Recent publications
In complex navigational environments, effective unmanned surface vehicle (USV) scheduling is critical. However, the obstacle avoidance problems are often ignored in the literature. This study addresses the multi-objective heterogeneous USV scheduling problems with obstacle avoidance. The objective is to minimize the maximum completion time and total carbon emissions. First, a mathematical model is developed to describe the concerned problems. Second, an A* algorithm is employed to obtain a path between task points with avoiding obstacles. Third, to obtain a high-quality scheduling scheme, an improved artificial bee colony (ABC) algorithm with deep Q-network (DQN) is designed. According to the problem nature, six novel rules are designed for finding high-quality solutions in initialized population. Five local search strategies are designed based on the structure of solution space. The scales of the instances and different objectives are utilized to designed a DQN, which recommends suitable local search strategies during iterations for improving convergence speed. Then, the Gurobi solver is employed to verify the proposed model. The effectiveness of the proposed strategies is verified by 13 instances with different scales. Finally, the experimental results and discussions show that the designed ABC with DQN has the strongest competitiveness among all compared algorithms.
Distributed manufacturing and fine-manufacturing are two typical scenarios of modern manufacturing industries in the context of globalization and customization. The distributed differentiation flowshop integrated scheduling problem (DDFISP) is a novel model that deals with the integrated scheduling problem of these two manufacturing scenarios. In the DDFISP, jobs have multiple customized types and are manufactured in a number of distributed factories. Each factory includes three fine-processing stages: parallel machine fabrication, single machine assembly, and dedicated machine differentiation. In the paper, a new distributed memetic evolutionary architecture is first built, which consists of four modules with distinct functions, including global exploration, local exploitation, knowledge transfer, and search restart. The exploration and exploitation are coevolved in the distributed way and communicated by knowledge transfer. This architecture can be used as a universal model to construct evolutionary algorithms. Following this architecture and devising each module innovatively, a novel knowledge transfer-driven distributed memetic algorithm (KTDMA) is constructed to solve the DDFISP. Specifically, global exploration is performed on multiple populations by dynamically selecting global exploration optimizers from predefined external repository. Local exploitation is executed on an independent elite archive by a destruction-construction local search and a key block local search. Knowledge transfer is conducted to communicate the superior information between exploration and exploitation based on a point-ring topology. Search restart is adaptively carried out to alleviate the search homogeneity. Computational results show the effectiveness of the proposed evolutionary architecture and special designs, and demonstrate that the KTDMA performs more competitive than the compared state-of-the-art algorithms.
We report an 850 nm surface-illuminated InGaAs modified uni-travelling-carrier photodiode (MUTC-PD) with distributed Bragg reflector (DBR) bottom mirror. By adopting InGaAs as the absorber, which exhibits a higher absorption coefficient than GaAs, the absorption layer thickness can be reduced to 960 nm, so as to minimize the transit time of the photogenerated carriers. The fabricated MUTC-PD demonstrates a 3-dB bandwidth of 27.5 GHz and a responsivity of 0.51 A/W under a bias of -2 V at the wavelength of 850 nm.
Due to the accelerating effect of chloride and sulfate on the hydration of clinker, seawater-mixed cement (SC) paste is more prone to brittle cracking. Herein, this research systematically investigates the early crack resistance of seawater-mixed sintered sludge cement (SSSC) paste based on splitting tensile test and restrained squared eccentric ring test. The microstructure characteristics characterized by mercury intrusion porosimetry and scanning electron microscopy are combined to reveal the enhancement mechanism of sintered sludge ash (SSA) on SC paste, and a life cycle assessment is conducted around its carbon footprint. The results indicate that the SSA incorporation causes a continuous increase of 24.4% in the splitting tensile strength of SSSC paste. Meanwhile, the digital image correlation technology accurately captures the strain and displacement fields composed of crack propagation, which tends to be symmetrically distributed and reduces the crack width by 30%. During the inhomogeneous restrained shrinkage process, as the SSA increases, the crack deviation of the SSSC paste first magnifies and then reduces, the crack width decreases, and the cracking time extends. The pozzolanic activity of SSA is more active in SC paste, which significantly promotes the secondary accumulation of C-S-H and the increase of gel pore volume. This effectively reduces the risk of brittle cracking caused by uneven distribution of paste stiffness due to hydration acceleration during seawater mixing. In addition, replacing 50% cement with SSA only results in a total of 4.1% carbon emissions in SSSC paste and a 47.8% reduction in global warming potential from sludge transportation to activation treatment, demonstrating significant environmental advantages.
