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Synthetic aperture radar automatic target recognition (SAR ATR) has ushered in a new era dominated by deep-learning (DL) techniques. However, the DL-based recognition systems inevitably confront catastrophic forgetting for learned knowledge and overfitting for the new, once deployed in openly dynamic scenarios where targets of new classes continually appear with few-shot instances. For practical applications, a decoupled self-supervised subspace classifier with few-shot class-incremental learning (FSCIL) ability is proposed for prompt knowledge transferring and stable discrimination, w.r.t., intrinsic and domain-specific challenges of the FSCIL of SAR ATR. Specifically, observing the significant componentity and azimuth sensitivity of targets in SAR imagery, two self-supervised tasks powered by a scattering mixup module and a rotation-aware transformation module are designed to synthesize virtual samples and related labels to unleash the classifier's transferability to future categories while enhancing its discriminability to fine-grained scattering patterns. Once deployed, the model's parameters are frozen to decoupled with dynamic worlds for general knowledge extraction. At inference, a subspace classifier with class-aware target priors proposed by a max-coverage feature selection mechanism is formed for stable point-to-space discrimination. Extensive experiments on three FSCIL datasets built from SAR-AIRcraft-1.0, Self-owned, and MSTAR datasets, which cover various categories captured by airborne and spaceborne SAR payloads, show the state-of-the-art performance achieved by our method compared to numerous latest benchmarks.

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.

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.

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.

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.

A Ka-band 1024-channel passive millimeter wave (PMMW) imager (BHU-1024) based on synthetic aperture interferometric radiometer (SAIR) technique has been developed by Beihang University for security screening. BHU-1024 uses linear phased array to obtain resolution in the horizontal direction and uses aperture synthesis to obtain resolution in the vertical direction. This imager is designed for detecting concealed weapons on the human body and operated under the near-field condition of the antenna array. Thus, the conventional direct Fourier imaging theory, which is based on the far-field approximation, can’t be applied any longer. In this paper, a novel near-field image reconstruction method based on the beamforming technique is proposed. In this method, we derive the near-field imaging formula by beamforming theory and demonstrate the feasibility of obtaining spatial brightness temperature distribution by beam focusing. In the design of the beamformer, we further optimize the weight vector to suppress the sidelobe levels of the synthesized beam. The results of simulation and measurement demonstrate that the proposed method is an advantageous, effective imaging method for near-field passive millimeter wave imaging. At present, the proposed method has been applied to the actual passive millimeter wave imaging system BHU-1024.

Classification is one of the most classic problems in machine learning. Due to the global optimization ability, evolutionary computation (EC) techniques have been successfully applied to solve many problems and the evolutionary classification model is one of the methods used to solve classification problems. Recently, some evolutionary algorithms (EAs) such as the fireworks algorithm (FWA) and brain storm optimization (BSO) algorithm have been employed to implement the evolutionary classification model and achieved the desired results. This means that it is feasible to use EC techniques to solve the classification problem directly. However, the existing evolutionary classification model still has some disadvantages. The limited datasets used in the experiment make the experimental results not convincing enough, and more importantly, the structure of the evolutionary classification model is closely related to the dimension of datasets, which may lead to poor classification performance, especially on large-scale datasets. Therefore, this paper aims at improving the structure of the evolutionary classification model to improve classification performance. Feature selection is an effective method to deal with large datasets, firstly, we introduce the concept of feature selection and use the different feature subsets to construct the structure of the evolutionary classification model. Then, the BSO algorithm is employed to implement the evolutionary classification model to search the optimal structure by search for the optimal feature subset. Moreover, the optimal weight parameters corresponding to the different structures are also searched by the BSO algorithm while searching the optimal feature subset. For verification of the classification effectiveness of the proposed method, 11 different datasets are selected for experiments. The results show that it is feasible to optimize the structure of the evolutionary classification model by introducing feature selection. Moreover, the new method has better classification performance than the original method, especially on large-scale or high-dimensional datasets.

3D point cloud classification is a hot issue in recent years. 3D point cloud is different from regular data such as image and text. Disorder of point cloud makes two-dimensional (2D) convolution neural network (CNN) hard to be applied. When features are acquired from input data, it is important to extract global and local information effectively. In this paper, we propose a 3D model classification method based on multi-head self-attention mechanism which consumes sparse point clouds and learns robust latent representation of 3D point cloud. The framework is composed of self-attention layer, multilayer perceptrons (MLPs), fully connected (FC) layer, max-pooling layer and softmax layer. Feature vector of point includes spatial coordinates and shape descriptors, and they are encoded by self-attention layers to extract relationships among them. Outputs of attention layers are concatenated and put into MLPs to extract features. When they are transformed into the expected dimension by MLPs, max-pooling layer will be applied to get features in high level. Then, they are put into fully connected layer. Softmax layer is used to determine category of 3D model. The proposed method is applied to ModelNet40. Experimental results show that the proposed method is robust to rotation variance, position variance and point sparsity.

