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The deflection of face angle is the most important factor affecting the accuracy in face recognition, non-frontal faces make some face recognition systems lose their due functions, the existing frontal face conversion methods often have the phenomenon of distortion and lack of identity. Aiming at the above problems, this paper proposes an face-frontal network which combines heat map of key points of face and improved attention mechanism. The network consists of a generator network and two discriminators networks, and the thermal map of the key points of the frontal face is used as a priori condition to guide the generation of the frontal face. In the generator part, the self-attention mechanism is introduced to obtain the dependence between feature points and other position features, which enhances the illumination perception ability of the network layer. At the same time, local attention is used in a discriminator to improve the local detail generation ability of the network in the face. Compared with other advanced frontal face generation methods, the proposed method has improved the accuracy of Rank-1 face recognition compared with other methods. The recognition rate of Rank-1 on Multi-PIE data set with small angle deflection is higher than other methods, the average recognition rate of Rank-1 on Multi-PIE setting2 is 97.41%, which is higher than the advanced method. Experimental results show that the proposed method can generate positive faces with corresponding identities from non-positive faces, which can be directly used in recognition tasks and has high recognition accuracy.

In the era of information warfare, the combat system-of-systems consists of interconnected entities that can be abstracted as heterogeneous combat network (HCN). Developing HCNs with exceptional performance is crucial to building effective combat system-of-systems. Currently, large-scale research on HCN suffers from significant time costs and poor convergence results. To address these problems, a graph embedding-based Heterogeneous Combat Network Optimization Framework (HCNOF) is proposed, which optimizes HCN features and reduces time overhead. In this paper, we design crossover operators and mutation operators for HCNs to iteratively search for the optimal HCN. Additionally, we developed a heterogeneous graph embedding method for HCNs to transform the high-dimensional HCN representation matrix into a low-dimensional embedding matrix. Finally, we designed experiments to optimize the functional robustness of HCN, verifying the effectiveness of HCNOF. The results demonstrate that, in the graph embedding task, the MAP value of HCNOF is higher than several conventional graph embedding methods, indicating better preservation of network structure. In the optimization task, HCNOF demonstrates superior performance, particularly in terms of convergence speed, which is a measure of optimization efficiency. It has shown a 17.23% improvement in this aspect compared to HCNOF0 and a 6.89% enhancement when compared to the GE-SU-EANet algorithm, underscoring its advantages in both optimization performance and cost reduction. Overall, our research findings motivate the establishment of stronger combat systems and provide an innovative approach for large-scale HCN optimization.

Sugarcane is a significant crop in terms of annual biomass in the world. Timely and accurate mapping of sugarcane planting is important for food security and sustainability. However, accurately remote-sensing-based mapping sugarcane remains challenging due to two reasons: 1) the scarcity of sugarcane training samples, and 2) the diverse sugarcane fields planting dates. This article proposed a novel transfer learning algorithm for accurate sugarcane planting mapping through space and temporary in the U.S. This algorithm only depended on one place's training data to mapping other places’ growing sugarcane. We distilled burning sugarcane fields as training labels from Sentinel-2 in Palm Beach County and neighboring area in 2021. The time-invariant phenology features were calculated from composite Sentinel-2 normalized difference vegetation index (NDVI) series using linear cosine regression (LCR). They integrated as training samples to construct a one-class support vector machine (OCSVM) classifier, generating Jun.–Nov. growing sugarcane maps in Plam Beach County and Lafourche County 2022. Meanwhile, a postprocess method was used to improve mapping quality. These sugarcane maps were validated using surveyed and observed field dataset and compared against the cropland data layer (CDL). As a result, the sugarcane maps achieved the best accuracy in its mature stage (Sep. 2022), which exhibited nearly complete phenological characteristics. The maps show low misclassification rates and higher accuracy compared to the CDL 2022. Moreover, the LCR-OCSVM method was confirmed to have superior transfer learning capability for sugarcane classification across different regions and periods compared to the Harmonic-OCSVM method.

For high-power silicon carbide (SiC) devices, breakdown voltage analysis is an important parameter, especially for guard ring design. This work explores the implementation of machine learning on SiC guard ring parameters such as ion implanted dose and energy. In this work, the reinforcement learning method has been successfully implemented on the 1.7 kV SiC guard ring device TCAD simulated data for the prediction of parameters. Our work has predicted the parameters successfully for the 2.5 kV guard ring design. For training, proximal policy optimization (PPO) and advantage actor-critic (A2C) RL agents were deployed. The network architecture was kept at “auto” with 3 hidden layers of 128 neurons in each layer. Our method is practically feasible and easily implemented as compared to other works, and has been shown in this paper. By using the limited design parameters of the 1.7 kV guard ring device, the trained agent has successfully predicted the design parameters for the 2.5 kV guard ring device, which has been confirmed using TCAD simulations. This work is more accurate, practical, and result-oriented, and we believe that this can significantly minimize the computational cost as compared to the standalone TCAD simulations. Also, this implementation of ML on TCAD data can substantially accelerate the design exploration for the power devices and ultimately lower product-to-market time.

Object detection based on images or videos is one of computer vision’s most common and practical tasks. In recent years, thanks to the development and advancement of deep learning methods, object detection algorithms and frameworks have made significant breakthroughs, allowing many detection methods to be applied to real-world scenarios. However, traditional object detection algorithms perform poorly in complex scenarios such as densely populated and crowded environments, which are frequently encountered in practical applications. Consequently, dense object detection has emerged as an important research branch within object detection, garnering increasing attention from scholars. New ideas and methods for different application scenarios and scientific problems continue to emerge. This paper comprehensively surveys the latest advancements in dense object detection, summarizing and analyzing the most advanced methods. We discuss the similarities and differences, as well as the advantages and disadvantages, of dense object detection algorithms based on five different approaches: NMS, LOSS, re-scoring, transform and YOLO. Additionally, we introduce several datasets and evaluation metrics relevant to dense object detection tasks. Finally, we summarize the current challenges in dense object detection research and discuss future research directions.

