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This article provides a comprehensive overview of on-board wireless motors based on WPT system. Based on the mathematical models of common wireless motors, the coupling strength of on-board wireless motors is divided, and their characteristics and advantages are comprehensively summarized and classified. In order to provide power to the three-phase wireless motors, the rotary transformer is used to replace the common coupling coil in the WPT system. This paper conducts electromagnetic model on the rotary transformer, as well as a comprehensive summary of structures and materials of the rotary transformer. The resonant compensation topology of WPT system is crucial. Comparing the transmission characteristics and advantages and disadvantages of various compensation topologies is beneficial for selecting appropriate compensation topologies in different application scenarios. For the control strategy of WPT system, this paper mainly focuses on the issues of output power and transmission efficiency. Finally, the research difficulties and directions of on-board wireless motors are proposed, such as electromagnetic interference and resonance, and this technology is extended to other fields, including wireless charging, robotics, etc.

With the increasingly widespread application of permanent magnet synchronous motors (PMSMs), it is required that PMSMs can still maintain high efficiency and high reliability in complex environments. Due to the nonlinearity of PMSMs and the influence of external disturbances, its precise control is still a challenge. As a modern control scheme, sliding mode control can improve the control PMSM drive system. It has been successfully applied in the control of PMSMs and has become a hot spot in the motor drive system. This paper provides an overview of the current research status of sliding mode control strategies based on PMSMs and introduces the design of sliding mode controllers, as well as the development and application of high-order sliding mode control. The method of suppressing the chattering problem is summarized, and the deficiencies of it are put forward. Finally, the development trend of sliding mode control technology is discussed, and the future development direction of SMC for PMSM is prospected.

Traditional nonlinear elements of the electromagnetic transient simulation program (EMTP) use the numerical integration method, often employing the trapezoidal integration method as an implicit algorithm. However, when the number of nonlinear elements is high, the iterative format becomes computationally cumbersome. The semi-implicit format algorithms, based on the Rosenbrock real-time integration method, do not require iterations and can be applied directly in a recursive manner. However, as the number of nonlinear elements increases, it becomes necessary to invert the matrix that contains the Jacobian matrix, resulting in an increase of both algorithmic computation and inversion time with the dimensionality of the system. In this paper, a new numerical solution method is adopted, which combines the precise integration method. Specifically, the Calahan algorithm, with 2-stage and 3-order, is used to numerically integrate the Duhamel integration terms, and the accuracy of the Calahan algorithm is enhanced through Richardson extrapolation. The new algorithm can avoid the inverse of the matrix containing the Jacobian matrix, and the computational efficiency is equivalent to an explicit method, which greatly improves the computational efficiency of the Rosenbrock integration algorithm. The results verify the effectiveness and accuracy of the algorithm for typical power system nonlinear element electromagnetic transient simulation cases.

To address the challenges of road extraction in high-resolution remote sensing images, this paper presents an enhanced UNet++ road extraction method that incorporates CBAM. The original UNet++ network is referenced, and the loss function is improved by introducing a new joint loss function. The enhanced UNet++ network utilizes an attention mechanism to enhance the network’s ability to identify road features, thereby improving the accuracy of road extraction. Additionally, a new joint loss function is employed to enhance the network’s stability and further improve its road extraction capability. Experimental validation is performed on the Massachusetts roads dataset and DeepGlobal road dataset. The experimental results demonstrate that this method outperforms U-Net, SegNet, and UNet++ networks in terms of IoU, Recall, OA, and Kappa. Specifically, on the Massachusetts road dataset, the OA and Kappa values are 94.92% and 0.9202, respectively. On the DeepGlobal road dataset, the OA and Kappa values for this algorithm are 98.12% and 0.9515, respectively. The ablation experiment confirms the effectiveness of the proposed enhancements. In conclusion, this paper presents a method that effectively extracts roads from high-resolution remote sensing images, exhibits a certain level of generalization ability, and can provide valuable support for road protection and planning.

Heavy rains pose a great threat to the reliable and secure power supply of urban distribution networks. A knowledge-informed data-driven resilience assessment approach is proposed to evaluate urban distribution networks’ abilities to resist heavy rains. Firstly, the rainstorm waterlogging process is simulated to obtain the rainstorm intensity and rainfall process, providing the input for the data-driven model. Then, input variables are grouped guided by expert knowledge, and a dynamic and static data-driven model is constructed to predict the line outages based on historical data. Finally, the Monte Carlo simulation method integrated with the data-driven model is developed to assess the resilience of urban distribution networks and the number of line outages is selected as the evaluation metric. The effectiveness of the proposed method is sufficiently validated by the historical data of an urban distribution network.