In order to improve classification performance and operating efficiency of the hash algorithm, this paper proposes a novel hash algorithm that combines three-dimensional global features and local energy features. During the stage of three-dimensional features extraction, the image is firstly compressed by SVD decomposition to form a secondary image. Then the statistical features of the secondary image at the three-dimensional visual angle are extracted as the global features. Finally, the global feature hash is generated by using the relationship between the statistical features of the image layers from different three-dimensional visual angles. In the energy feature extraction stage, the luminance image is divided into blocks, and then the energy value of each image sub-block is obtained. The multi-directional energy change features are taken as the local features of the image. Subsequent experimental results prove the effectiveness of the proposed algorithm. The algorithm not only has good robustness to the conventional content-preserving operations, but also achieves a good balance between discrimination capability and robustness. In addition, compared with several state-of-the-art schemes, this algorithm has the best ROC curve, the shortest running time and the best local tamper detection ability.

Public key encryption with equality test (PKEET for short) is a new cryptographic primitive which allows a proxy to check whether two ciphertexts encrypted under different public keys are of the same plaintext. PKEET has become a prospective candidate for various application scenarios, such as data management on encrypted databases, personal health record systems, spam filtering in encrypted email systems. In this paper, we propose an efficient all-or-nothing public key encryption scheme with authenticated equality test (AoN-PKEAET for short). In comparison with the existing PKEET schemes, the proposed scheme provides a new feature of ciphertext authentication before the plaintext equality test. It ensures that no misjudgement occurs when testing the equality between ciphertexts. Specially, the $\mathsf {Test}$ algorithm in the proposed scheme first authenticates the compared ciphertexts and the equality test is fulfilled only if the ciphertexts are authenticated as valid. With this new feature, the $\mathsf {Test}$ algorithm cannot output 0 (which means that the encrypted messages are different) when the two input ciphertexts are of an identical message, or it cannot output 1 (which means that the encrypted messages are identical) when the two input ciphertexts are of different messages. Under a redefined security model for AoN-PKEAET, we give the detailed security proof of the proposed scheme.

A Ka-band 1024-channel passive millimeter wave (PMMW) imager with 1 GHz bandwidth called BHU-1024 has been developed by Beihang University for security screening. BHU-1024 uses linear phased array to obtain resolution in the horizontal direction and uses aperture synthesis to obtain resolution in the vertical direction. The non-ideal characteristics of the hardware cause the decrease of system sensitivity and always blur the reconstructed image, hampering applications where high resolution and accurate recognition are emphasized. Hence, effective calibration is a prerequisite to ensure the quality of the millimeter wave image. In this paper, the overall calibration approach is presented, focusing on the main errors relevant to the BHU-1024 instrument design: The basic correlator counts are preprocessed to correct for comparators offset and quadrature error. The residual offset is estimated by continuously sweeping the local oscillator (LO) phase. And for the baseline error, a novel and effective calibration approach, which makes use of an external noise source mounted on the metal frame, is proposed to tackle this problem. The results of simulation and measurement demonstrate the validity of the overall calibration approach. Finally, the actual calibrated data is used to reconstruct the millimeter wave image, which further validates the calibration process.

Traditional deceptive jamming methods effectively generate false targets against three-channel synthetic aperture radar ground moving target indication (SAR GMTI). However, effectiveness of traditional methods is limited to azimuth distance between two jammers. When the distance requirement is not satisfied, the jamming amplitude coefficients will be too high to be used by jammers. To overcome this disadvantage, a novel multiple-jammer deceptive jamming method is proposed. The intercepted radar signal is retransmitted and performed by amplitude and time-delay modulation. Then, jamming signal is first modeled as a polynomial function where the polynomial order is equal to the number of jammers. The interferometric phase difference between a moving target and a false target is utilized as an objective function, the azimuth relocated position difference between a moving target and a false target is utilized as the constraint to reduce computational burden, and the optimal jamming amplitude coefficients are estimated by solving the optimization problem based on the particle swarm optimization (PSO). Hereby, the optimal jamming amplitude coefficients and gain calculated with the proposed method will be lower than those calculated with traditional methods when the distance requirement is not satisfied. By utilizing the optimal coefficients and jamming amplitude gain, a false target generated with the proposed method will be detected by displaced phase center antenna (DPCA) technique and be relocated in the desired position by along-track interferometry (ATI) technique. Experimental results verify the effectiveness of the proposed method.

