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Formation-containment control in multi-agent systems faces a critical challenge: how to stabilize all formation leaders when the $\alpha -$ leader’s influence diminishes over topology distance, especially under input delays? This challenge is particularly acute in PDE-based models, where information decay is intrinsic, making it difficult for the $\alpha -$ leader—a leader among leaders—to affect far-away agents, especially under speed constraints.Our work tackles this challenge by proposing a three-layer hierarchical control framework based on Partial Differential Equations (PDEs). Our key innovation is an integral-type delayed compensation input specifically designed for the $\alpha -$ leader. This control law uses kernel functions derived from backstepping transformation and equivalence principles, tailored to compensate for input delays. By providing the $L^{2}$ space expansion of these kernel functions, we show that formation control error for finite agents is acceptable, even with approximations.We organize formation leaders into a one-dimensional chain-like topology, categorizing them as leaders, anchors, or $\alpha -$ leaders based on their roles. For other agents, we derive distributed control laws from the discrete form of PDEs governing formation deployment. Followers converge to the convex hull spanned by leaders, forming an internal two-dimensional surface. This PDE-based approach ensures invariance in translation, rotation, and expansion.Our work also contributes to the mathematical foundations of PDE-based multi-agent systems. We discuss the numerical solution of kernel functions, offer a distributional interpretation, and—critically—analyze the discretization error between PDE and ODE models. This analysis reveals the relationship between stability, time step, spatial step, and control laws, addressing an often-ignored issue in the field.Simulation examples validate our theoretical findings, showing effective formation-containment control under our framework.

The lightning discharges generated by lightning strikes can reach several hundred kiloamperes, causing significant electromagnetic, mechanical, and thermal effects. These effects induce extremely high overvoltages in power transmission systems, leading to the tripping of transmission lines. Therefore, the study of lightning protection and early warning methods is crucial for safeguarding transmission lines. However, previous research in the realm of active lightning protection has been limited, focusing either solely on assessing the risk of lightning-induced tripping on the power grid side or merely predicting the patterns of lightning activity on the disaster side. This paper proposes a novel method for lightning protection and early warning of power transmission lines based on the analysis of lightning data. It encompasses both the risk of lightning-induced tripping of transmission lines on the grid side and the hazards of real-time lightning activities on the disaster side. In the warning phase, the method extensively mines historical lightning location data and, based on predictions of thunderstorm cloud movement trajectories, determines if thunderstorm clouds are nearing tightly packed transmission corridors. It then assesses whether these clouds present a high-risk storm or are approaching transmission lines with low lightning protection capabilities, subsequently integrating these findings to issue a continuous lightning-induced trip warning. In a case study conducted in a certain part of northern China, the proposed continuous lightning strike trip warning method achieved an accuracy rate of 80% for transmission corridors of particular interest to power grid companies’ dispatch departments, enhancing the efficiency of warnings and reducing false alarms and missed reports.

This paper investigates the dynamic observer (DO) design problem for a class of disturbed Lipschitz nonlinear vehicle systems with uncertainties, where the uncertainties are assumed to be in both system matrix and input matrix. The proposed DO has a unified form and the popularly used proportional observer and proportional integral observer can be considered as the particular cases of the proposed DO. Based on the algebraic constraints, the observer design issue is transformed into the asymptotically stability analysis problem. The sufficient criterion of the DO design is derived in terms of linear matrix equality (LMI), by constructing a Lyapunov function to guarantee the H-infinity performance. A numerical example is used to illustrate the performance of the proposed DO.

Accurate indoor positioning is critical to a variety of use cases including tracking, proximity, mapping, and navigation. Existing positioning methods are either inaccurate (e.g. RSSI-based ranging), impractical (e.g. fingerprinting), or uncommon (e.g. Ultra-Wide Band/UWB). WiFi Round-Trip Time (RTT) marries the ubiquity of WiFi infrastructure with the accuracy of such ranging mechanisms as those used in UWB. We present WhereArtThou, a commercial-grade, plug-and-play indoor positioning solution (IPS) based on WiFi RTT. Our base algorithm uses an extended Kalman filter with a random walk motion model (EKF-RW) which relies solely on RTT distance measurements. We propose two EKF components to enhance positioning accuracy. First, as distance measurements can further be fused with inertial sensor readings, we propose a step-and-heading-based filter (EKF-SH) when such readings are available. We devise a method to fit the non-Gaussian step error with a Gaussian random variable to remain within the computationally-efficient Kalman filtering framework. Second, we define a distance-dependent measurement model to match the true statistics of the measurement noise and approach optimal position estimation. Moreover, we measure the gain from the proposed enhancements not only through the almost-exclusively-used error metric in the indoor positioning literature, the Euclidean distance and its variations, but also through metrics commonly used in satellite and maritime navigation, the cross-track and along-track errors, in addition to a set of metrics of our own definition. Finally, we show that RTT is highly susceptible to the human body holding the device measuring it, and we case-study the impact of human-body blockage on positioning and ranging errors. We test our algorithms on over 18 hours of walking data collected on different devices, in different locations, and with different users, and we observe that the EKF-RW and EKF-SH achieve 90th percentile distance errors of 1.65 m and 1.45 m, and 90th percentile cross-track errors of 0.85 m and 1.55 m, making our solution primed for commercial deployment.

This article presents an elliptical circumnavigation control strategy for multiple quadrotors subject to uncertainties. At translational level, a novel horizontal elliptical guidance law capable of regulating the relative distance between different quadrotors and a vertical control law are synchronized to educe the translation controller. At rotational level, an USDE characterized by laconic constructure and simple parameter tuning, is introduced to estimate the unknown lumped uncertainties online. The primary advantage of this paper is that the elliptical circumnavigation with regulatable distance between circling quadrotors is achieved. Meanwhile, all the error signals in the closed-loop system are analyzed to be bounded. Finally, practical experiments verify the efficacy of suggested strategy.