The marine geoid can be determined through the utilization of the marine gravity anomaly model. The accuracy of retrieving marine gravity anomaly model with altimetry data has been improved due to the abundance and high quality of available altimetry data. The SDUST2022 GRA marine gravity anomaly model is the global marine gravity anomaly model constructed by integrating multi-source altimeter satellites, including ICEsat-2 laser altimeter satellite for the first time. The model optimizes the deflection of the vertical accuracy imbalance. The Stokes formula is employed in this study to construct the geoid model, while the two-dimensional fast Fourier transform convolution in planar coordinate is utilized to enhance computational efficiency. Additionally, the remove-computer-restore method is applied during the calculation process. Firstly, the accuracy of the algorithm is verified using the 2190-degree XGM2019e_2159 gravity field model and the 2159-degree EGM2008 gravity field model. Subsequently, the XGM2019e_2159 is removed from the SDUST2022 GRA marine gravity anomaly model, and the residual geoid height is computed utilizing the Stokes formula. Then, the geoid height of the corresponding reference field is restored. Finally, a geoid model for China Sea and its adjacent ocean is constructed. The results showed that the geoid of the China Sea and its adjacent ocean gradually increases from west to east, and the longwave and shortwave characteristics of the seafloor can be clearly seen. The reliability of the model established in this paper was verified by the least-squares collocation method, and the geoid model calculated by the two methods had a good consistency. The mean sea surface model was introduced to calculate the mean dynamic topography. According to the mean dynamic topography, the Kuroshio Extension and the mesoscale eddy phenomenon caused by the instability of background current could be clearly seen. Compared with the mean dynamic topography of DTU22MDT, the difference between the two mean dynamic topography was basically within the range of centimeters. This showed that the mean dynamic topography of the China Sea and its adjacent ocean calculated in this paper was reliable, that is, the geoid of the China Sea and its adjacent ocean constructed in this paper was reliable.

Reliability investigations are crucial for the advancement of light-emitting diode (LED) technology. Salty water vapor exposure poses a significant risk to the performance and longevity of AlInGaP LEDs, accelerating degradation. This study integrates multiple methods, including electrical and material analysis, machine vision, artificial intelligence (AI) recognition, and circuit design, to monitor the real-time degradation of LEDs. Our experiments reveal that exposure to saltwater vapor induces structural damage, including the detachment of the epitaxial layer and crack formation, leading to diminished of LED performance. Within 30 minutes of saltwater exposure, the device showed significant degradation in materials, electrical properties, and luminous areas, especially, the luminous area dropped to below 50% of its original brightness. Additionally, AI-based damage identification is implemented to monitor the LED’s state, triggering a protective circuit as the luminous area is less than 50%. This research offers valuable insights into LED degradation, real-time monitoring, and protective measures due to damage caused by saltwater vapor, and proposes a promising solution for early fault detection, paving the way for more robust LED-related systems in harsh environments.

Interval state estimation (ISE) can estimate state intervals of power systems according to confidence intervals of predicted pseudo-measurements, thereby analyzing the impact of uncertain pseudo-measurements on states. However, predicted pseudo-measurements have prediction errors, and their confidence intervals do not necessarily contain the truth values, leading to estimation biases of the ISE. To solve this problem, this paper proposes a pseudo-measurement interval prediction framework based on the Gaussian process regression (GPR) model, thereby improving the prediction accuracy of pseudo-measurement confidence intervals. Besides, a weight assignment strategy for improving the robustness of weighted least squares (WLS) ISE is proposed. This strategy quantifies the deviation between the pseudo-measurement intervals and their estimated intervals and assigns smaller weights to the pseudo-measurement intervals with larger deviations, thereby improving the estimation accuracy and robustness of the ISE. This paper adopts the data from the supervisory control and data acquisition (SCADA) system of the New York Independent System Operator (NYISO). It verifies the advantages of the GPR method for pseudo-measurement interval prediction by comparing it with the quantile regression and neural network methods. In addition, this paper demonstrates the effectiveness of the proposed weight assignment strategy through the IEEE 14-bus case. Finally, the differences in the estimation accuracy and the bad data identification between the robust interval state estimation and deterministic state estimation are discussed.

Staircase modulation is a switching technique ubiquitous in multilevel inverters utilizing large number of output voltage levels. With tens of levels, the output of a multilevel inverter employing staircase modulation approaches a sinusoid without requiring switching harmonics filters. Out of various multilevel inverter topologies, the modular multilevel converter (MMC) became prominent due to its modularity, scalability, and efficiency. However, balancing the submodule (SM) capacitor voltages poses a significant challenge in MMC operation. In this work, a staircase matrix modulation (SMM) strategy, which achieves sensor-less capacitor voltage balancing, is proposed for the switched – capacitor MMC (SCMMC), an MMC topology with a very small arm inductor. The proposed SMM utilizes a full rank, symmetric switching matrix, where specific switching patterns are assigned for each voltage level. The structure of the proposed matrix, its unique features, and the process of populating its entries for any converter voltage level are described. Theoretical analysis on the operation of the proposed SMM, simulations for an 11-level SCMMC, and experimental results on a single-phase, 2 kW, 425 V, 4-level SCMMC prototype are presented to illustrate the voltage balancing capability of the proposed SMM. The resulting switching frequency of the SCMMC under SMM is also analyzed.

This paper addresses the secure communication problem in a distributed active-passive reconfigurable intelligent surface (RIS) assisted multiple-input single-output (MISO) scenario with a single-antenna malicious eavesdropper. The objective is to maximize the secrecy rate of the single-antenna legitimate user under constraints on the base station (BS) power and RIS amplification coefficients. The study investigates a joint optimization algorithm for BS beamforming and RIS phase-shift matrices. To address the complex non-convex fractional programming problem with coupled variables, an alternating optimization (AO) algorithm based on semi-definite relaxation (SDR) and the method of minorization maximization (MM) is proposed to obtain a suboptimal solution to the optimization problem. Simulation results demonstrate that the proposed AO algorithm achieves better secrecy performance with fewer iterations compared to traditional approaches. The design of the distributed active-passive RIS effectively mitigates the “double fading” effect, leading to a significant improvement in secrecy rate compared to existing active RIS and passive RIS designs.

