin in core rod decomposes in a multi-factor aging environment, leading to the decay-like aging of the core rod surface. As the aging progress develops inward, the insulator will eventually lose its mechanical properties. It is inferred that the internal discharge is the main reason of heating and deterioration of composite insulators. Moreover, long-term discharge will lead to the decay-like aging of core rods.

The soybean futures market in China occupies an important position in the agricultural product futures market. The research on the fluctuation of soybean futures price and the prediction of the future price trend has always been the focus of extensive attention in the field of agricultural economics. This paper proposes an EEMD-NAGU hybrid prediction model for soybean futures price based on Ensemble Empirical Mode Decomposition (EEMD) and New Attention Gate Unit (NAGU). The model first uses EEMD to process soybean futures price data into multiple Intrinsic Mode Functions (IMFs) and residual sequence. Then calculates the sample entropy of each IMF and reconstructed the IMF into three components of low-frequency, medium-frequency, and high-frequency, according to the size of the sample entropy. NAGU is formed by embedding the Attention mechanism (Attention) into the Gate Recurrent Unit (GRU) structure, further improving the learning capability of the model. NAGU splits the original reset gate and update gate, sets up two-stage respectively, and uses different activation functions to capture the information in historical data better. Soybean futures price data is complex nonlinearities and contain more noise. In this model, EEMD plays denoises the time series data and fixes the model input. NAGU can perform differential learning on data and finally produce prediction results. EEMD-NAGU is compared with thirteen other prediction models (Support Vector Regression (SVR), LSTM, GRU, NAGU, EEMD-LSTM, EEMD-GRU, EEMD-NGU, Attention-LSTM, Attention-GRU, Attention-NGU, EEMD-Attention-LSTM, EEMD-Attention-GRU, and EEMD-Attention-NGU). The evaluation indexes of the experiment are Mean Absolute Error (MAE), Mean Square Error (MSE), and R Squared ( $R^{2}$ ). The experimental results show that the EEMD-NAGU model outperforms other models with better prediction performance. The model can be widely used to predict the price of wheat, corn, gold, oil, and other time series data.

Background and objective: Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. Methods: In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. $\mathtt {SAS}$ programs to implement these newly proposed hospital compare models were thoroughly developed. The $\mathtt {SAS}$ programs are freely available via a GitHub (https:\\www.GitHub.com) repository. Results: We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Conclusions: Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability.

Electric power fittings are the link between conductors and transmission towers, and it is essential to improve their ability to withstand damage. To obtain the actual load of electric power fittings and study the stress of each electric power fitting under the actual load, a transmission line model of catenary-shaped and an integral model of tension insulator string are established in this paper. Then, conductors with a uniform thickness of ice coating and ice shedding process are simulated by changing density method. The results show that the maximum dynamic tension of the conductor of the isolated span transmission line is greater than the static tension of conductor icing only when the length of the ice-shedding section is small. Under any ice-shedding condition, the maximum dynamic tension of the conductor of the multi-span transmission line will be close to or greater than the static tension after conductor icing. When the form conductor tension is loaded on the tension insulator string, it can be found that the most dangerous fitting in the tension insulator string is found, and the stress analysis is carried out on the key connecting fittings, which provides a reference for the design and optimization of electric power fitting.

Aimed at solving the problems of frequent handover and large overheads for positioning in 6G, this paper proposes a reinforcement learning (RL)-based visible light positioning (VLP) handover scheme by means of a stereo camera system in which a lower handover rate and higher positioning accuracy were achieved simultaneously. Because of the randomness of the distribution location of indoor light sources and obstacles, even if the parameters of light sources and receivers are consistent, users cannot rely on unitary and invariable parameters to determine whether to change the access point (AP) in the process of moving, especially in some special locations. The proposed scheme, which decomposed the user’s moving track at different speeds in a VLP system to optimize selecting the AP by using RL at each step, was exhibited and tested. Experimental results show that the proposed scheme achieved millimeter-level positioning accuracy, improved the normalized reward by over 40% and reduced the handover rate by 87% and 78% compared to the immediate handover (IHO) and dwell handover (DHO) methods concurrently.

3D reconstruction is the transformation of real objects into mathematical models. By using 3D models, we can observe the shape and measure the parameters, and help us to analyze the properties of objects. For the problems of incompleteness and inefficiency in the reconstruction of object 3D point clouds, a fast and automated system for panoramic 3D point cloud reconstruction of objects was proposed. First, we designed an automatic platform, which could acquire RGB image sequences of objects in two directions. Then we adopted the Structure From Motion (SFM) algorithm to generate point clouds. For the problem of different scales of point clouds, we obtained the scaling by calculating the length ratio of the axes of the oriented bounding box, and scaled the point clouds to a uniform scale. In addition, markers were placed around the object and used to acquire the rotation matrix of the object point cloud in two directions. Finally, we verified the point cloud models of different objects generated by the system, and found that the relative error didn’t exceed 6.67%. According to the results, the system proposed could reconstruct the panoramic 3D point cloud of the object better and provide a reference for related research.

Based on a developed three-layerair–ice–water reflection model, this article simulates the evolution of reflection coefficient versus elevation angle. Due to the interference between the signal components from the air–ice and ice–water interfaces, the reflection coefficient experiences an oscillating pattern versus elevation angle so that detecting sea ice using the power or amplitude of the reflected global navigation satellite system (GNSS) signal has to choose a suitable satellite to reduce the influence of the oscillating pattern. A sea ice surface is more stable and presents higher correlation than a dynamic ocean surface, this article explores the potential of detecting and measuring sea ice using the coherency of reflected GNSS signal for coastal scenario. Experimental results show that phase coherency can significantly detect sea ice without strictly limiting elevation and azimuth angle. This article also is to is to explore the potential of retrieving sea ice thickness using the oscillating phase pattern versus elevation angle. The phase compensation and the dual-polarization observation are proposed to remove the delay phase between the direct and reflected signal from the estimated phase of the reflected GNSS signal. The results show that the amplitude and frequency of the oscillating phase pattern, respectively, have an inversely proportional and positively linear relationship with sea ice thickness. Simulation shows that, compared to the oscillating amplitude, the oscillating frequency is a better choice to measure sea ice thickness. The frequency of the dual-polarization oscillating pattern could provide the measurement performance with a root-mean-square error of 0.05 m.

