bluuebunny/crossref_metadata_2025_split huggingface dataset:
Photovoltaic partial occlusion maximum power point tracking based on improved sparrow search algorithm
Authors: Yang Hao , Jiang Zhihong , Wu Han , Li Han , Zhu Yangyang , Gao Jiaqi 
Published: December 2024

Abstract
In scenarios of partial shading, photovoltaic array output characteristics can exhibit multiple peaks, which can cause traditional maximum power point tracking (MPPT) algorithms to get stuck in local optima. An enhanced sparrow search algorithm for MPPT is introduced to enhance the efficiency of photovoltaic systems. This improved approach utilizes Tent chaotic mapping for population initialization and incorporates an adaptive weight factor to refine convergence precision. Simulation results demonstrate that the improved sparrow search algorithm, compared to the conventional version, offers superior convergence speed and stability when applied to multi-peak MPPT scenarios.
Design of a new wide frequency measurement method for power system
Authors: Jiang Bo , Li Kang , Sun Peng , Yan Xueting , Hu Yili 
Published: December 2024

Abstract
Aiming at the wide application and complex characteristics of the new power system, this paper proposes a design of broadband measurement method and realizes the application of broadband signal analysis and broadband measurement by improving the wavelet packet algorithm. As a multi-resolution analysis method widely used in the field of signal processing, the wavelet transform is unique in that it can perform local analysis in both time and frequency domains at the same time and has significant advantages in analyzing time-varying non-stationary signals. This paper will start from the algorithm requirements of electronic measurement devices based on the basic theory of wavelet transform, build a wide frequency measurement method, and comprehensively simulate and analyze the test signal. By combining the SVMD (Successive Variational Mode Decomposition) adaptive extraction algorithm with frequency band decomposition, the main sub-harmonic components in the frequency band are accurately extracted and measured, which verifies the feasibility and superiority of the algorithm.
Research on the conversion mechanism of tradable certificates with emission reduction value in the “electricity-carbon-green certificate” market
Authors: Qian Bin , Tang Jianlin , Lin Xiaoming , Zhang Fan , Jin Hui , Zhang Qian 
Published: December 2024

Abstract
The concepts of tradable certificates with emission reduction value such as Carbon Emission Trading (CET), China Certified Emission Reduction (CCER), and Green Electricity Certificate (GEC) are vaguely defined, and the policy and mechanism design for connecting various certificates are relatively scarce. This paper summarizes the characteristics of the main tradable certificates with emission reduction value in China and abroad and the experience of connection mutual recognition. Then, combined with the actual development of China’s “electric-carbon-green certificate” market, it analyzes the current problems such as repeated certification of emission reduction on GEC and Green Electricity Consumption Certificate (GECC), green electricity is not considered in carbon emissions accounting, and the definition between CCER and green certificate is not clear. Based on the existing connection cases, this paper proposes a mutual recognition system covering the connection between GECC, GEC, non-hydro Renewable Energy Consumption (REC), and CET, and the connection between GEC and CCER.
Fake face detection with densenet and texture descriptor
Authors: Wu Xiaosheng 
Published: December 2024

Abstract
Deep generative models, notably Generative Adversarial Networks (GANs), have the capability to generate highly authentic facial images that can mislead human visual perception. In contrast, deep discriminative networks, exemplified by Convolutional Neural Networks (CNNs), demonstrate superior performance in distinguishing between authentic and synthetic images. In the present study, we introduce an algorithm for the identification of synthetic faces, which integrates comprehensive global characteristics with a dense neural network framework. Our methodology entails the initial extraction of comprehensive texture features from facial images, subsequent to which classification is conducted employing the DenseNet architecture. The empirical findings corroborate the efficacy of our proposed approach, indicating its robust performance on the given dataset, as well as its enhanced capability for generalized detection of fabricated faces produced by novel GAN architectures not encountered previously.
Efficient inference of Vision Transformer with structural pruning and operator fusion on GPU
Authors: Liu Shijin 
Published: December 2024

Abstract
Currently, Vision Transformer (ViT) has been widely applied in computer vision tasks and has achieved remarkable results. However, its storage, memory, and computational requirements hinder its deployment on general and common GPUs. We propose a framework for accelerating the inference of ViT models, which takes into account both model compression and hardware-friendly operator fusion. This framework facilitates the efficient execution of the model on a single GPU while minimizing any substantial reduction in model accuracy. Our approach consists of two stages. The first stage entails structured pruning, whereby an automated approach is employed to determine the dependency relationships among layers within ViT models. By leveraging these relationships, group pruning is implemented on the interdependent parameters, resulting in a pruned model. In the second stage, hardware-specific acceleration algorithms, tailored to the unique characteristics of the pruned model, are introduced. These algorithms aim to further enhance the performance of the pruned ViT models, enabling them to achieve inference speedup on GPUs. We conducted experiments on various ViT models, and the results indicate that our designed acceleration framework achieves a significant improvement in inference speed, ranging from 1.05 times to 3.08 times, while maintaining accuracy loss within a tolerable range of 3%.
Test study on the impact of high-speed signal transmission based on VPX connectors
Authors: Liu Jiahua , Zhang Liangjuan , Cao Zijian , Zhao Zihao 
Published: December 2024