This article presents the synthesis and design of 3-D microwave absorber with 70° angular stability for C-band and X-band. The operating principle is firstly investigated, where the absorber is considered as an energy convertor. With the help of our proposed universal equivalent transmission line (TL) model, the absorptive performance can be accordingly synthesized. Then, a design method for efficient absorption under large angles is presented. By selecting a proper synthesized angle (SA), the angular stability can be effectively improved. After that, the prototype with 70° angular stability is designed as an example with structural realization and practical implementation. Measurements agree well with synthesized and simulated results, successfully verifying the proposed design method. For specific C-band and X-band applications, the measured average absorption ratios (ARs) under normal incidence, 45° incidence, and 70° incidence are 94.2%, 94.0%, and 92.3%. Minimum measured ARs within the operating bandwidth are 88.4%, 81.5%, and 82.0% for normal, 45°, and 70° incidences. Besides, the proposed absorber element owns the advantage of simple structure with only one resistor. Such a class of microwave absorber is a potential candidate for wide coverage electromagnetic absorption.

In the field of vehicle path planning, the traditional Artificial Potential Field (APF) has the disadvantage that it is difficult to jump out of the local extremum. Therefore, an improved Artificial Potential Field based on the Gradient Statistical Mutation Quantum Genetic Algorithm (GSM-QGA) is proposed. Based on the traditional circular influence domain, the dynamic elliptical influence domain of the repulsive potential field is proposed by analyzing the motion state of the vehicle and obstacles. Through an analysis of the factors influencing the potential field function, the velocity factor is introduced to design the repulsive and attractive potential field functions. The GSM-QGA is introduced as a modified local optimal correction strategy for the improved APF. When the vehicle falls into the local extremum reciprocating motion, a pseudo-global map is constructed based on the current position of the vehicle, and a feasible path to jump out of the local extremum range is planned. The effectiveness of the improved algorithm in vehicle path planning is proved by simulation experiments. Compared with other algorithms, the path length planned by the improved algorithm is shorter and the path is smoother. The algorithm can effectively jump out of the local optimal state.

When AC system operates asymmetrically, poor structure and control parameters of the converter station controller will have a negative impact on the stable operation of the flexible DC power grids. In this paper, a small-signal model of flexible DC power grids under asymmetric conditions is proposed, considering the internal characteristics of converter station, positive-sequence current vector controller based on voltage-droop, negative-sequence current suppressor based on quasi-proportional integral resonant (quasi-PIR), and DC power grids. Then, the influence of control parameters in voltage-droop controller and quasi-PIR suppressor on the system stability is analyzed by using Lyapunov stability theory and the root locus method, and their reasonable ranges of values are determined. Finally, the Zhangbei four-terminal flexible DC grid project is simulated in PSCAD/EMTDC to verify the accuracy of the presented small-signal model and the conclusions about the impact of parameters on the system stability, which provides a theoretical basis for the stability analysis and the tuning of control parameters in flexible DC power grids under asymmetric conditions.

One of the main advantages of multi-level inverters (MLI) is their ability to achieve high power quality and high efficiency power conversion. With the emergence of wide-band-gap (WBG) devices, the tendency has grown towards using high switching frequencies to improve converters’ output power quality and minimize switching harmonic filters footprint. While high-frequency switching finds significant success in low and medium-power applications, it is not the best practice to reduce the filter size when applied to medium voltage (MV) high power (HP) inverters implemented with high voltage levels. In this paper, we show that the least filtering requirements for MV HP higher level inverters is achieved using the modulation strategy with the least switching, i.e., the staircase modulation. The paper compares the harmonic filter requirements and power losses for low-frequency and high-frequency pulse-width-modulated MLI. The analysis shows that low-frequency switching not only achieves the lowest losses, but also produces the lowest line-to-line voltage total harmonic distortion (THD), which allows eliminating both voltage and current harmonic filters in higher level inverters. Simulation results are provided to verify the theoretical analysis on the THD, and experiments on a 1 kW, 600Vdc MLI are presented to compare efficiency for low- and high-frequency modulation.

The conventional TO-247-3 packages with single-sided cooling limit the thermal and electrical performances of discrete SiC MOSFET devices. In this paper, a double-sided cooling press-pack (PP) packaging approach for the discrete SiC MOSFET device is proposed to optimize its thermal and electrical performances. First, a double-sided cooling PP structure for the discrete SiC MOSFET devices is designed with a copper foam gate pin and an embedded fixture. Then, based on finite element simulations, the steady-state thermal and electrical performances of the discrete SiC MOSFET device with the double-sided cooling PP package are analyzed, and the parasitic inductance of the designed SiC MOSFET device is extracted by the ANSYS Q3D software. Finally, a prototype of the double-sided cooling PP SiC MOSFET device is fabricated, and test platforms are established to verify its performance. The research findings demonstrate that the designed double-sided cooling PP SiC MOSFET device can reduce thermal resistance and switching loss by 47.4 % and 42.3%, respectively.

Image/feature decomposition plays a crucial role in image fusion tasks. However, in deep learning-based fusion methods, existing decomposition operations tend to be either overly simplistic or excessively intricate to learn effectively. In this paper, a novel Fusion network (MVF) based on the Morphing convolutional (MConv) structure and the light-weight Visual state space block (LWVB) is proposed, which is used to extract richer deep features and enhance the quality of the fused images. The proposed structure incorporates convolutional kernels of different shapes to extract detail features from various directions (horizontal, vertical, and diagonal). These kernels consist of fixed shapes with learnable values, ensuring a simplified learning process while retaining the advantages of non-deep-learning filters. Moreover, to efficiently capture global spatial information without incurring significant storage overhead, we modify the Visual State Space (VSS) Block of VMamba and propose the light-weight VSS block which can compress and extract global information at both high and low frequencies, thereby enhancing the spatial awareness capabilities of proposed module. Comparison experiments show that the proposed fusion framework, utilizing the morphing convolutional structure and LWVB, achieves superior fusion performance compared to the state-of-the-art fusion methods.

Deception detection is essential for protecting the public interest and maintaining social order. Its application in various fields helps to establish a safer and trustworthy social environment. This study focuses on the problem of deception detection in videos and proposes a visual deception detection method based on a capsule network (DDCapsNet). The DDCapsNet model predicts deception classification using the fusion of facial expression features and video-based heart rate feature via a channel attention mechanism. Supervised contrastive learning is further introduced to enhance the generalization ability of the DDCapsNet. The proposed model is evaluated on a self-collected dataset (physiological-assisted visual deception detection dataset, PV3D) and the public Bag-of-Lies (BOL) dataset, respectively. The results show that DDCapsNet outperforms the unimodal system and other state-of-the-art (SOTA) methods, where the ACC reaches 77.97% and the AUC reaches 78.45% on PV3D, and the ACC reaches 73.19% and the AUC reaches 72.78% on BOL dataset.