Remote sensing images acquired by the FY4 satellite are crucial for regional cloud monitoring and meteorological services. Inspired by the success of deep learning networks in image super-resolution, we applied image super-resolution to FY4 visible spectrum (VIS) images. However, training a robust network directly for FY4 VIS image super-resolution remains challenging due to the limited provision of high resolution FY4 sample data. Here, we propose a super-resolution and transfer learning model, FY4-SR-Net. It is composed of pretraining and fine-tuning models. The pretraining model was developed using a deep residual network and a large number of FY4 A 4 and 1 km resolution VIS images as the training data. The knowledge derived from 4 km to 1 km resolution images was incorporated into FY4 B 1 km to 0.25 km resolution VIS images. The FY4-SR-Net is fine-tuned by incorporating limited 1 km and 0.25 km resolution panchromatic images, and then producing 1km super-resolution VIS images of the FY4 satellite. Using the one-day FY4 test dataset for qualitative and quantitative evaluations, the FY4-SR-Net outperformed the classic bicubic interpolation approach with a 16.12% reduction in root-mean-square error and a 2.97% rise in peak signal-to-noise ratio averages. The structural similarity value average increased by 0.0026. This article provides a new precedent for improving the spatial resolution of FY4 series meteorological satellites, which has important scientific significance and application properties.

Few-shot object detection (FSOD) aims to localize and recognize potential objects of interest only by using a few annotated data, and it is beneficial for remote sensing images (RSIs) based applications, such as urban monitoring. Previous RSIs-based FSOD works often try to convert the support images from class-agnostic features to class-specific vectors, and then perform feature attention operations on query image features to be tested. However, such methods still face two critical challenges: 1) They ignore the spatial similarity of support-query features, which is indispensable for RSIs detection; 2) They perform the feature attention operation in a unidirectional manner, which means that the learned support-query relations are asymmetric. In this article, to address the challenges above, we design a few-shot object detector, which can quickly and accurately generalize to unseen categories with only a small amount of data. The proposed approach contains two components: 1) the self-adaptive global similarity module that preserves the internal context information to calculate the similarity map between the objects in support and query images, and 2) the two-way foreground stimulator module that can apply the similarity map to the detailed embeddings of support and query images at the same time to make full use of support information, further strengthening the foreground objects and weakening the unconcerned samples. Experiments are conducted on DIOR and NWPU VHR-10 datasets and their results demonstrate the superiority of the proposed method compared with several state-of-the-art methods.

The Y field of view of an off-axis optical system is generally less than 1°. Scholars usually use a freeform surface to achieve a rectangular field of view imaging. However, it adds to the difficulty of processing, testing, assembling, and adjusting, and at the same time has the drawbacks of high manufacturing costs and long processing cycles. In view of the above problems, this paper presents a computational imaging design method for expanding the Y field of view of an off-axis optical system. This method analyzes the aberration characteristics of the optical system, creates a point spread function model based on the wavefront aberration theory and Zernike polynomials, and processes images using a spatial variation deconvolution algorithm. In this paper, we used this method to simulate an off-axis three-mirror optical system with a focal length of 260 mm, the f-number of 2.5, and a field of view of 8° × 1° and compare the images before and after processing. The results show that the processed images have a clear outline, the overall image quality is improved, the field of view is improved to 8° × 6°, and the modulation transfer function of the entire field of view is above 0.4. The off-axis optical system rectangular field of view imaging is realized without using a freeform surface.

A GaN Reverse-Conducting HEMT (RC-HEMT) is proposed and fabricated on the GaN/AlGaN/GaN platform. It features an integrated Schottky barrier diode (SBD) to realize reverse conduction and the double-heterojunction to enhance breakdown voltage (BV). Compared with the inherent reverse conduction capability of the conventional HEMT (Con. HEMT), the built-in SBD exhibits a low reverse turn-on voltage ( $V_{\text{RT}}$ ) and its $V_{\text{RT}}$ is independent of the threshold voltage and gate bias. At the off-state, the fixed positive and negative polarization charges form the polarization superjunction (PSJ). Therefore, the depletion region is extended and more uniform E-field distribution is obtained. Experimental results show that the RC-PSJ-HEMT achieves a low $V_{\text{RT}}$ of 0.68 V, which decreases 69.1% compared with that of the Con. HEMT. The BV of the RC-PSJ-HEMT (with $7.5 \mu \text{m}$ $V_{\text{GD}}$ ) is increased to 723 V from 202 V of the Con. HEMT.

A subwavelength gratings (SWG) assisted directional coupler (DC) is proposed to work as beam splitter (BS) for 850 nm chip-based optical coherence tomography (OCT). The bandwidth of the SWG assisted DC is maximized to 110 nm by selecting appropriate SWG geometries including its duty cycle and period. The axial point spread functions (PSFs) derived from the BS transmittances proves the optimized bandwidth is broad enough to support axial resolution higher than $4 \;\mu m$. Meanwhile, the SWG period can be manipulated to match the center wavelength between BS and light source so that the wavelength mismatching induced resolution degradation is effectively mitigated. Furthermore, a statistical analysis is performed to demonstrate the SWG assisted DC is a high fabrication-tolerant device in terms of axial resolution, even the SWG geometries suffer from a fabrication jitter in 20 nm variance.

