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

Currently, defect detection for photovoltaic (PV) electroluminescence (EL) images faces three challenges: limited training data and complex backgrounds result in low accuracy in detecting defects; the diverse shapes of specific defects often lead to frequent false alarms; and existing models still require improvement in accurately recognizing these 12 specific defects. An innovative detector, called the Reconfigurable Adaptive Focus Background Suppression Detector (RAFBSD), is proposed to tackle these challenges. This approach utilizes the advanced Deformable Attention Transformer (DAT) technology to effectively focus on regions with defects. By doing so, it captures more informative features while significantly reducing noise interference. Additionally, the Omni-dimensional Dynamic Convolution (ODConv) and the Receptive-Field Attention convolutional (RFAConv) modules enhance accuracy by effectively allocating attention weights to optimize model performance. Compared to YOLOv8, RAFBSD has demonstrated substantial enhancements in both mAP50 (by 4.7%) and mAP50-95 (by 5.3%). Furthermore, RAFBSD demonstrates an outstanding capability to precisely identify flaws in 12 different classifications of PVEL images, exceeding the recognition capacity of YOLOv8 models by three additional types. These findings unequivocally establish RAFBSD as an efficient and groundbreaking object detection model.

Conventional evaluations of naval artillery’s destructive prowess are dependent on resource-intensive sea trials, which are neither economically viable nor advantageous for the cultivation of combat intelligence. To circumvent these limitations, we propose a method rooted in virtual reality for assessing the destructive efficacy of naval artillery ammunition against unmanned maritime targets. Leveraging the Unity3D engine, we construct a high-fidelity virtual testing environment and design corresponding testing tasks. The offensive strategy is devised considering the naval artillery’s damage radius and range, incorporating factors such as the warhead’s velocity, and determining its launch angle and direction. Following this, the projectile’s trajectory is computed employing the point mass motion equation, facilitating the simulation of the projectile’s entire life cycle trajectory. A Monte Carlo based model has been developed for assessing the damage effectiveness of naval artillery ammunition against unmanned boats. This model enables quantitative calculation of ammunition usage under varied damage probabilities, addressing issues related to the complexity of the testing process, the limited number of tests, and the challenges in computing damage probabilities for target impacts of naval artillery ammunition. The findings corroborate the precision and stability of the virtual reality based assessment method for naval artillery ammunition damage effectiveness against unmanned surface vessels in combat simulations. This verification holds substantial importance for enhancing the computation accuracy of damage effectiveness associated with naval artillery ammunition.

Orbital angular momentum (OAM) mode division multiplexing (MDM) amplification in a seven-ring-core erbium-doped fiber (7-RC-EDF) has been experimentally demonstrated, in which the 1st–4th order OAM modes within each core of the 7-RC-EDF can be supported. By combining the OAM modes with the pump laser into the 7-RC-EDF, 112 OAM modes are predicted to be amplified in the C-band with a net gain of more than 22.36 dB with the differential modal gain (DMG) of less than 1.7 dB. The optical signal-to-noise ratio (OSNR) can be higher than 30 dB. As a proof of concept, signals with three wavelengths were individually converted into the 2nd–4th higher order OAM modes, respectively, and then injected together into one ring core of the 7-RC-EDF. The amplification with a net gain of more than 27.7 dB and OSNR of more than 22 dB was achieved. The proposed wavelength division multiplexing (WDM) combined with the OAM-MDM technique verified its potential applications in large-capacity OAM-MDM optical fiber communication systems.

Accidents involving electric vehicle fires have increased as the number of electric vehicles has grown recently. The issue of charging safety is a key barrier to the growth of the electric vehicle sector because these accidents have resulted in large financial losses for car owners and charging facility operators. The approach for resolving the issue of electric car charging safety through an electric vehicle charging safety warning system is suggested in this research. The suggested solution uses an adaptive optimization of long short-term memory neural network (A-LSTM) to forecast voltage changes throughout the whole charging process by using the vehicle’s daily historical charging data. The warning threshold adjustment method is established by the difference between the predicted voltage data and the actual voltage data, which is dynamically adjusted as the charging process progresses. Finally, a real-time warning model for vehicle charging alert is developed. The daily charging data of electric vehicles is used in the paper to verify the precision of data prediction and the accuracy and timeliness of the model. The study’s findings demonstrate that the early warning model suggested in this paper can quickly send out early warning signals to safeguard the safety of car charging and can identify aberrant charging data.

Building roof segmentation is a key step in the process of 3-D building reconstruction using airborne light detection and ranging point cloud data. Voxel-based region growing is one of the most widely used methods to segment planes because of its high efficiency and easy implementation, but it is easy to omit roof planes due to the unreasonable voxel size and the complex roof structures. In addition, boundaries between adjacent roof planes are inaccurate. To solve the issues, a roof segmentation method using octree-based hybrid region growing and boundary neighborhood verification voting is proposed. First, an octree-based voxelization is performed on the raw building points to generate two basic units: planar voxels and individual points (i.e., points that are not in the planar voxels). Then, the hybrid region growing is conducted on these two basic units to segment coarse roof planes. A parameter-free boundary neighborhood verification voting strategy is used to assign the boundary points to the correct roof planes by verifying the neighborhoods of the boundary points and using reliable neighborhood information. Experimental results of four datasets, including two datasets provided by the International Society for Photogrammetry and Remote Sensing and two high-density datasets provided by OpenTopography, verify that roof planes can be successfully segmented by the proposed method with over 96.8% completeness and a minimum of 93.2% correctness. In addition, boundary points are assigned to the correct roof planes by the neighborhood verification voting strategy. Thus, the segmented roof planes can be used in various applications.

