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As a critical aviation support sensor, radar altimeters face imminent challenges due to the interference caused by the frequency overlap with the developing 5G telecommunications networks. This paper addresses the critical challenge posed by 5G interference with radar altimeter signals, which is crucial for maintaining aviation safety. It introduces a novel machine learning (ML) framework to preserve altimeter accuracy in the presence of 5G signals. This framework first classifies signals into pure or interfered categories, then applies regression models to predict altitudes when interference is detected, effectively quantifying and mitigating the interference impact. Distinguished by using real 5G signals from a Norman, Oklahoma base station, the approach offers a realistic evaluation and demonstrates the ML framework’s effectiveness in real-world conditions. Using various advanced ML models, the methodology showcases how critical aviation instruments can be safeguarded against the challenges posed by emerging telecommunications technologies, ensuring air travel safety and efficiency.

Compressed sensing theory is widely used to accurately reconstruct the original signal from a small number of random observations, i.e., obtain high-dimensional information from low-dimensional information. This feature has shown effectiveness in image super-resolution. In this paper, based on the compressed sensing theory, the ARSR (Adaptive Regularization term Super Resolution) algorithm is proposed to achieve super-resolution reconstruction of images. The algorithm models on the basis of exploiting the image local sparsity prior. On the one hand, by using adaptive regularization coefficients to weight the elements in the norm, we obtain more accurate sparse representation coefficients. On the other hand, we also add an adaptive regularization term behind the optimization model, which is able to take the correlation of the image into account as well. By generating a suitable coefficient, it can adaptively reconcile the relationship between sparsity and correlation. In addition, we derived an approximation to solve the model iteratively using the alternating direction method of multipliers (ADMM). In this paper, the sparse transform domain is trained by the K-SVD algorithm and the accuracy evaluation indexes of the experiments use peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Compared with existing classical sparse representation-based image super-resolution algorithms, our ARSR algorithm obtains the highest PSNR and SSIM values in different regions of the image with better subjective visual effects.

Spectral clustering, a powerful algorithm in the field of AI, holds a significant role despite its inherent high time complexity. For data owners grappling with limitations such as small datasets and restricted computational resources, harnessing the computational capabilities of cloud computing and aggregating data from multiple sources can yield precise spectral clustering results. However, explicit data uploading to cloud servers poses privacy risks. In response to this challenge, we explore the outsourcing dilemma of spectral clustering in a cloud and multi-user environment and propose a quantum-secure and efficient solution. Specifically, by employing the CKKS homomorphic encryption algorithm within a dual non-collusive server model, we formulate a comprehensive and multi-user spectral clustering outsourcing scheme. Our approach addresses privacy concerns by introducing secure computation protocols for $L_{2}$ norm, exponential function, and negative half power function. We elaborate on efficient computational algorithms for each stage of spectral clustering, ensuring accurate clustering outcomes without compromising dataset privacy. Moreover, in our scheme, users only need to upload their encrypted dataset without requiring direct interaction with each other or the cloud server until obtaining clustering results. Finally, we argue the IND- $\mathcal {CPA}$ security of our design and substantiate its accuracy and efficiency through theoretical comparison analysis and experimental evaluations.

The spatio-temporal action detection task requires the output of the temporal and spatial positions as well as the action category of the target action instances in the form of action tubes. However, the current definition of video-level metrics in spatio-temporal action detection tasks is not sufficiently clear and unified to fully describe the ability of network models to perform spatio-temporal detection. Furthermore, existing tube linking methods are not only heavily dependent on the quality of the detection stage but also lack reliable linking criteria, resulting in poor tube linking performance. To address these issues, this study proposes a hierarchical linking method based on multiple clues. This method first dynamically utilizes various correlation clues at two levels, including appearance features, spatial overlap, motion prediction, category scores, tube length, and tube confidence status, to reduce the negative impact of unreliable information on the correlation. Then, it employs inter-class correlation to handle the mutual influence between different categories, followed by joint probability data association to address the mutual influence between correlated objects, ultimately achieving robust and accurate online linking of action tubes. The method is experimentally compared with other correlation methods on the untrimmed UCF24 and MultiSports datasets, demonstrating state-of-the-art tube link performance. We also conducted ablation experiments to explore the impact of the different modules and stages in the proposed tube-linking method.

The human body is a typical non-rigid object, and its 3D reconstruction is a classic problem in the field of computer vision. Due to the inherent complexity and dynamism of the human body, it is not suitable for existing non-rigid 3D motion reconstruction algorithms that assume that the number of shape bases of non-rigid bodies is known. The number of shape bases is very important for 3D reconstruction methods. If the number of shape bases is estimated incorrectly in contour deformation estimation, it can lead to unreliable or even complete failure of the reconstruction algorithm. Therefore, this paper designs a 3D non-rigid motion human object reconstruction algorithm based on reliable estimation of contour deformation degree. This algorithm leverage Scale Invariant Feature Transform (SIFT) to obtain non-rigid moving human target features. Firstly, the contour appearance model of the moving human sequence is used to extract the contour feature sequence, which is preprocessed based on the contour appearance depth feature; Furthermore, the deformation degree of the contour is reconstructed and the calculation process of the number of shape bases was optimized, which is no longer simply defined as known. This method optimizes and solves the problem of missing data, improves the reliability of estimating the degree of contour deformation, and completes target reconstruction. The experimental results show that the three-dimensional reconstruction algorithm can accurately reconstruct the changes in the movements of athletes’ shots; The accuracy of 3D reconstruction can reach 95.98%; Moreover, PSNR, SSIM, and MSE indexes performed well with smaller fluctuation range, and the distribution of three-dimensional reconstructed scattering points is very close to the three-dimensional position distribution of real scattering points, and the three-dimensional reconstruction effect is good with strong reliability.

In typical traffic scenarios such as non-signalized roads or shared spaces where vehicles and pedestrians interact without clear separations. The interaction distance between objects is usually shorter due to the simultaneous motion of road users. Pedestrian-crossing scenarios in these areas make the scenario complex due to the unpredictability of the pedestrians intention and the need to balance between safety, comfort, and time consumption. To address this collision avoidance(CA) problem, a novel strategy using Social Force Model (SFM)-based adaptive parameters was proposed. The interaction system between the ego vehicle and the pedestrian was simplified as a Markov process to adopt the SFM-based dynamic model, and the validity of this simplification was demonstrated using real-world driving data. Based on the current state of the interaction system that consists of vehicle and pedestrian, this research adopted the optimal parameters that were generated by particle swarm optimization (PSO) to generate optimal parameters for the SFM-based vehicle dynamic model, which helps the vehicle avoid pedestrians with random motion. The proposed method was validated through bench testing, and the results showed that the proposed method balanced the safety, comfort, and time consumption requirements during the CA process in the studied scenario.