For a high-speed railway relay downlink communication system based on short-packet transmission, the problem of maximizing the minimum user throughput by jointly optimizing the transmission packet length and the transmission power control of the relay device is investigated in this paper. The optimization problem is a nonconvex and mixed-integer one that is difficult to obtain an optimal solution, and a low-complexity algorithm is proposed to obtain the solution to the joint optimization problem. First, a closed-form expression of the optimal blocklength is derived by fixing the transmission power; Secondly, in the case of fixed blocklength, the non-convex optimization subproblem of transmission power is transformed into a convex problem by introducing auxiliary variables and using the successive convex approximation method. Finally, the feasibility and effectiveness of the proposed algorithm in this paper are verified by simulation.

In the application of medium and long-term runoff forecasting, machine learning has some problems, such as high learning cost, limited computing cost, and difficulty in satisfying statistical data assumptions in some regions, leading to difficulty in popularization in the hydrology industry. In the case of a few data, it is one of the ways to solve the problem to analyze the data characteristics consistency. This paper analyzes the statistical hypothesis of machine learning and runoff data characteristics such as periodicity and mutation. Aiming at the effect of data characteristics inconsistency on three representative machine learning models (multiple linear regression, random forest, back propagation neural network), a simple correction/improvement method suitable for engineering was proposed. The model results were verified in the Danjiangkou area, China. The results show that the errors of the three models have the same distribution as the periodic characteristics of the runoff periods, and the correction/improvement based on periodicity and mutation characteristics can improve the forecasting accuracy of the three models. The back propagation neural network model is most sensitive to the data characteristics consistency.

Cross-database micro-expression recognition (CDMER) is a difficult task, where the target (testing) and source (training) samples come from different micro-expression (ME) databases, resulting in the inconsistency of the feature distributions between each other, and hence affecting the performance of many existing MER methods. To address this problem, we propose a dual-stream convolutional neural network (DSCNN) for dealing with CDMER tasks. In the DSCNN, two stream branches are designed to study temporal and facial region cues in ME samples with the goal of recognizing MEs. In addition, in the training process, the domain discrepancy loss is used to enforce the target and source samples to have similar feature distributions in some layers of the DSCNN. Extensive CDMER experiments are conducted to evaluate the DSCNN. The results show that our proposed DSCNN model achieves a higher recognition accuracy when compared with some representative CDMER methods.

In view of the lack of data association in spatiotemporal information analysis and the lack of spatiotemporal situation analysis in knowledge graphs, this article combines the semantic web of the geographic knowledge graph with the visual analysis model of spatial information and puts forward the comprehensive utilization of the related technologies of the geographic knowledge graph and big data visual analysis. Then, it realizes the situational analysis of COVID-19 (Coronavirus Disease 2019) and the exploration of patient relationships through interactive collaborative analysis. The main contributions of the paper are as follows. (1) Based on the characteristics of the geographic knowledge graph, a patient entity model and an entity relationship type and knowledge representation method are proposed, and a knowledge graph of the spatiotemporal information of COVID-19 is constructed. (2) To analyse the COVID-19 patients’ situations and explore their relationships, an analytical framework is designed. The framework, combining the semantic web of the geographic knowledge graph and the visual analysis model of geographic information, allows one to analyse the semantic web by using the node attribute similarity calculation, key stage mining, community prediction and other methods. (3)An efficient epidemic prevention and anti-epidemic method is proposed which is of referential significance. It is based on experiments and the collaborative analysis of the semantic web and spatial information, allowing for real-time situational understanding, the discovery of patients’ relationships, the analysis of the spatiotemporal distribution of patients, super spreader mining, key node analysis, and the prevention and control of high-risk groups.

The precise matching of the optical system and the detector has attracted more and more attention to satisfy requirements of future infrared (IR) imaging systems, both for miniaturization and long-distance detection. Considering the trade-off between the spatial resolution and the sensitivity, this paper proposes a general optimization method, which can optimize parameters of the IR imaging system automatically by modifying λF/d. Firstly, on the basis of the trade-off between the spatial transfer characteristics and the spatial sampling characteristics, we establish a more accurate minimum resolvable temperature difference (MRTD) model, and analyze the coupling relationship between the optical system and the detector under the optimum MRTD. Secondly, we analyze the relationship between parameters of the IR imaging system and MRTD model, and propose a trade-off optimization approach of the IR imaging system. Finally, this paper verifies the rationality of the optimization method by optimizing CA-295 system. The trade-off optimization method proposed in this paper can not only satisfy the design requirement, but also reduce the size, weight, power consumption and cost (SWaP$) of IR imaging systems.