Heart rate measurement technology based on Doppler radar can monitor heart rate in real time without direct contact with the skin, which possesses the advantages of convenient operation, comfort and insensitivity to the skin state. However, due to the limitation of radar’s performance, heartbeat signals obtained by radar sensor do not have clear peak value like electrocardiograms (ECGs) and pulse waves, which affects its measurement accuracy. Therefore, this paper proposes a non-contact short-term heart rate detection system based on 120 GHz narrow beam frequency-modulated continuous-wave (FMCW) millimeter wave radar. On the basis of improving the signal-to-clutter ratio (SCR) of intermediate frequency (IF) signals with narrow beam, this system applies stochastic resonance algorithm to enhance the amplitude of heartbeat signals submerged in radar system noise, thus enhancing the signal-to-noise ratio (SNR) of heartbeat signals and improving the accuracy of heart rate detection. The short-term heart rate of 10 volunteers is measured at 3 s interval under the condition that the radar sensor was 1 m from the human chest. Experimental results show that compared with the peak count of the reference pulse wave sensor, the results of this system are highly consistent with those of photoplethysmograph (PPG) detection. Finally, those results of heart rate detection are also used to analyze heart rate variability (HRV).

In this paper, a novel joint symbol rate-modulation format identification (SR-MFI) and optical signal-to-noise ratio (OSNR) estimation scheme using the low-bandwidth coherent detecting and random forest (RF)-based ensemble learning is proposed for intermediate nodes in the flexible dense wavelength division multiplexing (F-DWDM) networks. By leveraging low-bandwidth coherent detecting with small bulk wavelength scanning, no chromatic dispersion compensation and low-complexity RF, the proposed scheme could serve as a reduced-complexity and cost-effective option to realize joint SR-MFI and OSNR estimation at intermediate nodes in F-DWDM networks. To verify the feasibility of the proposed scheme, the comprehensive simulations of 8/16 GBaud polarization division multiplexing (PDM)-4/16/32/64 quadrature amplitude modulation (QAM) systems are conducted. The simulation results show that the identification accuracy of SR-MFI reaches 100% and the mean absolute error of OSNR estimation is within 1 dB. Moreover, the proposed monitoring scheme is verified by 8/16 GBaud PDM-4/16/32QAM coherent transmission experiments.

Traditional defensive techniques are usually static and passive, and appear weak to confront highly adaptive and stealthy attacks. As a novel security theory, Cyberspace Mimic Defense (CMD) creates asymmetric uncertainty that favors the defender. CMD constructs multiple executors which are diverse functional equivalent variants for the protected target and arbitral mechanism. In this way, CMD senses the results of current running executors and changes the attack surface. Although CMD enhances the security of systems, there are still some critical gaps with respect to design a defensive strategy under costs and security. In this paper, we propose a dual model to dynamically select the number of executors being reconfigured according to the states of the executors. First, we establish a Markov anti-attack model to compare the effects of CMD under different types of attack. Then, we use a dynamic game of incomplete information to determine the optimal strategy, which achieves the balance of the number of reconfiguration and security. Finally, experimental results show that our dual model reduces defensive costs while guarantees security.

Camouflage plays a fundamental role in modern electrical confrontation. Two important elements of camouflage are camouflage colors and camouflage textures. Many methods were presented to extract the main colors of the background. However, there are few methods to extract the background textures at present. The traditional methods based on watershed segmentation or background contour segmentation are computationally complex and time-consuming, being difficult to meet the real-time requirements. In this paper, a camouflage generation algorithm based on rectangle blocks scrambling and Fuzzy C-Means (FCM) clustering method is proposed. The algorithm consists of three modules, namely (1) the rectangle blocks segmentation module, (2) the rectangle scrambling module, and (3) the extraction of background dominant colors. Firstly, the texture features of the background image are simulated by rectangle blocks segmentation and scrambling algorithm, which avoids the complex calculation process and the loss of textures information compared with traditional algorithms based on description operators for background textures extraction. Next, Fuzzy C-Means (FCM) method is used to extract the main colors of background image with high accuracy and fast speed. In addition, experiments show that the proposed algorithm reduces the computing time and presents better concealment effect by retaining the similar domain colors. Compared with the template traversal algorithm and the watershed segmentation algorithm, the proposed algorithm features reduced computing time by more than 50%, and an increased similarity between the generated texture and background texture to more than 90%.

To improve the efficiency for managing power systems with constant time delay, an improved frequency-weighted model reduction is proposed. First, the Padé approximant allows to represent the time delay in the state-space form and build a linear model of the closed-loop power system containing a wide-area damping controller. Then, a weight function is introduced to solve the equilibrium transformation matrix and balance the original system. Finally, the residual order reduction is applied to the model of the power system considering time delay. The proposed method is evaluated in the two-area four-machine test system and New England system, and the effectiveness of the proposed reduction method is verified.