Absolute measurement has consistently been the primary focus in the development of precision linear and angular displacement measurements. The scheme design of binary zero position codes is an important factor for absolute measurement. Designing and optimizing high-bit zero position codes with over 100 bits face considerable challenges. Simultaneously, the working parameters of zero position codes [unit code width (b), distance (d), and yaw angle (α)] remarkably affect their post-installation performance, particularly in absolute positioning and limit code application in multi-degree-of-freedom measurement schemes. This study addresses these challenges by proposing a design method for zero position codes that considers diffraction based on generative adversarial networks and aims to explore a design with increased efficiency and accuracy as well as optimization for high-bit zero position codes. Additionally, the tolerance range of zero positioning performance for each working parameter is examined. By leveraging the adversarial network structure, this study generates the optimization of a 150-bit code and processes the tests of the zero position code by using simulation results. The following working parameter ranges for code design are recommended on the basis of theoretical and experimental results: b greater than 10 μm, d and α within 1000 μm and 3490 μrad, and avoidance of intervals with sharp changes in the full width at half maximum. The proposed code design and parameter optimization lay a solid foundation for research and engineering applications in absolute measurement field and have considerable potential for generalization and wide applicability.
The emergence of adversarial examples poses a significant challenge to hyperspectral image (HSI) classification, as they can attack deep neural network-based models. Recent adversarial defense research tends to establish global connections of spatial pixels to resist adversarial attacks. However, it cannot yield satisfactory results when only spatial pixel information is used. Starting from the premise that the spectral band is equally important for HSI classification, this paper explores the impact of spectral information on model robustness. We aim to discover potential relationships between different spectral bands and establish global connections to resist adversarial attacks. We design a spectral transformer based on the transformer structure to model long-distance dependency relationships among spectral bands. Additionally, we use a self-attention mechanism in the spatial domain to develop global relationships among spatial pixels. Based on the above framework, we further explore the influence of both spectral and spatial domains on the robustness of the model against adversarial attacks. Specifically, a weighted fusion of spectral transformer and spatial self-attention (WFSS) is designed to achieve the multi-scale fusion of spectral and spatial connections, which further improves the model’s robustness. Comprehensive experiments on three benchmarks show that the WFSS framework has superior defensive capabilities compared to state-of-the-art HSI classification methods.
Orthogonal Time Frequency Space (OTFS) modulation has exhibited significant potential to further promote the performance of future wireless communication networks especially in high-mobility scenarios. In practical OTFS systems, the subcarrier-dependent Doppler shift which is referred to as the Doppler Squint Effect (DSE) plays an important role due to the assistance of time-frequency modulation. Unfortunately, most existing works on OTFS channel estimation ignore DSE, which leads to severe performance degradation. In this letter, OTFS systems taking DSE into consideration are investigated. Inspired by the input-output analysis with DSE and the embedded pilot pattern, the sparse Bayesian learning based parameter estimation scheme is adopted to recover the delay-Doppler channel. Simulation results verify the excellent performance of the proposed off-grid estimation approach considering DSE.
In recent years, online condition monitoring methods have gained popularity for their ability to predict latent insulation failure in electrical machines. Most of the existing methods are based on leakage current measurement. While neutral-voltage-based methods were firstly introduced for transformer monitoring, recent research has explored their application to electrical machines. However, the process of selecting the most suitable method for specific condition monitoring requirements remains ambiguous. This aricle offers a comprehensive comparison of the advantages and disadvantages of these methods, grounded in a theoretical analysis of the stator windingmodel. Sensitivities to various insulation aging problems are also studied, considering signal-to-noise ratios (SNRs) to determine the most suitable features for monitoring different aging issues. Additionally, a novel phase-to-ground insulation aging monitoring method is introduced, which accounts for the aging position along the winding. This method is based on the common-mode impedance spectrum and offers the benefit of reducing sensor bandwidth requirements when compared to existing methods. This research contributes to the refinement of condition monitoring for electrical machines, enabling more precise and efficient early detection of insulation aging problems.