The scintillator crystal is a crystalline material that can light under the influence of high-energy rays and be widely used for detecting high-energy particles. With the development of industrial CT and other fields, the demand for inorganic scintillator crystals with high melting points and high performance is increasing yearly, which results in the abandonment of the traditional laboratory mode of manufacturing inorganic scintillator crystals. For achieving the needs of high-standard inorganic scintillator crystals, the market attaches great importance to the intelligent manufacturing of scintillator crystals. One of the critical technical difficulties in intelligent crystal manufacturing is the numerical assessment of crystal quality. The traditional laboratory research and development mode relies on manually measuring crystal quality, which has many defects, such as the inability to be numerical, and the quality evaluation varies from person to person. Therefore, this paper proposes a methodology for assessing crystal quality based on the residual depth network. At the same time, in order to make crystal quality evaluation more refined, this paper uses a target detection method based on depth learning to determine the crystal quality assessment area. The target detection model in this article is developed based on the yolov5 deep learning framework, which is suitable for crystal target detection, and its detection accuracy is 98%. The residual depth network proposed in this paper is based on the network developed by ResNet-18, which is more suitable for crystal grading, and its accuracy of crystal quality evaluation reaches 84.8%. The quality evaluation of inorganic scintillation crystals is a solution to the problem of numerical representation of the quality of inorganic scintillation crystals, which opens up a technical channel for closed-loop control of intelligent crystal manufacturing processes.

Few-Shot Segmentation (FSS) is challenging for intra-class diversity and support sample scarcity. Many works focus on the class-wise or pixel-wise similarity between the support foreground and query sample while neglecting the support background, which is vital for FSS to suppress the related query background. In this paper, we propose a Multi-Similarity Enhancement Network (MSENet) to remedy this issue by extracting the pixel-wise support-query similarity of the foreground and background. To remedy the shift issue, caused by the huge difference between support and query target objects, this study extracts and fuses multiple support-query similarity, and keep enhancing them with convolutional operations. Experimental results reveal that our approach achieves a performance of 66.8% in PASCAL and 43.8% in COCO, surpassing the state-of-the-art (SOTA) and outperforming other leading competitors.

Combining with the dispersion characteristics of ultrasonic guided waves, full waveform inversion method shows great application potential in the quantificational detections of defects in plate- and pipe- like structures. Owing to the inversion efficiency problem, the forward modeling is generally based on the acoustic equation approximation, and a reference signal unaffected by the defects needs to be artificially selected to correct the approximation forward simulation results. This study presents an auto-calibration (without artificial selection of the reference signal) and high-precision imaging method based on the combination of the full waveform inversion and ray tomography algorithm. The ray tomography results are not only used to automatically select the reference signal based on an auto-calibration process, but also used as the macro initial model for the full waveform inversion method, which decreases the possibility of losing in local minimum values during the inversion process and enhances the robustness of the inversion method. Therefore, compared with the classical guided wave tomography method based on full waveform inversion, relatively high-frequency transducers can be used to acquire high-frequency signals, and thus, the imaging accuracy could be effectively improved. Simulation and experiment results have verified that the global relative error of the auto-calibration method is smaller than the classical method. The good imaging results of irregular complex defects confirmed the effectiveness and applicability of the new method.

Two microwave photonic signal processing structures, which are capable to realise microwave frequency division with a tunable integer or non-integer division ratio, are presented. They are based on applying an RF signal into a Mach Zehnder modulator, which is biased to generate a carrier-suppressed double sideband (DSB) or single sideband (SSB) optical signal. The carrier-suppressed DSB optical signal consists of the upper and lower $1^{\rm st}$ order sidebands with a separation of two times the input RF signal frequency. The carrier of the SSB optical signal is suppressed by a fibre Bragg grating (FBG) connected to the modulator output. Hence the FBG output consists of the upper $1^{\rm st}$ order and lower $2^{\rm nd}$ order sidebands with a separation of three times the input RF signal frequency. The carrier-suppressed DSB or SSB optical signal is injected into a semiconductor laser. The semiconductor laser is oscillated in the period-one state that generates a number of equally spaced frequency components, which are frequency locked by the injection light wave. Beating of these optical frequency components at the photodetector produces an RF signal with a frequency of 2/ $N$ or 3/ $N$ times the input RF signal frequency where $N$ can be between 3 and 6. Hence the 2/ $N$ frequency divider can realise 2/3, 1/2, 2/5 and 1/3 frequency division operation, and the 3/ $N$ frequency divider can realise 3/4, 3/5 and 1/2 frequency division operation. The proposed 2/ $N$ and 3/ $N$ frequency dividers have a very simple structure compared to the reported photonics-based microwave frequency divider that can realise both integer and non-integer frequency divisions. The frequency division ratio can be tuned by simply adjusting the forward bias current of the semiconductor laser subject to optical injection. Experimental results demonstrate the two proposed structures can realise microwave frequency division with a tunable integer and non-integer division ratio for different input RF signal frequencies of 10 to 18 GHz, and over 60 dB output signal-to-noise ratio performance. More than 27 dB suppression in the unwanted frequency components around the frequency divided signal is also demonstrated.

The conventional laser triangulation displacement sensor (LTDS) is difficult to achieve large dynamic range and high precision measurement simultaneously, since LTDS is affected by factors such as limited detector size, nonlinear input and output variables, laser power fluctuation, speckle noise, electronic noise, etc. Given the above background, we proposed a modified LTDS using a diffraction grating to improve the dynamic range and precision simultaneously. Different from the conventional LTDS, the modified LTDS generates more than one spots on the image sensor, thereby the displacements with different ranges can be obtained from the multiple order diffraction spots. Ultimately, the dynamic range and measurement accuracy will be improved simultaneously by integrating these displacements. In this paper, the principle of the modified LTDS for large dynamic range and high precision measurement is described in detail, and we verified the validity and effectiveness of this idea through the experiment. Compared with the traditional LTDS, the experimental results show that the linearity of the modified LTDS is improved by a factor of 1.5685 and the dynamic range is improved 1.2986 times as well as maintaining the same linearity.