The overwhelming data rates in next generation wireless networks impose a burden on the high-capacity network planning. One promising strategy to meet the demand for high-capacity communications is to augment radio frequency (RF) based multi-channel multi-radio (MCMR) wireless mesh network (WMN) by free-space optics (FSO). In this paper, we construct a hybrid RF/FSO MCMR WMN topology and address its resource allocation (RA) problem in terms of interface assignment, channel allocation, routing, FSO link allocation, and topology control. Considering the weather effects on FSO link availability and the fading nature of RF links, the RA problem is formulated as a two-stage optimization problem with the objective of maximizing the network throughput. In our optimization model, we formulate each stage as a mixed integer linear program, and the bottleneck RF links are gradually upgraded by FSO links. To avoid the computational complexity of the second stage optimization, an improved iterated local search algorithm is proposed. Simulation results show that our RA scheme is efficient and the throughput can be enhanced dramatically by proper FSO link augmentation.

This paper analyzes the input-to-state stability (ISS) properties of subhomogeneous cooperative systems for the first time. A new definition of finite-time ISS (FISS) for nonlinear positive systems is given. For stability analysis, the max-separable Lyapunov function is used, and the min-separable Lyapunov-like function is presented for the first time in this paper. Using these functions, we obtain our main results on solution estimates for subhomogeneous positive systems. According to the proposed method of analysis, the ISS properties of these systems can be easily judged only on the basis of their differential and algebraic characteristics. Simulation examples verify the validity of our results.

Autism spectrum disorders (ASD) are generally defined as a development disorder typically characterized by social interaction and communication ailments and stereotyped actions due to combined genetic and environmental factors. Different critical aspects contribute to ASD, and consensus has been reached among autism researchers about its predominant genetic factors. However, the pathogenesis of ASD has not been fully revealed, and a systematic method must be developed to identify the genes related to this disease. Here, we predicted new ASD-associated genes by random walk method on the basis of prior-known ASD genes from AutDB. New genes such as RAC3, AC1 (ADCY1), PKC (PRKC) gamma, EPH receptor A5, WNT3A, calretinin, RAS-R, KLF4, and calpain 3 were found to play an irreplaceable role in ASD pathogenesis.

In this paper, a pair of clipping interference cancellation detectors based on symbol feedback and soft information are proposed for the generalized space-time-frequency index modulation (GSTFIM) systems. Specifically, the clipping interference is reconstructed by the prior information of the GSFTFIM symbol in these two detectors, then an iterative algorithm is developed for elimination and compensation of the clipping interference. The difference of these two proposed detectors lies in that the symbol feedback based interference cancellation (SFIC) detector completes the iteration through the hard decision symbol, while the soft information based interference cancellation (SIIC) detector iterates by the mean and variance of the estimated symbols. Simulation results show that the proposed two detectors can obtain flexible tradeoff between performance and complexity, compared to the traditional maximum likelihood (ML) detector with clipping interference.

In order to improve classification performance of the hash algorithm for color images, this paper presents an hash algorithm that based on three-dimensional color structure features and luminance gradient feature. Firstly, the algorithm preprocesses the input image to produce a secondary image, and extracts the color opponent component from the secondary image. Secondly, three-dimensional external structural features are constructed by using the peak curve, mean curve, and valley curve of the color opponent component. Thirdly, the overlapping points of the peak points in different visual angles and the valley points in different visual angles are selected as the salient points. The position information of the salient points are used to construct internal features. The gradient feature is constructed by using pixel changes in four directions of Y component of luminance image in YCbCr color space. Finally, the three-dimensional color structure features and luminance gradient feature are combined and disturbed to obtain the final hash sequence. Experiment results show that the proposed scheme has preferable robustness and discrimination. Compared with some existing schemes, the proposed scheme has better classification performance, shorter hash length and less average time of hash generation, and it has good detection performance in image copy detection and image tampered detection.

Heating load forecasting is the premise for guiding heating operation management and dispatching. Heating load forecasting is a time series prediction problem which requires us to predict the real-time heating loads in the next 24 hours using available historical records and weather information. In this paper, we propose a model for short-term heating load forecasting based on a properly designed strand-based long short term memory (LSTM) recurrent neural network. We present how the data are pre-processed, and the loss function is designed to improve the model's performance. Furthermore, an ensemble strategy is incorporated with the LSTM model to enhance its generalization and robustness. On offline (historical) testing data, the proposed model performs satisfactory predictions which meet the requirements of the local power plant. In addition to offline tests, we also implement the model to an online system of a power plant in Shandong province, China. The model made continuous forecasting without human interference for four months during the heating season of 2018. The model reported satisfactory online testing results that were comparable with the offline experiments using historical data.