Abstract
In high-speed digital signal transmission, VPX connectors play a crucial role as an important link for blind plug interconnection, ensuring the quality of high signal transmission. For high-speed circuit signal transmission, in order to ensure the absolute equal length of high-speed signals and avoid crosstalk caused by high-speed signals, this paper proposes a testing method that establishes a high-speed signal transmission link by designing equal-length sector modules and backplane blind plug interconnection through VPX; By using a vector network analyzer to test relevant parameters, we will analyze the differential characteristic impedance of VPX connectors after engagement under different structural tolerances, and obtain the quality of high-speed signal transmission. This testing research method can effectively analyze the main influencing factors of high-speed signal transmission in VPX connectors, and the research results provide effective support for engineering applications.
A near-field SIMO millimeter-wave imaging system based on RMA
Authors: Yu Ziyang , Liao Kefei 
Published: December 2024

Abstract
In the imaging of human security checks, high-resolution millimeter wave imaging systems are required. The millimeter wave imaging system has a large amount of data and requires a long time. So, in this design, in terms of data acquisition, the system uses the SIMO method, using one transmitting antenna and four receiving antennas to maintain imaging resolution while reducing data acquisition time by four times. In this system, MATLAB sends instructions to the rail control board through the serial port to control the movement trajectory of the radar. MATLAB implements collaborative radar acquisition through Lua scripts. The system scans 50 lines of data and finally obtains a raw data size of 400x200x256. Then, the RMA algorithm is used to calculate the imaging scene image. The imaging scheme of this system is simple to implement and has good imaging effects. It has great potential in millimeter wave high-resolution imaging direction and millimeter wave security inspection.
FPGA implementation of curve fitting algorithm for image diffusion spectrum
Authors: Li Shijie , Han Peng 
Published: December 2024

Abstract
In recent years, the application of image diffusion spectrum algorithm for high-concentration particle systems such as suspensions, coatings, emulsions, gels, etc. in multiple fields such as pharmaceuticals, energy, chemical industry, and food has become more and more extensive. Although the relevant technology has made significant progress at the algorithm level, the real-time and portability issues of the system are still the key factors restricting its application potential. The traditional image diffusion wave system needs to transfer the data to the host computer for processing after the image processing is completed, which has problems such as clock delay and large system volume. To this end, we proposed a fitting algorithm based on the image diffusion spectrum. By directly processing the data obtained in image processing in FPGA, clock delay is avoided and the miniaturization of the instrument is also achieved. Through comparative experiments, we can see that our algorithm can achieve the same processing effect on the host computer.
Street tree segmentation method combining image semantic segmentation and point cloud clustering
Authors: Zhu Hesen , Song Shuang , Gao Yunlong , Shao Guifang , Zhu Qingyuan 
Published: December 2024

Abstract
As a common object in the park scene, the 3D information of street trees is crucial for digital cities, and extracting individual trees from point cloud data accurately and efficiently is a hotspot and a difficult point-in-point cloud processing. Aiming at the interference problem of complex non-tree objects and dynamic objects in the park environment, a tree point cloud segmentation method combining a deep learning network framework with a point cloud clustering algorithm is proposed in this paper. First, this paper applies the YOLOv8 to the semantic segmentation of camera images, and fuses image semantics with point cloud data, to build the semantic point cloud model. Then, preliminary extraction for street trees is performed using the semantic information of this point cloud model, and radius filtering is used to reduce the outliers. Finally, the individual tree segmentation is realized using an enhanced multi-level Euclidean clustering algorithm based on KD-Tree. Experimental results indicate that the proposed method performs well in extracting and segmenting the roadway trees, with a recall of 94.5%, a precision of 80.7%, and an F1-score of 87.0%.
Optimized Kalman filter techniques for ultrasonic flow measurement
Authors: Liu Weiqun , Wang Xiaohui , Peng Zhengjun , Guo Fengjuan 
Published: December 2024

