bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of an axial field direct-drive tidal stream generator
Authors: Liu Wenzhou , Yang Jinghuan , Wang Pengyu , Pei Lixin , Yang Yilong , Zhang Xuejun , Zhang Baowen
Published: December 2024

Tidal stream energy generation is a technology that harnesses the kinetic energy of tidal or ocean currents to produce electricity. Unlike traditional thermal and wind power generation, tidal stream energy generation relies on the natural movement of seawater for energy conversion. Due to the periodic and predictable nature of tides and currents, tidal stream energy generation offers high stability and reliability. This paper introduces the design of a micro-scale tidal generator with the power of 300W. An appropriate electromagnetic configuration was chosen, and the initial materials and electromagnetic structure of the generator were determined. Three-dimensional simulations were performed using finite element analysis. The design parameters of the prototype are reasonable, and all performance indicators meet the design requirements, confirming the feasibility of this generator for tidal stream energy generation.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
A two-layer planning model for source storage in distribution networks based on SSA-AVOA intelligent combination algorithm
Authors: Feng Nan , Jiang Leilei , Wang Zhen , Gao Ming , Song Dongming
Published: December 2024

As renewable energy sources become more integrated into the power grid, their output uncertainty presents several challenges, including increased planning costs for the distribution network, potentially reduced economic benefits, and decreased operational stability, which can elevate safety risks. To address these issues, a two-layer planning model for the distribution network has been proposed. The upper layer focuses on minimizing the total cost, while the lower layer targets reductions in total operating costs and voltage deviations. This model utilizes a hybrid search algorithm that combines the Sparrow Search Algorithm (SSA) with the African Vulture Optimization Algorithm (AVOA) to optimize site selection and capacity determination for distributed photovoltaic (PV) systems and energy storage systems (ESS). Experimental results show that this model effectively achieves a Pareto optimal front, balancing both economic and security considerations in the distribution network system.
State of charge estimation for lithium-ion batteries based on Gated Recurrent Unit neural network and an Adaptive Unscented Kalman Filter
Authors: Song Wei , Zhao Chengji , Yang Chao , Wu Mingda , Qiu Shaoxiang , An Rongbang
Published: December 2024

For battery management systems, the accurate estimation of the state of charge (SOC) of lithium-ion batteries is crucial, yet it still poses challenges. Traditional model-based filtering methods typically require accurate battery models, but parameter uncertainties arise from various factors such as battery aging and temperature variations, leading to parameter uncertainties. In contrast, data-driven approaches can effectively capture SOC variations under different operating conditions and temperatures, but often exhibit significant prediction fluctuations. To address these challenges, a combined SOC estimation approach known as GRU-AUKF is presented in this paper. This method utilizes Gated Recurrent Unit (GRU) networks based on temperature, current, and voltage to estimate SOC, followed by filtering the output using an Adaptive Unscented Kalman Filter (AUKF) to reduce estimating errors. Experimental results demonstrate that when estimating battery SOC across different temperatures ranging from 0°C to 50°C, the RMSE is less than 1.68%, and the MAE is less than 1.44%. Compared to methods solely employing GRU models, the proposed GRU-AUKF method exhibits superior performance in enhancing estimation accuracy, thus validating its effectiveness.
Reliability assessment of distribution networks with medium-voltage and low-voltage flexible interconnections considering graded transfer
Authors: Liu Wenjun , Li Shuaihu , Ma Rui , He Shuyun
Published: December 2024