This paper presents a detailed experimental investigation of the nonlinear dynamics of a dual-beam optically injected semiconductor laser. Unlike previous works, we focus on the situation where the power and the frequency of the injection light wave are fixed and the free running frequency of the laser subject to optical injection is in between the two injection beams that have a large frequency separation. The nonlinear dynamics of the slave laser (SL), i.e., the laser subject to optical injection, for different SL forward bias currents are examined. Results show, in addition to the regenerated injection light wave, a fundamental oscillation together with its harmonics are generated when the SL interacts with its nearby injection beam. The SL output frequency components are stabilised when one of the harmonics of the fundamental oscillation is located at the injection beam that is far away from the free running SL. More than six-fold improvement in the frequency stability of the resultant output microwave signal, compared to that generated by optical mixing, is obtained. Experimental results also show the SL nonlinear dynamics have different behaviours when the two injection beams have different frequency separations.

With the improvement of the quality of life, the demands on button mushroom (Agaricus Bisporus) increase significantly. But traditional farming methods mainly rely on costly human picking, which is currently facing the time-consuming and labor-intensive problem. As machine vision technology becomes more mature, harvesting robot with flexible picking end-effector is an alternative method to address this issue. Though the conventional harvesting robot is capable of mushrooms picking, but the damage rate is relatively high for the improper picking force and motion. Thus, a novel picking end-effector for button mushrooms is designed based on vacuum negative pressure picking in this paper. The harvesting robot with flexible end-effector is proposed to solve the problem: 1) to avoid the injury when the end-effector touch the mushroom; 2) to increase the picking efficiency without damage rate increase. The structure of robotic mushroom picking end-effector is described in detail, and then the kinematic modeling and picking force analysis of this robot are presented. Lastly, the numerical simulation via the MATLAB is carried out to study the influence of the robot parameters. The bruise tests indicated that the maximal allowable stress is 0.196 MPa and mean allowable pressure is 13.82 N. Preliminary results demonstrate that the robot achieved 88.2% success rate and 2.9% damage rate in the factory environment, and there is potential for the automatic mushroom harvesting with the proposed harvesting robot.

Big data-driven technologies, especially machine learning and deep learning technologies, have been extensively employed in mineral prospectivity prediction. Several approaches have been proposed to learn the deep characteristics of geoscience data, enhance the accuracy of prediction and reduce uncertainty. Nevertheless, the approaches always contain the following two limitations. Firstly, the formation of mineral resources often involves the coupling of multiple factors on a certain spatio-temporal scale, resulting in rare labelled deposits and insufficient number of training samples. Secondly, training Deep Neural Network (DNN) is very challenging. Many approaches are subject to weak interpretability and lack of organic combination with geoscience knowledge. To address these two problems, we propose Geo-Rnet and GCAE (Geological Convolutional Autoencoder). Geo-Rnet is a multi-class mineral prospectivity prediction approach based on improved DNN. GCAE is able to effectively augment multi-disciplinary geoscience data by constructing upon an optimized Convolutional Autoencoder. The experimental results show that most of prospective areas predicted by Geo-Rnet overlap with the labelled mineralization locations, with an average accuracy of 91.1%. In addition, 89.98% of the ore deposits are located in the predicted areas. The results indicate the effectiveness of Geo-Rnet and GCAE for multi-class prediction of mineral resources. Finally, we classify the target area into several mineral prospectiviy areas according to their different mineral types. The research provides an innovative approach for mineral prospectivity prediction in the target area.

We propose and experimentally demonstrate an RF signal multiplier based on optical injection locking in a semiconductor laser followed by an optoelectronic oscillator (OEO). In the proposed scheme, the modulated master laser is injected into a slave laser, a single mode Fabry-Pérot laser diode (SMFP-LD), in such a way that one of the harmonics of the modulated beam is locked to the corresponding mode of the SMFP-LD. By optical beating at the output of the SMFP-LD, stable multiple RF signals with ahigh signal-to-noise ratio (SNR) are generated due to SMFP-LD, which provides a sufficient gain to the RF signal. Further, we employ an OEO structure at the output of the SMFP-LD to optimize one of the generated multiple RF signals which are determined by the electrical bandpass filter (EBPF) and the low noise amplifier (LNF) in the OEO loop. As the gain of the OEO loop is higher than the loss, a high-purity oscillating signal, which is the selected multiple RF frequency, can be generated. In the experiment, a sextuple frequency (20 GHz) of the modulated signal is observed with an SNR and the phase noise of 54.44 dB and −102.44 dBc/Hz@10 kHz, respectively. Compared with other photonic schemes, the proposed method provides high SNR, higher multiplication factor, and low phase noise.

Orthogonal Time Frequency Space (OTFS) as a two-dimensional modulation scheme designed in the delay-Doppler domain, is realized by inverse symplectic finite Fourier transform (ISFFT) and symplectic finite Fourier transform (SFFT), for combating the high Doppler channels toward future wireless communications. Meanwhile, generalized spatial modulation (GSM) offers an efficient implementation for multi-input multi-output (MIMO) systems, by activating only part of the transmit antennas to alleviate inter-channel interference (ICI). In this paper, the combination of the above two concepts is exploited, for bridging their unique advantages. On the other hand, an iterative detector based on generalized approximate message passing (GAMP) is also developed for GSM-OTFS. Simulation results demonstrate that the proposed GAMP detector can achieve better performance than the conventional minimum mean square error (MMSE) detector.

The role of demand response resources is particularly important under the new-type power system with new energy as the main source. Demand response resources are important for generation adequacy and can provide electricity capacity value for power system. The United States and the UK are the only countries in the world that offer capacity services from demand response resources as generation resources. Their experiences and lessons learned are of great significance to other countries and regions. This paper first analyzes the significance of DR resource participating in the capacity market from two perspectives: the development of demand response resources and system generation capacity. Secondly, a detailed analysis of the mechanism design of demand response resources in the US capacity market is presented and the development status of demand response resources since 2007 is provided, so as to provide meaningful reference for other countries and regions. Thirdly, this study introduces the current situation and dilemma of demand response resources in the capacity market of the UK, and provides corresponding suggestions so that demand response resources in other countries and regions can avoid repeating the pace of the UK in the capacity market. Finally, suggestions for demand response resources to participate in the capacity market are given from four aspects.

