Offshore wind turbine (WT) noise can affect marine organisms and seabirds, while the aerodynamic noise of WT blades serves as an indicator of turbine operating status. Investigating WT noise characteristics under varying wind conditions is thus crucial for monitoring operational states, such as wake interference. This paper presents an intelligent acoustic sensor system for WT health detection based on the IEEE 1451.9 standard. The system enables data transmission, processing, and display of real turbine blade sound, comparing results with blade self-noise calculated by the BPM model and also validating the model's accuracy. Furthermore, with the help of the aero-elastic coupling software FSAT. Farm developed by the National Renewable Energy Laboratory (NREL), the study also examines the effects of wind speed (8 m/s , 10 m/s, and 12 m/s) and turbulence (6%, 10%) on turbine aerodynamic noise. The results demonstrate a strong correlation between the simulation and measured data. Higher wind speeds increase aerodynamic noise emissions and amplify the amplitude modulation (AM) characteristics of the noise. At a wind speed of 8 m/s, increased turbulence intensity enhances the AM characteristics, whereas, at 10 m/s and 12 m/s, it diminishes these characteristics.
China's metro system ranks among the top in the world in terms of both its total operational mileage and the scale of planned construction. As the metro system enters a phase where construction and operation are given equal priority, there is an increased demand for improved management capabilities in metro operation and maintenance. This paper investigates the business requirements for lifecycle management of metro rails, exploring key technologies such as coding standards for individual rails, attribute data standards, the implementation of electronic tags, and service life evaluation models. A lifecycle management information system for metro rails has been designed and developed, which supports rail degradation prediction and optimizes maintenance and replacement decisions. This paper focuses on the system's technical architecture and core functionalities. The system has been successfully implemented on the actual lines of Tianjin Metro, effectively meeting operational requirements and yielding positive application outcomes.
This paper proposes a high-frequency (HF) square-wave voltage injection method to identify the parameters for three-phase permanent-magnet synchronous motor (PMSM) drives fed by cascaded H-bridge (CHB) inverters. The key is to identify the $dq-\text{axis}$ inductances independently. In the proposed method, the one module of each phase in the CHB inverter is specifically configured to inject HF square-wave voltages, while the remaining modules are maintained for reference voltage modulation. Furthermore, effective compensation measures are implemented to address the harmonic issues arising from the injection of HF square-wave voltages, the amplitude attenuation caused by filters, and the impact of the inherent nonlinear characteristics of CHB inverters on parameter identification. Simulation results are given to validate effectiveness of the proposed parameter identification method.
With the growing demand for electricity, live-line working for distribution network has gained increasing attention worldwide to ensure uninterrupted power supply. Live-line working robots have significantly enhanced the efficiency of maintance and ensured the safety of operators, leading to their growing application. In this paper, we propose a dual-arm collaborative live-line working robot system for the distribution network, with design considerations for both hardware and software. On the hardware level, the robot is primarily composed of two UR5e robot arms and is equipped with various visual sensors to get information about work environment. We use the Touch haptic device of 3D Systems to teleoperate the UR5e robot arm. On the software level, we propose a master-slave heterogeneous teleoperation method, where the position and orientation of UR5e end-effector are respectively controlled through incremental mapping and one-to-one mapping. This system not only enhances operators' safety but also improves the sense of immersion and operational accuracy, demonstrating significant practical value and broad application prospects.
This paper presents a new negative imaginary(NI) synthesis method for a linear-time-invariant(LTI) system with up to two poles at the origin. A dynamic parallel feedforward compensator(DPFC) is added to the controlled plant, which ensures that the augmented system exhibits generalized negative imaginary(GNI) property. And a set of linear matrix inequalities(LMIs) conditions are derived, leading to easy application of this scheme. The augmented system could be stabilized by any SNI controllers only if satisfying the DC loop gain condition. As an application of these results, an example of stabilizing a flexible satellite is carried out to verify the effectiveness of the proposed method.
The rapid development of information technologies such as the internet and big data has greatly facilitated people's work and life, but it has also led to security issues such as data breaches, online fraud, and system hijacking. Currently, in most research on deep learning-based clustering algorithms, issues such as the feature diversity of high-dimensional data, the representativeness of clustering features, and the precision of anomaly detection still need optimization. To address these shortcomings, this paper designs a Hybrid Clustering Deep Autoencoder (HCDAE) algorithm, which uses two nested deep autoencoders to learn normal data, extract the key features that are representative of the data, and then select the optimal cluster centers in the latent representation space through an adaptive cluster center filtering mechanism. This further enhances the significance and representativeness of the latent feature representations. Finally, this paper uses benchmark datasets for comparative experiments to evaluate the performance of HCDAE. The experimental results show that HCDAE generally performs better than other models and can effectively detect network attack anomalies, providing a new research approach for the field of network attack anomaly detection.