The features of unmanned aerial vehicles (UAVs), such as strong manoeuvrability, small size, and light weight, make them considered to participate in free space optical (FSO) quantum communication, becoming an important part of the Space-Air-Ground Integrated Network. However, research reports on small-sized drones based long-range optical quantum link are few. A major challenge is that traditional opto-mechanical structure is difficult to adapt to small UAV platforms. Here, a lower size, weight, and power (SWaP) acquisition and tracking system prototype is developed, and its greatest advantage is that the system can be easily integrated into small flying platforms and quickly build a long-distance quantum communication link. The prototype weighs less than 8 kg. A small-sized two-axis mirror mechanism is designed for wide-range acquisition and tracking. Moreover, the system can automatically point to ground station. The SWaP acquisition and tracking system can support quantum key distribution (QKD) link and general FSO link by drones. In the 7 km air-to-ground demonstration, the acquisition and tracking system prototype is tested in practice, which is suspended under the small six-rotor UAV platform, and a special passive vibration isolation unit is added in between. A ten-minute continuous and stable beacon tracking proves that the system can quickly and automatically align.

In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging with edge penalty. The superpixel generation algorithm consists of two stages, i.e., fast pixel clustering and superpixel merging. In the clustering stage, we define a new adaptive pixel dissimilarity measure for SAR image and then optimize the DBSCAN strategy, which considers the edge information and can achieve rapid clustering. In the merging stage, based on the initial superpixels, a new superpixel dissimilarity measure is defined, which can merge the small local superpixels into their neighborhood superpixels, making the final superpixel segmentation results compact and regular. Experimental results on two simulated and two real SAR images demonstrate that our method outperforms the state-of-the-art superpixel generation methods in terms of both efficiency and accuracy. The superpixel segmentation accuracy of our method is 5–10% higher and the time cost is 10–40% lower than other methods. Since the superpixel segmentation result can be used as a preprocessing stage for the SAR data interpretation applications, superpixel-based and pixel-based classification results with two real SAR images are also used for comparison, which can validate the advantages of our proposed method.

Cascaded Brillouin random fiber lasers (BRFLs) with laser linewidth compression were theoretically and experimentally demonstrated. Thanks to photon-phonon coupled interplay as well as randomly distributed feedback along silica fibers, the coherent time of cascading BRFL radiation is significantly prolonged whilst the phase noise is further suppressed, albeit with a quantum-noise-induced linewidth narrowing limit. To validate it, the linewidth narrowing ratio in BRFLs, defined as the ratio of the Brillouin pump and its sequent order of Stokes linewidth, was carried out and exhibited a dependence on the Brillouin pump linewidth, which is in good agreement with theoretical predictions. Furthermore, the relative intensity noises of cascading BRFLs were also investigated and discussed.

In order to achieve high-speed, high-sensitivity optical communication over long distances and high attenuation channels, we propose a photon-counting communication system based on the detection of multichannel single-photon avalanche diodes (SPADs). First, the ideal pulse position modulation (PPM) bit error rate (BER) expression for a multichannel detection system is derived, and then the effect of the detector's dead time is taken into account. The BER of the multichannel detection system was analyzed by means of a Monte Carlo simulation, after which we set up a four-channel receiving experimental verification system with a 1 × 4 fiber splitter and four single-photon counting modules (SPCMs). The simulation and experimental results demonstrated that the BER of the multichannel detection system could be greatly reduced and the communication speed substantially improved. Finally, a reliable photon-counting communication system with a rate of 50 Mbps and sensitivity of 4 photons/bit was developed using serially-concatenated pulse position modulation (SCPPM) technology.

In this paper, an adaptive fuzzy finite-time control problem for pure-feedback switched nonlinear systems with prescribed performance is investigate. By utilizing the prescribed performance technique, we mean that the system output is converge to the predefined bounds. By using the dynamic surface control (DSC) and the backstepping technique, we establish a novel adaptive fuzzy finite-time pure-feedback switching control method. In such a scheme, the Butterworth low-pass filter (LPF) is used to construct the fuzzy observer, which can estimate the immeasurable states and avoid algebraic loop problem. In accordance with the stability theory of common Lyapunov function (CLF), the designed controller can ensures that the tracking error converges to a small neighborhood of the origin in finite time. Finally, an example is given to illustrate this method and verify its validity and effectiveness.

Because of the rapid growth of multimodal data from the internet and social media, a cross-modal retrieval has become an important and valuable task in recent years.The purpose of the cross-modal retrieval is to obtain the result data in one modality (e.g., image), which is semantically similar to the query data in another modality (e.g., text).In the field of remote sensing, despite a great number of existing works on image retrieval, there has only been a small amount of research on the cross-modal image-text retrieval, due to the scarcity of datasets and the complicated characteristics of remote sensing image data. In this article, we introduce a novel cross-modal image-text retrieval network to establish the direct relationship between remote sensing images and their paired text data. Specifically, in our framework, we designed a semantic alignment module to fully explore the latent correspondence between images and text, in which we used the attention and gate mechanisms to filter and optimize data features so that more discriminative feature representations can be obtained. Experimental results on four benchmark remote sensing datasets, including UCMerced-LandUse-Captions, Sydney-Captions, RSICD, and NWPU-RESISC45-Captions, well showed that our proposed method outperformed other baselines and achieved the state-of-the-art performance in remote sensing image-text retrieval tasks.