To meet the needs of three-dimensional (3D) measurement and to achieve the high-precision fitting of the central axis of a screw thread, this study performed complex processing of the 3D structures on the surface of the thread central axis. Moreover, this study examined the problems of long processing time and low accuracy caused by the use of a point cloud with a large data volume. An improved particle swarm optimization algorithm was used to develop a fitting method for the central axis of the three-dimensional (3D) thread of a 3D convex hull. SolidWorks was used to simulate the 3D parts of a standard thread, and the Point Cloud Library was used to generate a 3D simulated point cloud for the thread surface. The maximum deviation between the fitted line and the line obtained in the two-dimensional (2D) projection method was 0.12 μm. An experiment was conducted using data regarding the tip of the plug gauge surface. The variation in the distance between the obtained straight line and the 3D point cloud was smaller than that between the obtained straight line and the 2D point cloud. Moreover, the calculation speed was higher when using the 3D method than when using the 2D method.

Medical image fusion technology has been widely used in clinical practice by doctors to better understand lesion regions through the fusion of multiparametric medical images. This paper proposes an automated fusion method based on a U-Net. Through neural network learning, a weight distribution is generated based on the relationship between the image feature information and the multifocus training target. The MRI image pair of prostate cancer (axial T2-weighted and ADC map) is fused using a strategy based on local similarity and Gaussian pyramid transformation. Experimental results show that the fusion method can enhance the appearance of prostate cancer in terms of both visual quality and objective evaluation.

Min-projection switching law, a related control method of switched linear systems model, is famous for its outstanding dynamic performance. However, under digital control framework, H6 inverter directly employing it performs unsatisfactorily. To improve the performance, a new control law based on projection-value-based switching strategy named synthetic velocity vectors control law (SVVCL) is proposed in this paper. Compared with min-projection switching law, SVVCL could be effectively applied on digital-controlled H6 inverter under the framework of pulse width modulation (PWM) technique. Firstly, fitness function of SVVCL considering static and dynamic performances is established. Secondly, control law is simplified to improve its accessibility. Further, a common approach to design compensator for improving output robustness is discussed. Finally, experiments on a 400W prototype are carried out. Results show that H6 inverter employing SVVCL has preferable static performance, strong robustness, as well as excellent dynamic response characteristics.

Soil organic matter (SOM) is rich in black soil area. It has a significant effect on soil health and luxuriant vegetation. The agricultural production activities can thus be guided by measurements of SOM at different periods. Most of previous work estimated SOM using reflectance spectra measurements on dried soil samples, to remove the effect of soil moisture content (SMC). However, it is time-consuming and almost conduct-prohibitive in the field. In this study, we utilized the continuous wavelet transform (CWT) on visible (VIS) and near infrared (NIR) spectra to test its effect on eliminating the SMC influence, and proposed to use the PCA-RF method coupled with CWT to predict SOM from the wet samples. Multi-scale coefficients can amplify the response of spectra to SOM in varying degrees, while improving the correlation of characteristic wavebands and minimizing the moisture interference on SOM specific wavelength. By analysing the multi-scale coefficients, we found that wavelengths ranging around the peaks of 580nm, 820nm, and especially the narrow region around 1400nm are highly correlated regions to SOM. Furthermore, the accuracy of SOM estimation models illustrated the effectiveness of the CWT. Results of the validation model using the dataset of wet samples on CWT scale 6 (R2 = 0.84, Mse = 0.23%, and RPD = 2.53) can be statistically equivalent to dataset of dried samples (R2 = 0.86, Mse = 0.20%, and RPD = 2.68). Combined with the PCA-RF method, SOM estimation can be perfectly performed with fewer features as input variables and has a great improved. The best prediction of validation model was on scale 6 with features extracted from 4 PCs, compared with the EPO-PLSR method (R2 = 0.85, Mse = 0.25%, and RPD = 2.54), the proposed method has a better result (R2 = 0.94, Mse = 0.09%, and RPD = 4.08).

An improved reversion method for ultrasonic surface wave characterization of coating layers is presented and validated by experiment. The experiment is implemented with seven specimens of thin Cu layers coated on Al substrates. 2-D Fourier transforms of the experimentally obtained surface wave pulse propagation signals were conducted to obtain frequency-wave number relations. The combined equations of two unknowns were derived using the relation. The two unknowns are the thickness h and the Young's modulus E of the coating layer of the specimen. The h and E are obtained by solution of the combined equations. Comparing with that used for many years which are based on 1-D Fourier transform and nonlinear regression, the new method is simpler, faster, and equally accurate and reliable. The standard error of 1.55μm is reached in the measurements of the Cu films thicknesses which vary in the range from 23.6μm to 224.8μm. The standard error of 2.97GPa is reached in measurements of Young's modulus of the Cu films and the exact value of Young's modulus of pure Cu is 110GPa.

The electric shovel is a bucket-equipped mining excavator widely used in open-pit mining today. The prolonged direct impact of the bucket teeth with hard and abrasive materials such as ore during the process of the mining excavation can cause the bucket teeth to break and fall off prematurely, resulting in unplanned downtime and productivity losses. In response to this problem, we have developed a vision-based bucket teeth fault detection algorithm with deep learning. Using a dataset based on the images of both real shovel teeth and 3D-printed models, we trained a Faster Region Convolutional Neural Network (Faster R-CNN) to obtain the number of normal bucket teeth and the positions of the bucket teeth from the images, using the additional bucket dataset from 3D-printed models to pre-train the network for improving its detection accuracy on the real bucket data. We compared the resulting Faster R-CNN model with the ZFNet, the ResNet-50, and the VGG16 and found our Faster R-CNN model to perform best in terms of accuracy and speed.