The exercise recommendation system is emerging as a promising application in online learning scenarios, providing personalized recommendations to assist students with explicit learning directions. Existing solutions generally follow a collaborative filtering paradigm, while the implicit connections between students (exercises) have been largely ignored. In this study, we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student (exercise-exercise). Specifically, a new framework was proposed, namely personalized exercise recommendation with student and exercise portraits (PERP). It consists of three sequential and interdependent modules: Collaborative student exercise graph (CSEG) construction, joint random walk, and recommendation list optimization. Technically, CSEG is created as a unified heterogeneous graph with students’ response behaviors and student (exercise) relationships. Then, a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG, which allows for full exploration of both similar exercises that students have finished and connections between students (exercises) with similar portraits. Finally, we propose to optimize the recommendation list to obtain different exercise suggestions. After analyses of two public datasets, the results demonstrated that PERP can satisfy novelty, accuracy, and diversity.

To improve vehicle performance and energy utilization, a novel planetary gear set based flywheel hybrid electric powertrain (PGS-FHEP) is proposed. The PGS-FHEP involves an internal combustion engine, a planetary gear set that integrated a control motor and an energy storage flywheel, which combines the high efficiency of the mechanical flywheel energy storage system with the flexible and controllable characteristics of the electric motor. The powertrain is analyzed and modeled using lever analogy method, and a rule-based control strategy is designed and verified under different test cycles. The simulation results indicate that compared with the traditional manual transmission vehicle, the fuel economy of the vehicle equipped with PGS-FHEP can be improved by more than 50%, and the acceleration performance can be increased by 28.01%. Up to 60.61% of vehicle kinetic energy can be recovered by PGS-FHEP, among which 37.85% can be directly captured by the energy storage flywheel. In addition, the battery charging power is reduced, which is beneficial to prolong the battery life.

High-frequency start-up strategy is usually used to limit the current overshoot in the resonant tank of LLC converter. However, the strong coupling among start-up frequency, initial duty cycle, and current limit increases the design complexity of parameters. This paper proposes a parameter optimization strategy of the LLC converter soft start-up process based on the numerical calculation method. With this strategy, the initial switching frequency and duty cycle are determined adaptively according to the given maximum current requirements during the start-up process. The current overshoot is suppressed and soft switching is achieved in the whole process. After the second switching period, 50% duty cycle operation occurs without oscillation. Furthermore, exponential frequency reduction is used in the process, and the current overshoot limiting condition in the initial period is discussed. Finally, a 240 W LLC converter prototype is built to verify the correctness of the theoretical analysis in this paper. In terms of current limiting, soft switching, and initial switching frequency, the comprehensive performance of the soft start-up control strategy proposed in this paper is better. Compared to the traditional control strategy, the current overshoot is reduced by 45.6%. The theoretical calculation, simulation, and experimental results are consistent.

Abstract The accuracy and reliability of circuit board fault detection are significantly influenced by the uncertainty inherent in the VI curve. Here, an ensemble neural network is proposed, which combines the neural network‐based prediction interval and ensemble approaches, to improve the accuracy of fault detection using the VI curve. First, a loss function with multiple objectives is formulated by integrating curve fitting and prediction interval. The aim is to achieve the curve fitting between current and voltage while simultaneously determining the optimal upper and lower bounds of the prediction interval. Second, an ensemble approach is employed to reduce model uncertainty and derive the ultimate current predictions and intervals. These predictions and intervals are then used in a comparative approach to automatically detect faults in circuit boards. Finally, the effectiveness of the proposed algorithm in improving the accuracy of fault detection is verified on experimental circuit boards.

Decarbonization has been a trend worldwide, especially in the shipping industry. A pure battery-powered all-electric ship (AES) may be the most flexible way to achieve ship decarburization in recent years. Compared with the conventional ship, the initial investment cost is high, and energy redundancy is low for battery-powered ships. Therefore, an energy management strategy that can minimize the operation cost and guarantee energy use safety is crucial. In this paper, considering the dynamic electricity price and environmental factors, a co-optimization method of ship speed and energy use is proposed for an inland battery-powered ship. Firstly, the energy consumption model is constructed by analyzing the interaction of the hull, propulsion motor, and propeller. Then the speed and energy optimization model is proposed to minimize the ship operation cost. Finally, a neighborhood search differential evolution (NSDE) is adopted to solve the proposed complex nonlinear optimization problem to obtain optimal sailing speed and departure time. Then case study is implemented with an actual battery-powered cargo ship. The results show that the method could provide ship operators a reference for better management of the inland battery-powered AES.

To improve the inertia and damping properties of the energy storage units (ESUs) interface converters in DC microgrids, an enhanced virtual DC machine (VDCM) control technique based on traditional P-U droop control is proposed in this paper. The control scheme actively introduces additional inertia and damping to the converter by equivalently analogizing the P-U droop control of the energy storage converter with the speed control of the DC machine. With the proposed control scheme, the operation stability of the DC microgrid can be improved effectively. Due to the problem that the energy storage interface converter under VDCM control cannot achieve power distribution, a coordinated control method of power proportional distribution of parallel energy storage converter is proposed. A small signal model is established to analyze the influence of control parameter changes on system performance. The effectiveness of the proposed method is verified by the simulation, the results show that the proposed strategy can actively introduce greater inertia and damping for the converters to improve the inertia and damping characteristics of the energy storage converter. At the same time, it can play a dynamic adjustment effect when the energy storage interface converters are connected in parallel, which can make each converter distribute power according to the set proportion in the three working modes of charging, discharging and charging and discharging switching.