An all-silica Fabry-Pérot interferometer (FPI) is demonstrated for high-temperature pressure monitoring. The pressure sensor is fabricated with single mode fiber and silica capillary by CO2 laser. Owing to slight mismatch of thermal expansion coefficient in all-silica structure, it can work stably under 600°C atmosphere. The influence of the temperature on the pressure sensor was investigated within a temperature range from 25°C to 700°C and a pressure range from standard atmosphere pressure to 4 MPa. The experimental results show that the wavelength shift versus the pressure at each temperature is linear, which indicates the proposed FPI pressure sensor has potential applications in industrial reactors, oil and gas wells, and so on.

The magnetic levitation system is a typical open-loop unstable system. To weaken the chattering phenomenon of sliding mode control (SMC), a hybrid control strategy of particle swarm sliding mode-fuzzy PID control was designed. This strategy uses particle swarm optimization (PSO) to optimize the SMC using the method of exponential approach law, so as to obtain the best sliding mode surface parameters and exponential approach rate coefficients, thus shortening the time required to approach the stable region when the nonlinear system is far from the stable point; a state supervision controller was designed by analyzing the stable conditions of sliding mode. When the system approaches the stable region, it smoothly transitions to fuzzy PID control, thereby weakening the chattering phenomenon of SMC. To verify the dynamic performance of the designed algorithm, a magnetic levitation ball system was selected as the controlled object. Through simulation and experiment, it was verified that the controller had strong robustness and strong anti-interference performance. It effectively weakened the chattering and had good adaptability to external disturbance and state variable crossing.

We propose a novel demodulation scheme based on measuring interference fringe visibility and demonstrate it using a typical Michelson interferometer (MI) configuration. The key component to achieve the scheme is a section of highly birefringent fiber (HBF) spliced into the sensing arm to function as the sensing element. The optical path difference (OPD) leads to interference fringes, while the birefringence of the HBF results in a visibility envelop. It is possible to detect measurands associated with birefringence through measuring the visibility, which is attributed to a flexible use of the fundamental principle of interferometry. As one of the potential applications, temperature sensing employing the MI configuration is performed. Moreover, the technique has the advantage of being immune to element instabilities, such as power fluctuation of the light source, phase shift of the reference arm, noise of the photo detector, and wavelength shift of the optical spectrum analyzer (OSA). This work may provide an alternative method to achieve high precision interferometry with low cost.

Photon-counting detectors can increase the range of underwater optical wireless communication (UWOC) by enhancing the detection sensitivity and have thus become an active research area in recent years. However, system performance still needs to be improved in the reported underwater photon-counting communication (UPCC) experiments. In this study, a real-time, high-speed UPCC system was designed and experimentally validated based on a single-photon avalanche diode (SPAD). A reliable symbol synchronization method based on pulse-position modulation (PPM) was developed. This method achieved slot synchronization by using a narrow-pulse-width laser and a simple matched filter and realizes frame synchronization through an improved synchronization sequence. In addition, channel errors were corrected by serially concatenated convolutionally coded PPM (SCPPM), and digital signals were processed in real time based on field-programmable gate arrays (FPGAs). Finally, desktop communication experiments were completed at a slot frequency of 25 MHz and a communication rate of 6.21 Mbps. The system exhibited a bit error rate (BER) of less than 10–7, a received optical power of only −84.3 dBm, and an efficiency of 1.35 photons per bit.

Using pseudospark discharge sourced electron beams for the generation of high-peak-power millimeter and terahertz radiation has attracted increasing research interest in recent years. However, one of the crucially important and hitherto unanswered questions is “what is the upper-frequency limit at which millimeter-wave devices can be driven by pseudospark discharge sourced electron beams?”. In this paper, we studied this question from the perspective of beam transportation in a plasma background, more specifically an ion channel using particle-in-cell simulations to find the limitations. The parameter ranges of the beam transportation with small oscillations in the beam diameter were investigated and summarized, through simulations of beam propagation in a large diameter drift tube with different ion densities, plasma electron densities, beam density distributions, and beam energies. The beam transportation in a small diameter beam tunnel was also simulated. It showed the maximum beam current with a small velocity spread that can be transported in the beam tunnel was determined by the diameter of the beam tunnel and the ion density. High injected current will cause significant beam loss and reduce the overall efficiency. The simulation results indicate a minimum diameter of the beam tunnel in a millimeter-wave circuit that can be effectively driven by a pseudospark-sourced electron beam. The equivalent upper limit in the operating frequency is about 400 GHz.

While the 5-th generation of communication networks (5G) is taking its first deployment steps, high doubts still concern the fulfilment of the stringent requirements of its slices. To meet these complex requirements, robust access networks should support the current 5G air interface. In this regard, space and air networks, namely satellites and unmanned aerial vehicles (UAVs) are expected to play a key role due to wide coverage and flexible deployment, respectively. Currently, the integration of these platforms in the terrestrial networks is weak though. Therefore, we suggest bridging this gap by designing a heterogeneous traffic offloading approach in the space-air-ground integrated network (SAGIN). Our innovative offloading approach covers the co-existing requirements of two heterogeneous slices of 5G by offloading smartly the traffic to the appropriate segment of SAGIN. Specifically, the ultra-reliable low-latency communications (URLLC) traffic is offloaded to the UAV link and to the terrestrial link to satisfy its stringent requirements in terms of latency. However, the enhanced mobile broadband (eMBB) traffic is offloaded to the UAV link, to the terrestrial link and to the satellite link because it is less sensitive to delay but needs high data rates. Our offloading approach boosts the network’s availability and reduces the latency experienced in SAGIN through an efficient resource allocation and an optimized design of the UAVs trajectory. Our findings highlight the key role that the concrete integration between SAGIN segments plays to achieve a better quality of service (QoS) for different slices with heterogeneous requirements.