Detecting and localizing the inner damage of the downhole casing is crucial for oil and gas production. The current methods for damage detection suffer from polarization effect, capacitance effect, and difficulty in measuring weak direct current signals. As an alternative, we propose a new instrument design based on a six-electrode array in this study. The basic detection principle is introduced, and the key factors that affect the detection performance are studied through modeling and simulation. Besides, the entire system of the instrument design consisting of several modules is presented. In addition, an experiment is conducted to demonstrate the detection capability of the proposed design. It is found that the detection dynamic of the instrument is 93 dB and a signal-to-noise ratio is 73 dB. It can detect damage when salinity is lower than 120 g/l and can identify the location of damage with high resolution in the longitudinal and radial directions.

A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs). The solution of PDEs can be formulated as the solution of a constrained optimization problem, and we design a hierarchical neural network framework to solve this optimization problem. Through decomposing a PDE system into components parts, our D3M builds local neural networks on physical subdomains independently (which can be implemented in parallel), so as to obtain efficient neural network approximations for complex problems. Our analysis shows that the D3M approximation solution converges to the exact solution of the underlying PDEs. The accuracy and the efficiency of D3M are validated and demonstrated with numerical experiments.

Sector antenna azimuth plays an important role in the operation and maintenance of mobile networks. It is adjusted frequently so as to guarantee the high quality of coverage and low interference among neighboring cells. As one of the key elements of base station almanac (BSA), the azimuth of all the base stations need to be precisely managed by the operator. However, currently it is generally acquired by on-site measurement and updated manually, which is neither timely nor cost-effective. In addition, it is not open for third parties who need the information for network analysis. In view of this, by transforming the problem of azimuth estimation into one of searching for the optimal cell boundaries under the constraint of site location, this paper proposes an azimuth estimation method based on an improved multi-class soft-margin support vector machine (SVM). The explicit expression of the objective function of the problem is deduced through Lagrange duality. A circular one-versus-one strategy is utilized in dealing with the multi-class classification problem. In addition, a so-called average confusion index is designed to evaluate the degree of boundary separability of a base station so as to prejudge whether it is worthwhile to estimate its azimuths. Experiments are undertaken to validate the proposed algorithm by utilizing the spatial-temporal wireless signal dataset crowdsensed from massive user terminals in the 4G network. The experiments show that the proposed algorithm gives a significantly higher accuracy compared to that of two existing methods, namely the Gauss and radial rasterization methods. The impacts of data volume, penalty term and the boundary separability on the azimuth estimation is also discussed. The new algorithm is a promising alternative to the conventional manual solution with a much higher efficiency and lower cost, thus improving the intelligence of mobile network operation.

Currently, many consumers prefer online shopping via e-commerce platforms because of the shopping convenience and product diversity. Meanwhile, offline retailers provide retail services to cope with the competition from e-commerce platforms. Additionally, environment protection has been regarded to be equally important as economic growth in meeting the challenges of sustainable development goals. This work uses consumer utility selection theory to form the online and offline market demand functions in a dual-channel closed-loop supply chain considering consumers' e-commerce preferences and retail services. Then, we further reveal the optimal system sales channel and coordination contract to acquire good economic and ecological benefits. We find the following: 1) As in practice, consumers' e-commerce preferences and retail services simultaneously affect the purchasing and sales channels. 2) Since both consumers' e-commerce preferences and retail service quality increase as service costs decreases, the recovery rate of used products increases, which contributes to improving the ecological benefits. 3) The “Double Marginalization” problem can reduce the operating efficiency of the decentralized system. Accordingly, we design a revenue-service cost sharing contract to solve this problem in a coordinated manner, and thus increase the economic and ecological benefits of the system. Consequently, all members can obtain profits in a win-win scenario by bargaining on the revenue rates and service costs.

We propose and experimentally demonstrate a noise mitigation scheme for coherent optical discrete multitone (DMT) signal transmission system. The proposed scheme can mitigate the laser phase noise and additive white Gaussian noise (AWGN) simultaneously when optical DMT signal is coherently detected. In the digital signal process (DSP), the laser phase noise is first estimated and compensated through moving average filter. The AWGN is then reduced by considering the time-domain real-valued or frequency-domain symmetrical conjugation characteristic of DMT signal. Finally, we conduct an experiment to transmit dual-polarization (DP-) DMT signal based on a low complexity coherent detection structure with only two pairs of input and output ports. The experimental results prove that the proposed scheme can enhance the signal-to-noise ratio (SNR) and effectively reduce the effect of laser phase noise even the laser linewidth is as large as 10 MHz. The DP-DMT signal is successfully transmitted over 1920-km standard single mode fiber (SSMF) in the experimental demonstration. The analysis of computational complexity also indicates that the proposed DSP scheme requires less number of complex multiplication operations and has larger laser phase noise tolerance than other DSP schemes.