In air combat, target intent recognition is the premise and foundation of battlefield situation awareness and intelligent decision-making. Aiming at the problem that traditional intention recognition methods cannot deal with a large amount of continuous target data, an air target combat intention recognition model based on one-dimensional convolutional neural networks and bidirectional long short-term memory (1DCNN-BiLSTM) is proposed. First, the target data is divided into fixed-size continuous subsequences by time sliding window on the basis of determining the target feature space and intention space. Second, the convolution operation is performed on the target sequence through the 1DCNN module as a means of extracting the features of the target attributes in the time dimension, and at the same time reducing the dimensionality of the target sequence, so as to facilitate the subsequent processing of the target data. Then, the BiLSTM module is utilized to capture the dependencies on the longer distance of the target sequence from both forward and reverse directions simultaneously. Finally, the optimal model structure and hyperparameters of 1DCNN-BiLSTM are not only determined through experiments, but also the validity of each part of the model is verified. Compared with the traditional methods, the model proposed in this paper effectively improves the accuracy of combat intention recognition of air targets, and provides the essential basis and auxiliary support for the decision-making of the commanders.

In the flexible DC grids, selections of slope coefficients in system level controls and designs of smoothing reactors have a crucial influence on the system stability. In order to obtain their optimal values, a comprehensive small-signal stability model with the DC voltage slope control and smoothing reactors is proposed in this paper. Firstly, the linear state-space model of the entire system based on the small-signal method is deduced according to the coupling equations between the inputs, the outputs, and state variables related to converter stations and the AC sides, controllers, and the DC grid with smoothing reactors. Meanwhile, the influences of the MMC bridge arm circulating current and the capacitor voltage fluctuation of the sub-module are considered in this model. Secondly, the impacts of control parameters, smoothing reactor parameters, and bridge arm inductances are analyzed on the system stability through the Lyapunov stability theory. Finally, the simulation of Zhangbei four-terminal DC grid project in China is established to testify the correctness of the presented small-signal model and the conclusions about the impact of parameters on the system stability. The proposed method will provide some theoretical guidance for the system design and the parameter selection in DC grid applications.

As an important partner of fifth generation (5G) communication, the internet of things (IoT) is widely used in many fields with its characteristics of massive terminals, intelligent processing, and remote control. In this paper, we analyze security performance for the cooperative non-orthogonal multiple access (NOMA) networks for IoT, where the multi-relay Wyner model with direct link between the base station and the eavesdropper is considered. In particular, secrecy outage probability (SOP) for two kinds of relay selection (RS) schemes (i.e., single-phase RS (SRS) and two-phase RS (TRS)) is developed in the form of closed solution. As a benchmark for comparison, the SOP for random RS (RRS) is also obtained. To gain more meaningful insights, approximate derivations of SOP under the high signal-to-noise ratio (SNR) region are provided. Results of statistical simulation confirm the theoretical analysis and testify that: i) Compared with RRS scheme, SRS and TRS may improve secure performance because of obtaining smaller SOPs; ii) There exists secrecy performance floor for the SOP in strong SNR regime, which is dominated by NOMA protocol; iii) The security performance can be enhanced by augmenting the quantity of relays for SRS and TRS strategies. The purpose of this work is to provide theoretical basis for the analysis and design of anti-eavesdropping for NOMA systems in IoT.

Reverse engineering is a powerful tool for duplicating real objects from data acquired by tracking systems, industrial X-ray computed tomography (CT), laser scanners, and image captures. This work proposed a novel method using the three-dimensional (3D) reconstruction and 3D printing to duplicate a mechanical key based on only one single naturally-captured photograph. A reverse engineering system of the mechanical key was self-developed which includes functions of projective transformation, curve extraction, point clouds generation, and 3D geometry reconstruction. The database technology was introduced into the image-based 3D reconstruction process for the first time to store basic dimensional information of key blanks, which provided supplementary information during the 3D key reconstruction process. Five different types of duplicated keys verified the effectiveness of the proposed method. The maximum geometric deviation was less than 0.39 mm. The duplicating time cost was 5 minutes for 3D geometry reconstruction and 18.3 minutes for 3D printing.

Silicon nitride waveguide (Si3N4), is used as the most important sensitive device because of its excellent high-polarizing characteristics. It has the potential to build the miniaturized, high-precision resonant integrated optical gyroscope (RIOG) in recent years. However, the back-reflection caused by the refractive index difference between the Si3N4 waveguide and the pigtail fiber has a non-negligible impact on the accuracy of the gyroscope. In this paper, we propose a method to suppress the back-reflection of the end face of the Si3N4 waveguide resonator. We use the Fimmprop module of the simulation software Photon Design to simulate the relationship between the back-reflection coefficient of Si3N4 waveguide and different tilt angles. The simulation result shows that when the end face of the Si3N4 waveguide is oblique cut by 15°, the back-reflection is the minimum, about -67 dB. The back-reflection obtained through the experiments are about -65 dB, consistent with the simulation result. Together, our data suggested that the back-reflection noise of the Si3N4 can be suppressed by the oblique cutting of the end face by 15°. The conclusion can lay a foundation for improving the performance of RIOG based on Si3N4 waveguide resonator.

Neutral-point clamped (NPC) three-level grid-tied converter is the key power electronic equipment connecting renewable energy and power grid. High temperature and other factors can lead to current sensor failure, control strategy failure, output current distortion, and even lead to the disconnection of renewable energy. To maintain the normal operation of NPC three-level grid-tied converter with current sensor fault, a model predictive fault-tolerant control strategy based on space voltage vector set and fault phase current reconstruction is proposed. Firstly, the relationship between fault phase current and voltage vector is studied. After the current sensor fault, only 12 out of 27 voltage vectors were found to be able to reconstruct faulty phase currents with a DC current sensor and an AC current sensor. Model predictive current control using the set of these 12 voltage vectors, and fault phase current is directly calculated by DC current and normal phase current, which improves the fault tolerance and stability of the converter system. Finally, simulation and experimental results verified the effectiveness of the proposed method.