For flexible interconnection, flexible interconnected devices (FID) are utilized to upgrade or build connection nodes, which can provide more ways for load recovery after faults, thereby improving the reliability level of the distribution network. Based on the characteristics of connection switch, medium-voltage (MV), and low-voltage (LV) flexible interconnection transfer methods, a reliability assessment method is proposed in this paper for medium-voltage and low-voltage flexible interconnection distribution networks (MLFIDN) considering graded transfer. First, a multi-state reliability model of the LV substation area and discrete probability distribution of DG output are established based on the system structure and composition. Then, the fault impact analysis based on graded transfer mode is proposed. A transfer recovery model that takes into account power balance, the node voltage, line current safety constraints, and flexible interconnection operation constraints is established. On this basis, a simulation method based on non-sequential Monte Carlo sampling is used for system reliability assessment. Finally, the effectiveness of the proposed method is verified by the IEEE-33 distribution network with MV and LV flexible interconnection reliability assessment. The reliability assessment results can quantify the improvement of flexible interconnection on the reliability of distribution networks.
Health status analysis of PEMFCs based on degradation mechanism model and data-driven method
Authors: Zhang Fan , Yin Yan , Qin Zhikun , Yao Junqi , Guo Ting
Published: December 2024

Proton exchange membrane fuel cells (PEMFCs), as an environmentally friendly energy conversion device, are considered an important solution to achieve the net-zero pathway. However, the widespread commercialization of PEMFC is still hindered by limitations in reliability and durability. This article combines the degradation mechanism model with data-driven optimization algorithms to achieve degradation mechanisms and internal health state analysis of PEMFCs. Firstly, a semi-empirical degradation model for PEMFCs is constructed and validated with experimental data from the polarization curve. Afterward, based on the 4750 h degradation test data, the aging factors are fitted based on genetic algorithms, including leakage current, electrochemical active surface area (ECSA), ion impedance, contact resistance, oxygen diffusion coefficient, and concentration loss coefficient. The health status of key components is reflected by these aging factors. This article provides an in-depth analysis of the aging mechanism observed in PEMFCs during long-term operation, thereby establishing a fundamental understanding of the health status pertaining to crucial components within PEMFCs.
Design and optimization of line CT energy harvesting device
Authors: Li Ke , Gao Kai , Zhang Jianfeng , Zhou Lei
Published: December 2024

The aim of this study is to design and optimize an energy harvesting device for line current transformers (CT). The main function of this device is to extract energy from CT on the line to supply power to related electronic devices such as sensors and monitoring equipment. Firstly, an in-depth analysis of the working principles and characteristics of CT was conducted to determine the design requirements and performance indicators of the energy harvesting device. Subsequently, an energy-harvesting device based on the principle of magnetic coupling was designed. Different circuit principles were developed to accommodate low-load and high-load scenarios on the line, and the structure and parameters were optimized to improve energy conversion efficiency and output stability. Through experimental validation, the performance and stability of the designed energy harvesting device under different working conditions were verified. Finally, the design scheme of the energy harvesting device was optimized, and further improvement and application suggestions were proposed. This study provides a theoretical and practical basis for improving the performance and reliability of line CT energy harvesting devices, which is of great significance for the safe operation of power systems and equipment monitoring.
Nonlinear modeling of ball screw-type inertial container suspension
Authors: Kou Farong , Li Jiaolian
Published: December 2024

In order to better reduce the influence of spring-loaded mass inertia on vehicle suspension performance and make it better adapt to road excitation, this paper proposes a ball screw inertial container suspension configuration with adjustable inertia coefficient. By adjusting the mass of the flywheel part of the passive ball screw inertial container in the radial direction, the rotating radius is changed under the action of centrifugal force, and then the rotating inertia is changed, so that the rotating inertia of the flywheel can be adjusted with the rotating speed of the flywheel itself to achieve the adjustable inertia coefficient. Considering the nonlinear influence of the ball screw inertial container suspension system, a two-freedom nonlinear dynamic model of 1/4 vehicle is established. The model is simulated and analyzed. The results show that, compared with the passive and fixed inertia coefficient ball screw inertial container suspension, the adjustable inertia coefficient is improved by 45%, 11%, and 37%, respectively, compared with the fixed inertia coefficient inertial container suspension. The ball screw inertial container active suspension can effectively suppress the vibration impact of vehicles under road excitation.
A hierarchical sorting method based on hierarchical clustering and its application in port evaluation
Authors: Gao Su , Cheng Cheng
Published: December 2024