In this paper, we demonstrate high light tuning efficiency in indium selenide (In2Se3) coated micro knot resonator (MKR). Tuning of output optical power is realized via externally pump light excitation at wavelengths of 405, 532 and 660 nm. High light tuning efficiency is achieved with improved coating method, enhanced light-matter interaction, strong absorption of In2Se3 and highly sensitive to change of the coupling condition. The tuning efficiency of the output optical power is up to 0.815 dB/mW. The temporal response of the structure is around 1.6 ms under 532-nm pump light excitation. The sensitivity depends on micro knot resonator resonance including Q factor and ER. The MKR tuning performance with various diameters of the resonator and the diameter of the microfiber is investigated. The advantages of this device are easy fabrication, all-optical tuning, high sensitivity and fast response. This proposed structure might find potential applications in all-fiber-optic based tunable devices such as optical modulator, detector, etc.

The remaining useful life (RUL) prediction is critical for the safe and reliable operation of lithium-ion battery (LIB) systems, which characterizes the aging status of the battery and provides early warning for battery replacement. Most existing RUL prediction methods rely on empirical aging models, and the role of the battery mechanism is not considered in the subsequent algorithm settings. The accuracy and stability of data-driven algorithms are severely limited by battery aging data. A new electrochemical-model-based particle filter (PF) framework for LIB RUL prediction is proposed in this paper. Parameters of a simplified electrochemical model (SEM) are used as state variables of the PF algorithm and these parameters can be identified by applying specially designed current excitations to the battery. The SEM-based capacity simulation process is taken as the observation equation in the PF algorithm framework. Therefore, the mechanism of the battery is fully considered when making the RUL prediction. The proposed method is validated through cyclic aging experiment of a cylindrical LFP/graphite LIB of 45Ah. The accuracy of the method is compared with a data-driven-based PF framework for RUL prediction and shows better accuracy and stability, which provides a choice for achieving high-quality RUL prediction.

Acoustic micro-imaging based on high-frequency ultrasound has been widely and effectively used for microdefect detection in microelectronic packages. With the miniaturization of microelectronic devices and the reduction of defects, edge blurring occurs in high-frequency ultrasonic scanning and directly affects the detection accuracy and signal-to-noise ratio, especially in spherical structures, such as ball grid arrays, wafer-level chip-scale packaging, and flip-chip solder bumps. This paper depicts the ultrasound interaction behaviors and the edge blurring effects during microdefect imaging, which provide a theoretical basis for improving the defect detection accuracy in subsequent research. A microdefect finite-element model was developed to simulate scanning in acoustic microscopy imaging. C-lines and C-scans of microdefects of various sizes were obtained, which can identify the location and size of the defects more easily. Furthermore, an improved method to obtain the acoustic propagation path map was developed for analyzing the acoustic energy transmission during detection. Different energy consumption paths around the microdefect lead to differences among the C-lines. The different sizes of microdefects show different blurred edges in the C-scans. The experimental data and simulation show consistent results, which prove the credibility of the developed method.

As stress is related to many mental and physical health problems, monitoring stress and its management is getting increasingly important in modern societies. Because of the advantage of convolutional neural network (CNN) in automatic feature learning, this study is proposed to use CNN to achieve accurate and fast detection of acute cognitive stress from heart rate variability (HRV). The traditional mental arithmetic calculation was adopted as the stressor for a total of twenty participants, during which one-lead electrocardiogram (ECG) was acquired. Six conventional HRV methods for inferring cognitive stress were extracted from the ECG signals, and their performance in identifying acute cognitive stress was compared with the proposed CNN-based method. The experimental results showed that with a super-short (10 s) time window, the detection error rate of CNN was 17.3%, which is significantly better than the performance of all six conventional HRV methods (> 7.2%, p

A novel broadband microstrip to waveguide transition is proposed in this paper. A microstrip circuit is inserted into a waveguide along the axis of the broadside wall, and the referred waveguide is WR-90 standardized rectangular waveguide. The microstrip circuit consists of a pair of symmetric step-like patches, a microstrip probe, and a metal ground plane. The transition has a smaller size as it is end-inserted. Meanwhile, the traditionally stepped metal ridge in the waveguide is replaced with bistratal symmetric patches, integrated design of patches, and microstrip probe. Therefore, the welding loss and structural instability have been avoided. Complementary split ring resonators are loaded in the patches to broaden the bandwidth and increase efficiency. Measured results show that the proposed microstrip to waveguide transition achieves a -15 dB return loss with the bandwidth of 8.65-12.33 GHz, and its insertion loss is better 0.5 dB over the band of 8.13-12.13 GHz. Furthermore, this design features lightweight, low cost, and simple processing methods.