This article focuses the denitrification processes control of selective catalytic reduction, which faces great challenges caused by high-order dynamics, strong nonlinearity, wide-range load variations, and multisource disturbances. A modified active disturbance rejection control based on gain scheduling (MADRC-gs) is proposed. A parameter-switching methodology with the selected scheduling parameter is provided and the visual analyses of performance guarantees are analyzed. MADRC-gs is applied to a practical denitrification process of an in-service 660MW power plant. Actual operational data illustrate that MADRC-gs can reduce hourly average integral absolute error by about 22.61% and 43.18% in high-load and low-load ranges, respectively. Even though the power plant experiences lifting and lowering powers frequently, MADRCgs can still obtain better control performance compared to the original controller and MADRC, and show a promising application potential in energy and chemical industries.
Modular multiactive-bridge (MMAB) converter is increasingly recognized as a potential solution for efficient power conversion in the integration of distributed energy resources, storage systems, and loads. However, optimizing MMAB converters for high-efficiency operation remains a challenge due to the inherent coupling across ports. To address this problem, this article proposes an inductance-current-minimization optimization scheme based on a hardware decoupling method. The decoupling across ports is achieved by eliminating the inductance at one of the ports, which simplifies the complexity of the system significantly. Based on the decoupled MMAB converter, an optimization scheme is proposed to minimize the root mean square value of the inductor current. Compared with the conventional modulation scheme, this scheme can reduce the inductor current and expand the soft-switching region across various power ranges and voltage ratios, thereby enhancing the operation efficiency. Furthermore, the proposed scheme is based on analytical solutions, enabling online implementation without the need for lookup tables and scalable to accommodate any number of ports. The effectiveness of the proposed scheme has been verified by a series of simulations and experiments based on a four-port MMAB converter prototype.
The vibroacoustic level has become a key factor for evaluating permanent magnet motor performance in recent years. However, the influence of tangential force is usually ignored, which will lead to errors in vibration noise calculation and limit the idea of reducing motor vibration noise. In this article, a comprehensive investigation of the tangential - and radial forces and their effects on the motor vibration is conducted. First, the analytical method of the electromagnetic force is presented. Next, the finite element method is applied to analyze the electromagnetic laws of radial force and tangential force. Then, the Theorem of Translation of A Force (TTF) is introduced to describe the influence of tangential force on radial vibration. After that, the influencing factors of stator teeth and load on the radial- and tangential-force are discussed, especially the phase relationship. Finally, the electromagnetic vibration law caused by radial force and tangential force is simulated and verified by experiments. The results can provide guidance for design of low vibroacoustic motor considering radial force and tangential force.
In the industrial assembly of flexible printed circuits (FPCs), the FPC connectors and receivers are required to connect successfully with the least possible buckling times. However, precise and autonomous positioning of the FPC connectors is still a great challenge, as they are very small and visually blocked during assembly. This article proposes a strategy for the industrial assembly of FPC by: 1) unlike the traditional vision based methods, the task of FPC position identification in this work is considered as a haptic signals alignment problem. We mount a haptic sensor at the top end of the robotic arm manipulator and record various mismatched haptic signals around the ideal FPC connector position; 2) converting both the correct and incorrect haptic signals into images; and 3) we introduce a novel deep Siamese-network (DSN) structure to align the incorrect haptic images to the ground truth. The experiments are carried out on a practical FPC assembly platform for the Redmi Note 11 mobile phone. It is found that the haptic images combined with DSN can significantly improve the FPC location accuracy. When the buckle times are limited to 10, 5, and 1, the proposed method outperforms the state-of-the-art methods in a realistic FPC assembly platform.
FPGAs are drawing increasing attention in resolving molecular dynamics (MD) problems, and have already been applied in problems such as two-body potentials, force fields composed of these potentials, etc. Competitive performance is obtained compared with traditional counterparts such as CPUs and GPUs. However, as far as we know, FPGA solutions for more complex and real-world MD problems, such as multi-body potentials, are seldom to be seen. This work explores the prospects of state-of-the-art FPGAs in accelerating multi-body potential. An FPGA-based accelerator with customized parallel dataflow that features multi-body potential computation, motion update, and internode communication is designed. Major contributions include: (1) parallelization applied at different levels of the accelerator; (2) an optimized dataflow mixing atom-level pipeline and cell-level pipeline to achieve high throughput; (3) a mixed-precision method using different precision at different stages of simulations; and (4) a communication-efficient method for internode communication. Experiments show that, our single-node accelerator is over 2.7× faster than an 8-core CPU design, performing 20.501 ns/day on a 55,296-atom system for the
Tersoff
simulation. Regarding power efficiency, our accelerator is 28.9× higher than I7-11700 and 4.8× higher than RTX 3090 when running the same test case.