This paper investigates the vehicular platoon control problem subject to variable communication delays, packets disorder, access constraints and resource constraints in vehicular and hoc networks (VANETs). Using a novel representation of the network delays as an uncertain variable belonging to the different bounded intervals, the discrete-time variable sampling interval platoon model is established with communication constraints. An event-based control and scheduling (EBCS) codesign strategy that can robustly stabilize the platoon system is given. Based on this model and a guaranteed performance cost function, the aforementioned problem is formulated as an LMI optimization problem, which can guarantee the Global Uniform Practical Stability (GUPS). A numerous simulation and experiments with laboratory scale Arduino cars show the efficiency and practicability of the proposed methods.

This article studies the model, control and simulation on transition flight of a novel tail-sitter with vertical takeoff and landing capability. The proposed tail-sitter adopts an innovative varying fuselage shape design to break the technical bottleneck in the balance of efficient horizontal flight and agile vertical flight. Overset grids and computational fluid dynamic methods are used to explore the vehicle’s prestall aerodynamics, which are relative to not only the angle of attack and sideslip angle but also to the varying angle between two rear fuselage’s parts. High angle of attack aerodynamics based on the improved Viterna and Corrigan stall model is also established for this novel tail-sitter. Meanwhile, an accurate model of the propeller is tested in a wind tunnel. Combining the forces and moments generated by propellers, aerodynamics and gravity, a 6DoF nonlinear time-varying dynamic model is built. A robust controller based on incremental dynamic inversion method is designed for this tail-sitter, which is good at dealing with uncertainties and external forces. Nominal and model mismatch conditions are simulated to verify the controller’s performance. Different varying strategies for the mechanism are analyzed during the transition flight. Simulation results show that this novel tail-sitter can transform between vertical and horizontal flight mode easily and the varying strategy related to pitch angle is a prior choice for transition flight.

Drone-mounted base stations (DBSs) are promising means to provide ubiquitous connections to users and support various emerging applications in mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we investigate the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network while minimizing the number of deployed DBSs. Since the BUD problem is NP-hard, it is then decomposed into three sub-problems: the joint UE association, power and bandwidth assignment (Joint-UPB) problem, the DBS placement problem (including the vertical and horizontal positions) and the problem of determining the number of DBSs to be deployed. We propose two approximation algorithms to solve the first two sub-problems and use the linear programming to solve the last sub-problem. Finally, another approximation algorithm, referred to as the AA-BUD algorithms, is proposed to solve the BUD problem with guaranteed performance based on the solutions of the three sub-problems. The performance of the AA-BUD algorithm has been demonstrated via extensive simulations, and is shown to be superior to two benchmark algorithms with up to 62% throughput improvement.

In an islanded microgrid with multiple distributed generations (DGs), the difference in line impedance may cause local voltage deviation, which leads to a series of problems such as lower power allocation accuracy and bus voltage drop under traditional droop control. In this respect, a method for optimizing the droop control using an improved particle swarm optimization (IPSO) is proposed. Firstly, the microgrid structure and influence of line parameters on traditional droop control strategy is analyzed. Then, an improved particle swarm optimization is proposed. Based on the basic particle swarm optimization (PSO) algorithm, a fuzzy inference system (FIS) is introduced to dynamically adjust the particle swarm optimization, which can effectively improve the global search ability and local search ability of the algorithm. After that, the improved algorithm is applied to the droop controller, simultaneously, the range of stable operation of the system is determined by small signal analysis. Finally, the simulation and experiment results show that the proposed improved droop control strategy can achieve accurate allocation of active and reactive power effectively while maintaining bus voltage and system frequency stability, enhance the dynamic performance and transient stability of the microgrid system.

With the development of new energy technology, the number of electric vehicles (EVs) will continue to increase, which will seriously threaten the safe operation of the power grid. The V2G information interaction system can help the power grid to achieve safe and reliable operation. Therefore, under the framework of the V2G information interaction system, this paper establishes the communication system between EVs and charging piles based on power line communication (PLC), which can reduce the cost and avoid the problem that the incompatible communication interface caused by different specifications of charging piles. Due to the severe impulse noise (IN) communication environments between EVs and charging piles, the transmission performance would be seriously degraded. Hence, an improved-priori-aided sparsity adaptive matching pursuit (IPA-SAMP) is proposed to reconstruct IN. In the process of IN cancellation, the measurement vector is obtained from the null sub-carriers. In order to efficiently reconstruct IN, as the input of the IPA-SAMP algorithm, partial support are improved by the optimized threshold, which significantly improve the accuracy and robustness of the recovered IN. The proposed IPA-SAMP algorithm can effectively recover IN in the channel between EVs and charging piles, improves the reliability of the information exchange system, which is validated by computer simulations.