Flexible printed circuit boards (FPCB) are widely used in smart devices with high wiring density and light weight. In this paper, the chemical etching process of FPCB with 18 $\mu \text{m}$ line pitch is investigated. A geometric model of the FPCB circuit with the shape of “T” is established and simulated by the finite element method. The time evolution of the etching cavity, concentration field and velocity field of CuCl2 solution are studied, as well as the effects of initial concentrations and inlet velocities on the etching cavity profile. Finally, the FPCB sample with 18 $\mu \text{m}$ line pitch is successfully fabricated by employing process parameters from the etching simulation. The results show that as the increase in the etching cavity, recirculating eddies form at the bottom of the photoresist in the corners of the etching cavity, resulting in more etching on the top sides of sidewalls over time. Higher initial concentration of the etching solution will result in a larger etching cavity profile, but the inlet velocity cannot affect the etching cavity profile significantly. Finally, the effectiveness of the simulation model is verified by comparing the etching cavity profiles with four experiments.

Air quality prediction is an important reference for meteorological forecast and air controlling, but over fitting often occurs in prediction algorithms based on a single model. Aiming at the complexity of air quality prediction, a prediction method based on integrated dual LSTM (Long Short-Term Memory) model was proposed in this paper. Firstly, the Seq2Seq (Sequence to Sequence) technology is used to establish a single-factor prediction model which can obtain the predicted value of each component in air quality data, independently. Each component of air quality is regarded as time series data in the forecasting process. Then, the LSTM model with attention mechanism is used as the multi-factor prediction model. The influencing factors of air quality, like the data of neighboring stations and weather data, are considered in the model. Finally, XGBoosting (eXtreme Gradient Boosting) tree is used to integrate two models. The final prediction results can be obtained by accumulating the predicted values of the optimal subtree nodes. Through evaluation and analysis using five evaluation methods, the proposed method has better performance in terms of error and model expression power. Compared with other various models, the precision of prediction data has been greatly improved in our model.

In recommender systems, measuring user similarity is essential for predicting a user's ratings on items. Most traditional works calculate the similarity based on historical ratings shared between two users, without considering the probability of users' different behaviors. To address this issue, our work designs a similarity measure based on the users' historical behavior probabilities. Based on the similarity method, a complex network of user relationships is modeled. The user degree and community information of the modeled network, as well as the number of shared ratings between users, are used with the proposed similarity measure to design a rating prediction algorithm for recommender systems. Experiments based on MovieLens and Netflix data sets show that this method is effective and can successfully improve the accuracy of rating predictions and reduce the number of neighbors required to achieve the optimal prediction accuracy. Our research shows that in a complex system, the relationship between users can be effectively measured by the users' historical behavior probability, providing a new perspective for future research on similarity measurement.

A robust asynchronous switching fault-tolerant control method is proposed to solve the problems of uncertainties, unknown disturbances, time-varying delays and partial actuator failures in multi-phase batch processes. Firstly, an asynchronous system composed of subsystems with different dimensions including stable and unstable case is established to describe such multi-phase batch processes more accurately. Then introducing the output tracking error, the established switching model of different dimensions is extended. On this basis, a robust asynchronous switching fault-tolerant control law is designed, which improves the system’s ability to cope with negative factors such as actuator failure and can obtain greater adjustment freedom. Secondly, by using relevant theories and methods, the sufficient conditions in the form of linear matrix inequality (LMI) are given to ensure the exponential stability of the system and the asymptotic stability at each phase. By solving these LMIs conditions, the shortest running time under stable case, the longest running time under unstable case and the control law gain of each phase are obtained. Finally, the effectiveness and feasibility of the proposed method are verified with injection molding process.

Number of detected atoms and contrast of Ramsey fringe are critical operation parameters in an atomic fountain clock. To optimize these operation parameters, a Monte Carlo method is utilized to simulate the evolution of the atomic cloud. It is found that the physical variables, including atomic temperature, launching direction of the atomic cloud, and the initial position of the atomic cloud, have a large influence on the ratio of the returning atoms to the launching atoms, and consequently the detected atomic number, and the atomic temperature affects the pattern of the Ramsey fringe. In the experiment, these relevant physical variables are optimized and a returning ratio as high as 8.6%±0.4% is obtained for a cesium atomic fountain with a launch height of 1 m. In addition, the Ramsey fringe is experimentally optimized and the contrast of the central Ramsey fringe reaches 90%. The Monte Carlo method we used is also applicable in similar cold atoms experiments.

CycleGAN can realize image translation and style transferring among unpaired images. However, it will easily generate inappropriate image results when the number and shapes of objects in the style offering image and the source image are greatly different. The paper proposed an improved network, named arCycleGAN, which introduced the mechanism of attribute registration into CycleGAN to solve the problem. The arCycleGAN can transfer the freshness styles from the style offering images to the unpaired input source images. The generated target images will have the freshness attributes of the style offering images, while maintaining the shapes and key features of the input source images. The realization of mechanism of attribute registration consists of three modules. The first module is attribute recognition module, which can identify and label the attributes of objects in images. The second module is image pre-screening module, which selects appropriate image subset as screened training set from raw image set according to the attributes of the input source images. The third module is similarity matching module, which matches the images in screened training set based on the similarity. The generator and discriminator in the new network are similar to that in the CycleGAN network. Experimental results demonstrate the effectiveness and better performance of the arCycleGAN. Compared with the CycleGAN, the new network can generate more convincing images. It can generate the target images of similar quality based on a smaller training set and less training time than the original CycleGAN. For generating images of similar quality, the number of images in the required training set can be reduced by 50%, while training time is reduced by 5.8%.