A hierarchical sorting method with semantic description is developed to address sorting issues in management decisions. This approach achieves optimal hierarchical clustering results by incorporating indicators of intra-similarity, inter-similarity, and total similarity. Additionally, it integrates the axiomatic fuzzy sets theory to produce a semantic description of the hierarchical sorting results. The developed method can provide a good semantic expression for sorting results from a multi-level perspective and assist decision-makers in making reasonable decisions at different levels. This study takes the 40 main seaports as application examples for analyzing the applicability and practicability of the designed method.
A research of word difficulty clustering based on improved k-means++ algorithm
Authors: Wang Dongjie , Liu Lina
Published: December 2024

Nowadays, Wordle, a daily puzzle game offered by the New York Times, is now becoming prevalent around the world by storm, and difficulty analysis of words has become a hot topic. However, the difficulty analysis of words is highly sensitive to the feature extraction of words, which undoubtedly creates great difficulties. Based on this, this paper proposes an improved K-means++ algorithm for clustering analysis of word difficulty, which is simulated and verified in the U.S. college students’ mathematical modeling C problem data. The results show that this paper clusters the 359 words in the data into 6 classes A, B, C, D, E, and F, and determines the class of word ERIE as B based on the distance between ERRIE and the word attributes of the six clustering centers. The DB index value of the model is as high as 0.815, and the results of the analysis of variance (ANOVA) of the clustering index are very significant, which proves the high accuracy of the model.
Numerical simulation study on critical initiation pressure of JO-11C booster explosive
Authors: Dang Shuaiying , Zou Debo , Yang Peiyuan , Cui Yongqiang , Lu Bingju , Cheng Dong
Published: December 2024

The booster pellet is the key component of a certain-shaped charge-cutting device. The booster pellet at the initiation point is excited by the electric detonator, and the booster pellet at the booster point is excited by the cutting cord. The initiation reliability of the booster pellet determines the reliability of the shaped charge-cutting device, so it is necessary to study the shock sensitivity of the booster pellet. In this paper, the critical initiation pressure of the JO-11C booster explosive is investigated by using numerical simulation. The results show that the critical gap thickness range for JO-11C booster explosive is 50-50.5 mm, and the critical initiation pressure range is 2.05-2.11 GPa.
All exact solutions to the generalized Maccari system with power law nonlinearity
Authors: Yang Yunfeng , Yang Yingquan , Hu Jinyan
Published: December 2024

In this study, we investigate the generalized Maccari system featuring power law nonlinearity. By employing envelope pattern transformations, the system is reformulated into an integral form. We provide exact solutions for the generalized Maccari system when n=1 and n=2 using the polynomial discrimination system method.
Observer design of discrete-time generalized quasi-one-sided Lipschitz nonlinear systems with multiple delays
Authors: Dong Wenqiang
Published: December 2024

In this article, the generalized quasi-one-sided Lipschitz condition is drawn into the topic of observer design for a kind of discrete-time nonlinear systems with multiple delays. The sufficient conditions for the asymptotic stability of full-order observer and reduced-order observer for systems are proposed, respectively. Under this condition, the two types of observers for systems can be designed, although the system parameter (A
0,C) is not detectable.
Stability analysis of a conventional SEIR epidemic model with relapse and general nonlinear incidence
Authors: Mu Xiaoqing
Published: December 2024

A conventional SEIR epidemic model is proposed, with the general nonlinear incidence rate. It is hypothesized that some recovered people may become infected again in this paper. Both the basic reproduction number and its critical effect on the system have been determined. We mainly used Jacobian matrices to discuss the local stability of the system and make use of geometric methods to research the system’s global stability. Eventually, we draw a brief conclusion.
A lightweight insulator string defect detection method based on improved YOLOv5
Authors: Gao Pengpei , Song Chunhe , Zhang Yue
Published: December 2024