Chatbot communication, in which a robot communicates with a human being in natural language in an open domain, has achieved significant progress. However, it still suffers from problems such as a lack of diversity and contextual relevance. In this paper, we propose a retrieval-polished (RP) model for response generation that polishes a draft response based on a retrieved prototype. In particular, we first adopt a prototype selector to retrieve a contextually similar prototype. Then, a generation-based polisher is designed to obtain a polished response. Finally, we introduce a polished response filter to choose whether the final reply should be the retrieved response or the polished response. Extensive experiments on a dialog corpus show that our method outperforms retrieval-based and generation-based chatbots with respect to fluency, contextual relevance, and response diversity. Specifically, our model achieves substantial improvement compared with several strong baselines.

To improve computational efficiency of obtaining local induced current distribution under multiple incidents angles when electromagnetic scattering problems of electrically large objects are considered, a novel method is proposed to form small-scale impedance matrix and reduce number of repeated calculation for different incident angles, which is implemented by following three steps. Firstly, an undetermined matrix equation including multiple incident angles is constructed by interrelating part field nodes and all source nodes on object surface. Secondly, the undetermined matrix equation is transformed into a form that conforms to the compressive sensing (CS) framework, which is solved by reconstructed algorithm to obtain approximate induced currents of all source nodes under a wide incident angle range. Finally, exact wide-angle currents of a given node are obtained by calculating total magnetic field, and neighborhood wide-angle currents of the given node can be expanded by choosing appropriate technology in different boundary conditions. Numerical results of electrically large objects shown the efficiency of the proposed method.

Residents clustering in different periods of load fluctuation and aggregated forecasting can increase the load prediction accuracy. But the strength of load fluctuation reflects the difference in the electricity consumption behavior of residents and affects the cluster results of residents. This paper presents a new day-ahead aggregated load-forecasting method for distribution networks based on the load fluctuation and feature importance (FI) profile clustering of residents. First, the input features are determined, the FI profile of residents is determined, and residents are clustered according to the FI profile. Then, the crow search algorithm is used to optimize the initial cluster centers for preventing the clustering results from falling into a local optimum. And the cluster verification index S_Dbw, the sum of the average scattering for the clusters and the inter-cluster density, is used to evaluate the cluster quality. The optimal clustering results of the aggregated load for different fluctuation periods are determined via statistical experiments. Finally, a random forest predictor based on ensemble learning is selected. According to the optimal clustering results in different fluctuation periods, a rolling forecasting model is constructed to realize day-ahead aggregated load forecasting in a residential distribution network.

Appropriate fault isolation and system reconfiguration scheme is of great significance to ensure the reliability and precision of the tightly coupled global navigation satellite system/inertial navigation system (GNSS/INS). Currently, the commonly used methods include fault isolation method and fault adaptation method. However, the performance of them is not compared and analyzed fully in the existing literatures. In this paper, the principle of them is analyzed and the performance of them under different conditions is compared in theory firstly. On this basis, to improve the effectiveness and adaptability of fault detection and isolation, an adaptive fault isolation and system reconfiguration method is proposed. The radial basis function neural network (RBFNN) is used to predict the pseudo-GNSS measurement for the measurement reconfiguration. Besides, taking the variety of observation conditions into consideration, an adaptive adjustment criterion is introduced to realize the switch of fault isolation and measurement reconfiguration. The performance of the proposed method is verified and compared with the traditional methods by using the field test data. The results show that the fault isolation method can obtain higher filtering precision than the fault adaptation method in theory, compared with these two methods, the proposed method has better adaptability to the complex environment, and can improve the reliability and precision of the navigation system effectively.

Dielectric elastomer generators (DEGs) provide a new solution for vibrational energy harvesting. Currently, a type of impact-based DEGs, which can harvest energy from ambient vibrations, has been proposed and studied through simulations. However, the energy conversion mechanism and the performance evaluation approach of such impact-based DEGs have not been fully studied yet, thus limiting the reliability of the research on the system design/optimization and performance evaluation. In this paper, a single-sided impact (SSI) model is proposed to reveal the impact-based energy conversion mechanism. Based on this model, a complete four-stage impact process is analyzed to reveal the energy conversion mechanism, and the electrical outputs and energy conversion efficiency are derived as the energy harvesting performance evaluation indexes. To use the developed analytical model to predict the system electrical response accurately, some important parameters including the coefficient of restitution (COR) and largest deflection of the membrane at impacts were obtained experimentally, and the system output voltages at impacts were measured to verify the theoretical approaches in calculating the system electrical outputs and studying the parameters' influences. Furthermore, the influences of the pre-stretched ratio, impact velocity, and input voltage on the system energy harvesting performance are studied through simulations. The research results can provide guidelines to improve the energy harvesting performance of the impact-based DEGs in real applications.

With the rapid development of social media, the number of online comments has exploded, and more and more people are willing to express their attitudes and feelings on the Internet. Under the influence of a series of major events all over the world, production order is facing a serious challenge, which severely impact on energy market. In 2020, a large number of investors' and consumers' comments related to social events began to appear on the Internet from China. However, the style and quality of online comments vary greatly, making it difficult to accurately extract users' views and tendencies. Based on the investors' and consumers' statements published on Chinese Internet, in this paper, we use statistical methods to classify the sentiment orientation of the Netizens firstly, and then use Bidirectional Encoder Representations from Transformer-Bidirectional Long Short Term Memory (BERT-BiLSTM) which is the combination forecasting method of Bidirectional Encoder Representations from Transformer (BERT) and Bidirectional Long Short Term Memory (BiLSTM), to model and forecast the sentiment orientation of users' statements, as well as being compared with its based models, BERT and BiLSTM. Among them, the accuracy and recall, which represent the predictive abilities on the overall samples and on the focus(the samples of Label 1) respectively, of BERT-BiLSTM model are 0.8620 and 0.7078 respectively, which are superior to those of BERT model (0.8559 and 0.5576) and LSTM model (0.7775 and 0.0747). The research results can accurately predict the sentiment orientation of Internet users during the social events so as to provide technical support for grasping the energy market trend.