Ionospheric phase delay can seriously affect the low-frequency spaceborne synthetic aperture radar (SAR) systems. Ionospheric correction is, therefore, vital to improving the measurement accuracy of Interferometric SAR (InSAR). InSAR ionospheric correction relies mainly on two kinds of approaches, i.e., one uses SAR azimuth offsets measures, and the other achieves through a range split-spectrum (RSS) technique. However, the two approaches have different spatial sensitivities to the ionosphere, but they can complement each other. In this study, we present an integration InSAR ionospheric correction method. This method uses the Helmert variance component estimation to reasonably allocate the weights for the ionospheric measurements obtained from the azimuth offset-based and RSS techniques to improve the ionospheric correction performance. The azimuth offset can be derived from a pixel offset-tracking procedure or multiple-aperture InSAR. We demonstrated the proposed method on 28-MHz FBS mode ALOS-1 PALSAR images acquired before and after the Wenchuan earthquake in Sichuan Province, China, 2008. This study also applied the proposed method to 14-MHz FBD mode ALOS-1 PALSAR images with no significant surface deformation and high coherence in Chile. After the ionospheric correction with the proposed method, we found that the ionospheric errors in the Wenchuan and Chile cases have varied from -31.0 to 21.1 cm and -4.7 to 93.9 cm, respectively. The results show that the proposed method can effectively remove the long-wavelength ionospheric delay. Our study reveals that this method can also alleviate the influence of local ionospheric disturbance simultaneously.

As one road network is often the backbone in a geo-spatial dataset, capturing and/or updating the road networks using remote sensing imagery play an important role in traffic management, urban planning, vehicle navigation, and emergency management. Along with the progress of remote sensing launching technologies and the successful applications of deep learning in the field of computer vision, it has become more and more efficient and economical to employ deep learning methods for road-features extractions from very high-resolution (VHR) remote sensing imagery. Meanwhile, as one of the most significant and popular volunteer geographic information data sources, the OpenStreetMap (OSM) including the complete road networks in the wide world has been accumulated in the past decades. In this article, a generic and automatic approach for extracting road networks from VHR remote sensing images has been proposed based on fully convolutional neural network, in which the road centerlines from OSM have been employed to construct the labels for the model training and validation. In the conducted experiments on various VHR image datasets with two different spatial resolutions of 0.3 and 1 m, the proposed model has demonstrated quite satisfactory results - the overall completeness and correctness of the roads extraction from VHR remote sensing images exceed 94.0% and 98.0%, respectively.

In this work, the influence of the acceptor-type trap on the threshold voltage and short-channel effect is analyzed and modeled for the short-channel GaN MOS-HEMT. Particularly, the analysis and modeling are carried out with the dependences of the traps’ ionization condition on the gate voltage and drain voltage considered. From the calculated results based on the proposed threshold voltage model, it is found that both the interface acceptor-type trap and buffer acceptor-type trap play a vital role in the threshold voltage reduction for the short-channel AlGaN/GaN MOS-HEMT, due to the acceptor-type trap’s detrapping effect induced by the drain voltage. The calculated results are well supported by the numerical simulation, which verified the correctness and accuracy of the presented model.

The object detection and recognition algorithm based on the fusion of millimeter-wave radar and high-definition video data can improve the safety of intelligent-driving vehicles effectively. However, due to the different data modalities of millimeter-wave radar and video, how to fuse the two effectively is the key point. The difficulty lies in the data fusion methods such as insufficient adaptability of image distortion in data alignment and coordinate transformation and also the mismatching of information levels of the data to be fused. To solve the problem of data fusion of millimeter wave radar and video, this paper proposes a decision-level fusion method of millimeter-wave radar and high-definition video data based on angular alignment. Specifically, through the joint calibration and approximate interpolation, projected to polar coordinate system, the radar and the camera are angularly aligned in the horizontal direction. Then objects are detected by a deep neural network model from video data, and combined with those detected by radar to make the joint decision. Finally, object detection and recognition task based on the fusion of the two kinds of data is completed. Theoretical analysis and experimental results indicate that the accuracy of the algorithm based on the two data fusion is superior to that of the single detection and recognition algorithm on the basis of millimeter-wave radar or video data.

A novel high performance SiC asymmetric cell trench MOSFET with split gate (SG) and integrated junction barrier schottky (JBS) diode (SGS-ATMOS) is proposed for the first time. The shielding effect provided by the SG structure not only reduces the gate-drain capacitance ( ${C}_{\mathrm{gd}}$ ) but also alleviates the electric field crowding in the dielectric layer at trench corner. The integrated trench JBS diode bypasses the PiN body diode while obtaining good double protection from the SG and p-type shielding region. Therefore, not only the MOSFET but also diode performance is significantly improved for the proposed structure. Numerical analysis results show that compared with the conventional asymmetric cell trench MOSFET (Con-ATMOS), the high frequency figure of merit (HFFOM1, ${R}_{\mathrm{on}} {\cdot } {C}_{\mathrm{gd}}$ ) is reduced by 92.5% and the Baliga figure of merit (BFOM, ${BV}^{2}/R_{\mathrm{on,sp}}$ ) is increased by 57.2%, respectively. In addition, the forward conduction voltage drop ( ${V}_{\mathrm{F}}$ ), reverse recovery charge ( ${Q}_{\mathrm{rr}}$ ) and peak reverse recovery current ( ${I}_{\mathrm{rrm}}$ ) of the diode are reduced from 3.10V, $1.95\mu \text{C}$ /cm2 and 68.0A for the Con-ATMOS to 1.56V, $0.97\mu \text{C}$ /cm2 and 35.9A for the proposed SGS-ATMOS, respectively. Compared with the Con-ATMOS, the turn-on loss ( ${E}_{\mathrm{on}}$ ) and turn-off loss ( ${E}_{\mathrm{off}}$ ) of the proposed device are reduced by 33.3% and 33.0%, respectively. The ${E}_{\mathrm{on}}$ and ${E}_{\mathrm{off}}$ of the proposed device are also 33.6% and 30.0% off compared with the Con-ATMOS with external JBS diode, respectively. The temperature characteristics of the SGS-ATMOS are also discussed and it is found that the proposed device exhibits good performance at high temperature.