In this paper, we propose and demonstrate a low-complexity fiber nonlinearity impairments compensation (NLC) scheme based on simple recurrent neural network (SRNN), combining with the triplets derived from the first-order perturbation solution of the coupled nonlinear Schrodinger equation. Benefitting from the time memory of SRNN, the proposed NLC can reduce the computational complexity without sacrificing the compensation performance compared with the schemes based on memoryless feedforward neural networks. To verify the feasibility of the proposed NLC, the simulation systems of single channel and three channel 30 GBaud polarization division multiplexing (PDM) 16 quadrature amplitude modulation (QAM) over 2400 km standard single mode fiber transmission are constructed respectively. Comprehensive numerical simulation results demonstrate that although the NLC performance of proposed scheme is inferior to that of digital back propagation (DBP) with much higher complexity and impracticality, it can still achieve the competitive NLC performance with lower computational complexity compared to the schemes based on deep neural network (DNN) and artificial neural network (ANN). Moreover, it is further proved that using triplets as the feature required for NLC can cause the pattern of pseudo random bit sequence to be destroyed, resulting in the neural network-based NLC scheme not suffering from the problem of overestimation for performance. Additionally, the time memory of SRNN enables the number of triplets in training set required by the SRNN-based NLC to be reduced by 169% compared to that of DNN and ANN. The experimental results of 16 GBaud PDM-16QAM wavelength division multiplexing coherent systems demonstrate that compared to with ANN and DNN, the comparable NLC performance can also be achieved by the proposed scheme with lower complexity.

Recently, terahertz (THz) nondestructive testing technology has prevailed in the defect detection of composite materials yet is limited by identifying deeper flaws. To solve this problem, a time-domain piecewise imaging method based on terahertz signal extraction is proposed in this article. First, the surface reflection echo signal is removed by applying a time correction, and the time-domain signal is then segmented according to the thickness of the sample. Finally, high-quality terahertz images are obtained by image fusion technology. All imaging methods, including traditional ones, are utilized to detect defects in glass fiber reinforced plastic (GFRP) sample then the signal-to-noise ratio (SNR) and defect edge contrast of each method are calculated. The experimental results show that the proposed method can effectively improve the terahertz imaging of deeper defects in GFRP sample especially for multi-depth defects and possesses the highest SNR and defect edge contrast.

Influential nodes in complex networks play more significant role than other nodes in many applications such as viral marketing. Identification of the most influential nodes and ranking them based on their spreading ability is a hot topic in the field of complex networks. However, when selecting a set of influential nodes, we need to consider the mutual influence between the nodes, rather than simply selecting nodes with strong influence. And how to select the initial node set to maximize the spreading scale after the propagation process has become a challenging issue. In this paper, based on node ranking methods, a novel re-ranking algorithm Re-rank by Information Spreading Function (RINF) is proposed to identify a set of influential nodes in complex networks. For each step in the proposed algorithm, select the node with the highest score, and then update scores of nodes based on local paths of the selected node with the help of information spreading probability function INF. The effectiveness of proposed algorithm is evaluated by Susceptible-Infected-Recovered (SIR) model. Compared with original methods, the performance of re-ranked versions of corresponding methods has increased from 3.1% to 61.1%. Compared with the best result of all benchmark methods, the performance of PageRank_INF (PageRank after re-ranking) improved by 2.9%, 1.4%, 1.5%, 3.6%, 3.2%, and 3.4% on BA, Jazz, PGP, Sex, USAir and Router network, respectively. Experimental results show that the proposed algorithm can well identify influential nodes in complex networks.

With the development of deep learning (DL) and synthetic aperture radar (SAR) imaging techniques, SAR automatic target recognition has come to a breakthrough. Numerous algorithms have been proposed and competitive results have been achieved in detecting different targets. However, due to the influence of various sizes and complex background of ships, detecting multiscale ships in SAR images is still challenging. To solve the problems, a novel network, called attention receptive pyramid network (ARPN), is proposed in this article. ARPN is a two-stage detector and designed to improve the performance of detecting multiscale ships in SAR images by enhancing the relationships among nonlocal features and refining information at different feature maps. Specifically, receptive fields block (RFB) and convolutional block attention module (CBAM) are employed and combined reasonably in attention receptive block to build a top-down fine-grained feature pyramid. RFB, composed of several branches of convolutional layers with specifically asymmetric kernel sizes and various dilation rates, is used for grabbing features of ships with large aspect ratios and enhancing local features with their global dependences. CBAM, which consists of channel and spatial attention mechanisms, is utilized to boost significant information and suppress interference caused by surroundings. To evaluate the effectiveness of ARPN, experiments are conducted on SAR Ship Detection Dataset and two large-scene SAR images. The detection results illustrate that competitive performance has been achieved by our method in comparison with several CNN-based algorithms, e.g., Faster-RCNN, RetinaNet, feature pyramid network, YOLOv3, Dense Attention Pyramid Network, Depth-wise Separable Convolutional Neural Network, High-Resolution Ship Detection Network, and Squeeze and Excitation Rank Faster-RCNN.