Aiming at the problems of large detection blind areas and difficult threshold settings in traditional passive islanding detection methods, this paper proposes an intelligent passive islanding detection method based on the adaptive boosting (Adaboost) algorithm. This method first uses feature screening technology to obtain the key feature electrical quantities of DC microgrid islands and form a sample set, and then rely on the Adaboost algorithm to generate a high-precision island classification model to realize the detection and classification of DC microgrid operation status. It has the advantages of automatic setting threshold, small detection blind area, and high accuracy. In addition, aiming at the problem that the classification results of the intelligent islanding detection algorithm based on machine learning cannot be directly used as the islanding discrimination results, an islanding criterion method based on the sliding window algorithm is added on the basis of the classification algorithm. This method realizes the accurate detection of the DC microgrid islanding operation state by the secondary analysis and judgment of the classification results of the islanding classification model. Finally, a DC microgrid model is built to simulate and verify the proposed method. The results show that the method can quickly and accurately complete the islanding detection of the DC microgrid.

Among major nondestructive testing technologies (NDT), such as X-ray and ultra-sound, Terahertz (THz) waves present significant transmission rate through most of nonconductive materials without the need of applying couplant while posing zero ionized radiation hazard. In comparison to microwave NDT, terahertz technology boasts a superior spatial resolution and measurement precision due to its shorter wavelength. As such, it is considered to be the most promising technology for industrial NDT applications. To analyze of THz wave transmission characteristics within porcelain insulator and obtain large quantity of testing data with repeatable measurement condition, this paper reports an experimental approach using Kaolin, in powdery form, to emulate the aging behavior of porcelain, and a THz sensor to detect such imperceptible aging process where micrometer-scale cracks slowly develop inside of porcelain insulators. To support the data acquisition from distance, a LORA communication module was integrated in the sensor for using under high-voltage environment. In addition, a dedicated user-interface software has been developed to receive the wireless data as well as conducting data analysis, crack recognition and graphic display. The experiment successfully demonstrated a very feasible solution for online monitoring of the aging process of porcelain insulators by using compact THz sensor buried in the insulator.

The interfacial properties between carbon fiber (CF) and thermoplastic resin are relatively weak, which can be problematic for composites in structural applications. Improving the surface roughness of CF is regarded as an effective way to enhance the interface of composites. However, most CF modifying methods are complex and time-consuming, which cannot meet the demand for industrial production. Therefore, it is of great significance to research a fast technique of CF surface modification to strengthen the interface of composites. Herein, a one-pot reaction based on the aryl diazonium salt modification was applied to enhance the interface between CF and poly ether ether ketone (PEEK) resin. Carbon nanotubes (CNTs) were linked to CF by p-phenylenediamine (PPD) via cyclic voltammetry (CV). The surface morphology, chemical characteristics and surface energy of modified CF illustrated the effectiveness of this method, and the interfacial properties of as-prepared modified CF/PEEK demonstrated the increased tendency. All the CF was treated within 5 min and the interfacial shear strength (IFSS) of CF/PEEK was increased to the maximum of 99.62 MPa by aryl diazonium salt modification. This work may shed some light on the industrialized application of CF reinforced high-performance engineering thermoplastic composites.

Carbon-fiber-reinforced polyimide (PI) resin composites have gained significant attention in the field of continuous-fiber-reinforced polymers, in which the interfacial bonding between carbon fiber and matrix resin has been an important research direction. This study designed and prepared a water-soluble thermoplastic polyamide acid sizing agent to improve the wettability of carbon fiber, enhance the van der Waals forces between carbon fiber and resin and strengthen the chemical bonding between the sizing agent and the alkyne-capped polyimide resin by introducing alkyne-containing functional groups into the sizing agent. This study found that the addition of a sizing layer effectively bridged the large modulus difference between the fiber and resin regions, resulting in the formation of an interfacial layer approximately 85 nm thick. This layer facilitated the transfer of stress from the matrix to the reinforced carbon fiber, leading to a significant improvement in the interfacial properties of the composites. Adjusting the concentration of the sizing agent showed that composites treated with 3% had the best interfacial properties. The interfacial shear strength increased from 82.08 MPa to 108.62 MPa (32.33%) compared to unsized carbon fiber. This research is significant for developing sizing agents suitable for carbon-fiber-reinforced polyimide composites.

The preparation of electrocatalysts with high performance for the ethanol oxidation reaction is vital for the large-scale commercialization of direct ethanol fuel cells. Here, we successfully synthesized a high-performance electrocatalyst of a AuPd alloy with a decreased alloying degree via pulsed laser irradiation in liquids. As indicated by the experimental results, the photochemical effect-induced surficial deposition of Pd atoms, combined with the photothermal effect-induced interdiffusion of Au and Pd atoms, resulted in the formation of AuPd alloys with a decreased alloying degree. Structural characterization reveals that L-AuPd exhibits a lower degree of alloying compared to C-AuPd prepared via the conventional co-reduction method. This distinct structure endows L-AuPd with outstanding catalytic activity and stability in EOR, achieving mass and specific activities as high as 16.01 A mgPd−1 and 20.69 mA cm−2, 9.1 and 5.2 times than that of the commercial Pd/C respectively. Furthermore, L-AuPd retains 90.1% of its initial mass activity after 300 cycles. This work offers guidance for laser-assisted fabrication of efficient Pd-based catalysts in EOR.

In this work, a metasurface-based, 1-bit electronically reconfigurable reflectarray antenna (RRA) is proposed for dual-polarization beam-steering applications. The proposed RRA consists of $16\times16$ Jerusalem cross-shaped unit cells loaded with PIN diodes, which are used as active and reconfigurable devices to generate a reflection phase difference of 180° between their on- and off-states. An optimal focal length to diameter ratio $(f/D)$ for the feed antenna is determined to maximize its radiation coverage on the RRA. Microcontroller-based shift registers are used to resolve the complexity of the individual DC bias networks for the PIN diodes. The proposed RRA is designed, fabricated, and measured to evaluate its beam-steering performance. The measurement results show that the proposed RRA can achieve beam-steering of ±60° for x- and y-polarizations in both azimuth and elevation planes. Moreover, the proposed RRA is further verified by full-wave simulations to evaluate its additional functions, such as the generation of sum and difference beams for monopulse radar systems. The proposed RRA is anticipated to have significant potential in areas such as wireless communications, imaging, and radar systems, and is a particularly suitable candidate for reconfigurable intelligent surfaces (RISs) due to its adaptability and capability of dynamically generating different radiation patterns.