With the rapid development of network technology, the number of digital images is growing at an alarming rate, people's demand for information gradually shift from text into images. However, it is very difficult for users to quickly find the images they are interested in from the large number of image libraries. The purpose of this paper is to study the image recommendation algorithm based on deep learning. In this paper, image classification algorithm is firstly studied. LReLU - Softplus activation function is formed by combining LReLU function and Softplus function, and CNN is improved. Then, an image retrieval model based on local sensitive hash algorithm is proposed in this paper. This model calculates the distance in hamming space for the binary hash code generated by mapping. Euclidean distance is calculated inside the result set after similarity measurement to improve the accuracy, and the image retrieval model is constructed. Finally, an image recommendation model based on implicit support vector machine (SVM) is proposed in this paper. This image recommendation method combines image text information and image content information. The experimental results show that the proposed image recommendation model can meet the practical needs. In this paper, the overlap rate between the CNN-based recommendation model and the human recommendation algorithm was tested, and the coincidence degree of the two recommended images reached 88%.

The traditional kernel correlation filter (KCF) algorithm has poor tracking results in complex scenes with severe occlusion, deformation, and low resolution and cannot achieve long-term tracking. To improve the accuracy of the tracking algorithm in complex scenes, an adaptive kernel correlation filter algorithm is proposed. First, a multifeature complementary scheme is proposed that linearly weights the responses of the histogram of oriented gradient (HOG) features and color features and learns a target position estimation model to realize target position estimation. Then, an adaptive scale model for estimating the scale transformation of the target is learned by extracting the HOG features of the object. Finally, according to occlusion judgment criteria, the Kalman filter is introduced to correct the position of the tracking target. The accuracy and success rate of the proposed algorithm are verified by simulation analysis on TC-128/OTB2015 benchmarks. Extensive experimental results illustrate that the proposed tracker achieves competitive performance compared with state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.899 and 0.635, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes. This study provides references for computer vision processing, such as image retrieval, behavior analysis, and intelligent driving.

Hyperspectral unmixing is a crucial task for hyperspectral images (HSIs) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF), and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this article, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.

Delineating the crucial waves in electrocardiogram records is a paramount work for the automatic diagnosis system of heart diseases. In this paper, a novel method is described to determine the boundaries and the peaks of P waves, QRS complexes and T waves by utilizing twelve-lead electrocardiogram signals. It avoids the difficulty of setting the thresholds when determining the boundaries of crucial waves and also the trouble of selection of wavelet basis as the wavelet-based method does. The signals are first preprocessed by a bandpass filter. After that, the locations of QRS complexes are identified. And based on the QRS locations, adaptive search windows are set to detect the locations of P waves and T waves. Then, a method called local distance transform decides the wave boundary in each lead. Finally, the final boundary determination rule is applied to obtain reliable boundaries. We justify the performance of our algorithm on LUDB database. When the tolerance window interval is 40ms, the peak accuracies of P wave, QRS complex and T wave are all beyond 98% and their boundary accuracies are all above 96%. Compared with the derivative threshold method and the wavelet-based method where the tolerance window interval is 150ms, the algorithm shows a sensitivity and a positive predictive value of peaks and boundaries greater than or equal to 98.43% and 96.44% for the P wave, 99.89% and 99.86% for the QRS complex and 99.21% and 99.85% for the T wave. For the critera of average error and standard deviation, our method has the performance similar to those methods. In addition, our algorithm can also handle such several situations where the boundary determination of crucial waves is tough as high T wave, high noise and baseline wandering well.

This paper addresses the problem of the fixed-time cooperative guidance (FxTCG) law for multi-missiles against a single maneuvering target with constraints of both the interception time and the line-of-sight (LOS) angle. Firstly, by utilizing the consensus protocol and the fixed-time consensus theory, a new FxTCG in the LOS direction is presented to guarantee that the multiple missiles intercept the incoming target synchronously. Moreover, the acceleration of the target causes disturbance, so a simple fixed-time disturbance observer (FxTDO) is introduced to compensate FxTCG with the disturbance estimation. Subsequently, the FxTCG in the normal direction of the LOS is designed based on an adaptive fixed-time convergence reaching law and the proposed FxTDO, which ensures the fixed-time convergence of the LOS angular velocity and LOS angle between each missile and the maneuvering target. Finally, numerical simulations and adequate analyses are carried out to illustrate the accuracy, effectiveness, and robustness of the proposed FxTCG scheme.

In the fiber sensing research area, a pair of sharp contradictions is highlighted: high sensitivity and large dynamic range. In addition, the question of how to enable user-editable features for fiber optic sensors is gaining more and more attention. This letter presents a fiber acoustic sensor with real-time controllable sensitivity based on the optical vernier effect, which is possibly a solution to such a problem. The optical vernier structure with amplified and real-time tunable detection sensitivity is formed by cascading a Sagnac interferometer and a laboratory-made tunable Fabry-Perot interferometer (FPI). Sound pressure and frequency of the acoustic signal can be all-optically demodulated simultaneously by spectral reading and spectral scanning method, respectively. Experimental results show a real-time controllable acoustic sensitivity (1×, 5×, and 10× are demonstrated) and a maximum sensitivity is as high as 37.1 nm/Pa (10× of single FPI) within relatively large sound pressure range of 62.2-92.4 dB. By adjusting the FPI cavity length in real time, sensitivity can be controlled to meet the needs of different users or occasions. The proposed sensor has merits of controllable sensitivity in real time, high sensitivity, and large dynamic range, enabling the applications related but not limited to acoustic sensing.