By improving a previously introduced Lyapunov functional,this paper addressed the stability issue about neutral-type Cohen-Grossberg neural networks with multiple time-varying delays in the states of neurons and the time derivative of states of neurons, a sufficient condition for global asymptotic stability is obtained. Since the Cohen-Grossberg neural networks considered in this paper is more general than those in the former literature, the obtained result developed stability condition of Cohen-Grossberg neural networks. At the end of the paper, two examples are employed to demonstrate the validity and superiority of the conclusion.

High-power wireless power transfer (WPT) is an attractive option for fast charging of electric vehicles (EVs), but the power capability of the WPT system is limited by power devices and resonant elements. This paper presents a scalable modular four-channel WPT system for fast EV charging. Owing to the modular design, the four-channel system achieves 50 kW output power with low-power Silicon devices, and the reliability and robustness of the system are significantly improved. Through the coil structure and placement design, the four-channel magnetic coupler achieves inter-channel decoupling, eliminating interference between channels, simplifying control logic, and enabling scalable modular design. The four-channel magnetic coupler also has rotational symmetry, making the system have a balanced tolerance to misalignment in both lateral and vertical directions. The proposed magnetic coupler is verified by 3-D finite element method (FEM) simulation. A modular four-channel WPT prototype is built and tested. Experiments show that the prototype can deliver 50.8 kW to load with 97% dc-to-dc efficiency over a 200-mm airgap in aligned case, and has a balanced tolerance to misalignment in both the lateral and vertical directions.

Accurate prediction of the large-scale channel fading is fundamental to planning and optimization in 5G millimeter-wave cellular networks. The current prediction methods, which are either too computationally expensive or inaccurate, are unsuitable for city-scale cell planning and optimization. This paper presents FadeNet, a convolutional neural-network enabled alternative for predicting large-scale fading with high computation speed and accuracy. By using carefully designed input features and neural-network architecture to capture topographical information, FadeNet accurately predicts the large-scale fading from a base station to each location in its coverage area. Evaluations on realistic data, derived from millimeter-wave cells across multiple cities, suggest that FadeNet can achieve a prediction accuracy of 5.6 decibels in root mean square error. In addition, by leveraging the parallel processing capabilities of a graphics processing unit, FadeNet can reduce the prediction time by 40X - 1000X in comparison to industry prevalent methods like ray-tracing. Generalizations of FadeNet, that can handle variable topographies and base station heights, and its use for optimal cell site selection are also explored.

This paper reveals the fundamental reason for the asymmetrical issue in magnetic-geared machines (MGMs) based on flux modulation theory. The analysis indicates that the magnetic circuits of three-phase windings in MGMs are inherently asymmetrical. This asymmetrical issue is even more severe in conventional consequent-pole MGMs (CP-MGMs) because of the more distorted magnetic field. Hence, to address the asymmetrical issue, a novel structure featuring enhanced harmonic elimination capability is proposed in this paper. Consequently, torque density can also be improved. In particular, the proposed CP-MGM employs modular structure and axially embedded permanent magnets (PMs) to achieve symmetrical back electromotive force (EMF) waveforms and improved torque capability, respectively. To further improve the electromagnetic performance of the proposed CP-MGM, the PM arc ratio and flux modulator width ratio are analytically designed, which provides a general design guideline for CP-MGMs. To illustrate the merits of the proposed CP-MGM, a few other MGMs are included for a fair comparison based on finite element analysis (FEA). Results show the proposed CP-MGM can achieve more symmetrical back EMF waveforms and lower torque ripple, as well as lower PM consumption and higher torque density, as compared with its MGM counterparts.

Power line carrier communication (PLC) is one of the important technologies to construct and realize ubiquitous power Internet of Things, while the degree of network matching severely affects the communication quality of the network. Combined with the actual topology of a medium voltage (MV) distribution network, the transmission line theory is used to derive the impedance matching relationship between the two slave nodes in the master-slave network mode. Based on this, a matching optimization method for the carrier communication of MV distribution Internet of Things based on network looseness is proposed. Firstly, a large complex distribution communication network is selected and divided into several small impedance-matching networks according to the network looseness on the trunk lines. Then, in each region, the particle swarm algorithm is applied for matching coordination to avoid power wasting and impedance mismatching among multiple communication nodes. The coordinated matching method has fast optimization speed and strong portability, and its feasibility and effectiveness are verified by theoretical derivation, simulation and laboratory test results, which provides an important theoretical basis for the planning, design and popularization of PLC technology in the ubiquitous power Internet of Things.

Automatic extraction of buildings from high-resolution remote sensing images becomes an important research. Since the convolutional neural network can perform pixel-level segmentation, this technology has been applied in this field. But the increase in resolution prone to blurry segmentation because the model needs more edge detail and multi-scale detail learning. To solve this problem, a method is proposed in this paper, which consists of three parts: (1) an improved model named Holistically-Nested Attention U-Net (HA U-Net) is designed, which integrates the attention mechanism and multi-scale nested modules to supervise prediction; (2) During model training, an improved weighted loss function is proposed to make the designed model more focused on learning boundary features; (3) watershed algorithm is exploited for image post-processing to optimize segmentation results. The designed HA U-Net performs well on WHU Building Dataset and Urban3d Challenge dataset, and achieves 9.31%, 2.17% better F1-score and 10.78%, 1.77% better IOU than the standard U-Net respectively. The experimental results indicate that the proposed method can well solve the building adhesion problem. The research can serve as updating geographic databases.

According to a previously built situation-awareness (SA) model based on attention allocation, with the ACT-R (Adaptive Control of Thought, Rational) theory for analyzing pilot cognitive processes for situation elements, an SA mathematical model was improved to predict pilot SA during exposure to different display interfaces and missions. An indicator-display monitoring task was performed under different experimental conditions for SA model verification, while the SA global assessment technique (SAGAT), performance measures, 10-dimensional SA rating technique (10-D SART), and eye movement measures were adopted to comprehensively assess the operator’s SA. The experimental results revealed that theoretical prediction values calculated using the improved SA model were strongly correlated with the operation performance, and thus confirming the model validation. The SAGAT was shown to be a more effective method than SART in this research, and the overall SAGAT accuracy rate, as well as the accuracy response time, are effective indices for SA measurement. Eye-movement indices, such as the fixation/saccade ratio, which corresponds to the mode of information perception and extraction, was examined to be sensitive to operator’s SA changes. The Advances of the improved SA model have been achieved in predicting and indicating SA by using human behaviors, including operation performance, SAGAT response behaviors, and visual behaviors. Thereby, it provides a new auxiliary tool for quantitative characterization of pilot’s SA during cockpit interface design optimization and ergonomic evaluation.