To solve the problem of industrial control network (ICN) security situation prediction, this paper proposes a security situation prediction model for ICN based on evidential reasoning (ER) and belief rule base (BRB). First, this paper analyzes multiple factors influencing the security situation of the ICN, establishes a framework for security situation assessment, employs the ER algorithm for attribute fusion, and derives the security situation value of the ICN. Second, using historical data combined with expert knowledge, a security situation prediction model for ICN based on the BRB is constructed. Additionally, an extended projection covariance matrix adaptive evolution strategy (EP-CMA-ES) optimization algorithm is proposed, which is employed to optimize the parameters of the prediction model. The model not only comprehensively uses qualitative knowledge and quantitative data, but also integrates more uncertain information, and the reasoning process is interpretable. It also solves the subjectivity problem of expert knowledge, overcomes the problem of small amount of data caused by the difficulty of collecting industrial control safety data, and improves the accuracy of model prediction. Finally, prediction experiments were conducted on industrial datasets, confirming the feasibility and effectiveness of the security situation prediction model for ICN and the EP-CMA-ES optimization algorithm proposed in this paper.

Faults are a type of geological structure easily “reactivated” by underground mining, which destroys the internal movement and deformation laws of rocks and soil masses and causes anomalous surface sinking features. Therefore, studying the spatiotemporal evolution and deformation mechanism of mining subsidence in regions of fault occurrence is crucial for logical planning of the operating face and preventing and providing early warning of geological disasters. Considering the abnormal surface damage phenomenon in a town in the Hebei Province as the research background, this study used 46 Sentinel-1A radar images from January 3, 2019, to June 26, 2020, to investigate the spatiotemporal evolution of surface movement and deformation during the mining of the working face using the time-series InSAR deformation analysis method. Particularly, a four-threshold permanent scatterer point selection method was proposed and applied to small baseline subset InSAR monitoring. Compared with the monitoring data of 69 benchmarks in the same period, the mean absolute error and root mean square error between InSAR and leveling were 6.7 and 5.3 mm, respectively. These values indicated that the deformation of the mining area inverted by InSAR was reliable. Subsequently, the response mechanism of surface deformation in a region of fault occurrence was analyzed based on separation space theory using a rock beam mechanical model. The results revealed that the abnormal damage area had been deformed since September, 2019, and the abnormal deformation continued to develop with the advancement of the working face. The termination position of the abnormal deformation was approximately linear and relatively spatially independent of the conventional subsidence basin. Additionally, the time-series deformation characteristics of different regions within the influence range of mining were different, and the surface subsidence curve in the direct influence area conformed to the conventional law of subsidence. Surface subsidence in the indirect influence area experienced a process of acceleration and then slowed; the deformation mechanism was different from the conventional movement law of rock strata. The surface outside the affected area exhibited a small uplift, and its active time was consistent with the fault slip instability time. Furthermore, after the fault was disturbed, the rock mass in the fault zone slipped towards the goaf, and a separation space was generated at the fault plane and transferred to the loose layer, which promoted the abnormal expansion of the movement range of overburden and severe deformation at the outcrops. Simultaneously, the footwall of the fault was slightly uplifted owing to the lever principle. Our research results provide data-driven support for the study of abnormal damage laws and the disclosure of their causes. These findings are imperative for the development and improvement of the subsequent subsidence control theory.

Existing image processing methods usually divide image denoising and image fusion into two directions for research, and even the best current image denoising methods such as DnCNN can cause information loss during image processing, and the image fusion method mainly considers the fusion between multiple source images to complement the image information, but does not take into account the degradation of the fusion quality due to the noise in the source images. Therefore, aiming at the problem that various existing image denoising methods cannot reduce noise efficiently in complex noise situations while performing multi-source image fusion, preserving texture details, highlighting edge contour structures, and enriching image energy, a method of reconstructing remote sensing images by simplified adaptive dual-channel PCNN (Dual-PCNN) fusion in the NSCT transform domain is proposed to unify image noise reduction and image fusion under the same framework to obtain a noisy image information reconstruction method, which completes the complementary advantages between two image processing methods. Firstly, the impulse noise in the image is removed using IMFLED filtering, and then the Gaussian noise is processed by DnCNN and FFDNet respectively, and the results are imported into simplified parametric adaptive dual-channel PCNN to fuse the preprocessed images respectively. Then the DnCNN image and the preprocessed image are decomposed by NSCT to obtain each low-frequency sub-band and high-frequency sub-band. The low-frequency sub-band is stimulated with detail using the guided filter, and the high-frequency sub-band is enhanced by separating the detail layer through the guided filter to obtain the energy-detail-enhanced high-frequency sub-band. Finally, the corresponding sub-bands are fused into the simplified parametric adaptive dual-channel PCNN respectively, and the fused sub-band coefficients are reconstructed by NSCT inversion to obtain the final reconstructed remote sensing image. The experiments on grayscale images and remote sensing images show that this method achieves excellent results in both visual subjective and quantitative index evaluation, and the reconstructed images perform well in texture details, contour structure, and energy enrichment. After the information reconstruction of remote sensing images, the quality and resolution of remote sensing images are effectively improved, so that terrain information, landform features, and structural features can be extracted more accurately, which are widely used in remote sensing and geographic information fields such as landform analysis, agricultural monitoring, building inspection and environmental protection.

Supervision of Livelihood Issues (SLI) refers to the supervision and participation of the general public in government actions and public affairs, with a particular focus on issues closely related to people’s lives. However, current regulatory systems often suffers from information imbalances, low authenticity, and credibility. To address these challenges, this study proposes a blockchain-based SLI framework that leverages the decentralized, tamper-proof, traceable, and transparent nature of blockchain technology to provide a reliable platform for SLI. Furthermore, we designed an information data structure on the blockchain and presented the information flow process. We developed smart contracts that utilize automation capability to automatically aggregate, analyze, and publish warnings based on on-chain data, enabling the evaluation of relevant departments’ performance. Moreover, in the context of SLI applications, we propose an optimized consensus algorithm called the SLI-PBFT. This algorithm is based on the PBFT consensus protocol and incorporates dynamic scalability mechanisms, scoring models, and simplified consistency protocols to enhance the consensus efficiency of SLI applications. Finally, the experiments were conducted. We developed an SLI prototype system and compared and analyzed the performance of SLI-PBFT with other PBFT-based consensus algorithms within the SLI prototype system. The experimental results demonstrated the feasibility of the SLI framework. Moreover, SLI-PBFT exhibits significant improvements in throughput and reduced latency compared to other algorithms in the context of SLI applications. In addition, they possess strong fault tolerances and security capabilities. This study provides theoretical and practical guidance for the SLI in real-world scenarios.