A novel carrier stored trench bipolar transistor (CSTBT) with split gate (SG) and recessed emitter trench (SGRET CSTBT) is proposed. The proposed device features a SG structure with thicker oxide layer under the trench gate and recessed trench emitter, respectively. Compared with the conventional CSTBT with recessed emitter trench (RET CSTBT), the proposed device not only significantly reduces the gate-collector capacitance ( ${C} _{\mathrm{ GC}}$ ) but also alleviates the negative impact of the heavily doped n-type carrier stored layer on the breakdown voltage (BV). Simulation results show that with the similar BV of about 650V, the on-state voltage drop ( ${V} _{\mathrm{ ceon}}$ ) at ${J} _{\mathrm{ ce}}$ =200A/cm2 for the proposed SGRET CSTBT is only 1.11V, which is 0.19V lower than that of the conventional RET CSTBT. Moreover, compared with the conventional RET CSTBT, the ${C} _{\mathrm{ GC}}$ at the ${V} _{\mathrm{ ce}}$ of 25V, total gate charge ( ${Q} _{\mathrm{ G}}$ ) and miller plateau charge ( ${Q} _{\mathrm{ GC}}$ ) for the proposed device are reduced by 84.3%, 38.6% and 51.6%, respectively. As a result, the trade-off relationship between the ${V} _{\mathrm{ ceon}}$ and turn-off loss ( ${E} _{\mathrm{ off}}$ ) as well as trade-off relationship between the turn-on loss ( ${E} _{\mathrm{ on}}$ ) and $\text{d}{V} _{\mathrm{ ak}} / \text{d}{t}$ of the free-wheeling diode (FWD) are significantly improved for the proposed device. At the same ${V} _{\mathrm{ ceon}}$ of 1.16V, the ${E} _{\mathrm{ off}}$ is reduced from 9.2mJ/cm2 of the conventional one to 3.3mJ/cm2 of the proposed device. At the same ${E} _{\mathrm{ on}}$ of 8.3mJ/cm2, the $\text{d}{V} _{\mathrm{ ak}} / \text{d}{t}$ of FWD for the proposed device is reduced by 24.5% compared with that of the conventional RET CSTBT, which significantly suppresses the EMI noise.

The major challenge for wireless power transfer (WPT) is to extend transfer distance and to broaden transfer direction, while a stable power can be transferred with high and constant efficiency. The multi-coil WPT system can transfer power on the plane or space omnidirectionally, but its output power and efficiency are usually difficult to keep stability. For a nonlinear wireless power transfer system based on parity-time (PT) symmetry, when the coupling condition of the system can be satisfied, it can attain a stable power with high and constant efficiency, but it still has a dead-spot problem in the transfer direction. Therefore, this article proposes a design of a plane-omnidirectional WPT system, of which the coupling condition can be maintained across the plane. The transmitter coils are a pair of orthogonal coils which are named transmitter coils 1 and 2. Meanwhile, in the transmitting circuit, the positive direction of transmitter coil 1, which determines the positive direction of current and flux, is controlled by a reversed switch group (RSG) with a logic-check loop (LCL). Moreover, the theoretical analysis shows that the proposed system can reverse the flux direction of transmitter coil 1 while the angle of transfer direction is overly large, then the coupling condition of the system after reversal can still be satisfied. Finally, the experimental results show that when the receiver coil moves around the transmitter coils on the whole plane, the stable output power of 14 W is maintained with an approximately constant transfer efficiency of 87%.

Accurate detection of multiclass instance objects in remote sensing images (RSIs) is a fundamental but challenging task in the field of aviation and satellite image processing, which plays a crucial role in a wide range of practical applications. Compared with the natural image-based object detection task, RSIs-based object detection still faces two main challenges: 1) The instance objects often present large variations in object size, and they are densely arranged in the given input images; and 2) Complex background distributions around instance objects tend to cause boundary blurring, making it difficult to distinguish instance objects from the background, resulting in undesired feature learning interference. In this article, to address the abovementioned challenges, we propose a novel RSI anchor-free object detection framework that consists of two key components: a cross-channel feature pyramid network (CFPN) and multiple foreground- attentive detection heads (FDHs). First, an anchor-free baseline detector with the CFPN structure is developed to extract features from different convolutional layers and incorporates these multiscale features through parameterized cross-channel learning processes, learning the semantic relations across different scales and levels. Next, each FDH is designed to predict an attention map to enhance the features of the foreground region in RSIs. Furthermore, under this scale-aware anchor-free baseline detector structure, we design a curriculum-style optimization objective that dynamically reweights training instances during the current training epoch, enabling the detector to receive relatively easy instances that match with its current ability. Experimental results on three publicly available object detection datasets demonstrate that the proposed method outperforms existing object detection methods.

Aiming at the problem of noise easily polluting the voltage measurement link of an inverter DC bus in photovoltaic grid, an improved linear active disturbance rejection control technology based on filter function is constructed by combining linear active disturbance rejection control technology with filter. The core of the method is to expand the filtered voltage into a new state variable and use the linear extended state observer to estimate the filtered voltage, which is used as the feedback quantity, to improve the ability of linear active disturbance rejection control technology to suppress high-frequency noise. Then, considering that the output of the system contains noise, the frequency domain characteristics of the improved linear active disturbance rejection control are analyzed theoretically and verified by Bode diagram simulation. Finally, the feasibility and effectiveness of the proposed control strategy are demonstrated by building a simulation platform for grid-connected PV inverter.