RF signal recognition is an important element toward RF situational awareness and dynamic spectrum management. In this work, machine learning-based signal recognition algorithms are proposed. Our key contribution is to engineer feature learning such that the classifiers can perform robustly even when a mixture of heterogeneous signal classes is observed, although the training is still done using non-mixture single-label samples. To achieve this, discriminative dictionary learning algorithms are developed with various feature-shaping constraints. The signal detection can then be done in a way reminiscent of the multi-user detection in wireless communication, employing linear equalizers. The algorithms are tested using real wideband RF measurement data. It is verified that the proposed algorithms can robustly classify the component signals even when their powers are widely different and their number is not known a priori.

This article investigates a finite-time attitude control with chattering suppression for quadrotors based on a high-order extended state observer (HESO). To improve the anti-disturbance capacity of quadrotors, a HESO capable of estimating fast time-varying disturbances is presented to enhance system robustness by regarding higher-order disturbance derivatives as extended arguments. Then, with the estimates of HESO, a finite-time attitude control policy including a double hyperbolical reaching law (DHRL) is introduced, alleviating control chattering and guaranteeing rapid response. The most significant feature is a finite time regulation for quadrotors without incurring severe oscillations can be obtained in the event of fast time-varying uncertainties. Moreover, the stability analysis is proved by a Lyapunov theorem. Finally, the effectiveness of proposed control scheme is demonstrated by numerical and experimental results.

All-solid-state 228.5 nm Deep Ultraviolet (DUV) laser has been studied with two-step second harmonious generation (SHG) of Nd:YVO4 914 nm fundamental laser, by optimizing its resonator design and SHG configurations. Adopting three-mirror folded V cavity and acousto-optical Q-switching, high peak power 457 nm laser output has been achieved by intracavity frequency-doubling of LD pumped Nd:YVO4 914 nm fundamental laser. At pump power of 41 W, the average output power for 457 nm laser has reached 600 mW at repetition frequency 10 kHz, 50 ns pulse width. With Type-I phase matching BBO crystal, external frequency doubling of 457 nm blue output was realized and optimized. Under LD pump power of 41 W, DUV laser at 228.5 nm with average output power of 35 mW has been achieved, at repetition frequency 10 kHz and pulse width of 46 ns. Under these conditions, the frequency doubling conversion efficiency is 5.8%, and the DUV laser output power instability is less than 2% in 2-hour test.

Language-modeling-based methods for story plot generation aim to generate a plot with a language model (LM). LM methods have limitations of user-assist plot generation of goal control, refinement for editing, causing the generated plots not clear sense for specific goal, lack coherence, and edit flexible. We present a control-and-edit transformer technique which uses controlled imitation learning of editing distance from dynamic programming to support deleting policy, inserting policy, a weighting-reward with prepossess of corpus statistic, and measures continues reward for the controlled goal. Automated evaluation and Haman judgement show our method is promising in comparison with the baselines.

That alleviating the heavy computing task, improving spectral efficiency and prolonging battery lifetime have been the key design challenges in Internet of Things (IoT) and intelligent connected vehicles (ICV). This paper studies the optimization of communication, computation and energy resource to minimize the energy consumption in the mobile terminal, where some superior technologies are included, such as Full-Duplex (FD), Simultaneous Wireless Information and Power Transfer (SWIPT), Mobile-Edge Computing (MEC) and Multi-input Multi-output (MIMO). In this model, the MEC-assisted Base station (BS) works in FD mode, then it can transmit and receive signals in the same frequency and time. Moreover, the mobile devices offload some computation tasks to the BS and complete local computations at the same time. Besides, the mobile device harvests the energy from the BS to support its energy consumption. And, our target is to minimize the energy consumption of mobile devices. Since the problem is non-convex, we propose an iterative solving algorithm including a multi-step optimization. First, we obtain the closed-form solution of the CPU frequency. And then, we transform the remain problem into a convex one to solve it by the interior point algorithm. Finally, we obtain the approximate solution by multiple iterations. Simulation results show that the proposed algorithm is superior to the compared schemes.

Reliability in the electrical drives is becoming an important issue in many applications. In this context, the reliability associated to the switched reluctance machine (SRM) is also an important area of research. One of the major problems, that strongly affect its operation, are drive power semiconductors faults. Typical power converter topologies used in SRM drives cannot handle faults in their power semiconductors. So, this paper presents a power converter topology that provides fault-tolerant capabilities to the drive under a switch fault. This power converter will be used considering that a change in the direction of the current that flows in the SRM windings does not affect the behavior of the machine. Besides that, the proposed power converter will allow to generate multilevel phase voltages in order to apply different voltage levels as function of the SRM speed. A laboratory power converter was developed to test the SRM drive in normal and faulty conditions. From the obtained results it was possible to verify the fault-tolerant capability of the drive under switch faults in different devices and failure modes. It was also possible to confirm the multilevel operation of the drive.

In recent years, the hazy weather in China occurs frequently, and image dehazing has gradually become a research hotspot. To improve the dehazing effect of the hazy images, this paper has proposed a multilevel image dehazing algorithm using conditional generative adversarial networks (CGAN). The hazy image is used to generate the composed image K jointly estimated by a transmission map and atmospheric light value through a generator network, and a dehazed image is calculated through an improved atmospheric scattering model. The generator network and the joint discriminator network are subjected to adversarial training and reconstruction constraints. The experimental results show that the proposed method achieved good dehazing effect in synthetic hazy images and real hazy images, and is ahead of other advanced dehazing methods in subjective evaluation and objective evaluation.