During the production and assembly process of televisions, the efficiency of assembly is limited by manual assembly methods. Conventional image matching algorithms such as SIFT and ORB fail to meet the real-time accuracy requirements of intelligent assembly processes. To address this issue, a novel algorithm called Adaptive Threshold oFAST and rBRIEF-BRISK (AFK) is proposed. This algorithm utilizes the Hamming distance as a similarity measure and integrates AToFAST and rBRIEF-BRISK fusion feature descriptors. Through a comparison of 12 sets of image matching scenarios, it is demonstrated that the AFK algorithm achieves a matching speed 4.5 times faster than the SIFT algorithm, with an image matching accuracy of up to 90%. Additionally, this paper presents the GLMLBP-TOP (Global-Local and Michelson Contrast LBP-Three Orthogonal Planes) dynamic texture recognition method. Through a comparison of 21 sets experiments, it is shown that the comprehensive classification accuracy of the GLMLBP-TOP method is approximately 94.5%, improving the performance of VLBP (87.71%) and LBP-TOP (91.1%) by 6.79% and 3.4% respectively. Real-world application results demonstrate that this method meets the requirements of intelligent assembly processes and has been successfully applied to the LB intelligent assembly process of a certain company’s televisions.

The network security status assessment (NSSA) method can evaluate the network security status of cloud manufacturing systems (CMSs), which is of great significance to reduce the network security risk and loss of CMSs. At present, the NSSA of CMSs suitable for semiquantitative and uncertain information conditions is commonly used, which has certain limitations and low accuracy. This paper proposes an NSSA method for CMSs based on semiquantitative information. First, through the detailed analysis of the influencing factors of the network security status of the CMSs, the security evaluation indicators are selected, and the evaluation framework containing multilevel indicators is established by combining semiquantitative information. Second, the evaluation level is established according to the data distribution and the properties of the indicators. Third, a process of data fusion reasoning based on an evidential reasoning algorithm is designed, and a strong reference fusion case is given. Finally, the effectiveness of the proposed security state assessment algorithm in the NSSA of CMSs is verified and analyzed by simulation experiments. The experimental results show that the proposed method can make full use of semiquantitative information and uncertain information to evaluate the network security status of CMSs, and the evaluation results can reflect the actual security status of CMSs.

The establishment and improvement of an innovation ecosystem have become important issues in building an innovative powerhouse. Built on the background of innovation ecosystem, from the perspective of cooperative interaction and dynamics, this study introduces value co-creation and dynamic capabilities, and analyzes how the proximity between enterprises and scientific research organizations and between enterprises affects enterprise innovation performance. The results demonstrate that, on the one hand, the proximity between enterprises and scientific research organizations and between enterprises can directly have an inverted U-shaped effect on enterprise innovation performance. On the other hand, it can indirectly have an inverted U-shaped effect on enterprise innovation performance through the partial mediating effect of value co-creation. Dynamic capabilities play a major role in regulating the relationship between proximity and enterprise innovation performance, as well as value co-creation and enterprise innovation performance. The research results expand the research in the field of innovation ecosystems, reveal the marginal effect of value co-creation on enterprise innovation performance, and help enterprises in the system to clarify how to improve innovation performance through proximity strategies and value co-creation activities with scientific institutions and other enterprises.

Network intrusion detection is an important technology in national cyberspace security strategy and has become a research hotspot in various cyberspace security issues in recent years. The development of effective and efficient intelligent network intrusion detection methods using advanced machine learning algorithms is of great importance for defending against various network intrusions in complex network environments. In this study, a network intrusion detection method based on decision tree twin support vector machine and hierarchical clustering, named HC-DTTWSVM, is proposed, which can effectively detect different categories of network intrusion. First, the hierarchical clustering algorithm is applied to construct the decision tree for network traffic data, where the bottom-up merging approach is used to maximize the separation of the upper nodes of the decision tree, which reduces the error accumulation in the construction of the decision tree. Then, twin support vector machines are embedded in the constructed decision tree to implement the network intrusion detection model, which can effectively detect the network intrusion category in a top-down manner. The detection performance of the proposed HC-DTTWSVM method is evaluated on NSL-KDD and UNSW-NB15 intrusion detection benchmark datasets. Experimental results show that HC-DTTWSVM can effectively detect different categories of network intrusion and achieves comparable detection performance compared to some of the recently proposed network intrusion detection methods.

In this paper, the glass-fiber-fabric-reinforced resin-based absorbing composites were prepared, and their microwave-absorbing properties were studied via simulation and experiment. The simulation results show that the absorption bandwidth of the absorbing material can cover the X\C\S band, respectively, at different thicknesses. The minimum reflection loss (RL) of the composite with a thickness of 2.2 mm is −27.4 dB at 5.95 GHz. However, the experiment results are quite different from those of the simulation. The metallographic results indicate that it is the change of the mass fraction of the absorbents in the composites after curing that causes the difference. According to the metallographic results, three shape approximation methods were proposed to calculate the real mass ratio of the absorbents in the composites, namely, parallelogram approximation, bows approximation, and elliptical approximation. Meanwhile, the structural parameter Kf was introduced to optimize the calculation results. The electromagnetic parameters of the material based on the calculation results were measured, and the results show that the simulation results obtained via bow approximation have a better coincidence to the experiment results, and the mass ratio of the absorbent raises by around 9.95%, which lays a foundation for the subsequent design of microwave-absorbing composites.

To investigate the mechanical properties of graphene (G) and calcium silicate hydrate (C-S-H) composites in different directions, molecular dynamics (MD) simulations and experiments were used, and the effects of temperature, loading rate, and graphene defects were also investigated. The experimental results show that the addition of graphene can improve the flexural, compressive, and tensile strength of the composite. The results of molecular dynamics simulation show that the addition of graphene in x and z directions can enhance the tensile strength of G/C-S-H in three directions, while the addition of graphene in y direction can reduce the tensile strength of G/C-S-H. At the same time, the tensile strength of G/C-S-H decreases with the increase in temperature and increases with the increase in loading rate. Meanwhile, the mechanical properties of G/C-S-H can be improved using a certain concentration of monatomic vacancy defects, diatomic vacancy defects, and S-W defects.

