bluuebunny/crossref_metadata_2025_split huggingface dataset:
Twisted hyperbolic van der Waals crystals for chip-scale full Stokes mid-infrared polarization detection
Authors: Sahoo Nihar Ranjan , Prasath S S Jatin , Kumar Brijesh , Kumar Anshuman 
Published: September 2024

Abstract
Investigating the polarization properties of light in the mid-infrared (mid-IR) spectrum is crucial for molecular sensing, biomedical diagnostics, and IR imaging system technologies. Traditional methods, limited by bulky size and complicated fabrication process, utilize large rotating optics for full Stokes polarization detection, impeding miniaturization and accuracy. Naturally occurring hyperbolic van der Waals (vdW) material based devices can address these challenges due to their lithography-free fabrication, ease of integration with chip-scale platforms and room-temperature operation. This study designs a chip-integrated polarimeter by performing multi-objective optimization for efficient exploration of the design parameter space. The spatial division measurement scheme used incorporates six precisely designed linear and circular polarization filters, achieving high extinction ratios exceeding 30 dB and transmittance surpassing 50%, with fabrication tolerance of film thickness up to 100 nm. The proposed device represents a significant advancement in polarimetric detection, providing a compact, cost-effective solution and opens new avenues for on-chip mid-IR polarimetric detection in next-generation ultra-compact optical systems.
Rolling bearing fault diagnosis based on efficient time channel attention optimized deep multi-scale convolutional neural networks
Authors: Li Ou , Zhu Jing , Chen Minghui 
Published: September 2024

Abstract
In rolling bearing fault diagnosis, the collected vibration signal has nonlinear and non-Gaussian characteristics, which makes the signal feature extraction incomplete during the feature extraction process, leading to reduced fault diagnosis accuracy. This article proposes a model based on efficient time channel attention deep multi-scale convolutional neural network (EMCNN) to solve the above problems. This method designs a multi-scale hierarchical expansion strategy in the multi-scale CNN (MSCNN), which can effectively extract different ranges of information from the signal. In addition, the efficient time channel attention module is designed and embedded into the MSCNN to enhance the attention to the important features in both channel and time dimensions, and also to avoid the problem of feature redundancy. Adamax optimization algorithm is used as the optimizer, which realizes the automatic adjustment and optimization of the learning rate and greatly improves the model training efficiency and performance performance. The effectiveness of the method was verified using the datasets from Case Western Reserve University and Xi’an Jiaotong University. By comparing with other diagnostic models, it was verified that the method had a high diagnostic rate and good generalization performance under nonlinear and non-Gaussian complex characteristics.
Semi-supervised sparse feature optimization transfer learning for fault diagnosis under cross-condition and imbalanced data
Authors: Zhou Ziyou 
Published: September 2024

Abstract
Fault diagnosis in intelligent manufacturing faces challenges from cross-condition variations and data imbalances, especially with rare faults. Existing methods typically address these issues separately, yet both often coexist in industrial settings. To tackle these dual challenges, this study proposes a semi-supervised sparse feature optimization transfer learning diagnostic method (SSFOD). This method introduces two strategies: (1) improved enhanced sparse filtering to optimize feature sparsity and improve detection sensitivity for minority class faults, and (2) adaptive resampling maximum mean discrepancy to dynamically adjust data distributions, enhancing model adaptability and generalizability. Experimental results show that SSFOD achieves an average accuracy of 99.3%, significantly outperforming existing methods. This approach effectively addresses the combined challenges of cross-condition and imbalanced data fault diagnosis, advancing the field in complex industrial applications.
Realization and optimization of the gold-mesoporous silica based bimodal surface plasmon resonance sensor with digital Gaussian filter
Authors: Xu Xiaoxue , Xie Pengpeng , Ren Jiao , Wang Guiqiang 
Published: September 2024