The flux-linkage characteristics of bearingless induction motors (BIMs) are nonlinear, and the models established by the general analytical method cannot accurately reflect the actual characteristics of BIMs. Thus, a novel method for nonlinear modeling of BIM flux linkage is proposed in this paper. The main objective of this method is to improve the accuracy of the flux linkage model based on the least square support vector machine (LSSVM) technique by applying the gray wolf optimization (GWO) algorithm to determine the optimal kernel parameter and regularization parameter of the LSSVM automatically. In this method, all BIMs flux linkage data are obtained from the finite-element method. In this paper, the relationship between input and output of the nonlinear flux linkage model is studied, and the precision model of GWO-LSSVM flux linkage is obtained. The simulation results demonstrate that the GWO-LSSVM model has high prediction accuracy and strong prediction ability. In addition, the GWO-LSSVM model is compared with other models. From this simulation comparison, it can be concluded that GWO-LSSVM modeling has the characteristics of higher accuracy.

In this paper, a fault diagnosis method that is based on the deep structure and the sparse least squares support vector machine (SLSSVM) is proposed. This method constructs the structure of a multi-layer support vector machine (SVM). First, the SVM on the first layer is trained by using the training samples, and it learns the shallow features of the data. Then, the “feature extraction formula” is used to generate a new expression of the sample, which is used as input of the next layer. The new layer of the SVM trains on the new sample, and it extracts and learns the deep features of the signal layer by layer; eventually, after multiple feature mapping, it outputs the diagnostic results on the last layer. Because of the deep structure, the algorithm complexity and operation time increase. Therefore, in this paper, the least squares support vector machine (LSSVM) is combined with the sparse theory. By constructing the approximate maximal linearly independent vector set in the feature space, we conduct the sparse expression of samples and obtain the discriminant function for classification, which effectively solves the problem of sparsity deficiency for the LSSVM. Last, the method is used to diagnose centrifugal pump faults and rolling bearing faults and compares with the several methods of the SVM, the SLSSVM, deep SVM, and convolutional neural networks. The diagnostic results indicate that the method in this paper has good performance.

As a new paradigm of information technology, the Internet of Things (IoT) is attracting increasing attention from various industrial fields. It is foreseeable that the applications of IoT will be prevalent in the public transportation system and bring changes to the system in the near future. In this paper, we analyze the impact of IoT environment on the public transportation system, propose a new framework of the intelligent public transportation system based on IoT, and present the deployment of the elements, the communication network, and the three-tier architecture of the system in detail. We also present the information flow, technical scheme, optimization model, and algorithm of the main modules of dynamic optimization of the system. The innovative points of this paper lie in: (1) a new framework for public transport system based on IoT, which integrates the scheduling problems of subway, bus, and shared taxi, is proposed for better-coordinated transfer solutions; (2) transport flow prediction methods based on periodic patterns mining is proposed for road flow analysis and passenger flow analysis, and; (3) mathematical model and DSS-based evolutionary computation algorithm are proposed for solving the dynamic bus scheduling and controlling problems. The proposed intelligent transport system based on IoT can assist the decision makers to increase the utilization rate of the transport resources, improve the efficiency of scheduling, and reduce passengers' traveling time.

This paper presents a novel tri-band antenna with dual polarizations and shared aperture. The proposed antenna operates at L-, S-, and X-bands with an approximate frequency ratio of 1:1.9:5.5. Three types of radiation elements resonating at different frequencies, including the square microstrip patch, microstrip dipole, and printed monopole, are interlaced in the same aperture. Printed monopole with a fork-shaped stub is adopted as the L-band element due to its wideband ability. Both the S- and X-band elements adopt a stacked structure to enhance the bandwidth. Besides, the S-band element employs a stepped gradient structure that can obtain a wider bandwidth than the normal microstrip dipole. The measured results agree well with the simulations. The measured impedance bandwidths of L-, S-, and X-bands are 21.7%, 11.6%, and 18.9%, respectively. The measured isolation at L-, S-, and X-bands is higher than 25, 38, and 29 dB, respectively. The antenna also exhibits an excellent radiation performance with the high cross-polarization discrimination at the three bands. To the best of the authors' knowledge, this is the first shared-aperture L-/S-/X-band dual-polarized broadband antenna in a true sense, which is useful for potential synthetic aperture radar applications.

Micro-expression recognition (MER) has attracted much attention with various practical applications, particularly in clinical diagnosis and interrogations. In this paper, we propose a three-stream convolutional neural network (TSCNN) to recognize MEs by learning ME-discriminative features in three key frames of ME videos. We design a dynamic-temporal stream, static-spatial stream, and local-spatial stream module for the TSCNN that respectively attempt to learn and integrate temporal, entire facial region, and facial local region cues in ME videos with the goal of recognizing MEs. In addition, to allow the TSCNN to recognize MEs without using the index values of apex frames, we design a reliable apex frame detection algorithm. Extensive experiments are conducted with five public ME databases: CASME II, SMIC-HS, SAMM, CAS(ME)2, and CASME. Our proposed TSCNN is shown to achieve more promising recognition results when compared with many other methods.