In the design of High-temperature proton exchange membrane fuel cells (HT-PEMFC), the thickness of the catalyst layer (CL) determines to some extent the strength of the electrochemical performance of the cell and its manufacturing cost. To achieve the goal of improving the cell performance and reducing the manufacturing cost, the genetic algorithm was employed to optimize the thickness of the cathode and anode catalytic layers. The thicknesses of the anode/cathode CLs were set as independent variables. The results showed that the best minimum adaptation value was determined as 0.03072. The thicknesses of the anode and cathode CLs are 0.0231 mm and 0.0315 mm, respectively. The performance improvement of the optimized model is approximately 6.8%. The optimization method used in the study can effectively improve the performance of HT-PEMFC and provide a reference for the design of HT-PEMFC.

So as to decrease those cacoethic impact of a huge amount of wind energy generation systems associated with the electric power system and improve the utilization rate and the budgetary profits of wind power era, this paper raises a neural network in view of CEEMDAN-LSTM-TCN. Firstly, CEEMDAN is used to break down the wind velocity arrangement to decrease the sway of arbitrariness Furthermore variance about wind velocity. Secondly, the ultra-short-term wind power forecast depend upon LSTM and TCN is built to realize the real-time prediction for wind energy. Finally, the simulation results show that LSTM-TCN can deal with multi time order characteristics and predict ultra-short period wind energy with effect, which is better than LSTM and TCN. It also has a scientific reference for local power dispatching.

In the construction of the energy Internet and the environment of national environmental protection, China has proposed the establishment of carbon trading markets, such as policy mechanisms, and energy conservation and emission reduction have also become the most popular topic. To this end, the article proposes a low-carbon operation mode of an integrated energy system. First, build a combined electricity-gas-heat-storage structure based on energy conversion and storage devices; then, introduce a stepped carbon trading mechanism and establish a hierarchical calculation model, that is, calculate carbon trading costs based on the carbon emission range; then, Establish a low-carbon scheduling model, in which the goal is to minimize the sum of power generation costs and CO2 emissions transaction costs, and take into account safety constraints. Finally, a case study will be conducted to test the effectiveness of the proposed model.

The large-scale development and utilization of new energy resource extremely promotes the construction and application of the flexible DC power grid especially in China. This paper focuses on the operation stability and new energy transmission of an actual regional power grid in North China, including new energy plants, the flexible DC power grid, a pumped storage hydropower plant (PSHP) with variable speed pumped storage unit (VSPSU) and regional AC power grids. According to the operation rules and goals, the economic operation model of the flexible DC-PSHP system is established by the mixed-integer second-order cone programming. Considering the transmission and consumption of the new energy generation, two operation strategies and three simulation scenarios are proposed. The day-ahead optimal operation schemes of PSHP in different scenarios are given by the case simulation. The results show that the proposed strategies can satisfy the operation demands of the regional power grid both in new energy generation transmission and operation economic benefits.

In view of the problem that load frequency fluctuation of the electrical power systems with energy storage when it is disturbed, a load frequency control (LFC) model is constructed, furthermore, a load frequency controller which bottom on disturbance observer is devised, the accommodate reaching law is used to improve the system performance. These actions have improved the immunity and robustness of the system. It is verified by MATLAB simulation that the control strategy can decrease load frequency deflection under conditions of load fluctuation with effect, and the Lyapunov principle is used to analyze the stability. The analysis shows the method proposed can effectively ameliorate the robustness and operational stability of power systems with energy storage.

Hainan Free Trade Port Layout decided to gradually ban fuel-based vehicles so as to achieve the goal of carbon emission peak by 2030, in 2021, HaiNan further required 100% use of new energy vehicles in official field. Therefore, as a complementary job, it is important to recognize key factors that influence purchasing will of electric vehicles of local residents. The study bases on field interviews and grounded theory, and we utilized qualitative analysis method to recognize four main scopes, namely: risk perception, value perception, mental pressure and self-control that influence the purchase intention of the public after three levels of notion encoding. Then a ridge regression model is built to get the relative weight of the factors, and we adopted theory of planned behavior by integrating factor weights and categories extracted from qualitative analysis into the TPB model so as to find psychological reasons of people’s choice, finding risk perception and value perception are dominate features that influence public purchase choice. At last, targeted suggestions are put forward based on above results.

The utilization efficiency of renewable energy can be improved by the effective control of time-shifting load. In this paper, the characteristics of the time-varying energy source and load model including wind, diesel storage, and seawater desalination are studied. The optimal operation strategy of the island microgrid is established. The objective of this optimization model is to minimize the annual investment cost of joint functions. On this basis, to further improve the renewable energy consumption rate of the system, the real-time control optimization strategy under the operation of the island microgrid is studied. The calculations show that the real-time power prediction through wavelet packet neural network and the optimal allocation strategy of island microgrid capacity based on time-shifting load can improve the acceptance of renewable energy. In the long run, it can solve the problem of lack of fresh water on the island and help to improve the economy.

In order to improve the accuracy of integrated energy microgrid cluster scheduling operation in existing technologies, a scheduling optimization measure based on an improved bat algorithm was proposed in integrated energy system in this study. Firstly, the integrated demand response (IDR) was used to enhance the load-side energy utilization link. Secondly, the front-end data fragments of the integrated energy system are optimized and integrated based on fuzzy clustering and Mahalanobis distance. Finally, the bat algorithm is improved and substituted by increasing the adaptive inertia weight coefficient. In the simulation stage, the operation simulation test of the system was realized by MATLAB. Experiments show that the optimized integration method based on fuzzy clustering and Mahalanobis distance can make the front-end data achieve reliable clustering, and reduce the redundancy by more than 80%. After improving the bat algorithm, not only the MAPE and RMSE error of scheduling prediction can be reduced to 1.52% and 17.45%, but also the total cost of system scheduling operation can be reduced by about 2.5%. This study can improve the accuracy, economy and prediction speed of scheduling prediction, and has a good application prospect in the field of integrated energy scheduling optimization.