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

This paper introduces a practical design of distributed and simple millimeter-wave phased arrays for 5G mobile handset. Three types of antenna arrays and four different arrangements are investigated in perspective of coverage efficiency and coverage map. According to the analysis, a distributed design, including four 1 × 2 quasi-Yagi arrays along each edge of the chassis and one 2 × 2 planar patch array along the longer edge of the chassis, is adopted, considering both the antenna performance and the complexity of the feeding networks. The gain of the handset antenna system is always above 2 dBi for all the incident angles. The coverage efficiency reaches 50% for a gain of above 7 dBi, with the relatively low-gain area mainly towards the backward direction. Feeding networks are designed to verify the beam shaping ability of the arrays. The prototype was fabricated and measured. All the arrays can cover the frequency band from 27.85 GHz to 28.62 GHz, with an isolation of above 35 dB. The maximum measured realized gain is 12.1 dBi and 12.6 dBi for the Yagi and the patch arrays, agreeing well with the simulated results. High gain, large coverage efficiency, robust performance and simple feeding networks make the proposed design a good candidate for 5G mobile handsets.

The development of advanced metering infrastructure (AMI) in smart grid (SG) had enabled consumers to participate in demand-side management (DSM) using the price-based demand response (DR) programs offered by the distribution companies (DISCO). This way, not only the consumers minimize their electricity bills and discomfort, but also the DISCOs can handle peak power demand and reduce the carbon (CO2) emissions in a controlled manner. Building an optimization framework that will minimize cost, peak demand, waiting time, and CO2 emission is not only a challenging task but also a concern of DSM. Most analyses are based on cost and peak-to-average ratio (PAR) minimization, but the effectiveness of the DSM framework is equally determined by user comfort and CO2 emission. Considering only one objective (cost) or two objectives (cost and PAR) is not sufficient. Thus, for DSM framework to achieve these four relatively independent objectives at the same time, minimized cost, PAR, CO2 emission, and user discomfort, an energy management controller (EMC) based on our proposed algorithm hybrid bacterial foraging and particle swarm optimization (HBFPSO) is employed that return optimal power usage schedule for consumers. A novel DSM framework consists of four units: (i) DISCO, (ii) multi-layer perceptron (MLP) based forecast engine, (iii) AMI, and (iv) demand-side energy management modules is successfully developed in this work. To validate the proposed model, extensive simulations are conducted and results are compared with the benchmark models like genetic algorithm (GA), bacterial foraging optimization algorithm (BFOA), binary particle swarm optimization (BPSO), and a hybrid combination of genetic and binary particle swarm optimization (GBPSO) in terms of electricity cost, PAR, user comfort, and CO2 emissions. The simulation results demonstrate effectiveness of our proposed model to outperform all the benchmark models in optimizing the consumer and DISCO objectives. The proposed scheme has reduced electricity cost, user discomfort, PAR, and CO2 emission for the residential sector by 15.14%, 4.6%, 61.6%, and 52.86% in scenario 1, 62.60%, 4.56%, 60.77%, and 27.77% in scenario 2, and 26.03%, 4.54%, 63.78%, and 23.02% in scenario 3, as compared to without an EMC. Similarly, for commercial sector the proposed HBFPSO algorithm reduces electricity cost, user discomfort, PAR, and CO2 emission by 11.31%, 5.5%, 60.9%, and 38.18% in scenario 1, 64.9%, 5.56%, 44.08%, and 58.8% in scenario 2, 15.31%, 5.26%, 78.22%, and 15.58% in scenario 3. Likewise, the proposed algorithm also has superior performance for the industrial sector for all the three scenarios.

Under the explosive growth of information available on the Web, recommender systems have been used as an effective technology to filter useless information and attempt to recommend the most useful items. The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive with a higher quality-of-service. Under such circumstances, the data sources contain four key characteristics (i.e., sparsity, heterogeneity, mobility, volatility). The conventional recommender systems based on cloud computing are incapable of digging the information of user demands. Mobile edge computing is a novel computing paradigm via pushing computation/storage resource from the remote cloud servers to the network edge servers to provide more intelligent and personalized service. This paper comprehensively reviews the state of the art literature on the convergence of recommender systems and edge computing, and identify the future directions along this dimension. This paper can provide an array of new perspectives on the convergence for researchers, practitioners, and tap into the richness of this interdisciplinary research area.

Silicon photonic interconnects on chip is an emerging technology for future ultra-scale and data-intensive computing chips, e.g., many-core processors, owing to its high transmission speed and low latency. However, in the Wavelength Division Multiplexing (WDM)-based architecture, the communication reliability can be significantly affected by the signal losses and crosstalk, due to the inherent characteristic of photonic devices. This paper studies the influence on reliability of managing application mapping and wavelength assignment separately. To deal with this issue, we propose a reliability-aware joint design method coordinating application mapping and wavelength assignment schemes. For a given application core graph and a network architecture, the design method can obtain a result of application mapping and wavelength assignment with improved reliability, compared to the separate scheme. According to the evaluation of Optical Signal-to-Noise Ratio (OSNR), the proposed method enables more reliable communication under given applications.

When the asymmetric fault occurs in power grid, it will cause the oscillation of the electromagnetic torque of the doubly-fed induction generator(DFIG). The oscillation of the electromagnetic torque will cause the torsional vibration of the drive chain, and the long-term torsional vibration will shorten the service life of DFIG. The analysis shows that the positive and negative sequence current components generated by DFIG terminals are the main factors that cause electromagnetic torque oscillation when the asymmetric faults occur. Therefore, the suppression of positive and negative sequence current components is the key to achieve active control of torsional vibration of drive chain, the traditional PID control can only adjust the direct-current(DC) component without static error, and cannot suppress the negative sequence alternating-current(AC) component. In this paper, based on the traditional PID regulator, the resonance(R) current regulator with the specific frequency is incorporated into the grid-side converter(GSC) to form the PID-R current controller. The PID-R current controller can realize accurate and effective control of the positive and negative sequence components in the forward rotation synchronous speed rotating coordinate system without decomposing the positive and negative sequence components, and can actively suppress the electromagnetic torque oscillation, thereby realize the active control of the drive chain torsional vibration. MATLAB/Simulink verifies the effectiveness of PID-R current controller.