Abstract
Traditional mechanical water meters have gradually failed to meet the needs of modern society due to low accuracy, high maintenance costs, poor durability, and lack of digital management capabilities. In order to improve the measurement accuracy and adaptability of the water meter, this study uses the ultrasonic water meter, based on the time difference method, and proposes an improved Kalman Filter Algorithm for the measurement of the ultrasonic water meter. In this paper, the measurement principle of an ultrasonic water meter is described in detail. The measurement accuracy under different flow rates and temperature conditions is verified by experiments. The experimental results show that the improved Kalman Filter Algorithm can effectively reduce the influence of noise. Even under conditions of low flow rate and large temperature change, the measurement error can be controlled within 1 %, which improves the measurement accuracy of the ultrasonic water meter.
Research on a non-sensing measurement method for the G/T values of transparent satellite transponders in orbit
Authors: Zhang Jingui 
Published: December 2024

Abstract
In the design of satellite communication systems based on transparent satellite transponders, satellite companies generally only provide a range of satellite G/T values. To ensure system availability, users use the minimum G/T value for link indicator allocation, resulting in low utilization efficiency of system equipment. The characteristics of transparent satellite transponders and the basic principles of direct sequence spread spectrum communication were studied and analyzed. A non-sensing in-orbit satellite G/T value measurement method based on the measurement signal and the original communication signal of the transponder being transmitted in the same frequency band was proposed. Compared with traditional satellite G/T value measurement methods, this detection method does not require the application of dedicated satellite resources, reduces the cost of satellite G/T value measurement, does not affect the normal communication of the original communication signal of the repeater, improves the utilization efficiency of the satellite repeater, and constructs an experimental testing environment for in-orbit satellite G/T value measurement. The experimental results show that the non-sensing satellite G/T value measurement method is consistent with the traditional satellite resource application G/T value measurement method. The test results can be used to guide the design of satellite communication system links.
Research on small target detection algorithm for water surface in complex weather environment
Authors: Chen Guanqiao , Cui Bin , Chen Yuanming , Hong Xiaobin , Zhang Yajun 
Published: December 2024

Abstract
Detecting small targets on water surfaces in varying weather conditions presents considerable challenges, primarily due to changing lighting and precipitation such as rain or fog. To mitigate these issues, an enhanced YOLOv5 algorithm for the recognition of small targets on water surfaces is proposed. This approach leverages optical flow to trace ship movements and create synthesized frames, thereby improving image clarity via histogram equalization. The algorithm integrates CBAM attention mechanisms and feature pyramid structures, which significantly boost the detection of small targets. Additionally, this method incorporates data augmentation to enlarge the training dataset, coupled with class weight balancing and the SIOU loss function to improve target localization accuracy. Experimental results demonstrate a marked improvement in mIoU, increasing from 0.7538 to 0.8392, thus effectively recognizing small targets in complex environments.
Design and implementation of a portable high-altitude combustible gas alarm detection device
Authors: Chen Daidong , Feng Li , Huang Sijun , Zhang Qiuquan , Zhang Haitao 
Published: December 2024

Abstract
High-altitude operations present significant safety risks and challenges in accurately detecting combustible gases due to the inherent dangers of working at heights and the technical limitations of traditional detection methods. This study proposes a portable high-altitude combustible gas alarm detection device designed to address the safety risks and accuracy issues associated with high-altitude operations. By integrating wireless communication technology, precise gas concentration control, and automated flow adjustment, this device enables remote ground-based detection, eliminating the safety hazards of high-altitude operations while ensuring measurement accuracy. The device’s real-time monitoring of gas concentration within the calibration hood mitigates the issue of standard gas dilution in the air, and automated control further enhances detection precision. Experimental results demonstrate the device’s suitability for high-altitude alarm calibration, with a concentration measurement error of less than ±5% FS. This research offers a novel technical solution for the on-site detection of high-altitude combustible gas alarms.
Defect detection of engine cylinder liner inner wall in multi-interference environments based on Large Vision-Language Models
Authors: Chen Yongbin , Zhang Zhijun , Yu Long , Huang Guojian , Huang Jiasi 
Published: December 2024

Abstract
This study presents a defect detection method for engine cylinder liner inner walls using Large Vision-Language Models (LVLMs) to overcome the limitations of traditional 2D methods in multi-interference environments. Traditional methods often lead to misjudgments and missed detections when dealing with watermarks, iron filings, and unclear textures. In contrast, LVLMs, by learning from large-scale image-text pairs, capture rich visual-linguistic relationships, enabling high-precision defect detection under zero or few-shot conditions. A dataset with various interference conditions was created, and the model was trained and optimized accordingly. Experimental results show that LVLMs significantly outperform traditional 2D methods in detection accuracy, exhibiting greater robustness and generalization. This research offers an efficient and flexible solution for defect detection in engine cylinder liner inner walls, with significant practical application value.
Research on temperature compensation of MEMS pressure sensors based on optimized multi-kernel relevance vector machine
Authors: Zeng Jun , Chen Xuanwei , Du Xiaohui , Zhu Qingyuan 
Published: December 2024