The wireless power terminals are deployed in harsh public places and lack strict control, facing security problems. Thus, they are faced with security problems such as illegal and counterfeit terminal access, unlawful control of connected terminals, etc. The intrusion detection system based on machine learning and artificial intelligence significantly improve the terminal side’s abnormal detection capacity. In this article, we aim at identifying the abnormal behavior of wireless power terminals based on a double Hidden Markov Model (HMM), which solves the computational complexity problem caused by high dimensions in intrusion detection systems using a single HMM. The lower-layer HMM is used to identify the discrete single network abnormal behavior. Simultaneously, the upper-layer can obtain more extended period attack behavior in multiple independent abnormal events identified by the low-level. The experiment results indicate that the intrusion detection system using proposed double HMM can effectively detect the terminal’s abnormal behavior and identify the network attack behavior for an extended period.

Due to the diffuse scattering on sea surface and dynamic geometry of coastal global navigation satellite system-reflectometry, the altimetry performance is strongly dependent on elevation angle. As the elevation angle decreases, the bias in retrieved height using the peak of the derivative waveform and the fractional power point of the waveform leading edge increases. In this article, a multiparameter estimator is proposed to combine the peak of the derivative waveform and several fractional power points to obtain unbiased height. The simulated results show that the bias of the uncalibrated height using the proposed algorithm has no significant dependence on elevation angle, but the standard derivation for the low elevation angle case still is larger than that of the high elevation angle. In addition, the calibrated performance depends on the used fractional values so that proper fractional values should be chosen. The data from the Dongying experiment acquiring GPS L1 CA and L5 signal and the Qingdao experiment for BeiDou B1I signal are used to demonstrate the proposed algorithm. After calibrating using the proposed algorithm, regardless of GPS L1 CA, L5, or BeiDou B1I signal, the calibrated heights all are in agreement with in situ heights. Finally, the calibrated heights from the BeiDou B1I signal are used to retrieve sea surface height. The results show that retrieved sea surface heights from geostationary Earth orbit (GEO) and inclined geosynchronous orbit/middle Earth orbit (IGSO/MEO) satellites all appear in the same trend as that of the in situ ones; furthermore, GEO satellites can provide a better retrieved performance than IGSO/MEO satellites. Through averaging the heights from several satellites and smoothing with the half-hour span, compared to in situ sea surface height, the standard derivation of 0.67 m is obtained.

Domain adaptation (DA) aims to enhance the feature transferability of a model across different domains with feature distribution differences, which has been widely explored in many computer vision tasks such as semantic segmentation and object detection, but has not been fully studied in hyperspectral image (HSI) classification task. Compared with the natural image-based DA, HSI-based DA still faces two main challenges: First, due to the strong spectral variability of HSIs, it is difficult to extract discriminative and domain-invariant features from different domains, resulting in the misalignment of cross-domain features; Second, class-wise (or fine-grained) spectral feature inconsistency between domains also inevitably degrades the classification accuracy. To address these issues, in this article, we propose a novel coarse-to-fine joint distribution alignment (JDA) framework for cross-domain classification of HSIs. Specifically, the training samples from source and target domains are first fed into a coupled variational autoencoders (VAE) module, which is composed of two well-designed VAEs equipped with mutual information metric to learn high-level domain-invariant representations in a shared latent space, so that the network can learn a coarse-grained source-target feature consistency. Furthermore, to alleviate the class-wise inter-domain feature inconsistency, a JDA module is constructed to perform a fine-grained cross-domain alignment by matching the joint probability distributions between the source and target domains through adversarial learning. Extensive experiments on both simulated and real hyperspectral datasets demonstrate the superiority of the proposed method in comparison with several conventional and state-of-the-art methods.

An ultralow specific on-resistance ( ${R} _{\mathrm{ on,sp}}$ ) accumulation-mode LDMOS (ALDMOS) is proposed and investigated by simulations and experiments. The proposed ALDMOS features two separated integrated diodes (SID) above the N-drift surface, which forms high density electron accumulation layer in the on-state. Meanwhile, the SID not only assists depleting the N-drift to increase the N-drift doping centration ( ${N} _{\mathrm{ d}}$ ) in the off-state, but also modulates the lateral electric field to improve the breakdown voltage (BV). Thus, the proposed SID ALDMOS could achieve ultralow ${R} _{\mathrm{ on,sp}}$ and maintain high BV simultaneously. The layout and key fabrication processes of the SID ALDMOS are demonstrated. The measured results show that the SID ALDMOS realizes a BV of 483V and ${R} _{\mathrm{ on,sp}}$ of 29.3m $\boldsymbol{\Omega } $ .cm2, with a high FOM value of 7.96MW/cm2. Its ${R} _{\mathrm{ on,sp}}$ is decreased by 33.7% compared with triple RESURF LDMOS at the same BV.

Block pulse functions (BPFs) are piecewise constant and not sufficiently smooth. Therefore, their accuracy is limited when it comes to identifying the parameters of fractional order systems (FOSs). This in turn means that BPFs are incapable of offering highly accurate parameter identification results. However, using a great number of BPFs would significantly increase the dimension of the operational matrix and thereby adds to the computational complexity and burden. To overcome this problem, we present here a hybrid function method for identifying FOSs. The method utilizes a hybrid of Bernoulli polynomials and block pulse functions (HBPBPFs) as the base functions to approximate input and output signals. The fractional integral operational matrix of HBPBPFs is derived and used to convert an FOS to an algebraic system. The parameters of the FOS are successfully identified by minimizing the mean square error between the output of the true system and that of the algebraic representation of the FOS. The simulation experiment verifies that our proposed HBPBPFs method is effective and can generate more accurate identification results than existing BPFs methods.