Vehicular Ad Hoc Network (VANET) is an important foundation of intelligent transportation system and is widely used in traffic management, automatic driving, and road optimization. With the gradual popularization and further development of VANET, a large amount of VANET data has been produced. However, it poses huge challenges to the security and privacy when using VANET data provides services for users. In this paper, combining the technologies of blockchain and ciphertext-based attribute encryption (CP-ABE), we propose a fine-grained access control scheme for VANET data based on blockchain (FADB). In FADB, we employ the blockchain to replace the third-party service providers for user identity management and data storage. And different VANET data access rights can be established according to user attribute. By improving the CP-ABE, the lightweight VANET devices can outsource complex encryption and decryption operations to powerful RSUs and further improve the efficiency of data access. Final, we carry out a series of simulation tests and security analysis, proving that the FADB can provide effective data security and low performance overhead.

Cloud storage system provides data owners with remote storage service, which allows them to outsource data without local storage burden. Nevertheless, the cloud storage service is not fully trustworthy since it may not be honest and remote data would be corrupted. One way to ensure trustworthy preservation of cloud data is the remote data auditing method, through which data owners can check storage reliability of cloud system on demand and avoid potential data corruption in time. However, private auditing methods fail to promise the mutual trust in auditing results. Thus, public auditing methods are introduced, in which traditionally a third party auditor is delegated to interact with cloud service providers for auditing tasks. Although the third party auditor serves as a medium to exchange trust, a centralized third party is hard to stay neutral, which exposes the remote data auditing to some threats such as collusion attacks. To address the trust problem between data owners and cloud service providers, we propose a collaborative auditing blockchain framework for cloud data storage. In this framework, all consensus nodes substitute for the single third party auditor to execute auditing delegations and record them permanently, thereby preventing entities from deceiving each other. Security analysis shows that the proposed framework has advantage of preserving remote data integrity from various attacks. Performance analysis demonstrates that the framework is more functional and resource-friendly than existing schemes.

QR code is designed as machine readable symbol, which is widely used in various fields of life due to its large message capacity and fast decoding speed. However, as a public standard, it will give rise to the security issue when delivering sensitive information with QR code. To overcome this weakness, this paper explores the characteristic of QR code to propose an efficient secret hiding mechanism to protect the sensitive information within QR code. The secret message would be embedded into cover QR code based on (8, 4) Hamming code. The error correction capacity (ECC) of QR code would correct the errors produced in the secret embedding procedure, and the valid marked QR code would reduce people's curious. Compared to the state-of-art works, the proposed scheme achieves a better performance on the aspects of secret payload and embedding efficiency.

Freight train positioning and axle safety monitoring are the key to railway traffic supervision, which can be achieved by sensors based on Internet of Things (IoT) technology, and the energy harvesting technology is the best solution to those sensors due to neither electrical power nor communication system is available on railway wagons. This paper proposes a self-powered railway wagon monitoring sensor (SpRWMS) solution based on the geophone, which has the characteristics of mature technology and simple structure, and can convert mechanical vibration of wagons into electrical energy. Since the vibration is so weak that the voltage induced by the geophone is very weak, hence, this paper proposes an electromagnetic generator (EMG) constructed by geophone matrix and an energy management interface (EMI) to convert the induced potential into the voltage that can drive the circuit system of sensors. In this paper, the implementation and performance of EMI will be focused on, and the energy harvesting system analysis prior to developing prototypes and conducting field trail. The design principles and prototypes of EMG and EMI are proposed based on the parameters of vibration profiles collected by a data logger, and the performance of the SpRWMS prototype is evaluated on a trial run freight train. Experimental results show that the proposed EMI can extract the energy generated by EMG more effectively and increase the voltage across the storage capacitor from 2V to 3.3V, that is, our proposed EMI and EMG can convert the railway wagon vibrational energy into the electrical energy form required by the circuitry of sensors.

IP protocol is the core of TCP/IP network layer. However, since IP address and its Domain Name are allocated and managed by a single agency, there are risks of centralization. The semantic overload of IP address also reduces its scalability and mobility, which further hinders the security. This paper proposes a co-governing Multi-Identifier Network (MIN) architecture that constructs a network layer with parallel coexistence of multiple identifiers, including identity, content, geographic information, and IP address. On the management plane, we develop an efficient management system using consortium blockchain with voting consensus, so the network can simultaneously manage and support by hundreds or thousands of nodes with high throughput. On the data plane, we propose an algorithm merging hash table and prefix tree (HTP) for FIB, which avoids the false-negative error and can inter-translate different identifiers with tens of billions of entries. Further, we propose a scheme to transport IP packets using CCN as a tunnel for supporting progressive deployment. We deployed the prototype of MIN to the largest operators' network in Mainland China, Hongkong and Macao, and demonstrated that the network can register identifier under co-governing consensus algorithm, support VoD service very well.

State-of-the-art autonomous micro-robotic turtles suffer from various limitations, such as power restrictions that minimize their deployment times. In this paper, an Ionic Polymer Metal Composite (IPMC) actuator-based centimeter-level biomimetic underwater robot was designed and developed as a robotic turtle with self-charging capabilities to overcome such limitations. It could move forward and make turns driven by five IPMCs on the water. The underwater charging station was able to transmit wideband ultrasonic and electromagnetic fields for electromagnetic induction charging. An ultrasonic communication system with one ultrasonic transmitter and two ultrasonic receivers was first fabricated to implement communication between the underwater station and the biomimetic underwater robot for autonomous tracking and rechargeable capabilities. Experiments were carried out to confirm the operation of the biomimetic underwater robot, which verified the centimeter-level rechargeable capabilities and autonomous target tracking features. The micro-robot demonstrated a self-tracking radial displacement error of approximately 6 mm and a charging reliability rate of more than 73%.

Neurodegenerative diseases are common progressive nervous system disorders that show intricate clinical patterns. The gait fluctuations reflect the physiology and pathologic alterations in the locomotor control system. Using gait fluctuations for disease state evaluation is an essential way for clinical trials and healthcare monitoring. The classification of gait fluctuations helps improve the life quality and enhance clinical diagnosis ability in neuro-degenerative patients. In this work, we firstly embed the time series of multiple gait fluctuations into the phase space. Then we use persistent homology to extract the topological signatures of barcodes. Together with a random forest classifier, we proposed a topological motion analysis (TMA) framework to analyze the gait fluctuations. Further, we proposed a comprehensive comparison study using the TMA framework in the neuro-degenerative classification tasks for stance-, stride-, and swing-based gait fluctuations. In the tasks of comparing amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD) to the healthy control (HC) group, the best-achieved AUC scores were 0.9135, 0.9906 and 0.9667 respectively, which show the effectiveness of TMA framework. In summary, our study proposed a TMA framework towards gait fluctuations classification in the neuro-degenerative analysis tasks. The proposed method shows promising clinical application value in earlier interventions and state monitoring for neurodegenerative patients.