Pulse frequency modulation (PFM) is the traditional method to drive CLLC resonant converters. However, PFM suffers the problems of unsatisfactory voltage regulation and low efficiency under light-load conditions under light-load conditions. Phase-shift modulation techniques, such as inner phase shift (IPS) modulation, can overcome this challenge. However, more improvements are demanded due to high circulating currents and difficulties in achieving zerovoltage switching (ZVS) in phase-shift modulations. Therefore, this article proposes an optimization for extended phase-shift (EPS) control to improve the light-load efficiency. In order to study the relations between phase-shifts of EPS control and converter efficiency, a detailed circuit model is established to solve time-domain expressions of circuit variables, the voltage gain and root mean square (RMS) values of resonant currents. Finally, based on these analyses, a proper selection of phase-shift values of EPS is determined to achieve higher efficiency of the CLLC converter at light-load conditions. The validity of the proposed optimized EPS control is verified on a 200V/30.,60 V, 300 W Si-based CLLC resonant converter prototype.
To efficiently plan the coverage path of UAVs during the visual inspection of building facades, the task is divided into two parts: viewpoint planning and path planning. First, a K-medoid clustering method is proposed to generate the initial set of candidate viewpoints based on the point cloud model of the building facade. A greedy optimization algorithm is then employed to select the optimal subset of viewpoints, addressing the viewpoint planning problem. Second, an incomplete graph is constructed, and the objective function is defined for path planning, which is solved using the Gaussian Zenith-Ant Colony Optimization (GZ-ACO) algorithm. The path length and turning angles are considered optimization objectives. Simulation results demonstrate that the proposed viewpoint planning method reduces the number of viewpoints by 66.67% and 98.67%, respectively, compared to the iterative random sampling method and the displacement method. Furthermore, the GZ-ACO algorithm outperforms the traditional ant colony algorithm, achieving a 19.97% reduction in the objective function value and a 21.76% decrease in the average steering angle. The validity and feasibility of the proposed method are thus verified.
In this paper, an improved ant colony algorithm is proposed and applied to the UAV trajectory planning. Based on three-dimensional rasterization, the algorithm constructs a comprehensive trajectory cost function by taking into account the threat cost, energy consumption cost and flight altitude cost. Meanwhile, the state transition strategy and pheromone update strategy of the ant colony algorithm are optimized. This enhances the global search ability and improves the iterative efficiency. Simulation results show that, compared with the original ant colony algorithm, the algorithm proposed in this paper has better performance in aspects such as flight distance, flight altitude and iterative efficiency.
Phase jumps, discontinuities, and slow unfolding speed are common drawbacks of traditional spatial phase unfolding methods. To overcome these problems, this paper proposes an absolute phase unfolding method based on dual-frequency stripe and lookup tables. First, the dual-frequency stripe method is presented, which integrates the high-frequency stripe and the unit-frequency stripe into one stripe pattern, necessitating just over five stripe patterns to obtain high-quality phase data. Secondly, a fast phase unfolding algorithm with lookup tables is used to precalculate modulation intensity and phase principal values and store the respective results. This algorithm circumvents the extremely time-consuming actangent function, reduces the number of quadratic operations, and achieves fast data processing. The experimental results show that the proposed method is 31.60 times faster than the traditional three-frequency four-step phase-shifting method, while also having the number of stripes required for processing. Our findings hold significant implications for the pursuit of rapid 3D reconstruction.
As the high-speed rail network grows, unexpected disruptions affecting train operations have become increasingly common. Consequently, efficiently adjusting train schedules to minimize delays has become a critical challenge in high-speed rail timetable rescheduling (TTR). To address this issue, this paper proposes a Multi-Agent Game Deep Reinforcement Learning (MAGDRL) approach to tackle the TTR problem during a complete blockage. Initially, the paper defines the MAGDRL state and action spaces, incorporating spatiotemporal distribution information, and develops an effective reward feedback system. Next, the Nash-Q learning algorithm ensures convergence to the Nash equilibrium strategy. Additionally, an approximate Nash equilibrium solving method is applied to enhance the computational efficiency of determining the Nash equilibrium strategy. Finally, simulation results based on the Beijing-Shanghai high-speed railway demonstrate that the proposed method effectively handles timetable rescheduling across multiple disruption scenarios involving complete blockages of segments.
The fuel cell propulsion system is one of the potential development directions for future green aviation. Due to the slow dynamic response of fuel cells, they need to be used together with lithium-ion batteries to form a hybrid power system. The implementation of most functions of lithium-ion batteries requires the State of Charge (SOC) as the basis, and accurate estimation of SOC is of great significance for the stable operation of the battery. Taking into account the requirements for modeling accuracy and complexity, this paper presents an improved second-order RC Equivalent Circuit Model (ECM) that incorporates hysteresis effects. The model parameters were identified using data obtained from Hybrid Pulse Power Characteristic (HPPC) tests. Recognizing the limitations of the Extended Kalman Filter (EKF) algorithm in terms of its interference rejection capabilities, this paper introduces a noise correction matrix via the windowing method to develop an Adaptive Extended Kalman Filter (AEKF). A simulation model was constructed to validate the SOC estimation performance of the proposed algorithm under various operating conditions. The simulation results demonstrate that the AEKF exhibits superior convergence and interference rejection capabilities, with SOC estimation error of less than 1%, meeting the required precision standards.