Abstract
This work presented a novel biomdal surface plasmon resonance (BSPR) sensor with mesoporous silica film (MSF) and digital Gaussian filter. A modified Stöber solution growth approach was applied to prepare MSF film on the gold film. The conventional SPR sensor was coupled with a digital Gaussian filter to realize the BSPR sensor. After that, the Gaussian parameters were modified and the BSPR angular spectrum was obtained for further sensing experiments. Porosity and thickness of the MSF film were measured and calculated by means of a combination of simulation and measurement. The simulation results indicate that the refractive index (RI) sensitivity of the BSPR sensor could reach 75.11 deg/RIU which is 66.91% higher than the 45 deg/RIU of the conventional gold-MSF SPR sensor. Based on the experiments, the RI resolution of BSPR sensor was improved by 37.1% to 6.61 × 10−6 RIU, the limit of detection (LOD) for glucose was raised from 320 mg l−1 to 131 mg l−1, and the LOD for CTAB molecule was raised from 124.98 nM to 63.78 nM when compared to the gold-MSF SPR sensor.
An improved morphological filtering and feature enhancement method for rolling bearing fault diagnosis
Authors: Ren Xueping , Guo Liangjian , Liu Tongtong , Zhang Chao , Pang Zhen 
Published: September 2024

Abstract
The defects-induced periodic pulse is one of the important indices for the characterization of bearing failure. To solve the problem that the weak impact features caused by the early fault of the rolling bearing are easily to be interfered with by noise and strong background signal and are difficult to extract, an improved morphological filtering method combined with the Teager energy operator (TEO) is proposed to extract weak shock features. Firstly, according to the correlation between the periodic pulse induced by defects and the Morlet wavelet, the Morlet wavelet is used as the model to construct the structural elements. Then, capturing the Pearson correlation coefficient of the structural elements and the original signal and the signal is filtered by the variable scale morphological filter after threshold screening. Finally, the TEO is used as the post-enhancement link to suppress the noise in the signal after morphological processing and further highlight the fault characteristics. Simulation signals, experimental signals, and field signals verify the effectiveness and robustness of the proposed method.
Breakdown modes of capacitively coupled plasma: II. Non-self-sustained discharges
Authors: Wu Hao , An Ran , Jiang Can , Zhong Dong , Jiang Wei , Zhang Ya 
Published: September 2024

Abstract
This paper constitutes the second part of a two-part series investigating the breakdown modes of capacitively coupled plasma across varying pressures, employing an implicit particle-in-cell/Monte Carlo collision model. This segment focuses on non-self-sustained modes, namely normal failure discharge (NFD), bias failure discharge (BFD), and runaway failure discharge (RFD). NFD results from a failed electron avalanche, BFD stems from the charging effect of the blocking capacitor, and RFD arises from a decrease in electron emission rate during sheath formation. The effects of background pressure and voltage on these failure discharges are examined and analyzed. The RFD, which leads to periodic electron avalanches, is discussed in detail. Studying these non-self-sustained cases facilitates understanding the reasons for failure discharge in extremely low-pressure environments and determining the parameter limits of self-sustained discharge, crucial for preventing plasma cracks, enhancing equipment product yield, and ensuring equipment safety, thereby mitigating industrial losses.
Decoherence of high-dimensional orbital angular momentum entanglement in anisotropic turbulence
Authors: Yan Xiang , Zhang Peng-Fei , Fan Cheng-Yu , Zhao Heng , Zhang Jing-Hui , Wang Bo-Yun , Wang Jun-Yan 
Published: September 2024

Abstract
The decoherence of high-dimensional orbital angular momentum (OAM) entanglement in the weak scintillation regime has been investigated. In this study, we simulate atmospheric turbulence by utilizing a multiple-phase screen imprinted with anisotropic non-Kolmogorov turbulence. The entanglement negativity and fidelity are introduced to quantify the entanglement of a high-dimensional OAM state. The numerical evaluation results indicate that entanglement negativity and fidelity last longer for a high-dimensional OAM state when the azimuthal mode has a lower value. Additionally, the evolution of higher-dimensional OAM entanglement is significantly influenced by OAM beam parameters and turbulence parameters. Compared to isotropic atmospheric turbulence, anisotropic turbulence has a lesser influence on high-dimensional OAM entanglement.
Innovative research on hybrid absorption refrigeration system with complementary solar energy and waste heat
Authors: Qu Zhuangzhuang 
Published: September 2024