Mapping socio-economic indicators with a raster format is still a great challenge. The nighttime light (NTL) datasets have been widely utilized to estimate the socio-economic parameters. However, the precision of the published datasets was too coarse to meet related issues such as flood losses assessment, urban planning, and epidemiological studies. The present study calibrated gross domestic product (GDP), population (POP), electric consumption (EC), and urban build-up area (B-A) at 100 m resolution for 45 cities of China in 2018 using Luojia1-01 NTL datasets via random forest (RF) as well as geographically weighted regression (GWR) model. The linear regression (LR), back propagation neural network (BPNN), and support vector machine (SVM) methods were selected for comparison with GWR and RF models. Besides, the Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) was chosen for comparison with Luojia1-01. The ten-folded cross-validation (CV) has been used for evaluating accuracy at county and city scales. Finally, the distribution maps of socio-economic parameters were illustrated and some findings were obtained. First, the validation results revealed that the calibration at the city-scale outperformed the county or district scale. Second, the precision of the Luojia1-01 NTL dataset surpassed the NPP-VIIRS NTL dataset on the same administrative scale except for some specific situations. Third, the precision of the simulation for the gross domestic product (GDP) is the highest than the others, followed by electric consumption (EC), build-up area (B-A), and population (POP). Fourth, the optimum model varied according to the socio-economic parameters. Fifth, the distribution of socio-economic parameters exhibited obvious spatial heterogeneity. This paper can supply scientific support for calibrating socio-economic parameters in other regions.

In this article, we study the cycle structure of (3, 19)-regular Tanner’s quasi-cyclic (QC) LDPC codes with code length $19p$ , where $p$ is a prime and $p\equiv 1~(\bmod ~57)$ , and transform the conditions for the existence of cycles of lengths not more than 10 into polynomial equations in a 57th root of unity of the prime field $\mathbb {F}_{p}$ . By employing the Euclidean division algorithm to check whether these equations have solutions over the prime field $\mathbb {F}_{p}$ , the girth values of (3, 19)-regular Tanner’s QC-LDPC codes of code length $19p$ are determined. In order to show the good performance of this class of QC-LDPC codes, numerical results are also provided.

The modular multilevel converter (MMC) with the clamp double submodule (CDSM) performs outstandingly on the handling of DC fault current. As CDSM is composed of multiple switching devices, different operating modes can be produced according to the combination of switching states. Among the effective operating modes, there are inevitably some unexpected modes, which may cause abnormal operation or even failure of the MMC. In order to improve the reliability of MMC, it is necessary to identify all the effective operating modes of CDSM and analyze their trigger conditions. In this article, a novel mode identification method for power converter with multiple switches is used. According to reasonable combination of switching states, the components of CDSM are connected in an orderly manner to obtain all effective operating modes. Based on the operating principle of CDSM, the unexpected modes are further recognized from the effective operating modes, so that the impact of the unexpected modes on the MMC can be obtained. Finally, the recognition and analysis results of the unexpected modes in CDSM is verified by Simulink and Plecs RT Box.

At present, China’s power load development is facing a new situation in which policies such as the new economic norm, industrial structure adjustment, energy conservation and emission reduction, etc. are being deeply promoted, load growth in some areas begin to ease, and volatility of load gradually become prominent, which increases the difficulty of medium and long-term load forecasting. In this context, in view of multi-correlation, uncertainty of influence of policy factors on power load, in order to improve the accuracy of load forecasting under the influence of policy factors, and solve the problem that policy factors are ambiguous, difficult to be quantified, and difficult to be integrated into load forecasting model, a medium and long-term load forecasting model considering policy factors is proposed. First, by analyzing the influence of various policies on power load, a hierarchical policy influencing factor index system that combines macro and micro levels is constructed to systematically reflect the influence of economy and policies on load under the new situation. Then, in view of the traditional grey relational analysis model’s insufficient consideration of the difference of historical data and future power development situation, by respectively weighting historical periods and factor indexes, a quantification analysis model of power load influencing factors based on two-way weighted grey relational analysis is proposed to quantify the influence of various policy factors on power load, achieve the combination of subjective weighting and objective weighting,and obtain quantification weights. Finally, the weighted fuzzy cluster analysis method combined with weights is used to predict load under the influence of policy factors. The proposed model can better solve the difficulty of medium and long-term load forecasting caused by the volatility of load under the influence of policy factors, and is suitable for medium and long-term load forecasting under the background of policy changes. The analysis of calculation examples shows that compared with traditional grey relational analysis model, the quantification results of proposed methods are more realistic, compared with traditional prediction methods such as time series extrapolation and elasticity coefficient, proposed method has better prediction accuracy and engineering application value.

The MOS-controlled Thyristor (MCT) has been characterized by MOS-gating, high current rise rate, and high blocking capability. The anode short MCT (AS-MCT) is distinguished from conventional MCT by an anode-short structure, which develops a normally-off characteristic. As a composite structure made of metal-oxide-silicon and bipolar junction transistors, AS-MCT is susceptible to displacement damage induced by energetic radiation. The anode trigger current which denotes the latch-up of internal thyristor structure is a key parameter for AS-MCTs. From the aspects of devices physics, we propose a model to describe the displacement damage on trigger current. Our model provides an excellent fit to the experimental data of the AS-MCT samples subjected to fission neutrons with flux in the range of $3.1\times 10^{9}-5.5\times 10^{13}\,\,\mathrm {cm}^{-2}$ . Moreover, this work shows that the high injection effect can alleviate the displacement damage of trigger current following high flux exposures.

