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When using Augmented Reality (AR) to overlay virtual textures onto real objects, a significant discrepancy between the tactile and visual characteristics of the real objects can cause users to experience dissonance. This study aims to quantitatively determine the acceptable range of differences in surface roughness that do not induce this dissonance. Specifically, it focuses on macro-roughness, characterized by spatial periods of $200~\mu $ m or more. The experiment system employed ensures accurate occlusion between the user’s finger and virtual textures, a factor often neglected in most conventional studies. Adopting people occlusion in the AR environment reduces the burden on users, while ensuring that the sense of dissonance is not from occlusion rendering. Two materials were used in the experiments: acrylic and medium density fiberboard. The findings indicate that users are more sensitive to dissonance when the real object is rougher than the virtual texture, compared to the opposite case. Additionally, the friction magnitude between the materials and the user’s finger was found to be proportional to the finger pressure applied. Based on this study’s quantitative analysis of the visual-haptic interaction in AR environments, it provides a basis for further research on multisensory interactions and its applications, such as AR material design.

This paper focuses on the wake features of autorotation status mainly including the induced velocity and tip vortex trajectory in order to preliminary explanation the principle of rotation. In this work, the flow features of autorotating rotor were investigated in water tunnel via particle image velocimetry (PIV) technique. The experiments were conducted with a two-blade single rotor which can freely rotate. The stable rotational speed with various pitch and shaft backward angle was recorded and analyzed. The results show that there is a threshold value of stable rotational speed at shaft backward angle and an optimal region at pitch angle. The PIV results showed that the velocity of the internal wake was much lower than the external, indicating a consuming of energy from the flow, which was in accord with momentum theory. The vortices have three prominent parts: tip vortices, wake sheets and root vortices. There is no apparent interference between wake sheets and tip vortices. The vorticity of the tip was observed to increase in near wake due to lesser interference with the sheets. The Beddoes’s model was employed to calculate the tip vortex location with respect to the rotor. The experimental results are in good agreement with the calculated results. It is meaningful for the study to enrich the autorotation theory of rotor and develop underwater rotorcraft.

In wafer fabrication, it is imperative to minimize the transient process of cluster tools for the sake of on-demand and preventive maintenance. Due to the trend of multi-type and small-batch production, transient processes appear more and more frequently. Thus, the optimization problems of transient processes have gained increasing attention from both industry and academia. The requirement for wafer revisiting tend to complicate this problem significantly. However, only a few studies take such a challenge for cluster tools with wafer revisiting. This paper focuses on the schedule optimization of transient processes for dual-arm cluster tools with wafer revisiting. To accelerate transient processes, including both start-up and close-down ones, we adopt a program evaluation and review technique to analyze and harness a cluster tool’s state evolution. We then propose computationally efficient algorithms to speed up transient processes. Finally, we provide illustrative examples to show their applications and validate their effectiveness.

A method for designing a wideband low radar cross section (RCS) antenna is proposed based on hybrid absorptive and diffusive frequency selective reflector (AD-FSR) in this paper. The low RCS antenna exhibits manifold frequency responses in three artificial bands, which are created by its different components. A simple U-slot patch antenna is borrowed, which is integrated with the diffusion metasurface, and placed under the absorptive sheet with about quarter wavelength. Then, the out-of-band incident wave is captured by the absorber in the lower frequency band, and diffused by the metasurface in the upper frequency band achieving the RCS reduction. The design strategies are explained and verified with the aid of the corresponding equivalent circuit model and current distributions. To illustrate the efficacy of the proposed approach, the low RCS antenna using the proposed hybrid AD-FSR structure is fabricated and tested, and the results demonstrate that the proposed structure is an attractive candidate for designing wideband low RCS antennas.

In this paper, we realize high-accuracy side-effect prediction of Traditional Chinese Medicine Compound Prescription by introducing network embedding and deep learning. A random walk network that could efficiently interpret the information in the prescription is established from a conventional Bag-of-Word network. After the validation of this random walk network, the highest prediction accuracy reaches 0.908 where a simple five-layer artificial neural network is implemented, rendering this method is promising for Traditional Chinese Medicine side-effect prediction and other medicines with a similar structure such as the compound drugs.