This paper evaluates the possibility of implementing a production line that incorporates the strategies of Industry 4.0 into its operation system, which includes the use of the Internet of Things (IoT), artificial intelligence (AI), big data, cloud computing, and robotics to develop a smart factory. These factories have a high level of connectivity and integration of systems, allowing an effective sharing of information in real-time. Topics include the function of smart transducers in real-time management and the improvement of process interoperability through the application of the IEEE 1451 standard. This is achieved through the integration of robotics with AI to enable the adaptive and precise control of production variables, thus improving the efficiency and quality of products. The paper also discusses the issues and scenarios of using these technologies where standardization plays a major role.
In eddy current non-destructive testing (EC NDT), the electrical conductivity and magnetic permeability of the sample are two fundamental factors influencing the eddy current signal. The coupling effect between the electrical conductivity and magnetic permeability of metallic materials not only reduces the accuracy of the eddy current signal but also affects the sensitivity of magnetic permeability measurements. In recent years, the conductivity invariant phenomenon (CIP) has been demonstrated to effectively eliminate the influence of electrical conductivity in magnetic permeability detection. Therefore, this study employs the finite element method (FEM) to systematically analyze the effects of magnetic permeability variations and probe parameters, including excitation signal frequency, coil turns, inner diameter, and outer diameter, on the CIP. We find that magnetic permeability variations affect the reference value of CIP but have a negligible impact on the depth of the conductivity invariant point. Additionally, optimizing probe parameters such as excitation frequency and coil turns can significantly enhance CIP performance in practical applications. The results reveal the mechanism of magnetic permeability changes that influence CIP and provide a theoretical foundation for optimizing probe design in future CIP applications. This study aims to enhance the stability and applicability of CIP, providing theoretical support for its further promotion and widespread application in engineering practices.
In sectors such as more electric aircraft and electric vehicles, the requirement for DC power is escalating, leading to heightened interest in DC-DC converters. LLC resonant converters, noted for their high efficiency and power density, are extensively employed within these fields. To satisfy the demands for high power output, multiple LLC converters are frequently connected in parallel. However, parameter mismatches between modules in a parallel system inevitably result in current imbalance issues. This paper proposes two current-sharing control strategies for parallel systems to mitigate this issue. During the soft-start phase, current compensation is incorporated into the traditional phase-shift soft-start technique to facilitate current sharing. In the steady-state operating phase, current sharing is achieved via adaptive droop control. The proposed current-sharing control strategies diminish the risk of overload for individual converters and enhance power conversion efficiency. The effectiveness of these proposed control methods is confirmed through extensive simulation experiments.
Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models.
Due to the requirements by climate change, significant advancements have been made in the electric power system (EPS) of More Electric Aircraft (MEA), leading to the widespread use of High Voltage DC (HVDC) EPS. An effective protection measure for HVDC is the Solid-State Circuit Breaker (SSCB). To guarantees the power density of bi-directional SSCB, this paper proposes a bi-directional Γ-Z-source SSCB (ΓZSCB) with a high device reuse rate. A mathematical model is established by analyzing the steady state and fault transient. Furthermore, a Saber simulation is conducted to validate the topology's autonomous shutdown characteristics and load mutation capability. The effectiveness of the proposed topology is verified along with the modeling method.
Interleaved converter configurations are a common solution for the charging of electric vehicles. Carrier phase shift is a common and important design element for these converters. Recent research shows that a proper regulation of the phase shift can improve the system performance. This requires more complex controllers and actuation capabilities. Finite Control Set Model Predictive Control (FCS-MPC) has the capability to manage multiple control. Recent research has managed to achieve a satisfactory switching frequency control through a period control approach (PCA) for a single power converter. However, it does not control the switching phase required for an interleaved operation with parallel converters. This paper presents the design and implementation of a switching phase control for PCA-FCS-MPC, allowing the operation of an interleaved configuration. The performance is evaluated for different operating point through experimental validation.
The three-phase system is a crucial trend in high-power wireless charging. However, the evaluation of interoperability in wireless power transmission (WPT) technology is currently confined to the single-phase stage. To address this issue, this paper proposes a method for interoperability evaluation based on three-port synthesized impedance. First, the correlation between the three-port impedance and interoperability is analyzed based on the various configuration of the three-phase WPT systems. Next, the evaluation factors and standard areas are defined for interoperability evaluation. Finally, a simulation model of the wireless charging system is employed to validate the accuracy of the proposed method. This study aims to enhance the interoperability evaluation framework for WPT systems and promote the technological advancement of fast wireless charging.
Accurate navigation and localization are essential for autonomous vehicles in complex environments. Visual place recognition (VPR) provides an efficient and cost-effective method for environmental representation. Our study introduces MDPR-Net, an autonomous vehicle positioning network utilizing 360-degree images. The dynamic interference removal module (DIR) eliminates dynamic targets filtering, ensuring precise environmental perception. Following DIR, a multi-view image encoder module (MIE) encodes the filtered panoramic images with shared weights, capturing comprehensive features. The image-relation attention module (IRA) then associates these features across multi-view images, enhancing the model's ability to understand the scene contextually. This approach is demonstrated on the nuScenes dataset, yielding promising results.