The ability to extract image features largely determines the accuracy of image classification. However, external interferences in images such as translation, rotation, scaling, occlusion, light, and non-linear deformation, result in greater intra-class differences and inter-class similarity, which substantially increases the difficulty of image classification. Benefitting from the excellent ability of feature learning and extraction, Convolutional Neural Networks (CNNs) have achieved good results in the field of image classification. However, they are also characterized by a complex training process and high-demand parameter adjustment. This paper propose a convolutional neural network optimization method to optimize the model parameters with hierarchical attribute constraints and achieve intelligent recognition of specific image features. Based on the optimized model, we further construct weak supervision and label correlation optimization models and provide a visualization solution for feature recognition results. Experimental results demonstrated that the proposed algorithm can efficiently realize intelligent image feature recognition with high accuracy.

Calibration is a technique used to obtain accurate probability estimation for classification problems in real applications. Class imbalance can create considerable challenges in obtaining accurate probabilities for calibration methods. However, previous research has paid little attention to this issue. In this paper, we present an experimental investigation of some prevailing calibration methods in different imbalance scenarios. Several performance metrics are considered to evaluate different aspects of calibration performance. The experimental results show that the performance of different calibration techniques depends on the metrics and the degree of the imbalance ratio. Isotonic Regression has better overall performance on imbalanced datasets than parametric and other complex non-parametric methods. However, it performs unstably in highly imbalanced scenarios. This study provides some insights into calibration methods on imbalanced datasets, and it can be a reference for the future development of calibration methods in class imbalance scenarios.

This study addresses the causal identification of air pollutants from surrounding cities affecting Beijing's air quality. A novel compressive sensing causality analysis (CS-Causality) method, which combines Granger causality analysis (GCA) and maximum correntropy criterion (MCC), is presented for efficient identification of the air pollutant causality between Beijing and surrounding cities. Firstly, taking the spatiotemporal correlation into consideration, the original data is mapped into low-dimensional space. Valid information is then obtained based on compressive sensing (CS), which can greatly reduce the dimensions of the data, thus decreasing the amount of data analysis required. Secondly, to analyze the causal relations, GCA, represented by the prediction from one time series to another, is extended to rule out “Non-Granger” causes of air pollutants in Beijing originating from its surrounding cities. Thirdly, the greatest impact on Beijing's air quality is confirmed based on MCC. Finally, the accuracy of these results is verified using the transfer entropy.

Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice that a hybrid fitting model with multiple types of function may have a better fitting result. Moreover, this also shows better interpretability. However, a perfect smooth hybrid fitting model depends on a reasonable combination of multiple functions and a set of effective parameters. That is a high-dimensional multi-objective optimization problem. This paper proposes a novel data fitting model construction approach. In this approach, the model is expressed by an improved tree coding expression and constructed through an evolution search process driven by the genetic programming. In order to verify the validity of generated hybrid fitting model, 6 prediction problems are chosen for experiment studies. The experimental results show that the proposed method is superior to 7 typical methods in terms of the prediction accuracy and interpretability.

In recent years, researches are concentrating on the effectiveness of Transfer Learning (TL) and Ensemble Learning (EL) techniques in cervical histopathology image analysis. However, there have been very few investigations that have described the stages of differentiation of cervical histopathological images. Therefore, in this article, we propose an Ensembled Transfer Learning (ETL) framework to classify well, moderate and poorly differentiated cervical histopathological images. First of all, we have developed Inception-V3, Xception, VGG-16, and Resnet-50 based TL structures. Then, to enhance the classification performance, a weighted voting based EL strategy is introduced. After that, to evaluate the proposed algorithm, a dataset consisting of 307 images, stained by three immunohistochemistry methods (AQP, HIF, and VEGF) is considered. In the experiment, we obtain the highest overall accuracy of 97.03% and 98.61% on AQP staining images and poor differentiation of VEGF staining images, individually. Finally, an additional experiment for classifying the benign cells from the malignant ones is carried out on the Herlev dataset and obtains an overall accuracy of 98.37%.

The audible noise of AC filter capacitors is one of the major noise sources in High Voltage Direct Current (HVDC) converter stations. In order to avoid the sharp noise of AC filter capacitors, it is necessary to design a Multiple Harmonic Current Injection System (MHCIS) for power capacitors to analyze their audible noise characteristics. Simulating effectively the noise of AC filter capacitors under their actual working conditions in converter stations is the primary target of this proposed MHCIS. To ensure the accuracy of the harmonic currents injected into the power capacitors, each harmonic current is detected and controlled separately. On the basis of the instantaneous reactive power theory, a selective harmonic current detection algorithm is proposed in this paper for the single-phase MHCIS. The amplitude of the selected harmonic current can be tracked timely and exactly in d-q synchronous rotational coordinate system, such that a Proportional Integral (PI) controller can be directly operated to eliminate the steady-state errors. The experimental parameters are usually invariable in this system, the phase deviation of the selected harmonic current is also fixed. Thus the phase angle offset of the corresponding harmonic current can be detected and calculated to compensate the system inherent delay. Owing to the accurate detection and control of multiple harmonic currents, the actual working conditions of AC filter capacitors can be accurately simulated, and the reliable noise analysis results of power capacitors can be obtained. Finally, the proposed selective harmonic current detection and control algorithm is verified by the theoretical analysis, simulation and experimental results.

With the rapid popularity of Internet shopping, session-based personalized recommendations have become an important means to help people discover their potentially interesting items in real-time. Most existing works only model a session as a sequence and use recurrent neural networks for recommendation. Despite their effectiveness, the results may not be sufficient to capture the potential relationships between items. In this work, we integrate a graph convolutional layer based on Auto-Regressive Moving Average (ARMA) filters into the Graph Neural Network (GNN). In particular, it is a new session-based recommendation framework Graph Convolution ARMA Filter (AUTOMATE), which can capture complex transformations between items through a sequence of sessions modeled as graph-structured data. Each session is then represented as the composition of the current interest and the global preference of that session using an attention network. The rationality and efficacy of the proposed AUTOMATE model are extensively evaluated on two public real-world datasets. Experimental results show that our model is significantly better than other state-of-the-art methods.