The development of energy-resolving photon-counting detectors provides a new approach for obtaining spectral information in computed tomography. However, the responses of different photon counting detector pixels can be inconsistent, which will always cause stripe artefacts in projection domain and concentric ring artefacts in image domain. Traditional ring artifacts processing methods are mostly based on averaging and filtering. In this paper, we propose to use deep learning methods for ring artifacts removal respectively in image domain, projection domain and the polar coordinate system. Besides, by incorporating reconstruction process into neural networks, we unite the information from image domain and projection domain for ring artifacts removal under the framework of deep learning for the first time. A traditional ring artifacts removal method, which is based on wavelet and Fourier transform, is implemented for comparison. Quantitative analysis is performed on simulation and experimental results and it shows that deep learning based methods are promising in solving the problem of non-uniformity correction for photon-counting detectors.

With the rapid development of technologies such as big data, intelligent data analysis and cloud computing, the application of Internet financial technology has become more and more extensive, and with the advent of the era of large asset management in the domestic wealth management industry, in order to improve the efficiency of financial services, traditional finance is needed. The products and services provided by the industry have been innovated, resulting in smart investment. Compared with traditional investment, smart investment as a new business model has the advantages of low threshold, low cost and high efficiency. However, as far as its nature is concerned, smart investment must first play the role of an investment adviser. Therefore, for enterprises or individuals who invest, the investment efficiency of smart investment is the most important. At present, the research on the efficiency analysis of smart investment, due to the improper selection of algorithm models or the lack of deep data mining, leads to the analysis of the investment efficiency of smart investment products is inconsistent with or even deviated from the actual situation. In view of these problems, this paper selects China Merchants Bank’s Capricorn Intelligence as the research object, and analyzes the investment efficiency of smart investment based on K-means cluster analysis and data mining technology. The results show that Capricorn has a certain randomness in the selection process of the fund, and chooses to reduce the rate of return in order to control the risk. The investment portfolio formulated for the customer has obvious timing. The results show that the machine learning based on K-means algorithm makes a concrete analysis of the investment efficiency of Capricorn Smart Investment, this method can also be used for the efficiency analysis of other smart investment products.

Multichannel signal processing in azimuth is a vital technique to enable a wide-swath Synthetic Aperture Radar (SAR) with high azimuth resolution. However, the multichannel high-resolution and wide-swath (HRWS) SAR system always suffers from the problem of the azimuth nonuniform sampling resulting in the image ambiguity, when it does not satisfy the uniform sampling condition. In this paper, to suppress the azimuth image ambiguity, we propose a novel unambiguous reconstruction method based on image fusion. During this reconstruction processing, the Back Projection (BP) algorithm is first utilized for SAR imaging to obtain the designed sub-images. Then, the reconstruction expression is derived as the summation of the sub-images weighted by the interpolation coefficient. This method integrates the reconstruction into the imaging process and the image fusion makes the procedure simple. In addition, the interpolation period, which affects the reconstruction image quality and efficiency, is further analyzed. Moreover, as the curved trajectory platform brings more challenges for the unambiguous reconstruction, the performance of the proposed method applied to the curved trajectory platform is studied. Finally, experimental results clearly verify the effectiveness of the proposed method for ambiguity suppression and demonstrate its applicability to the curved trajectory.

Based on advances in image processing technology and Web-enabling technologies for mobile devices, mobile Augmented Reality (AR) and Virtual Reality (VR) has developed rapidly. The rendering and interaction of 3D models is an important part of AR and VR applications and is closely related to user experience. However, since the existing WebGL 3D JavaScript libraries for Web-based mobile 3D (represented by three.js and babylon.js) load the entire model file at once, large-size 3D models with complex interactions cannot be rendered smoothly due to limited data transmission, the weak computation capabilities of mobile Web browsers, and the latency of 3D model rendering. In this paper, we first propose model-animation data separation and an on-demand loading mechanism to improve the data request and loading process of Web 3D models. The main mechanisms are the following: (1) The model data are segmented into topological data and animation data sequences, and only the necessary data of the model are loaded when the Web-based mobile 3D model is first rendered. (2) The 3D model animation data sequence is semantically decomposed, and a multigranular model animation data service is established to provide continuous animation data support. (3) An asynchronous request-response mechanism is used to optimize the loading method of the model data. The model rendering mechanism uses an on-demand request and rendering method to transform the centralized loading process of the 3D model into a decentralized process. According to the testing and verification results, this optimization method can reduce the latency of mobile Web 3D in model data transmission and rendering by 24.72% for the experiment models. The interaction experience of Web-based mobile AR and VR is substantially improved relative to existing Web 3D rendering engines and rendering mechanisms, especially in complex interactive service scenarios.