In order to control the influence of wind power generation incorporated into the power grid and keep the original power grid stable, this paper investigates the Takagi–Sugeno fuzzy model and the nonlinear switched system for permanent magnet synchronous generator in wind power generation system. By introducing fuzzy sliding mode control method, the stability of power system can be guaranteed. To do this, the integral sliding mode surface and fuzzy sliding mode control law are designed to reduce the influence of nonlinear disturbance on power system. In addition, the appropriate Lyapunov function is selected for derivation and proof. At the same time, the gain matrix is solved by linear matrix inequalities, which makes the model globally asymptotically stable. Finally, the stability of the system model is verified by simulation results.

A microwave transmitter/receiver using the low-temperature co-fired ceramic substrate and ball grid array packaging demonstrates superior properties, including high integration, miniaturization, and high electromagnetic shielding. However, it holds limitations of inadequate hermeticity (that is, gas or moist impermeability), high cost, and low reproducibility. In this work, we aim to overcome these difficulties by introducing a new packing technique. The packaging utilizes an electroless plated Ni/Pd/Au surface, resulting in a significant enhancement of the packaging hermeticity by orders of magnitude, approaching the level of −9 Pa·m3/s. Both Sn63Pb37 and Au80Sn20 solder alloys demonstrate exceptional solderability, attributed to Pd atoms diffusing to the Au layer during soldering at 310 °C. A reliability test of the packaging shows that the shear strength of the solder balls drops after thermal shocks but negligibly affects the hermeticity of the packaging. Furthermore, a meticulously designed internal vertical interconnect structure and I/O interconnections were engineered in the ball grid array packaging, showcasing excellent transmission characteristics within the 10–40 GHz frequency range while ensuring effective isolation between ports.

A topology optimization method based on the variable density method is used to optimize the design of the forced convection air heat sink structure, with minimum pressure drop as the optimization objective and heat transfer performance as the constraint, using a two-layer 2D model instead of the traditional 3D model. The technique of changing the interpolation parameters is introduced in the optimization process to effectively avoid the formation of blocking structures in the optimization. The optimized heat sink effectively enhances the heat transfer performance. Compared with the straight fin heat sink, the average temperature of the topology optimized heat sink decreases by 5.76%, indicating a lower temperature with the same pump power. This new type of heat sink contributes to energy saving and emission reduction.

The analysis of load characteristics is the basis and premise of load actively participating in power grid regulation. This paper proposes a multi-factor load classification method considering the load of clean energy power generation, the rapidity of load classification, and various subjective and objective factors that may affect the behavior of load consumption. First, it describes the characteristic index of load consumption behavior and analyzes the subjective and objective factors that affect the power grid consumption behavior. The effect of clean energy generation on load side is considered. Based on the load characteristics, the K-means algorithm is used for main clustering. Then, the confidence level of the uncertainty of the actual load adjustable capacity is analyzed by quantifying the load adjustable potential index and the fuzzy C-means clustering method was used for secondary clustering of the adjustable capacity. Finally, DBI and SC indexes are used to evaluate the clustering results, standard values of evaluation indexes are set, and unqualified clustering results are recalculated and corrected. 31 industrial users in a province are selected as research objects, and the load data of the past 365 days are collected to verify the effectiveness and practicability of the proposed method. The classification results show that the classification accuracy is still good when the noise is 30%, and the maximum deviation between the clustering results and the actual load regulation potential is 12%. It can meet the actual engineering error standard.

A single-phase voltage source inverter (VSI) still suffers from a long settling time and significant voltage overshoot/undershoot under the abrupt step-change of load current. This article analyzes the comprehensive large-signal dynamic process and limitation of the linear controller during step change in load current. The combination of linear and nonlinear control is presented which is a realistic, simple, and low-cost technique for enhancing the dynamic response of the single-phase VSI. A nonlinear controller based on the simplified capacitor charge balance algorithm is employed to drive the inductor current and capacitor voltage to attain the target value by precisely following the projected optimal trajectory during the transient process in a brief period. A linear PI controller is utilized during the steady-state operations such as input voltage variations, temperature drifts, and component ageing. The complete mathematical derivation as well as a design method is presented to guide the practical hardware implementation for the given main circuit parameters and identified load step change. Even though accurate time instants can be obtained using off-line numerical calculations, reasonable simplifications are provided for real-time practical engineering applications in DSP. Finally, simulation and experimental verifications prove that the single-phase VSI achieves a substantial settling time decrease and reduced voltage oscillations.

With the advent of big data era and the rapid improvement of raw data scale, feature selection, as the basis and critical technologies for data mining, plays an increasingly important role. However, most studies on feature selection methods, mainly directed to treat the single feature or overall feature subset, while the influence of the correlation and redundancy of features in the feature subset on the classification results is ignored. In this paper, a hybrid feature selection method based on feature subsets generated by factor analysis (FAFS_HFS) is proposed. Firstly, this method generates feature subsets from the maximum load (maximum explanatory power) of each feature through factor analysis. Then, minimal redundancy and maximal relevance (mRMR) and sequential forward selection (SFS) are used to remove the redundancy of each feature subset. Finally, fisher score based on feature subset (FSF-score) is utilized to evaluate and obtain the optimal feature subsets. Experiments are conducted on 14 datasets, the results show that FAFS_HFS method has higher classification accuracy and lower dimension on almost all datasets, especially in high-dimensional datasets, and it has competitive efficiency and classification performance compared with other methods.

Interconnect development for the new technology node requires coordinated efforts of multiple module teams working to co-optimize patterning and metallization solutions to meet performance and yield, and reliability targets. This paper presents the results of interconnect patterning optimization to improve overlay and process window control using a novel methodology, Design-for-Inspection™ (DFI). Using design-assisted voltage contrast measurement techniques, the method enables in-line test and monitoring of process induced overlay and CD variation of back-end-of line (BEOL) features built with litho-etch-litho-etch (LELE) patterning. While only some of the features of multi-color patterning scheme are chosen to be aligned directly, other combination of metal line and via colors may have uncontrolled misalignment risking open or short failures. The paper shows how the complete metrology coverage of multi-color combinations between dual patterned via and dual patterned metal lines helps driving the improvement of overlay and process margins in 14nm technology. The optimized process margin for via opens enables higher yields and better reliability.