With increasing complexity of the power system, and the gradually expanding scales, the probability of low-frequency oscillation (LFO) is increasing, and its performance is more diverse. New oscillation types appear, such as close modes oscillation and intermittent modes oscillation. Due to the frequency resolution limitation, it is difficult to identify model parameters of the new oscillation types accurately by the classical methods. Based on this, this article proposes an improved analytical modal decomposition (IAMD) method. Analytical modal decomposition (AMD) has the advantage in processing low-frequency signals, narrow-band signals, and intermittent signals. However, when we use AMD method to process the above signals, the cut-off frequency is a fixed bisection frequency. This frequency cannot adjust adaptively according to the signals. This article proposes to utilize chaotic particle swarm optimization (CPSO) algorithm and auxiliary signal to obtain the optimal cut-off frequency of AMD, quickly and accurately. The optimal decomposed signals are also obtained at the same time. Finally, the Hilbert Transform (HT) is used to identify the parameters of the LFO signals. Through the analysis of simulated and measured signals, it is verified that the IAMD method is suitable not only for online analysis and monitoring of typical LFO signals, but also for new oscillation types such as close modes oscillation and intermittent modes oscillation.

Integral imaging has been considered as the most applicable technology of true 3D display in the near future. In this paper, we propose the method to achieve full parallax and large-screen integral imaging display based on mini LED. Compared to the existing integral imaging systems based on the LED, the proposed method can present both horizontal and vertical parallaxes. We explore the optical parameter matching method between mini LED display unit and lenslet unit and design three different shapes of lenslet arrays. The imaging performance of lenslet arrays of circular-shape, square-shape, and hexagonal-shape is analyzed. The method of window interception is proposed to generate elemental images for the lenslet arrays with different apertures and arranging structures. The experimental results show that the true 3D display with full parallax and screen scalability has been realized using the proposed method and the display effect of lenslet array with hexagonal-shape is the best according to the evaluation of both subjective and objective merits.

Fatigued driving detection in complex environments is a challenging problem. This paper proposes a fatigued driving detection algorithm based on multi-index fusion and a state recognition network, for further analysis of driver fatigue states. This study uses a multi-task cascade convolutional neural network for face detection and facial key point detection, corrects the face according to the key points of the eye, intercepts a binoculus image to recognize the eye state, and intercepts a mouth image according to the left and right corner points to recognize the mouth state. This can improve the detection accuracy of the driver's head tilt, deflection, and so on. Next, an eye state recognition network is constructed for the binoculus image to identify the eye closure state, and a mouth state recognition network is used to identify the mouth state. Finally, a fatigue judgment model is established by combining the two characteristics of the eye state and the mouth state to further analyze the driver fatigue state. The algorithm achieved 98.42% detection accuracy on a public eye dataset and achieved 97.93% detection accuracy on an open mouth dataset. As compared with other existing algorithms, the proposed algorithm has the advantages of high accuracy and simple implementation.

In this paper, we propose to use deep neural networks for image compression in the wavelet transform domain. When the input image is transformed from the spatial pixel domain to the wavelet transform domain, one low-frequency sub-band (LF sub-band) and three high-frequency sub-bands (HF sub-bands) are generated. Low-frequency sub-band is firstly used to predict each high-frequency sub-band to eliminate redundancy between the sub-bands, after which the sub-bands are fed into different auto-encoders to do the encoding. In order to further improve the compression efficiency, we use a conditional probability model to estimate the context-dependent prior probability of the encoded codes, which can be used for entropy coding. The entire training process is unsupervised, and the auto-encoders and the conditional probability model are trained jointly. The experimental results show that the proposed approach outperforms JPEG, JPEG2000, BPG, and some mainstream neural network-based image compression. Furthermore, it produces better visual quality with clearer details and textures because more high-frequency coefficients can be reserved, thanks to the high-frequency prediction.

Naked eye augmented reality technology has the capability of satisfying the immersion feeling. Therefore, light field three-dimensional (3-D) display technology has received considerable attention. In this paper, a novel pose estimation method in light field 3D display based on H∞ optimal control is proposed. Compared with the current pure hardware platform method, the proposed method releases hardware resources such as the graphic processing unit and avoids numerous parameter adjustments. The proposed method comprises three key parts. First, we calculate the normal of the rigid objects and apply it to obtain the normal estimation. Second, we calculate H∞ of the normal estimation result on the Hardy space, which is the maximum singular value of the rational function matrix parsed in the right half plane of the complex plane. Finally, we use the optimal result of H∞ for pose estimation and transformation. The experiments are carried out on the virtual and real classical datasets acquired by Kinect, Mian, and Clutter. The experiments show that the proposed method can be used to obtain high-quality display effects with low cost and high efficiency.