The bearingless induction motor (BIM) system has the characteristics of non-linear, strongly coupled, and highly accurate. To achieve the stable and accurate control of BIM, the methods of direct torque control (DTC) and direct suspension force control (DSFC) based on space vector modulation (SVM) are introduced first. However, the dynamic performance and uncertain anti-jamming robustness of the system need to be further researched. Based on the BIM control system, the active disturbance rejection control (ADRC) technology is selected in this paper. The total disturbance of the system is estimated in real time by using the extended state observer (ESO). According to the estimated value of the ESO, the disturbance can be compensated by the ADRC in time to eliminate the steady-state tracking error to achieve good anti-interference performance. The ADRC-based direct torque and suspension force control system for the BIM is designed and simulated by the MATLAB software. The simulation and experiment results show that the ADRC technology can effectively suppress the impact of load disturbance, reduce the overshoot, and have strong robustness.

The default mode network (DMN) has been extensively investigated in the literature. However, previous studies have mainly focused on the age-related changes in the DMN between old and young participants. Age-dependent changes in specific regions within the DMN have not been adequately explored across the entire adult lifespan. Thus, in this paper, we performed a seed partial least squares (PLS) analysis to investigate the lifespan-wide changes in the regions of the functional and structural DMNs using the resting-state functional magnetic resonance imaging (fMRI) and the structural magnetic resonance imaging (MRI) data from healthy subjects aged 16-85 years. The posterior cingulate area was selected as the seed region based on prior fMRI studies. The single-group functional connectivity analysis showed a stable connection between the seed and the posterior cingulate cortex (PCC), middle temporal gyrus (MTG), and inferior temporal gyrus (ITG); a decreased connection between the seed and the medial prefrontal cortex (MPFC), anterior cingulate cortex (ACC), and superior frontal gyrus (SFG); and an increased connection between the seed and the precuneus (PreC), inferior parietal lobule (IPL), and middle frontal gyrus (MFG) across the entire lifespan. In contrast, in the single-group structural covariance analysis, the covariance connections of the seed to the DMN regions demonstrated a stable covariance trend to the PCC, MTG, superior temporal gyrus (STG), and ITG; an inverted U-shaped covariance trend to the MPFC, ACC, SFG, MFG, and inferior frontal gyrus (IFG); and a U-shaped covariance trend to the PreC with age. Full-group analyses found significant linear decreases in functional and structural DMN integrity. Our findings provide crucial information regarding the influence of age on the function and structure of the DMN and may contribute to the understanding of the underlying mechanism of the age-related changes in the DMN over the lifespan.

Cerenkov luminescence tomography (CLT) can reproduce the location of the tumor inside the body and the physiological processes that are related to its biological behavior, it has thus attracted more attention in the field of imaging technology. The inadequacy of signals that are measured on the body surface might cause ill-posed problems during image reconstruction, thus affecting the quantitative accuracy of CLT. This can be improved using a multispectral strategy by providing more measurements. However, current reconstruction algorithms for multispectral CLT are based on the light transport model that uses a single equation (a diffusion equation or a 3rd simplified spherical harmonics equation) for each spectrum, which cannot guarantee the accuracy and efficiency of the reconstruction. In order to make full use of the broad-spectrum characteristics of Cerenkov luminescence and ensure an accurate and efficient reconstruction, in this work we apply an adaptively hybrid model to the multispectral CLT, that can automatically select the light transport equation. Here, the hybrid model was not only applied to different spectra, but also to different tissues at a certain spectrum. The selection of the light transport equation was accomplished by automatically comparing the index with a predefined threshold. Our proposed method was evaluated with numerical simulations and mouse-based experiments and the results showed the feasibility and effectiveness of the adaptively hybrid model based multispectral CLT.