Abstract
With the increasingly prominent global energy crisis and environmental problems, the search for efficient and environmentally friendly energy utilization has become a hot spot of current research. The large-scale application of solar energy and waste heat in absorption refrigeration technology is one of the important technical means to achieve the goal of “double carbon”, while the traditional absorption refrigeration system has disadvantages such as discontinuity and instability. To improve the stability of the absorption refrigeration system and extend the operation time of the absorption refrigeration unit, this paper explores the application of the hybrid absorption recycling refrigeration system to the energy system with complementary solar energy and waste heat. In the future, the system design will be further optimized, the performance and economy of the system will be improved, and the wide application of the technology in industrial production and daily life will be promoted.
Research characteristics of aquifer energy storage system based on sandbox experiment
Authors: Huang Lihan , Li Wenxin , Chen Zhenqian 
Published: September 2024

Abstract
Based on the principle of thermal similarity, a complete sandbox experimental platform is established, and a corresponding three-dimensional unsteady-state heat transfer model is constructed. The study investigates the influence of boundary size on the energy storage characteristics of aquifer experiments. The wall boundary of the existing experimental platform has an impact on the experimental results. There exists a time in the experiment with the greatest discrepancy from the actual model, which is approximately 35 % of the cycle time. A new well-pipe structure with a unilateral water intake is proposed. The aquifer energy storage system that employs this structure exhibits smaller variations in extraction temperature during operation, resulting in an increase of energy storage capacity by 13.9% and an improvement of energy storage efficiency by 19.6%.
Sun tracking control technology of solar furnace
Authors: Shi Congcong , Zang Chuncheng , Sun Feihu , Wang Zhifeng 
Published: September 2024

Abstract
In this study, to improve the sun-tracking accuracy of the solar furnace, a sun-tracking control system is developed, and the control strategy of combining open-loop and photoelectric semi-closed-loop control is adopted for the system. A solar furnace prototype is built to verify the performance of the control system. Through sun tracking experiments, the tracking accuracy of the solar furnace is analyzed under open-loop and photoelectric semi-closed-loop control. The experimental results show that the tracking error of open-loop control is less than 12.2 mrad. The photoelectric semi-closed-loop control can effectively improve the sun tracking accuracy of the solar furnace with a tracking error of less than 3.5 mrad.
Power allocation strategy for electrolytic cell cluster system for high proportion photovoltaic consumption
Authors: Zhang Shuyan , Agu Damu , Jiang Cheng 
Published: September 2024

Abstract
Limited by the capacity of a single electrolytic cell, the large-scale hydrogen production system must be operated by a combination of multiple electrolytic cells to form an electrolytic cell cluster. Meanwhile, photovoltaic power generation has real-time changes of uncertainty. Allocating the operating power safely, efficiently, and reasonably to each electrolytic cell in the cluster is a crucial issue for large-scale hydrogen production systems. Therefore, this paper proposes a power allocation strategy for the electrolytic cell cluster system. Firstly, the operation characteristics of a single electrolytic cell and cluster are analyzed. Then, considering constraints such as power balance, electrolytic cell power limitations, and grid restrictions, a power allocation model for electrolytic cell clusters is established to maximize photovoltaic consumption. Based on this, a power coordination allocation strategy for the single electrolytic cell is provided. Finally, the effectiveness of the proposed strategy is verified through an example analysis.
Research on photovoltaic and energy storage systems in substation based on virtual DC machine control
Authors: Zou Bin , Liang Jianan , He Dehui 
Published: September 2024