This paper is concerned with the cooperative output regulation problem for multi-agent systems under the directed interaction topology. Comparing with the existing work where the exosystem is common for all the agents, a scenario in which the agents track multiple trajectories with different exogenous signals is considered. The main aim of the study is focused on the design of distributed feedback control laws, such that the agents in diverse clusters track the different reference trajectories exponentially, while rejecting the disturbances asymptotically. Considering that all the agents in each cluster cannot access the exogenous signal, the cooperative output regulation problem of the linear single-integrator agents is solved by devising a distributed state feedback control scheme. Furthermore, it is considered that all the states of the plant cannot be accessed by the agents within each cluster either, the cooperative output regulation problem of linear agents is solved by designing a distributed output feedback controller. Sufficient solvability conditions for those two problems are established, which are shown to be necessary and sufficient under some constraints.

In this paper, a Lagrange multiplier method is investigated for designing distributed optimization algorithm, which convergence is analyzed from the view of multi-agent networks with connected graphs. In the network, each agent is with both private and shared information. The shared information is shared with the agent's neighbors via a network with a connected graph. Furthermore, a Lagrange-multiplier-based algorithm with parallel computing architecture is designed for distributed optimization. Under mild conditions, the convergence of the algorithm, corresponding to the consensus of the Lagrange multipliers, is presented and proved. The experiments with simulations are presented to illustrate the performance of the proposed method.

Research on facial expression recognition (FER) technology can promote the development of theoretical and practical applications for our daily life. Currently, most of the related works on this technology are focused on un-occluded FER. However, in real life, facial expression images often have partial occlusion; therefore, the accurate recognition of occluded facial expression images is a topic that should be explored. In this paper, we proposed a novel Wasserstein generative adversarial network-based method to perform occluded FER. After complementing the face occlusion image with complex facial expression information, the recognition is achieved by learning the facial expression features of the images. This method consists of a generator G and two discriminators D1 and D2. The generator naturally complements occlusion in the expression image under the triple constraints of weighted reconstruction loss lwr, triplet loss lt, and adversarial loss la. We optimize the discriminator D1 to distinguish between real and fake by constructing an adversarial loss la between the generated complementing images, original un-occluded images, and small-scale-occluded images based on the Wasserstein distance. Finally, the FER is completed by introducing classification loss lc into D2. To verify the effectiveness of the proposed method, an experimental analysis was performed on the AffectNet and RAF-DB datasets. The visual occlusion complementing results, comparison of recognition rates of facial expression images with and without de-occlusion processing, and T-distributed stochastic neighbor embedding visual analysis of facial expression features all prove the effectiveness of the proposed method. The experimental results show that the proposed method is better than the existing state-of-the-art methods.

In this paper, we propose a dual-broadband multiple-input multiple-output (MIMO) indoor base station antenna for 2G/3G/LTE systems. The proposed MIMO antenna has a low profile with its overall dimensions of 220×220×42 mm3 and utilizes both spatial and polarization diversities. The antenna operates at 800-960 and 1700-2700 MHz simultaneously, with the return loss of higher than 14 dB across both frequency bands. The isolations of the antenna are 18 and 25 dB, with antenna gains of 3.6 and 7.2 dBi for the lower and upper frequency bands, respectively. The proposed MIMO antenna is designed and fabricated with its practical deployment in mind, such as the durability and overall low cost. Comparing with existing commercial 2G/3G/LTE antennas, our proposed design offers a more compact size and simpler feeding structure.

The construction of Green Mine has become an essential policy for the management of mineral resources in China. This study aims to propose a favorable approach to assess the performance of Green Mine. The evaluation criteria are recognized based on the specific characteristics of Green Mine. Considering that some of these criteria could not be mutually compensated, a novel multi-criteria decision making (MCDM) method is presented. It integrates the TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method with the elimination and choice translating reality (ELECTRE) method in a picture fuzzy environment. In most existent methods, the evaluation information of Green Mine is described by real numbers. However, in this study, picture fuzzy numbers (PFNs) are first used to express such information to effectively indicate the uncertainty and fuzziness in the evaluation process. Another innovation is that the entropy weights model and the best-worst method (BWM) are combined to determine the comprehensive criteria weights. The practicability and advantages of the proposed method are verified by a case study and a comparative analysis with other methods. In addition, the influences of some parameters are discussed using sensitivity analysis to judge the flexibility and robustness of the proposed method. The results show that the proposed approach is feasible for solving the non-compensatory problems of evaluation criteria and can provide references for the performance evaluation and management of Green Mine.