As the system becomes more and more complex, we are usually in the state of indeterminacy. In the real world, the states of uncertainty and randomness are the two most common types of indeterminacy. An uncertain random graph is applied to describe a graph model with uncertainty and randomness simultaneously. This paper mainly focuses on the connectivity of two vertices in an uncertain random graph. Firstly, a local connectivity index is proposed to unveil the chance measure that two special vertices are connected in an uncertain random graph. Furthermore, a method for calculating the local connectivity index is formulated. In addition, some simplified forms of the method are developed, and an algorithm is designed to obtain the local connectivity index. Finally, the information relevant to the relationship between the local connectivity index and the connectivity index is discussed.

The futures market's forecasts are significant to investors and policymakers, where the application of deep learning approaches to finance has received a great deal of attention. In this study, we propose a multivariate financial time-series forecasting method. Our model addresses the long- and short-term features, multimodal and non-stationarity nature of multivariate time-series by incorporating the improved deep neural networks and certified noise injection. Specifically, multimodal variational autoencoder is used to extract deep high-level features of multivariate time-series, Long- and Short- Term recurrent neural network is applied for multivariate time-series forecasting, and certified noise injection mechanism, inspired by differential privacy, is proposed to improve the robustness and accuracy of prediction. Extensive empirical results on real-world agricultural commodity futures price time series and relevant external data demonstrate that our model achieves better performance over that of several state-of-the-art baseline methods.

Increasing the specific power and efficiency of a power converter has been the forever-lasting task for power electronics engineers. With the Wide bandgap (WBG) semiconductor devices, new opportunities and challenges have been found. In this article, a 100-kW SiC grid-connected Photovoltaic (PV) string inverter is proposed. This SiC inverter is free of a grid interface filter, the size, weight, and cost of magnetic components are therefore reduced. In addition, to further achieve high power density high efficiency at 100kW, design aspects including closed-form equation to avoid overdesign passive components, ICT design optimization, and interphase crosstalk mitigation with high switching frequency have been presented. A 100-kW three-phase two-stage prototype has been built in the laboratory, which achieves a specific power of 5 kW/kg, measured peak efficiency of 99.5%, and California Energy Commission (CEC) efficiency of 99.2%.

Salient object detection aims to obtain the most attractive objects from the input images, which severs as a pre-processing step for many image processing tasks. This paper presents a novel deep neural network design for salient object detection by formulating a pyramid spatial context module, PSC module for short, to capture the spatial context information at multiple scales. To achieve this, we first adopt convolutional operations with different dilated rates to generate the feature maps with different respective fields, and then use the two-round recurrent translations to explore multiple types of spatial context features on these feature maps. By further inserting this module in a deep network, namely PSCNet, we are able to optimize the network in an end-to-end manner for salient object detection. We evaluate the proposed method on six public benchmark datasets by comparing it with 25 salient object detection methods. The experimental results demonstrate that our PSCNet performs favorably against all the other methods.

This article proposes an approach to identify intrinsic power losses of the medium frequency transformer that interfaces two modular multilevel converters (MMC) operated with phase-shifted square wave modulation (PS-SWM) to form a DC solid state transformer (DC SST) module. Cascaded MMC module based DC SST architecture is an excellent choice for HVDC to MVDC conversion for DC grid application as well as for renewable and distributed energy resource integration into the DC grid. Operation with PS-SWM allows reduced cell capacitor size and lower total semiconductor device ratings. However, this modulation generates a unique quasi-square waveform at the SST internal medium frequency (MF) AC interface, which differs from other known square-wave modulation types. This produces a similarly unique transformer flux density, resulting in a transformer core loss that cannot be predicted by existing methods which makes it challenging to identify. In the interest of presenting a useful and optimizable MF transformer design methodology, this article develops a tool to predict the loss of the MF transformer subjected to PS-SWM. The proposed loss estimation technique is validated through an experimental testbed operated in two different configurations and is demonstrated to provide a valid basis for the use of the proposed MF transformer design methodology. Furthermore, this article provides an analysis of the load-dependent MF transformer core loss, which demonstrates a secondary usefulness of the proposed loss prediction tool in identifying load-dependent converter losses.

The integrated satellite-terrestrial network has the characteristics of large scale, complex, high dynamic and heterogeneousness. Adopting traditional routing methods in the integrated satellite-terrestrial network will cause problems of poor scalability and large routing overhead. Greedy forwarding strategy based on network mapping with hyperbolic geometry works well in large scale network. However, there is no study on applying the network mapping with hyperbolic geometry to complex networks beyond two dimensions, including the integrated satellite-terrestrial network. Based on the method of spherical polar projection, this paper proposes a hyperbolic coordinates mapping algorithm in three-dimensional geographic space suitable for the integrated satellite-terrestrial network. This algorithm gives nodes of heterogeneous layers in the integrated satellite-terrestrial network a unified expression based on four-dimensional hyperbolic coordinates, which helps to quickly identify and locate nodes without global information distribution and scheduling when routing. The routing strategy using greedy forwarding strategy based on this algorithm only costs low storage overhead, as it does not need routing tables. Simulations demonstrate that the performance of the algorithm is hardly affected by the exponential expansion of the network size, which means the property of scalability is excellent. Also, it is stable under heterogeneous network structure, and maintains a stable routing success rate for optimal path selection around 93% with a time complexity of O(n).

Controlling an underactuated robot is an attractive research issue as some of the robot joints cannot be controlled directly. Besides, facing the unknown time-varying disturbance, the nature of the plant makes the stability of such uncertain nonlinear system more challenging. This work presents a robust finite-time control scheme that combines Hölder condition based extended state observer and sliding mode control approach to cope with the mentioned issues. The unknown time-varying disturbance and other uncertainties can be well recognized and actively compensated under the proposed scheme. Besides, this control scheme is easy to be implemented due to the small amount of information required. Simulation results demonstrate the performance of the proposed scheme and validate its better robustness and convergent speed compared with other schemes.