Abstract
To address the issues of insufficient inertia and damping support caused by massive power electronic equipment devices connected to DC substations, and the large fluctuation or instability of the 220 V DC bus voltage caused by the variation of power on the load side, an enhanced inertial control strategy based on VDCM (EI-VDCM) for DC substation is proposed. Firstly, the structure of the PV and energy storage DC substation is described. Secondly, the correspondingly mathematical model of the EI-VDCM control is derived. Then, the auxiliary voltage regulation power link is introduced into the control system to suppress the fluctuation of DC bus voltage caused by power change. Finally, the simulation model is built to verify the control strategy, and the actual waveform results show that the EI-VDVM control strategy can improve the transient stability of the DC bus voltage.
Estimation of lithium battery state of charge using PSO-AEKF algorithm
Authors: Zhu Tianye , Shi Zhihan , Zhang Tianyang , Zhang Guangming 
Published: September 2024

Abstract
For the construction of a second-order model, it is necessary to accurately identify five key parameters: R
0, Rp
, Cp
, Rp
, and Rd
. Traditional offline parameter identification methods rely solely on fitting the curve during the quiescent period after discharge to determine these parameters. This paper employs the Particle Swarm Optimization (PSO) algorithm combined with a second-order RC discrete model to fit the operating curve, thereby enhancing the model’s accuracy. In subsequent estimations, an adaptive Kalman filter is introduced to compare these two sets of parameters.
Research on torque ripple suppression of in-wheel fractional slot permanent magnet synchronous motors based on auxiliary slots
Authors: Yang Zhimeng , Tian Shaopeng 
Published: September 2024

Abstract
To address the issue of significant torque ripple in in-wheel fractional slot motors, a method involving the introduction of stator auxiliary slots is employed to weaken cogging torque and reduce vibration and noise generated during motor operation. Through analysis of cogging torque, the impact of the size, spacing, and quantity of auxiliary slots on torque performance is investigated. When four appropriately parameterized auxiliary slots are introduced, the cogging torque decreases from 7.81 Nm to 2.38 Nm, a reduction of 69.53%. Underrated conditions, the output torque increases from 131.84 Nm to 137.32 Nm, and the ripple of torque decreases from 14.87% to 6.97%. Thus, the motor’s torque performance is significantly improved.
PAS-YOLO: improved YOLOv8 integrating parameter-free attention and channel shuffle for object detection of power grid equipment
Authors: Xu Meng , Li Baofeng , Su Junchi , Qin Yu , Li Qiangwei , Lu Jiansheng , Shi Zhiheng , Gao Xin 
Published: September 2024

Abstract
Using an object detection network to identify power grid equipment in low-voltage stations automatically can effectively improve the accuracy and efficiency of information inspection, which is of great significance to the safe and stable operation of a power system. The current advanced object detection model YOLOv8 has high accuracy and efficiency in detection tasks. However, it still suffers from leakage and misclassification in complex scenarios, and there are higher lightweighting requirements in the face of real-time detection processes. This paper proposes a lightweight power grid equipment object detection model, PAS-YOLO. It uses a parameter-free attention module to extract features through convolution. A ShuffleNet Block, which consists of multiple channel shuffle modules, is introduced to reduce the convolution kernel parameters and improve computational efficiency. Extensive experiments on several benchmark and power grid equipment datasets demonstrate that our method outperforms the original YOLOv8 on multiple object detection metrics.
Traversal-based automatic EMT modeling approach for new type of power systems with massive power electronics
Authors: Xu Yizheng , Liu Min , Qiao Xiaomin , Wang Feng , Wang Xiangxu , Mu Qing 
Published: September 2024

Abstract
The increasing penetration of renewable energy devices and HVDCs/VSC-HVDCs has induced expansion of the grid as well as a large number of power electronics, which could extend the modeling process, induce more mistakes during modeling, increase the computing tasks, and slow down the simulation process. To address the issues, this paper proposes a traversal-based automatic EMT modeling approach for large grids with high penetration of power electronic devices, where traversal process and depth-first search are used. The approach is written in a C++ program and implemented with models of both benchmark case studies and actual UHVDC projects. The simulation result shows that the proposed approach can guarantee the error of the model within 3%, improve the modeling efficiency by four times, and enhance the simulation efficiency by almost twice that in the manual modeling process.
Simulation of electric potential distribution and assessment of personal safety risks near grounding devices of urban high-voltage transmission line towers
Authors: Hu Yaorong , Hu Wei , Feng Yuwei , Wang Di , Zou Jiang 
Published: September 2024