This paper devotes to the image compression and encryption problems. We develop a novel hybrid scheme based on block compressive sensing. Concentrate on taking full advantage of the different frequency coefficients sparsity, the nonuniform sampling strategy is adopted to improve the compression efficiency. First, the discrete cosine transform coefficients matrices of blocks are transformed into vectors by zigzag scanning. The different frequency components are extracted in the front, middle, and back of vectors, respectively. Using the measurement matrices with different dimensions, the combination of low- and high-frequency components, together with the medium-frequency coefficients are compressed simultaneously. Second, the recombinational block measurements are re-encrypted by the permutation-diffusion framework. The logistic map is introduced for key stream generation. In order to accomplish a sensitive and effective cryptosystem, the control strategy for secret keys is employed. The simulation results indicate that the proposed scheme forms a high balance between reconstruction performance, storage and computational complexity, and hardware implementation. Moreover, the security analyses demonstrate the satisfactory performance and effectiveness of the proposed cryptosystem. The scheme can work efficiently in the parallel computing environment, especially for the images with medium and large size.

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

Polarimetric high resolution range profile (HRRP) contains the geometrical structural information along radar line-of-sight and has shown great potentials in target recognition. In existing researches, H/α decomposition has been applied to exploit global polarimetric features of a single HRRP from its spatially averaged coherent matrix, which loses target local scattering information. In this paper, we propose to apply the H/α decomposition along the slow time dimension in a dual polarimetric HRRP sequence. The H and α features are extracted for each target scattering center by averaging its samples in different observations of the HRRP sequence, which provides both target local high resolution and polarimetric scattering characteristics. We also design a novel six-zone H-α plane for geometrical structure classification of target scattering centers. Simulation and experimental results show the effectiveness of the proposed method and display its good potentials for practical applications.

In recent years, the Chinese government is making great efforts to develop electric vehicle (EV) technologies to reduce carbon emissions and to protect the environment. A large number of EVs and facilities, such as charging stations (CSs), battery swapping stations (BSSs), and central charging stations (CCSs), have been deployed. However, a huge number of EVs connecting into the power grid may cause problems, such as voltage fluctuation etc. Meanwhile, the peak demand for charging will also lead to an increase in the cost of deployment of charging infrastructure. A price-based demand response (DR) program can be used to achieve goals, such as cost savings and reduction of the peak demand for charging. But to maximize the charging service capacity while minimizing the total cost is a challenging task as it is usually a tripartite game between the government, EV owners, and EV battery service providers. We considered it as a multi-objective optimization problem. First, a battery-swapping cost model is designed to describe the objective problem. It includes a battery demand model, a DR-based subsidy cost model and a charging cost model. Then, we leverage a covariance matrix adaption evolution strategy (CMAES)-based algorithm to deal with the multi-objective high dimension optimization problem and achieve the goal of maximizing the charging service capacity while minimizing the total cost. The experimental results confirm that the peak demand for battery swapping service can be reduced effectively and the total cost can be reduced by nearly 12% with our optimized subsidy strategy. Furthermore, the standard deviation evaluation results confirm the stability and the effectiveness of the proposed algorithm.

The modular multilevel converter (MMC) is now the most attractive topology for medium and high voltage power conversion applications with several advantages over the traditional voltage source converter (VSC). However, due to a large number of sub-modules (SMs) in the MMC, system reliability is a big challenge in its practical application, where each SM may be considered as a potential point of failure. In this paper, a reliability evaluation based on the Markov model is proposed for the MMC. The failure rates of the power electronic devices and SMs are firstly analyzed. Then, the Markov model and the state transition equation of the system are built in detail. A general reliability evaluation function is established, in which the mean time to failure and reliability evaluation of the MMC with redundant SMs are also discussed. Finally, a practical direct current (DC) distribution example for reliability evaluation is analyzed, and the results verify that the reliability evaluation based on the Markov model could provide a useful reference for project design.

An approach to improve the side-coupling ratio of the macro-bend polymer optical fiber (POF) coupler is proposed in this paper. Two naked POFs are twisted and twined around a cylinder to achieve continuous liquid level sensing. Through filling the gap between the two twisted POFs with UV optical cement, both the sensitivity and the measurement range are increased substantially. The reversibility is also improved since the gap is eliminated, and little liquid can exist in the structure with the liquid level decreasing. The design turns out to be a good method to enhance the performance, and could also be applied in the displacement or stress sensing field.

Graphene-based temperature sensor is characterized by low absorption coefficient and large thermal conductivity. We report a grapheme-coated microfiber (GCM) structure based sensor, which is demodulated with a dual-wavelength erbium-doped fiber laser (DWEDFL). The “thermophoresis effect” of GCM and gain competition between the two lasing wavelengths allow us to achieve an ultrahigh temperature sensitivity up to 2.10 dB/°C. To the best of our knowledge, it is the highest one among the intensity-type temperature sensors reported so far. The sensor has the potential in sensing application for its merits including high sensitivity and convenience of demodulation. Our results demonstrate that the GCM structure combined with the gain competition scheme offers an alternative way for high-sensitivity optical fiber sensing applications.