Driving intention prediction is one of the key technologies for the development of advanced assisted driving systems (ADAS), which could greatly reduce traffic accidents caused by lane change and ensure driving safety. In this paper, an advanced predictive method based on Multi-LSTM (Long Short-Term Memory) is proposed to predict lane change intention effectively. First, the training data set and test set based on real road information data set NGSIM (Next Generation SIMulation) are built considering ego vehicle driving state and the influence of surrounding vehicles. Second, the Multi-LSTM-based prediction controller is constructed to learn vehicle behavior characteristics and time series relation of various states in the process of lane change. Then, the influences of prediction model structure change and data structure change on test results are verified. Finally, the verification tests based on HIL (Hardware-in-the-Loop) simulation are constructed. The results show that the proposed prediction model can accurately predict the vehicle lane change intention in highway scenarios and the maximum prediction accuracy can reach 83.75%, which is higher than that of common method SVM (Support Vector Machine).

Skeleton-based action recognition has attracted extensive attention recently in the computer vision community. Previous studies, especially GCN-based methods, have presented remarkable improvements for this task. However, in existing GCN-based methods, global average pooling is applied to the extracted features before the classifier. This may hurt the recognition performance since it neglects the fact that not all features are equally important in the temporal dimension. To tackle this issue, in this article, we propose a feature selection network (FSN) with actor-critic reinforcement learning. Given the extracted feature sequence, FSN learns to adaptively select the most representative features and discard the ambiguous features for action recognition. In addition, conventional graph convolution is a local operation, it cannot fully capture the non-local joint dependencies that could be vital to recognize the action. Thus, we also propose a generalized graph generation module to capture latent dependencies and further propose a generalized graph convolution network (GGCN). The GGCN and FSN are combined in a three-stream recognition framework, in which different types of information from skeleton data are further fused to improve the recognition accuracy. Extensive experiments demonstrate that the proposed FSN is a flexible and effective module that can cooperate with any existing GCN-based framework to enhance the recognition accuracy, the proposed GGCN can extract richer skeleton features for skeleton-based action recognition, and our method achieves superior performance over several public datasets, e.g. 95.7 top-1 accuracy on NTU-RGB+D, 86.7 top-1 accuracy on NTU-RGB+D 120, etc.

Deep learning, especially convolutional neural network (CNN), has been widely applied in remote sensing scene classification in recent years. However, such approaches tend to extract features from the whole image rather than discriminative regions. This article proposes a self-attention network model with joint loss that effectively reduce the interference of complex backgrounds and the impact of intra-class diversity in remote sensing image scene classification. In this model, self-attention mechanism is integrated into ResNet18 to extract discriminative features of images, which makes CNN focus on the most salient region of each image and suppress the interference of the irrelevant information. Moreover, for reducing the influence of intra-class diversity on scene classification, a joint loss function combining center loss with cross-entropy loss is proposed to further improve the accuracy of complex scene classification. Experiments carried out on AID, NWPU-NESISC45 and UC Merced datasets show that the overall accuracy of the proposed model is higher than that of most current competitive remote sensing image scene classification methods. It also performs well in the case of fewer data samples or complex backgrounds.

In this paper, an efficient nonstandard finite difference scheme for the numerical solution of chaotic fractional-order Chen system is developed. In the new method, an appropriate nonlocal framework in conjunction with the Grünwald-Letnikov approximation are applied for the discretization of fractional differential system. By constructing the discretization with the nonstandard finite difference scheme, high resolution of the system can be obtained, and the numerical instabilities of the nonlinear fractional-order Chen chaotic system can be also addressed to some extent. In addition, a new fractional derivative of the Caputo type is employed in the context of fractional-order Chen system to further decrease the computational complexity in the long-term treatment of fractional model. Numerical simulations demonstrate the applicability, accuracy and efficiency of the developed method.