Aiming the problems that the clinical data of different patients is difficult for reasonable representation and the time interval between medical events is different, which lead to the difficulty of clinical prediction, a clinical prediction model based on the long short-term memory (LSTM) network optimized by fruit fly optimization algorithm in health time series data is proposed. First, FastText method is used to represent the interpretable vector of medical events, which can extract the concept relationship rich in medical information more effectively. Then, considering the strong dependence of clinical data on time stamp, LSTM network is used to model clinical events for better extraction of long-term and short-term information, so as to improve the prediction performance of the model. Finally, the fruit fly optimization algorithm is used to find the optimal super parameters of LSTM network, which can improve the training efficiency and prediction precision of the network. Experimental results on MIMIC datasets show that the prediction precision, Recall@k and MAP@k of the proposed model are better than those of other models. The validity of the model is proved.

Lung cancer is one of the leading causes of death over the world. Detecting and identifying malignant nodules on chest computed tomography (CT) plays an important role in the diagnosis and treatment of lung cancer. Computer-aided diagnosis (CAD) systems have been developed to identify lung nodules. However, the problem of a high false positive rate is still not well solved. In this paper, we propose a novel multi-task convolutional neural network (MT-CNN) framework to identify malignant nodules from benign nodules on chest CT scans. MT-CNN learns three-dimensional (3-D) lung nodule characteristics from nine two-dimensional (2-D) views, which are decomposed from different angles of each nodule. Each of 2-D MT-CNN model consists of two branches, one is the nodule classification branch (main task) and the other is the image reconstruction branch (auxiliary task). The motivation of the auxiliary task is to preserve more microscopic information in the hierarchical structure of CNN, which is beneficial to malignant nodule identification. The final classification result is obtained by integrating nine 2-D models. We test our method on the benchmark LUNA-16 and LIDC-IDRI datasets and compare it with state-of-the-art models. MT-CNN achieves the lowest false positive rate (3.2%) and highest AUC (97.3%) in LUNA-16 dataset and achieves an AUC of 95.59% in LIDC-IDRI. These results demonstrate the advantage of our method.

Existing self-supervised monocular depth estimation methods usually explore increasingly large networks to achieve accurate estimation results. However, larger networks are more difficult to train and require more storage space. To balance the network size and the computational accuracy, we propose in this article a compact lightweight network for monocular depth estimation, named LW-Net. First, we construct a compact network by designing an iterative decoder with shared weights and a lightweight pyramid encoder. The proposed network includes significantly fewer parameters than most of the existing monocular depth estimation networks. Second, we exploit a self-supervised training strategy by combining the proposed LW-Net model with a pose network, and we then use a hybrid loss function to train the decoder and encoder separately. The proposed training strategy results in the LW-Net model achieving a better performance in terms of estimation accuracy than other methods. Finally, we respectively run the proposed LW-Net model on the KITTI and Make3D datasets to conduct a comprehensive comparison with several state-of-the-art methods. The experimental results demonstrate that our method performs the best in terms of computational accuracy while utilizing the fewest parameters. Specifically, the model parameters of our method are reduced by 46.6%, the time cost is decreased by 7.69%, and the frame rate is increased by 5.19% compared with the existing state-of-the-art method.

To overcome the fast-changing block withholding attacks among multiple mining pools composed of miners in the blockchain system, this paper proposes a mining pool computing power allocation (MPPA) algorithm, which significantly improves the revenues of mining pools with block withholding attacks. MPPA first establishes the revenue optimization model of mining pools, which includes current adequate total computing power, the revenues of honest mining, and the revenues of block withholding attacks. Then MPPA calculates the revenue gain generated by block withholding attacks on other mining pools. To adjust the fixed computing power in each iteration, we have the mining pool computing power allocation algorithm with a fixed change of computing power (MPPA_F). To adjust the optimal recovery and attack computing power, we have the mining pool computing power allocation algorithm with an optimal change of computing power (MPPA_O). The simulation results demonstrate that MPPA_F and MPPA_O can find the optimized solutions of power computing allocation for each mining pool and outperform the state-of-arts such as WSFS, ALLC, and ALLD.

Sparse Multinomial Logistic Regression (SMLR) is widely used in the field of image classification, multi-class object recognition, and so on, because it has the function of embedding feature selection during classification. However, it cannot meet the time and memory requirements for processing large-scale data. We have reinvestigated the classification accuracy and running efficiency of the algorithm for solving SMLR problems using the Alternating Direction Method of Multipliers (ADMM), which is called fast SMLR (FSMLR) algorithm in this paper. By reformulating the optimization problem of FSMLR, we transform the serial convex optimization problem to the distributed convex optimization problem, i.e., global consensus problem and sharing problem. Based on the distributed optimization problem, we propose two distribute parallel SMLR algorithms, sample partitioning-based distributed SMLR (SP-SMLR), and feature partitioning-based distributed SMLR (FP-SMLR), for a large-scale sample and large-scale feature datasets in big data scenario, respectively. The experimental results show that the FSMLR algorithm has higher accuracy than the original SMLR algorithm. The big data experiments show that our distributed parallel SMLR algorithms can scale for massive samples and large-scale features, with high precision. In a word, our proposed serial and distribute SMLR algorithms outperform the state-of-the-art algorithms.

Retina images acquired by an adaptive optics confocal scanning laser ophthalmoscope (AOSLO) usually need to remove image warp to improve image quality. The most significant task of AOSLO image dewarping is image registration. Most traditional feature-based registration algorithms used for AOSLO images are based on Gaussian scale space. However, the homogeneous Gaussian blurring reduces the localization accuracy of feature points and the distinctiveness of feature descriptors. In this paper, the accelerated KAZE (AKAZE) feature based on nonlinear scale space was utilized to register AOSLO retinal images for the first time, and an efficient strategy based on matched feature points for frame selection was proposed to automatically accomplish AOSLO retinal image dewarping. Moreover, a flexible method based on power spectra analysis is proposed to study the minimum number of frames needed to accomplish image dewarping. The extensive experiments demonstrated that the AKAZE method is more suitable for AOSLO image dewarping, benefitted with better accuracy, robustness, and rapidity compared with several traditional registration methods based on Gaussian scale space.