Deep convolutional neural networks (DCNNs) have been driving significant advances in semantic image segmentation due to their powerful feature representation for recognition. However, their performance in preserving object boundaries is still not satisfactory. Visual mechanism theory indicates that image segmentation tasks require not only recognition, like DCNNs, but also local visual attention capability. Considering that superpixel is good at grasping detailed local structure, we propose a probabilistic superpixel-based dense conditional random field model (PSP-CRF) to refine label assignments as a postprocessing optimization method. First, the well-known fully convolutional networks (FCN) and Deeplab-ResNet are employed to produce coarse prediction probabilistic maps at each pixel. Second, we construct a fully connected CRF model based on the PSP generated by the simple linear iterative clustering algorithm. In our approach, an effective refining algorithm with entropy is developed to convert the pixel-level appearance and position features to the normalized PSP, which works well for CRF. Third, our method optimizes the PSP-CRF to obtain the final label assignment results by employing a highly efficient mean field inference algorithm and some quadratic programming relaxation related algorithms. The experiments on the PASCAL VOC segmentation dataset demonstrate the effectiveness of our methods which can improve the segmentation performance of DCNNs to 82% in mIoU while increasing the computational efficiency by 47%.

This paper investigates the event-triggered H∞ filtering for networked control systems (NCSs) with quantization and network-induced delays. With consideration of the limited capacity of the communication channels in NCSs, a quantizer is proposed to quantize control signals before being transmitted into the next node. First, an event-triggered scheme is addressed between the quantizer and the fuzzy filter, which aims to mitigate transmission rate and improve the usage of network resource. Based on the event-triggered scheme, the fuzzy filtering error system is established with quantization and delays. Second, a novel Lyapunov-Krasovskii functional is constructed, and the Writinger inequality is used to deal with the integral item of the derivative of the Lyapunov-Krasovskii functional, which can obtain more useful items. Then, a new stability criterion is addressed to ensure that the filtering error system is asymptotically stable with a prescribe H∞ performance level. By introducing matrix decoupling technique, the fuzzy filter is designed without the coupling matrices. Finally, numerical simulations are given to show the effectiveness of the proposed method.

The observability and observable degree of attitude errors in tightly coupled global navigation satellite system/inertial navigation system attitude determination system are studied in this paper. The mathematical relationship between the observable degree and the attitude dilution of precision (ATDOP) is determined by the singular value decomposition of the observability Gramian. The influence of the ATDOP on the observable degree is analyzed. The observability analysis of the attitude errors under different numbers of visible satellites is performed via a rank test of the observability Gramian. The results of a field test demonstrate that, a higher observable degree of attitude error will be obtained for the pitch, roll and yaw at a given time under a lower ATDOP. When the number of visible satellites is equivalent, a lower ATDOP leads to a higher observable degree of error for any one of the three attitudes at different times. Moreover, the results show that the three attitude errors are all externally observable under the condition of at least three visible satellites with three antennas.

Real-time kinematic (RTK) ambiguity resolution can be improved by engaging multiple reference antennas. The baseline information between the antennas can be taken as true values and used as a hard-constraint; or it can be regarded as weighted, a priori measurements, and used as a softconstraint. In this contribution, a comparison between the two constraints is made to explicate their difference in RTK ambiguity resolution for some specific applications, in which the coordinates of the reference antennas cannot be accurately calibrated in advance. The functional and stochastic models of the hard and soft-constrained RTK positioning are given. The float ambiguity precision of the unconstrained, the hard and the soft-constrained cases is compared. The closed-formulae of the ambiguity dilution of precision (ADOP) for the unconstrained and the hard-constrained models are derived, and they are proven to be the upper and lower bound of the soft-constrained ADOP, respectively. A simulated and a real data show that, with the hard or soft-constraints, the single-frequency ambiguity resolution success rate, as well as the performance in time-to-first-fix (TTFF), is improved significantly. When there is a bias smaller than 6 cm in the a priori baseline vectors between the reference antennas, the two constraints have a comparable performance in ambiguity success rate. Also, a sharp increase in the hard-constrained TTFF is observed at the same time. When the bias gets larger, the hard-constrained ambiguity success rates are decreased drastically. In contrast, the soft-constraint maintains a relatively good performance, showing a much greater tolerance for bias.

In successive cancellation (SC) polar decoding, an incorrect estimate of any prior unfrozen bit may bring about severe error propagation in the following decoding, and thus it is desirable to find out and correct an error as early as possible. In this paper, we investigate a progressive bit-flipping decoder which corrects at most L-independent errors in SC decoding. In particular, we first study the distribution of the first error position in SC decoding, and a critical set which with high probability includes the bit where the first error occurs regardless of the channel realizations is proposed. Second, a progressive bit-flipping decoding algorithm is proposed based on a search tree, which is established with a modified critical set in a progressive manner. The maximum level of the search tree is shown to coincide well with the number of independent errors that could be corrected. On this basis, the lower bound on BLER performance of a progressive bit-flipping decoder which corrects at most L errors is derived, and we show the bound can be tightly achieved by the proposed algorithm for some L. Moreover, an early-terminated bit-flipping (ET-BitFlipping) decoder is proposed to reduce the computational complexity and decoding latency of the original progressive bit-flipping scheme. Finally, numerical results show that the proposed ET-bit-flipping decoders can provide almost the same BLER performance as the state-of-the-art cyclic redundancy check-aided SC list decoders, with an average computational complexity and decoding latency similar to that of the SC decoder at medium to a high SNR regime.