The environmental interference and the noise in 3D synthetic aperture radar (SAR) image, considered as the background, are inescapable and ought to be eliminated. For 3D SAR image, there is a spatial separation of the target and the background. Therefore, it is possible to achieve the separation of the target and the background by image segmentation. Due to the complexity of the target shape and the large dynamic range of the SAR image, the background cannot be accurately separated through the amplitude information alone. In this paper, a method based on region growing is proposed to achieve 3D SAR image background separation utilizing the plural and the spatial information. The image enhancement matrix, constructed by the plural information of the SAR image, is implemented to improve the contrast of the image. The seeds are extracted by the weighted Otsu, and the weight is determined by the structure and amplitude information from the target. For the region growing, the growing process is achieved by the accumulation of the growing rate, which can suppress the growing of the noise. During the region growing, the stopping growing condition of each seed is independent and controlled by the seed threshold. The global threshold constrains the almost unrestricted growing of the seed whose amplitude is close to the noise amplitude. The results of the simulation and the experiments verify the performance of the proposed method is higher than that of the compared methods with three image evaluation criteria. Besides, we discuss the cost of computation and the influences of three important parameters to achieve a complete analysis.

This paper considers the problems of observing system states of some special system and proposes a novel $H\infty $ proportional–integral–derivative ( $PI^{\varepsilon }D$ )-based observer designed by linear matrix inequality (LMI). First, an augmented system is designed to ensure the asymptotic stability of the system with the $H\infty $ -based $PI^{\varepsilon }D$ observer. Then, the proportional, integral, and derivative gains of $PI^{\varepsilon }D$ observer are designed based on $H\infty $ synthesis. Finally, the $H\infty $ synthesis problem is translated into LMI, and through solving the LMI, the proposed observer can ensure the performance of $H\infty $ . The observation of vehicle longitudinal tire force is followed as an example, whose closed-loop system with traditional PID observer could not guarantee asymptotic stability. Both simulation and experimental results illustrate the great performance of the proposed observer.

The performance of the wireless mesh information-centric Internet of Thing (IC-IoT) networks can be greatly enhanced by adopting multi-radio multi-channel (MRMC) and partially overlapping channels (POCs). However, the network interference and channel assignment in IC-IoT networks become more complicated while using both MRMC and POCs. In this paper, a logical link-based partially overlapping channels interference model is analyzed to mitigate the inter-channel interference, and a channel selection scheme is formulated as a potential game. Moreover, a variable probability learning algorithm is proposed by selecting a channel in the strategy space based on its probability. The channel usage probability can be changed by its link utility. The channel with a larger link utility is then with a bigger probability in the strategy space. The simulation results show that our proposed algorithm can achieve high system throughput with fast network convergence.

The study of crowd movement has recently become a popular research topic due to the increasing frequency of public safety issues. Compared with human evacuation experiments and drills, which may have personal safety risks and require a large number of volunteers, a simple and convenient computer simulation has become the mainstream research method. The computer simulation first needs to characterize small groups in the crowd to model the motion state of crowds for more accurate crowd modeling. In this paper, a top-bottom hierarchical clustering algorithm based on off-line crowd trajectories is proposed to provide small group information for crowd motion simulation. First, an unmanned aerial vehicle and a tracking technology are used to capture the pedestrian flow and extract the pedestrian trajectory. Second, a top-bottom hierarchical clustering strategy is proposed to divide the crowd into groups, which solves the problem of the difficulty of ascertaining small groups. This method solves the problem of automatically determining cluster centers by using the improved density peak clustering algorithm combined with a greedy algorithm. One factor of distinguishing small groups is improved by replacing the angle of the direction of motion with the distance difference, thus reducing the computational complexity. Specifically, the mean distance between trajectories based on the Euclidean distance is used for the top-level coarse-grained clustering; then, the improved Hausdorff mean distance is determined in the bottom-level fine-grained clustering. Third, the proposed algorithm is validated by classifying the groups of pedestrians in real videos. The experiments show that the proposed method is applicable and effective.

A large number of mobile multimedia terminals are prominent features of smart cities. Device-to-device (D2D) communication takes advantage of the limited bandwidth resources of cellular networks to accommodate more mobile devices. However, when D2D pairs reuse cellular users channels, serious interference leads to energy consumption, which dissatisfies the requirements of green communication. This paper focuses on energy efficiency maximization of D2D communication under the constraints of both D2D pairs and cellular users quality of service. The formulated resource allocation problem is NP-hard, which is usually difficult to solve within polynomial time. To make the problem easy to handle, we divide it into power control and channel allocation sub-problems. In particular, we propose a power control algorithm based on the Lambert W function to maximize the energy efficiency of a single D2D pair. The preference values of D2D pairs and cellular users are calculated using the power control results, respectively. A channel allocation scheme based on the Gale-Shapley algorithm utilizes preference values to match two sides, which aims at maximizing the signal to interference plus noise ratio of cellular users and the energy efficiency of D2D pairs. The simulation results show that the proposed algorithm could not only guarantee the transmission rate of cellular users but also improve the system and D2D pairs energy efficiency.