The MOS-controlled Thyristor (MCT) has been characterized by MOS-gating, high current rise rate, and high blocking capabilities. The anode short MCT (AS-MCT) is distinguished from the conventional MCT by an anode-short structure, which forms an extracting path for the leakage current at the gate-ground and develops a normally-off characteristic. The AS-MCTs are ideal switches for pulse discharge application. As a composite structure made of metal-oxide-silicon and bipolar junction transistors, AS-MCT is susceptible to ionization damage. This work reports the experimental results for the degradation of zero-load (or short) pulse discharge circuit characteristics induced by the ionization damage of its AS-MCT switch following cobalt- $60~\gamma $ -ray dose up to 9160 Gy(SiO2). The radiation-induced leakage current in AS-MCT accounts for the degradations of charging time and peak surging current of the pulse discharge circuit. These degradations show a “tick”-like dependence on the $\gamma $ -ray dose which are recoverable after high dose exposures, thousands of Gy(SiO2). From device and circuit physics perspectives, the damages on pulse discharge circuit are modelled, and then, this article proposes, the mechanism behind the characteristics degradation of pulse discharge circuit from the total ionization dose damage of AS-MCT switch.

Wireless power transfer (WPT) using magnetic-coupling resonant has been proved to be a promising transmission method. At present, most of the existing resonant WPT systems adopt integer-order inductors and capacitors. In fact, in addition to integer-order components, there also exists fractional-order inductors and capacitors, which possess richer characteristics than integral-order inductors and capacitors. However, there are few studies on the application of fractional-order components in resonant WPT system. Therefore, this paper proposes a fractional-order resonant WPT system, which is based on a fractional-order resonant circuit only incorporating a fractional-order capacitor. The coupled-mode theory model of the proposed WPT system is established, and the impact of different loads and orders on transfer characteristics is explored. The theoretical analysis demonstrates that the proposed system can achieve constant current output that is independent of load, just by choosing appropriate parameters of the fractional-order capacitor. Finally, an experimental prototype of the fractional-order resonant WPT system is built, and the experiment results verify the correctness of the theoretical analysis.

In this paper, the influence of the silver glue on circuit performance and the reasons for its formation are analyzed in a terahertz hybrid integrated mixer. A validation analysis was carried out in the 600 - 700 GHz band with the aid of simulation software. Then a terahertz hybrid integrated mixing circuit with large pads was proposed in order to reduce the influence of the silver glue. Through the comparison of simulation results, it can be seen that after using large pads, the silver glue used for connection mainly concentrated on pads, which cannot have a significant impact on the matching of the circuits. The use of large pads also to a certain extent eliminates the resonance effect caused by silver glue. A terahertz hybrid integrated mixer with large pads was processed, assembled and tested. The test results show that the conversion loss is less than 14 dB under an RF frequency of 600 - 667 GHz. The experiments prove good consistency between measured and predicted conversion loss of the mixer using large pads. The difference between the measured and predicted conversion loss is only within 1.8 dB.

Paralleled DC-DC converters system normally is operated with current-sharing control to achieve equal current distribution among the converters connected in parallel. However, the added current-sharing control might make the paralleled DC-DC converters system suffer from potential degradation of stability and dynamic performances. Hence, the stability analysis of paralleled DC-DC converters system is a hot issue. In order to analyze the stability of paralleled DC-DC converters system accurately and effectively, an extended stability analysis method based on Floquet theory is proposed for paralleled DC-DC converters system in this paper with considering the periodic disturbance. And, a paralleled buck converters system with average current-mode control is taken as an analysis object. The extended stability criterion of paralleled buck converters system based on Floquet theory is derived. Finally, the simulation and experimental results verify the correctness and effectiveness of the proposed stability criterion with considering the periodic disturbance. This paper provides a new choice for the stability analysis of paralleled DC-DC converters system.

Opinion-leader mining in social networks is a critical problem in research of the information dissemination process and in public opinion guidance and supervision. Not every social network user has a high probability to be an opinion leader. However, most mining methods identify opinion leaders among users in the whole network, which adds unnecessary calculations. To solve this problem, we propose a rank after clustering (RaC) algorithm to mine opinion leaders in social networks with a phased-clustering perspective, which has the following aspects: (1) Aiming to reduce the scale of calculation, the clustering stage clusters users in social networks using a K-means algorithm according to topological information to find the set of opinion leader candidates; (2) The ranking stage determines the user ranks of opinion leader candidates by both their activeness and influence, and we accumulate the followers' influence weighted by degree of attention when assessing user influence. In experiments, a new indicator, the C-value, and simulations based on the linear threshold model are used to evaluate the performance of the RaC algorithm. The results show that RaC is effective and accurate.

A wideband 220 GHz subharmonic mixer based on monolithic integration technology is proposed in this paper. It features 12 Mm-thick GaAs membrane with anti-parallel Schottky diodes working at THz band monolithically integrated on the membrane. The optimization principles of low-parastics Schottky diodes and the fabrication process of GaAs MMIC membrane diodes are elaborated. To realize optimum performances of the mixer, the dimensions of the integrated diodes and the matching network are optimized with harmonic balance simulation and load-pull techniques. An IF low pass filter with compact microstrip resonance cell (CMRC) configuration and an improved perpendicular coax-to-microstrip connection are used to realize wide IF band. The measured results show that the single sideband (SSB) conversion loss of this mixer is less than 10.5 dB from 185 to 255 GHz with fixed IF of 1 GHz, while the double sideband (DSB) noise temperature is better than 1400 K in this frequency range. Using fixed local oscillator (LO) frequency of 110 GHz, the measured SSB conversion loss is 7.4-10.7 dB within 185-255 GHz, indicating the good performances of the mixer with IF from DC to 35 GHz. The GaAs monolithic integration technology provides an approach for massive manufacturing of identical circuits and the proposed mixer with wideband characteristics will be applied in 220 GHz imaging systems in the near future.