At present, visual and parametric designs have been widely used in architectural design, urban planning and landscape design for design inspiration. Highly integrated auxiliary design software has become more prevalent and greatly changed the traditional way of working with architectural design and urban planning with parameterised operations, convenient editing methods and efficient workflows. However, these methods for layout optimisation have more or fewer problems, such as humdrum forms, lack of aesthetic principles, and difficulty adapting to the layout optimisation of complex architectural environments, and usually presented obstacles to the users’ understanding of hierarchical relationships in complex environments. This paper presents a novel 3D scene layout optimisation method that combines scene view information with an interactive genetic algorithm to optimise the virtual environment’s layout. We apply the visual perception information of 3D building models to improve the scene’s layout and integrate the evaluation of visual salient features with other factors to realise the intelligent scene’s layout based on optimal 3D viewpoints and a genetic algorithm. The experimental results show that our method can efficiently optimise the layout of the 3D building environment, and it has innovation and practical application value in the fields of architectural visualisation, 3D animation, landscape planning and game design.

An interesting application of the link prediction technique is detecting the potential new links in collaboration networks. In this study, we construct collaboration networks based on the co-authorship information of the papers published in 43 statistical journals from 2001 to 2018. We construct training and testing networks according to the timestamps of the papers and construct a classification dataset for link prediction. We calculate 20 similarity indices based on the training network to perform link prediction. Additionally, we consider nodal attributes (institutes and research interests) to develop two novel predictors called the same institute (SIN) and keywords match count (KMC). Several machine-learning classifiers including support vector machine, XGBoost and random forest are implemented to combine all predictors. After incorporating SIN and KMC, we observe that the area under the receiver operating characteristic curve values of all classifiers improved, indicating that SIN and KMC can significantly improve classification accuracy. Finally, we provide collaborative recommendations for researchers based on the proposed model.

An adaptive on-line approximator-based finite-time trajectory tracking scheme is proposed to solve the problem of trajectory tracking of underactuated surface vessel affected by dynamic uncertainties and external disturbances. In this scheme, underactuated transformation is performed by using kinematic virtual control law transformation and bounded constraint. By designing adaptive on-line approximator to approach the upper bound of uncertainty and unknown disturbance, the problem of parameter uncertainty and external disturbance are solved. It is proven by Lyapunov theory that the error signals in the system can converge quickly to the stable region in finite time. Finally, by comparing the simulation results, it is verified that the proposed control scheme can make the underactuated ship track the desired trajectory in finite time. Compared to the traditional control method, the convergence rate of the system error is faster, and it exhibits strong robustness in the face of unknown external disturbances. This has a certain reference value for practical engineering applications.

To solve the problem of low message delivery rate and high network resource consumption when forwarding messages in opportunistic networks, an opportunistic routing algorithm based on trust relationships for wireless mesh networks is proposed. Firstly, the wireless mesh network is analyzed and the opportunistic routing model is constructed; By analyzing the security mechanism and security threat of communication entities, then measuring the trust degree of links and nodes, establishing the trust relationship between nodes, and defining and quantifying a new security measurement method based on the trust model; Finally, according to the security measurement method defined by the model, select the node with high trust value to participate in the message forwarding process. At the same time, give priority to the node with greater trust with the destination node as the relay node, and allocate the message copy according to the trust degree to make the message pass along the direction of increasing trust, to complete the design of opportunistic routing algorithm in wireless mesh networks. Experimental results show that the routing algorithm can effectively improve the message delivery rate, up to about 95%, and reduce the consumption of network resources.

The well-known compressed sensing reconstruction ( $\mathcal {CSR}$ ) uses the sparse characteristics of the signal to obtain discrete samples with the compression (i.e. measurement) algorithm, and then perfectly reconstructs the signal through the reconstruction algorithm. Benefiting from the storage savings, the $\mathcal {CSR}$ has been widely used in the field of large-scale image processing. However, the reconstruction process is computationally overloaded for resource-constrained clients. Therefore, designing a cloud-aided $\mathcal {CSR}$ algorithm becomes a hot topic. In this paper, we investigate the existing secure $\mathcal {CSR}$ algorithms within a cloud environment and propose a new privacy-enhanced and verifiable $\mathcal {CSR}$ outsourcing algorithm for online medical image processing services. Compared with previous work, our new design can efficiently achieve more extensive security. Precisely, (1) our algorithm realizes the privacy preservation of the original image, as well as the input/output information of the reconstruction process under the chosen-plaintext attack, (2) our design is based on a malicious cloud server model and can verify the correctness of the cloud returned result with a probability of approximating 1, and (3) our algorithm is highly efficient and can make the local client achieve decent computational savings. The main technique of our design is a combination of linear transformation, permutation and restricted random padding which is concise and high-efficiency. We analyze the above claims with rigorous theoretical arguments and comprehensive experimental analysis.

To improve the performance of anti-jamming communication in dynamic and adversarial jamming environment, an improved anti-jamming method is proposed based on deep reinforcement learning and feature engineering. Different from the existing studies that use computer vision of deep learning based on the infinite state of spectrum waterfall, the proposed method relays on analyzing spectrum differences between adjacent time slots which contains information and features of jamming patterns. First, anti-jamming strategy is trained by countering the jammer which carries out a random jamming patterns switching strategy. Second, an improved state space is introduced by containing historical spectrum of communication and jamming signal between adjacent time slots, which can help an anti-jamming agent effectively extract the features of jamming patterns to reduce computational complexity. In addition, an improved reward function based on channel switch cost is improved for considering propagation characteristics which may cause communication performance lost. Taking advantage of both feature engineering and deep reinforcement learning, an improved anti-jamming method is proposed to improve reliable anti-jamming performance. Compared with the traditional CNN-based deep reinforcement learning anti-jamming method, simulation results show that the improved method can obtain better performance and lower computational complexity.

Social identity theory and self-classification theory provide theoretical supports for explaining the formation and action of the technological divisive faultlines. The purpose of this study is to explore the effects of technological divisive faultlines on a clique’s innovation performance and the moderating effects of geographical proximity and closeness centrality. The data of 119 firm alliance and 351 firms in cliques are collected in China’s biomedical industry from 2010-2017. Then, we perform the negative binomial regression by STATA software with these data as samples. The results show that there is an inverted U-shaped relationship between the technological divisive faultlines and clique’s innovation performance; geographical proximity has no moderating effect on the inverted U-shaped relationship; closeness centrality has a significant negative moderating effect on the inverted U-shaped relationship between them. Besides, it suggests that the clique’s innovation performance under high closeness centrality is higher than that under low closeness centrality, which provides a new perspective for the previous research that forming cliques could break the relational redundancy.