Abstract
This study investigates the electric potential distribution near transmission line towers, focusing on the impact of contact voltage and step voltage on power system safety and personal safety. It establishes a model correlating contact voltage and step voltage with total human body current by analyzing key parameters of human electrocution accidents. Using finite element computational models, it accurately simulates the potential distribution of common tower grounding devices under fault currents, revealing distinct characteristics of potential distribution among different device configurations. The simulation reveals complexities in the potential distribution of square grounding devices with horizontal rays, proposing innovative suggestions to optimize grounding systems and reduce potential differences. Additionally, it analyzes current flowing through grounding devices under various fault conditions, highlighting potential risks of personal electrocution. Finally, the study proposes optimization schemes for electric potential distribution near transmission line towers’ grounding systems, offering crucial insights for power system safety and engineering design.
Simulation research on generator leading phase test and main transformer tap selection
Authors: Jiang Dawei , Yu Longbin , Niu Li , Liu Chang , Zhou Yuxiao , Guo Tie , Gao Yue 
Published: September 2024

Abstract
In a hydropower plant, the generator terminal voltage was found to be too low during a leading phase test, preventing the generator from achieving the expected leading phase capacity. An analysis of this issue led to a proposed solution involving adjustments to the main transformer tap changer. To validate this approach, a generator leading phase test simulation system was employed. This system simulated both the lagging power factor operation and leading phase test data for various tap settings of the main transformer. The simulated data were then compared with actual data from a second leading phase test conducted after adjusting the transformer taps. This case study also introduces the application method of the generator leading phase test simulation system and underscores its critical role in conducting effective leading phase tests.
Error prediction of VMD-GRU current transformer based on bayesian optimization
Authors: Chen Xu , Zhang Chao , Zhang Haomiao , Cheng Zhiqiang , Xu Yinzhe , Yan Yu , Ou Long , Su Yingchun 
Published: September 2024

Abstract
With the aim of accurately predicting the measurement error of the current transformer, a Bayesian optimization-based error prediction method combining variational mode decomposition (VMD) and gated recurrent unit (GRU) was proposed. Firstly, the secondary current signal of the CT is decomposed by VMD, and the arrangement entropy of each sequence signal is calculated, so as to determine the best decomposed mode number. Then, the decomposed modal components are utilized as inputs to the recognition model. Finally, a GRU model serves as the prediction model for each component, forecasting individual components, after which the predicted results of these components are reconstructed to yield the ultimate prediction outcome.
Improved stability analysis of delayed frequency regulation systems for a cyber-physical microgrid
Authors: Xu Haotian , Ba Lei 
Published: September 2024

Abstract
In a microgrid, the types of power supply and users are diverse. To use the frequency support potential of the multi-type of power supply units in the microgrid, these units can be connected to form a frequency control system with the help of an open network. However, a signal transmission delay exists in the network, which may affect the control performance and further lead to instability. Considering the effects of the delay type, this paper analyzed the micro-grid system stability during frequency support, and the allowable time delay range is estimated when the frequency control system can work normally. Firstly, based on the frequency support principle in a micro-grid, the mechanism equations of each part involved in the micro-grid frequency support are presented, and the time delay of the control loop is considered so that the state-space model of the frequency support control system is built. Secondly, based on Lyapunov stability theory, a time delay-related discriminant approach is proposed for the control system to maintain stability using increasing range function, integral inequality of auxiliary function, and matrix injection. Finally, the delay tolerance in different scenarios of a specific micro-grid system is estimated by the proposed approach. The result shows that the proposed approach is less conservative than the existing methods. Meanwhile, the relationship between controller gain and delay tolerance is given, which can be used as a reference in the controller design.