The fault detection of spacecraft electronic load systems is a crucial part of the spacecraft prognostics and health management system. To detect the abnormal state of spacecraft electronic load systems, complex electrical signals should be processed rapidly and accurately. For the fault detection of spacecraft electronic load systems, a robust unsupervised clustering analysis method and an accurate supervised classification method are of great importance. However, the traditional machine learning methods have poor performance when processing high-dimensional signal data because of the lack of ability to extract complex features from the signals. Therefore, neural-network-based deep learning (DL) models which can extract features from signals automatically are more suitable in this situation. In this paper, a novel convolutional neural network (CNN) module, the multi-branch residual module with dilated convolutions (MRD module), is proposed to extract multi-scale features from the electrical signal. Then, a well-designed CNN model named MRD-CNN is presented for the supervised classification task of signal. Furthermore, for the unsupervised clustering task, the clustering variational autoencoder with MRD modules (MRD-CluVAE) is proposed. The MRD-CluVAE can extract high-quality features from signal data and output the clustering results directly. To evaluate the performance of the proposed models, comparisons among the proposed models and other baseline algorithms are carried out. The experimental results show that the MRD-CNN model achieves higher classification performance and stability, while the MRD-CluVAE has a higher clustering accuracy than other algorithms. These methods can be utilized to resolve the classification and recognition problems of spacecraft electronic load signals.

In this paper, a novel wideband Low-Radar Cross Section (RCS) reflector is proposed. It consists of metallic hexagonal patches on the top layer of dielectric substrate and a metallic film on the bottom. The patches are consists of different sizes of hexagonal patch arrays. They are corresponding to different resonance frequencies, respectively. And the wideband polarization rotation is achieved. A 180°-phase difference is created by the Polarization Rotation Surfaces (PRS) using chessboard-like geometry, which introduced 90° and -90° phase-shift, respectively. The scattering energy is redirected into off-normal directions by the PRS, which enables the effective RCS reduction. In order to obtain more RCS reduction, the arrangement of the PRS reflector has been optimized. The results show that, more RCS reduction can be achieved by the improved arrangement. The monostatic RCS of the reflector can be reduced more than 20 dB by this method.

Failure probability evaluation for complex physical and engineering systems governed by partial differential equations (PDEs) are computationally intensive, especially when high-dimensional random parameters are involved. Since standard numerical schemes for solving these complex PDEs are expensive, traditional Monte Carlo methods which require repeatedly solving PDEs are infeasible. Alternative approaches which are typically the surrogate based methods suffer from the so-called “curse of dimensionality”, which limits their application to problems with high-dimensional parameters. For this purpose, we develop a novel hierarchical neural hybrid (HNH) method to efficiently compute failure probabilities of these challenging high-dimensional problems. Especially, multifidelity surrogates are constructed based on neural networks with different levels of layers, such that expensive highfidelity surrogates are adapted only when the parameters are in the suspicious domain. The efficiency of our new HNH method is theoretically analyzed and is demonstrated with numerical experiments. From numerical results, we show that to achieve an accuracy in estimating the rare failure probability (e.g., 10-5), the traditional Monte Carlo method needs to solve PDEs more than a million times, while our HNH only requires solving them a few thousand times.

Conditional preference networks (CP-nets) are used as intuitive graphical tools to represent conditional preference statements regarding the values of a set of attributes. Making use of CP-nets to solve certain learning problems has attracted growing attention from scholars and become a hot topic in the field of artificial intelligence. Therefore, many methods have been proposed to solve these learning problems. However, these approaches suffer from two main disadvantages: the amount of time required and their lack in concrete structures. To overcome these limitations, in this paper, we first provide theoretical support for the use of a conditional independent test for learning the structure of CP-nets. Second, we propose the dependent degree to calculate the dependency relationship among attributes. Finally, we present an algorithm to obtain the structures of CP-nets. Beyond that, a number of database samples have been reduced by filtering out insignificant or noise data, and a concrete structure of learned CP-nets with 18 attributes is given. The experiments show that our approach can obtain a better structure of CP-nets without materially increasing the time required for the process and put forward contrast to methods presented antecedently.

A novel pattern-reconfigurable two-port multiple-input multiple-output (MIMO) antenna system at Wi-Fi band has been designed by resorting to characteristic modes analysis. The design method of the proposed MIMO antenna array is based on the selective excitation of three orthogonal characteristic modes. The selective excitation is achieved by integrating two pairs of capacitive coupling elements (CCEs) on the same plane with the ground. Two feeding networks related to two ports have been designed for achieving a good impedance matching and providing the feeding to the CCEs. By switching the p-i-n diodes in each feeding network, the selective excitation mode of the two ports can be varied, thus leading to the reconfigurable patterns. Three operating states can be obtained for the proposed antenna design. Simulation results demonstrate that the envelope correlation coefficients of the proposed MIMO antenna array are far less than 0.5 due to the orthogonality of radiation patterns between the characteristic modes in each of the three states. Measured S-parameters and the radiation patterns of a realized prototype are in good agreement with simulation ones confirming the reliability of the design method.