This paper introduces a novel inverter structure tailored for direct-drive marine propulsion applications, integrating SiC-MOS and Si-IGBT devices in a hybrid bridge arm configuration. The scheme aims to enhance torque control performance by raising the switching frequency of the SiC-MOS and to minimize losses in the propulsion system by lowering the switching frequency of the Si-IGBT. Methods for torque ripple suppression and efficiency enhancement are proposed in this paper. The latter includes introducing the Optimal Switching Frequency Combination (OSFC) and Optimized Load Distribution (OLD) methods. The OSFC method aims to determine the optimal combination of switching frequencies for Si-IGBT devices relative to the predetermined frequency of SiC-MOS devices, thereby maximizing system efficiency at the rated torque operating point. On the other hand, the OLD method adjusts the load distribution between the motor windings of SiC-MOS and Si-IGBT devices to enhance the propulsion system's efficiency, particularly under light load conditions. Moreover, experimental validation of the proposed methods is provided. Experimental results show that the proposed control strategy can reduce total drive system losses by 30% compared to an all-Si-IGBT inverter and save 35% in costs compared to an all-SiC-MOS inverter while maintaining torque control performance.
In SiC MOSFET's short-circuit fault, large short-circuit current spikes during the delay time of short-circuit protection will increase its risk of damage and accelerate device degradation. This paper proposed an ultrafast and low-invasive short-circuit current limiting method by gate voltage control for SiC MOSFETs with Kelvin-source. By introducing a negative feedback composed of a low-invasive analog circuit into the gate driver, the proposed current limiting method can effectively limit SiC MOSFET's short-circuit current. It offers the advantages including ultrafast response, almost no additional power losses, excellent universality, and easy devices selection. The basic physical principles of the current limiting method are explained and the detailed circuit design is given. Furthermore, considerations in circuit design are discussed, including the analysis of the laws governing the negative feedback system to aid in device selection. A prototype of a gate driver for SiC MOSFET with DESAT protection and the proposed short-circuit current limiting circuit is constructed. Double pulse tests and short-circuit tests are conducted using two types of SiC MOSFETs with varying rated voltage and current ratings. The results indicate that the proposed current limiting method can reduce the peak current by 52.2% and the short-circuit energy by 57.1% with almost no additional conduction loss and less than 1% increase in switching loss. Additionally, during the short-circuit tests, the proposed current limiting method shows insensitivity to varying drain-source voltage and current rising rates, highlighting its exceptional universality.
In order to improve the reliability of current source converters, a new fault diagnosis method that can identify the single-switch and double-switch short-circuit faults is proposed in this paper. It only needs output current signal. Firstly, the amplitude and phase of the output current vector is calculated in a real-time manner. Secondly, the amplitude is compared with a diagnosis threshold. Combined with the phase information, the switch that has a short-circuit fault can be detected and located. The method is simple to achieve, which can be implemented in the existing control program directly. No additional sensors or other auxiliary equipment are needed. Compared with the existing fault diagnosis methods, the detection speed is faster and the implementation is simpler. Experimental results are presented to verify the effectiveness of the proposed solution.
Two-dimensional transition metal carbides, nitrides, or carbonitrides (MXenes) have garnered remarkable attention in various energy and environmental applications due to their high electrical conductivity, good thermal properties, large surface area,...
Fast, accurate, and data-light (small dataset) core loss modelling is critical to the design of high-density high-efficiency power electronics systems. However, conventional core loss modelling methods often demonstrate a significant lack of accuracy, design efficiency and/or cost-effectiveness especially under complex operating conditions. To tackle this problem, this paper proposes a core loss modelling method, Magnetization Mechanism-Inspired Neural Network (MMINN). MMINN is a hybrid data-driven and physics-driven model, which is designed to capture the fundamental magnetization process of magnetic materials at the micro-level. Therefore, MMINN can not only achieve high accuracy and computational efficiency as many other fully data-driven models do, but also enjoy the simplicity (fewer parameters to tune) and generality of a purely physic-driven model. The model training and testing are based on the open-source magnetic core loss database – MagNet, which includes dynamic hysteresis responses under various excitations at high frequencies. An abstract performance comparative analysis among a transformer-based neural network model, a LSTM-FNN model and the proposed model is discussed in the context of the core loss estimation. MMINN shows significant advantages in model accuracy and memory requirements, especially with small datasets, making it well suited for power magnetics design.
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Beijing, China
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Yong Qiu