Automatic steering system of self-driving vehicles has significant influence on vehicle safety. The performance of automatic steering system is affected by external disturbance, parameter perturbation, and real-time ability of control algorithm. First, to develop a high-performance automatic steering control strategy, a vehicle-road system model based on vehicle dynamics and kinematics is established. Then, the system state equation with direction-angle and lateral-position deviations is derived to describe the tracking accuracy. Finally, based on the sliding mode variable structure control method, an automatic steering control algorithm is proposed and verified. The results show that the dynamic quality of the system is improved with the optimization of the sliding mode. Furthermore, good robustness against vehicular velocity, real-time performance, and tracking accuracy is achieved.

Autonomous driving is a crucial issue of the automobile industry, and research on lane change is its significant part. Previous works on the autonomous vehicle lane change mainly focused on lane change path planning and path tracking, but autonomous vehicle lane change decision making is rarely mentioned. Therefore, this paper establishes an autonomous lane change decision-making model based on benefit, safety, and tolerance by analyzing the factors of the autonomous vehicle lane change. Then, because of the multi-parameter and non-linearity of the autonomous lane change decision-making process, a support vector machine (SVM) algorithm with the Bayesian parameters optimization is adopted to solve this problem. Finally, we compare a lane change model based on rules with the proposed SVM model in the test set, and results illustrate that the SVM model performs better than the rule-based lane change model. Moreover, the real car experiment is carried out to verify the effectiveness of the decision model.

In this paper, we study the distributed cooperative spectrum sensing in heterogeneous cognitive radio networks, where each secondary user (SU) adopts a different spectrum sensing algorithm and experience a different channel environment. To solve the spectrum sensing problem, we propose a fusion rule based on dynamic grouping, which can be used in the mobility of the primary user and SUs. By introducing a grouping parameter, the proposed algorithm can realize the dynamic grouping of SUs. The SUs in group-1 will not be able to participate in the cooperation, and the SUs in groups-2 and groups-3 have different weighting factors. The proposed fusion rule is only required to process the independent decision result of each SU, which significantly reduces the data transmission overhead and processing delay. The simulation results show that the proposed fusion rule can effectively improve the dynamic grouping of SUs and fuse detection information, and outperform the OR rule, AND rule, equal gain combining, and maximum signal-to-noise ratio under the same conditions.

In smart grid (SG), internet of things (IoT) has drawn much attention around the years due to increased volume of traffic. Various types of electrical devices in SG demand a differentiated quality of service (QoS). However, the spectrum resource for SG wireless communication is quite limited. The network performance and spectrum utilization in a SG-IoT network can be greatly enhanced by adopting wireless mesh networks (WMNs) with multi-radio multi-channel (MRMC) and partially overlapping channels (POCs). In this paper, a game-based efficient POCs assignment algorithm is proposed to satisfy the diverse QoS and improve the spectrum utilization efficiency. The utility function in the proposed game model is taken both of the differentiated QoS and interference into consideration to optimize the network capacity. Then Nash Equilibrium (NE) of the game model is analyzed, and a variable learning step algorithm (VLSCA) is designed to improve its convergence speed. In the simulations, seven SG application services divided into four priority levels are considered to compare with the other two typical channel assignment algorithms. The results show that our algorithm has better performance to ensure a higher priority level traffic with lower end-to-end delays and packet loss ratios.