Abstract
The high-temperature coefficient in MEMS pressure sensors significantly affects sensor accuracy. Software compensation effectively reduces temperature drift. However, compensation methods such as neural networks tend to overfit sparse and noisy datasets used for calibration, leading to reduced measurement accuracy. To address the above-mentioned crucial problems, this paper proposes a multi-kernel relevance vector machine (MKRVM) based on a snow ablation optimizer (SAO) to establish the temperature-voltage-pressure relationship of pressure sensors. Optimized weight distribution from this method mitigates noise impact on the model in sparse data scenarios. Experimental results show that within the range of -15°C to 60°C, the proposed temperature compensation method achieves a mean square error as low as 0.058, effectively reducing temperature influence on pressure sensor outputs and ensuring high precision performance of the sensor.
Classification of defect levels of power equipment based on improved BERT
Authors: Liu Qi , Shi Ying , Zhang Rui 
Published: December 2024

Abstract
Accurate defect level classification in power systems is crucial for preventing outages and ensuring safety. Traditional methods rely on manual inspection, which is inefficient and highly subjective, leading to potential misjudgments. To address this issue, this paper proposes the AMF-BERT model, which dynamically integrates multi-layer output features of the BERT model through an attention mechanism, capturing semantic information at different levels and significantly improving classification accuracy and robustness. The study also analyzes the impact of feature fusion at various layers on defect category classification accuracy. Extensive experiments on power defect text validate the model’s effectiveness, achieving an F1 score of 92.11%, which is an improvement of 3.58% over the baseline BERT model. The results indicate that AMF-BERT outperforms mainstream text classification models in accurately identifying major and urgent defects. This research enables power companies to allocate resources more efficiently, respond swiftly, and reduce safety risks and economic losses caused by improper defect handling, highlighting its practical application value in smart grid management.
Influence of fast switching fault current limiter on electromagnetic transient of extra-high voltage transmission line
Authors: Zhang Kun , Hu Jianqiao , Lu Xiaoyan , Peng Zhengwei , Ma Peihua , Gao Feng 
Published: December 2024

Abstract
The rapid advancement of power systems has led to an increase in short-circuit currents. The short-circuit current can be effectively limited by adding a fast-switching fault current limiter. However, incorporating a current-limiting reactor into ultra-high voltage lines may impact electromagnetic transients. This study utilizes PSCAD/EMTDC electromagnetic transient simulation software to develop a typical 330kV transmission line model for simulation analysis. The paper aims to study whether the current limiting reactor will affect the switching overvoltage in the automatic reclosing overvoltage. The results show that the input of the current-restricting transformer efficiently constrains the magnitude of short-circuit currents and exerts minimal influence on the automatic reclosing overvoltage in ultra-high voltage transmission lines.
Wavelet detection method for transient electrical energy disturbance with noise
Authors: Gong Jing 
Published: December 2024

Abstract
Electric energy signals are often contaminated by noise during the collection process, which affects the accurate positioning of disturbances. The discontinuity of traditional hard threshold functions can lead to oscillations, while excessive smoothness of soft threshold functions can lead to signal distortion. The optimized threshold function and scale adaptive processing on the selection of the threshold are proposed. The wavelet thresholding method is used to process voltage sag, rise, and interrupt signals containing noise. The denoised signal is decomposed into five scale wavelets using db7 wavelet and detected by using wavelet transform modulus maximum and signal singularity principle. Experiments have shown that optimized threshold and threshold function not only effectively denoise but also retain the effective mutation point information in the original signal, and the new method is reliable in detection.
Modeling and transfer characteristics simulation analysis of electronic current transformers
Authors: Yu Wenbin , Shen Hongming , Hu Diangang , Ni Saisai , Wu Congying , Lu Xi 
Published: December 2024

Abstract
Compared with conventional electromagnetic current transformers, electronic current transformers have potential transfer advantages to transient current. This paper establishes the equivalent circuits of typical electronic current transformers and constructs transfer functions to analyze their frequency response characteristics. The PSCAD simulation model is established based on the transfer functions to simulate and analyze the transfer characteristics of typical electronic current transformers, and compare them with conventional current transformers in terms of steady-state response, transient response, impulse response, and step response. The analysis results demonstrate that electronic current transformers exhibit superior frequency response and transient response characteristics compared to conventional current transformers. The magnet-optic transformer and fiber-optic current transformer show better performance and are ideal for wide-band current sensing. However, low-power current transformers and Rogowski electronic transformers can only be used for AC measurement as they cannot transfer DC or non-periodic components. The transfer characteristics of fiber-optic current transformers are influenced by closed-loop control loop parameters and need to be designed according to specific application scenarios. The simulation analysis results in this paper can provide guidance for selecting suitable current transformers for new-type power systems.