Cooperative vehicle infrastructure system (CVIS) is an essential part of the future intelligent transportation system (ITS). However, CVIS is uneasy to meet different QoS requirements of various information flows in the existing traffic environment. In fact, some real-time applications highly related to traffic safety early warning cannot be easily achieved or passengers' Internet accessing experience is always not very good. To overcome the above problem, this paper proposes a communication protocol named data networking for vehicle infrastructure cooperation (NDNVIC), which can respond to the vehicle's data query timely. In this paper, we utilize some ZigBee nodes as vehicle and RSU nodes and conduct several simulation experiments under a large-scale deployment with 150 RSU nodes to verify the effectiveness of NDNVIC. Compared with the traditional TCP/IP protocol, the response rate of NDNVIC is about four times, as fast as, that of TCP/IP.

The pebble shape is a critical factor that affects the bearing characteristics of truck escape ramps. In this paper, a reconstruction method was proposed for randomly shaped pebbles used in truck escape ramps, and statistical analysis was conducted on the results of pebble compression tests. First, 100 pebble samples were randomly selected, and the pebble outlines were fitted in three views. The overlapping method was implemented to fill the three views with ball elements, and the ball elements were then extended into three-dimensions. The results demonstrated that the proposed method satisfactorily monitored the real pebble shapes. Second, statistical analyses were conducted on the parameters of the outlines, and corresponding data sets were built. The key parameters of the pebble DEM model, such as the friction coefficient, were calibrated based on the compression test results, and repeated simulation results verified the feasibility of the proposed pebble DEM model. Next, statistical analysis was conducted on the probability distribution of the contact forces. Third, simulations were conducted with different pressing plate velocities and pebble shapes. The results indicated that the pressing plate velocities had minor effects on the contact forces. As the pressing plate velocity increased, the slope of the contact forces correspondingly increased. The particle shape was a critical factor affecting the simulation results. With the same contact parameters, the simulation results of different pebble shapes varied greatly, which further indicated the importance of the proposed pebble shape reconstruction method. The proposed pebble DEM model could be further used in the design of truck escape ramps.

A dynamically tunable coaxial attenuator based on graphene is presented in this paper. The attenuator is achieved by inserting a graphene sandwich structure (GSS) inside the dielectric of coaxial line along the propagation direction of electromagnetic waves to dissipate the electromagnetic field. The mechanism of attenuator is theoretically analyzed, and an approximate formula to calculate attenuation with different parameters is proposed. We experimentally demonstrate that attenuation of the attenuator can be tuned from 3 dB to 14.5 dB with good flatness in broad operation frequency from 2 GHz to 9 GHz when graphene impedance is changed by different bias voltage. The theoretical calculation and simulation results have a good agreement with experimental results, while the $\vert S_{11}\vert $ can always remain low level under -15 dB.

In this paper, a novel multilayer hidden conditional random fields (MHCRFs)-based cervical histopathology image classification (CHIC) model is proposed to classify well, moderate and poorly differentiation stages of cervical cancer using a weakly supervised learning strategy. First, the color, texture, and deep learning features are extracted to represent the histopathological image patches. Then, based on the extracted features, artificial neural network, support vector machine, and random forest classifiers are designed to calculate the patch-level classification probabilities. Third, effective classifiers are selected to generate unary and binary potentials. At last, using the generated potentials, the final image-level classification results are predicted by our MHCRF model, and an overall accuracy around 77.32% is obtained on six practical cervical histopathological image datasets with more than 600 immunohistochemical (IHC) stained samples. Among the six test accuracies, the highest reaches 88%. Furthermore, we also test our MHCRF method with a gastric hematoxylin-eosin (HE) stained histopathological image dataset including 200 images for an extended experiment, and achieve an accuracy of 93%.

The coding units (CU) partitioning in the 3D extension of the high efficiency video coding standard (3D-HEVC) is recursively conducted on different block sizes from 64 × 64 to 8 × 8. Besides, the depth coding in 3D-HEVC introduces several new coding tools for each CU to improve the coding efficiency, however, with great computational complexity. It is noted that only a small number of the CUs in recursive partitioning are encoded into the final bitstream. Among these CUs, the CUs with Intra 2N × 2N or Intra N x N as optimal modes have a very small proportion. In this paper, we thus propose an early determination of depth intra coding, where the coding stage of Intra 2N × 2N, Intra N × N or CU Splitting for the CUs could be early skipped based on several Decision Trees. Simulation results show that, with restrict the results from the tree leaves by different Gini thresholds, the proposed algorithm could save 41.14%-71.63% of the depth coding time with only a slight increase in BDBR.

Detecting high-order single-nucleotide polymorphism (SNP) interactions is of great importance for the discovery of pathogenic causes of human complex diseases. However, a considerable computing challenge exists in analyzing each SNP combination at a genome-wide scale. Swarm intelligence search (SIS) is an effective and efficient method for solving NP-hard problems and has been extensively researched for detecting high-order SNP interactions. In this review, we first analyze the strengths and limitations of existing methods such as exhaustive search using cluster computing and parallel computing, stochastic search and high-performance computing. Then, SIS algorithms for the detection of high-order SNP interactions are introduced in detail. The algorithms discussed are the genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), particle swarm optimization (PSO), differential evolution (DE), cuckoo search (CS), fish swarm (FS) and artificial bee colony (ABC). Finally, we discuss the characteristics and limitations of the involved methods and provide several suggestions for improving SIS algorithms to detect high-order SNP interactions.

Due to the advantages of high torque density, high efficiency, and wide constant power speed range (CPSR), interior permanent magnet synchronous motors (IPMSMs) are gaining more and more attention in electric vehicle (EV) applications. There are many geometrical parameters in IPMSMs, and some of them have significant impact on the performance of such machines. This paper presents a parametric sensitivity study of the rotor geometrical parameters for a V-shaped magnet IPMSM. In which, both the individual sensitivity and the combined sensitivity with considering the interaction effects of these parameters are performed by finite-element analysis (FEA). The electromagnetic characteristics in the low-speed range, including average torque, torque ripple, cogging torque, power density, power factor, efficiency, etc. and the flux-weakening capability in the high-speed range are investigated in detail. Based on the parametric study, the V-shaped magnet IPMSM is optimized. The results show that the performance of the optimal design is significantly improved. In addition, the validity of the FEA simulation results is verified by experiment with a prototype.