In the trend of urgent demand of energy saving for public transportation, the series-parallel plug-in hybrid electric bus (SPPHEB) with energy saving potential is proposed. The fuel economy of hybrid power train depends to a significant degree on its control strategy, and then an energy management strategy based on nonlinear model predictive control (NMPC) is obtained for better fuel economy performance. Firstly, the quasi-static model of the plant is described and the reference curve of the state of charge (SOC) and the predictive torque are formulated and illustrated. Then the NMPC framework for SPPHEB, which explored the torque prediction method in the prediction domain and the prediction method of the reference SOC trajectory on the whole working condition, is introduced and completed by adopting dynamic programming (DP) algorithm to solve the nonlinear optimization problem. Finally, the NMPC strategy is simulated in Simulink, and its optimization performance is compared with other strategies such as DP, equivalent consumption minimization strategy (ECMS) and charge-depleting and charge-sustaining (CDCS). The simulation result is that compared with the CDCS strategy, NMPC strategy shows an economic improvement by 18.86%, and 10.36% improvement compared with the ECMS strategy. The good performance of the NMPC strategy is due in part to the consideration of the reference SOC trajectory mechanism and the prediction of the expected torque. The NMPC-based EMS considered both optimization performance and computation burden, which may provide a prospect for further practical application of real vehicles.

Conventional radar cross section (RCS) measurements require far-field or compact-antenna-test-range (CATR) conditions, which have strict restrictions and high implementation costs. By contrast, RCS extrapolation methods in the near zone are more convenient and practical, which become the research emphasis in recent years. This paper presents a novel RCS extrapolation method based on the combination of near-field 3D synthetic aperture radar (SAR) technique and planar projective transforms (PPT) algorithm. Firstly, to extract the target's reflectivity distribution, we apply near-field 3D SAR imaging and obtain 3D near-field RCS (NFRCS) images. Secondly, to meet the requirement of RCS measurements, we derive a novel correction factor used to precisely expand 3D NFRCS images. Finally, we extrapolate the plane-wave responses in the azimuth-vertical dimensions, which presents as planar patterns. The proposed method utilizes the complete 3D image-based information to overcome the inherent constraints of classical RCS extrapolation methods on application scenarios, which has the advantages of broad applicability and high flexibility. The detailed derivation and implementation of this method are described in this paper. The simulation and experiment results verify that the proposed method can provide the equivalent CATR result within ±11° azimuth and elevation angles, and it is applicable to precisely measuring the point, surface, and complex scatterers.

Compressed sensing (CS) algorithms are used for linear array synthetic aperture radar (LASAR) three-dimensional (3D) imaging. However, it is difficult to obtain imaging results with both high computational efficiency and promising imaging quality. Because of the high-dimensional matrix-operations, the computational complexity of several CS algorithms is huge such as the iterative adaptive approach (IAA), bayesian compressed sensing (BCS), and sparsity bayesian recovery via iterative minimum (SBRIM) algorithm. Besides, the greedy pursuit algorithms such as the orthogonal matching pursuit (OMP) algorithm cannot acquire ideal imaging results on account of the preset sparsity of the imaging scene. To solve the problem, we present a fast sparse recovery algorithm via resolution approximation (FSRARA) in this paper. Firstly, the whole imaging scene is divided into 3D scattering units with large spacing, and SBRIM algorithm is used to obtain its low-resolution imaging results quickly. Secondly, the low-resolution imaging results are conducted image segmentation by the fuzzy c-means (FCM) clustering algorithm to extract the possible targets' areas coarsely. Then we re-divide the imaging scene by higher imaging resolution and extract the possible targets' areas according to the coarsely possible targets' areas. FSRARA achieves improved computational efficiency with low-dimensional matrix-operations on the possible targets' areas instead of the high-dimensional one on the whole imaging scene. Meanwhile, FSRARA performs better in suppressing the false targets and sidelobe interference and improves the imaging quality than the SBRIM algorithm. Simulation and experimental results prove that FSRARA improves the computational efficiency by hundreds of times at most than SBRIM algorithm and its computational efficiency is higher than smoothed $L_{0} $ norm (SL0), IAA, and BCS algorithm. Besides, FSRARA improves the imaging quality compared with OMP, IAA, SL0, BCS, and SBRIM algorithms.

In the conventional electromagnetic tube expansion, the end effects generated by the conventional helix coil may lead to inhomogeneous tube deformation in the axial direction. This paper is aimed at overcoming this issue by proposing a new concave coil structure to replace the helix coil currently used by the industry practice to generate a radial electromagnetic force on the tube. The proposed concave coil is expected to reinforce the electromagnetic force distribution profile and, hence, improving the axial inhomogeneous deformation of the tube. In this context, a new R-L criterion of deformation uniformity is first proposed. Second, an electromagnetic-structural coupling finite element model is established to investigate the relationship between the distribution of electromagnetic force generated by the concave coil and the uniformity of the tube under various voltage levels. The effectiveness of the proposed method is validated through a series of experimental and simulation analyses. Furthermore, based on the characteristics of the electromagnetic tube expansion, a modified multilayer concave coil structure is proposed to overcome the axial inhomogeneous deformation of long tubes.