A novel 2×2-element corporate-feed horn sub-array antenna for the 77-GHz band is proposed in this paper. The stair-stepping horn is employed instead of a traditional pyramid horn to reduce the profile of the array. A set of composite structure composed of waveguide divider and cavity is designed and fabricated integrally with a computer numerical control milling technique to simplify the feed-network. Most of all, a metallic cross bar with the wall is innovatively loaded to reduce the mutual coupling and suppress the side lobe level of the sub-array. Measured results show that the proposed antenna achieves a -10 dB |S11| with a bandwidth of 72-85 GHz which is corresponding to 16.6%. Moreover, the average gain of the antenna is about 19 dB, and the side lobe level of the antenna is less than -9.3 dB within the working band.

This paper presents a fast and efficient method that combines physical optics with physical optics to solve the composite scattering problem of a coated object above 3-D random rough surfaces. The object coated with lossy electric or magnetic radar absorbing materials is modeled as a stratified structure, and 3-D random rough surfaces are generated using Monte Carlo method with Gaussian spectrum. The proposed method utilizes fast and efficient physical optics as a solution for the coated object and the underlying random rough surface. Then a reradiation physical optics method based on Huyghens' principle is used as a solution for coupling scattering between the coated object and the underlying rough surface. The proposed method is numerically validated by comparing it with the commercial software FEKO and a multihybrid Kirchhoff approximation with the hybrid finite element-boundary integral algorithm. Runtime and memory consumption are also compared. Our method is found to reduce considerable time and memory while maintaining sufficient accuracy. The difference scattering radar cross section of a coated sphere above a 3-D perfectly electric conductor rough surface is numerically simulated. Furthermore, the dependence of difference scattering characteristic on the object coating parameters and the underlying rough surface parameters under different polarizations is discussed.

This paper presents a novel low-profile high gain frequency reconfigurable patch antenna with unidirectional radiation pattern by using a grid-slotted patch with tunable varactors loading. The antenna consists of two stacked substrates and three metal layers. A grid-slotted patch with two tunable varactors is placed on the top layer, a microstrip line is placed in the middle of two substrates, and the ground plane is on the bottom layer. A single dc voltage applied on two varactors is used to control the working frequencies of the proposed antenna. By altering the bias voltage, the working frequency of the proposed antenna can be continuously changed within a wide range from 2.45 to 3.55 GHz. The antenna maintains broadside radiation and stable radiation pattern in all the operating modes. The measured antenna gain of the proposed antenna rises from 4.25 to 8.49 dBi with the working frequency increases from 2.45 to 3.55 GHz. Compared to other frequency-reconfigurable antennas available in the literature, the proposed antenna has advantages of a wide frequency tuning range over a bandwidth of 1.45:1, high frequency selectivity, low profile (0.016 free-space wavelength at 2.45 GHz), high gain, stable unidirectional pattern, simple structure, and low cost. These advantages make it a promising candidate for cognitive radio and future wireless communication systems.

This paper reports a novel polarization-reconfigurable antenna based on the theory of characteristic mode (CM), which can electronically alter its polarization states between linear polarization (LP), left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP). A square patch with an I-shaped slot is used as a main radiator, which has two LP orthogonal CMs with the same modal significance and 90° characteristic angle difference at 2.4 GHz. A simple feeding network is designed to excite the different combinations of the two modes. By properly choosing the state of the excitation, the LP, LHCP, and RHCP waves can be generated. A 1 × 4 antenna array is designed for the demonstration of array application, and experiments are carried out to validate the proposed design. The measured overlapping impedance bandwidth (BW) (|S11| ≤ -10 dB) and 3-dB gain BW is 2.25-2.7 GHz in the LP state. The overlapping BWs in conjunction with the 3-dB axial ratio BW are 2.16-2.65 and 2.1-2.55 GHz in the LHCP and RHCP states, respectively. The measured maximum gains are 7.2 dBi, 9.78 dBic, and 9.8 dBic for the LP, LHCP, and RHCP states, respectively. The common BW for all three polarization states is 2.25-2.55 GHz or 12.5%. Experimental results show a good performance of the proposed antenna array.

In view of the facts that traditional traffic models are less effective in describing the new mobile internet applications and that the mobile user service behavior among regions with different levels of economic development are always different, a new modeling methodology of user service behavior specifically for mobile internet applications is proposed in this paper. Based on the crowdsourcing measurement data of the service behavior collected through massive end users in live mobile networks of China, the properties of three typical mobile internet services in three typical geographical regions are analyzed and compared with each other, by utilizing the proposed modeling methodology. And the relationships between some key properties of service behavior are analyzed with the maximal information coefficient method and the Pearson correlation coefficient method. The key parameters, differences, and internal relations among these services in typical geological regions of China are then identified. It provides a solid foundation for wireless product design, network planning, and optimization of mobile Internet services-oriented networks specifically in China, and is also a valuable reference for other countries of the similar situation.