This paper proposes an efficient way to calculate the transient electromagnetic field coupling to overhead multiconductor transmission lines (MTLs) in the time domain based on the Gauss-Seidel iterative algorithm analytically. This method is performed in the time domain directly. Thus, the case that transient electromagnetic field coupling to MTLs terminated by nonlinear load can be analyzed conveniently. In this method, the cross-coupling between the conductors is represented by the equivalent distributed voltage and current sources along every lines. Then, the analytical expressions of the voltage and current response along MTLs are derived iteratively in time domain based on the Gauss-Seidel iterative algorithm. The proposed method is validated by several numerical examples, which demonstrate that the proposed approach could handle the case that MTLs terminated by the linear load and the case that MTLs terminated by the nonlinear load with the acceptable computational accuracy. The proposed method exhibits a higher convergence rate than the previous Jacobi iterative method, especially for the case where the MTLs have a high ratio of length to wavelength and the case in which the MTLs have a high coupling factor. Additionally, due to the analytical methodology that the proposed method adopts, the computational accuracy is not affected by the simulation time step. Therefore, the proposed approach is very efficient for modeling of the transient electromagnetic field coupling to long overhead multiconductor transmission lines.

The star image suffers inevitably from degradation due to the high-speed motion of the space target and the long exposure time of the camera, therefore the attitude information of the star is hard to accurately obtain. This paper proposes a blind deblurring algorithm that combines shape features of space targets. First, the astronomical image is preprocessed using saliency detection. Then, considering the shape characteristics of space targets, the Minimum Bounding Rectangle (MBR) is introduced to describe the space targets. Next, the parameters of the MBR are used to estimate the point spread function (PSF). Finally, a regularization method is employed to recover the astronomical images. Experimental results on both simulation and real star images demonstrate that the proposed method reduces the error of point spread function estimation and decreases the error rate of identified space targets. At the same time, the accuracy of the star centroid extraction improves 0.2786 on average and the error of the star centroid location extraction reduces 0.059 comparing to the state-of-art method. The proposed method is of great significance for positioning, recognition and attitude determination of space targets.

Faced with the contradiction between economic growth and environmental pollution, implementing cleaner production has become a good choice for many mining companies to achieve sustainable development. This paper aims to evaluate the cleaner production for gold mines by exploring applicable decision making methods. First, the picture fuzzy numbers with four membership degrees are adopted to describe experts’ assessment information under complicated fuzzy environment. Thereafter, the extended stepwise weight assessment ratio analysis and mean-squared deviation models are combined to obtain the comprehensive criteria weights. Meanwhile, a novel evaluation method, which integrates the evaluation based on distance from average solution (EDAS) with elimination and choice translating reality (ELECTRE) module, is proposed to judge the cleaner production level. Subsequently, according to the characteristics of gold mines, an evaluation criteria system of cleaner production is constructed. Finally, a case of evaluating the cleaner production performance for four gold mines is provided to explain the application of the proposed method. Besides, a systematic comparison analysis with other existent methods is conducted to reveal the advantages of our method. Results indicate that the integrated EDAS-ELECTRE method is suitable and effective for gold mines to evaluate their cleaner production performance, and has important reference values for the cleaner production management and implementation.

In this paper, we propose two designs of slot-fed metasurface antenna for beam steering applications. Both designs have the same two-layer stacked configuration consisting of either a single-slot or a double-slot radiator and a metasurface superstrate. In contrast to existing phase-gradient metasurfaces with varying unit elements for beam steering, our proposed metasurface is formed by uniform square rings. At operating frequencies, each square ring acts as an individual resonating element. By exciting the elements in sequence with different phase delays through the slot radiator, the generated antenna beam can be steered. In the first design, the position of the single-slot radiator is shifted to achieve the beam steering functionality; while for the second antenna, to avoid mechanically moving the feeding slot, a double-slot-feeding structure is proposed with two slots excited with varying phase differences. Simulation and experimental results show that beam steering angles of -35° to 35° can be achieved for the single-slot-feeding design and beam steering angles of -30° to 30° are realizable for the double-slot-feeding configuration. Comparing with conventional phased array antennas, our proposed designs have compact size with moderate to high antenna gain.

Initial allocation modes and schemes are studied in this paper to provide guidelines for allocation of carbon emission permits in power system. We first introduce different allocation modes and the need to apply free allocation to assignability of emission permits. Then, we compare two different allocation schemes, which are based on historical emissions and generation performance standard. Further, a new allocation scheme based on Boltzmann distribution is proposed. Finally, a case study on Shanghai power grid in China is conducted to compare the allocation effects of these schemes respectively. The analytical results show that Boltzmann distribution based scheme has the best performance and should be adopted when developing initial allocation of carbon emission permits in Chinese power systems.