Current benchtop x-ray fluorescence computed tomography (XFCT) devices, which use x-ray tubes to stimulate x-ray fluorescence (XRF) photons, suffer from the contamination of Compton scatter background produced by the polychromatic incident beam. The conventional maximum-likelihood expectation-maximization (ML-EM) algorithm only considers the noise model of the XRF signal, which results in high statistical noise in reconstructed images caused by scattered photons. In this study, we proposed a scattering noise model enhanced EM-TV algorithm for benchtop XFCT image reconstruction in order to reduce the noise of scatter background and improve the sensitivity of XFCT images. The statistical noise of scattered photons was considered in the likelihood function and the EM iteration step was modified correspondingly to suppress the statistical noise caused by Compton scattered photons. The robustness of the EM iteration was improved by applying the reweighted total variation (TV) norm as the penalty function. Numerical simulations and imaging experiments of a PMMA phantom consisting of gadolinium (Gd) solutions were performed to validate the proposed algorithm. The phantom was irradiated by a cone-beam polychromatic source and the projection was recorded by a linear-array photon counting detector. For comparison, the XFCT images of Gd were reconstructed using different algorithms. Results indicate that compared with the conventional ML-EM algorithm, the proposed algorithm can obtain XFCT images with lower background noise and higher contrast, which may further improve the sensitivity and image performance of current benchtop XFCT systems.

A load-independent wireless power transfer system with constant current and constant voltage output for electric vehicles charging is designed and optimized in this paper. Wireless charging system based on LCL-S or LCL-LCL compensation topology is systematically analyzed. And dynamic LCL-S/LCL switching topology is designed and simplified to achieve constant current in the transmitting coil and load-independent constant current and constant voltage output, which can be controlled easily. Moreover, the coupling structures composed of different coil shapes and shielding structures are comparatively studied to improve the coupling stability under misalignment. Figure-of-merit and coupling change rate Δkt defined in this paper are the key parameters in the process of coupling structure design and optimization. The combination of rounded rectangular spiral coil and splicing magnet core units is optimized as the coupling structure of the wireless charging system. Finally, the resonant wireless charging system prototype is being built and tested. The experimental results show that the load-independent and other characteristics of the implemented system are well correlated with the theoretical analysis and design.

The co-tier and cross-tier interferences become serious when the electric power communication networks are deployed densely in the coverage area of the primary network base station (PBS). To reduce the co-tier interference in the electric power communication networks and guarantee the quality of service of the primary networks users (PUEs), we propose a distributed power control method. First, a reasonable interference threshold of the PBS is chosen to guarantee the PUE's transmission requirement. Then, a fictitious power-price game with interference compensation is employed to update the power of gateways in smart grid on the condition of the interference threshold. Finally, a modified distributed power control method is proposed to protect the primary network link. Simulation results show that the co-tier interference among gateways is reduced efficiently, the target signal-to-interference plus noise ratio of the PUEs can be achieved, and the network performance is improved.

Dual-energy mega-electron-volt (MeV) X-ray container radiography has become a well-established technique in customs security application, because of its material discrimination capability. The main difficulty of X-ray radiography is dealing with the materials overlapping problem. When two or more materials exist along the X-ray beam path, its material discrimination performance will be obviously affected. Computed tomography (CT) collects many X-ray measurements taken from different angles surrounding an object to produce cross-sectional (tomographic) images of the scanned object. Therefore, CT can provide real 3-D images inside the object. However, due to the bulky container volume and complex types of cargos, it is very hard to develop such a huge CT system for container inspection. To the best of our knowledge, there has no such commercial X-ray CT system for container inspection yet. This paper presents the design of a dual MeV energy X-ray CT system for cargo container inspection which uses an accelerator with fast 6/9 MeV switching spectra, an arc detector array and rotating mechanism. A dual MeV energy X-ray CT image reconstruction and material decomposition algorithm are developed. An experimental system was built with the same accelerator and detector array as used in the designed container CT system. Experimental results that prove the validity and effectiveness of the algorithm and CT system are presented.