In recent years, the use of drones to assess the safety status of insulators in power transmission systems has become a trend. To facilitate the application of intelligent algorithms on drone platforms, this paper proposes a lightweight insulator string defect detection method based on an improved YOLOv5. Firstly, to decrease the parameter count in the YOLOv5 backbone module, we opted for GhostNet. Considering the limitations of GhostNet in feature extraction, a lightweight attention mechanism is designed and integrated with GhostNet to enhance the accuracy of capturing defect features in insulator strings. Additionally, to further reduce parameters in the neck part of the YOLOv5 model, Ghost Convolution is introduced. Experimental results demonstrate that the proposed model performs excellently on the insulator string dataset and meets the requirements for real-time defect detection of insulators using drones.
Machine learning-based 2D model optimization design for fusion target pellets
Authors: Liu Pan , Wang Jian , Yuan Zilong
Published: December 2024

This paper proposes a machine learning-based method for the optimal design of a two-dimensional model of a fusion target pellet. The optimized design of the 2D model of the fusion target pill based on machine learning includes: parameterization of the fusion target pill, mesh division, construction of a database, data dimensionality reduction, construction of a neural network, and genetic algorithm optimization. On the basis of the original 2D model grid of the fusion target, the H-type grid is kept unchanged, only the O-type grid is replaced, and the total number of grid points is kept unchanged, so that the 2D model of the modified fusion target with any modeling parameter can quickly extract the physical parameters at the specified position in the flow field, with better geometrical adaptability, and improve the efficiency of data processing effectively. By combining the principal component analysis method and artificial neural network method to achieve the reconstruction of the flow field of the two-dimensional model of the fusion target, under the premise of guaranteeing the accuracy requirements, reducing the dimension and compression of the computational volume, thus reducing the time-consumption and saving the computational resources, realizing the rapid evaluation of the radiation hydrodynamic performance of the two-dimensional model of the fusion target and the rapid search for the optimization of the two-dimensional model of the fusion target, and providing a new idea for the high-efficient design of the two-dimensional model of the high-performance fusion target.
Evaluation of robotic process automation tasks in large-scale environments: success rates for performance and Z scoring for non-conformity
Authors: Hartikainen Eija , Hotti Virpi
Published: December 2024

Automation software is utilized to enhance efficiency and precision in various industries. Nonetheless, the performance and consistency of these software programs require careful monitoring through measurements. At the Finnish Government Shared Services Centre for Finance and HR (Palkeet), robotic process automation logging systems and experiments have been conducted to evaluate these measures. Success rate calculations are based on the logging system’s information and the findings (100 107 robot runs and 69 657 084 log levels during 2021-2022 and 2023/1-6). Through control charts and Z scoring, the experiments assess the proportion of non-conformity purchase invoices (987253 invoices, and 263822 defectives during 2020-2022) that the software cannot handle due to certain reasons, and the findings support an implementation of the Z scoring to observe out-of-control robot runs and analyze the causes. These results can be used to adapt and control the automation software’s processing capabilities.
Torrefaction Pre-Treatment Process for Biomass Fuel Pellet Characterization as a Renewable Energy Source
Authors: Yustira A , Arif H
Published: December 2024

This article focuses on the utilization and processing of biomass waste into a product that has economic value, namely fuel pellets which will later be used as fuel. Fuel pellets are produced by the torrefaction method, this method is used to improve the quality of fuel pellets in terms of density and calorific value. The biomass that will be used in the torrefaction method is empty palm oil bunches, cocopeat, oil palm fronds, oil palm fronds and corn cobs. The resulting fuel pellets will be analyzed to see the quality of fuel pellets from which biomass is the best. Analysis of fuel pellets includes the value of water content, ash, volatile matter, fixed carbon, calorific value (HHV) and bulk density.
A rough calculation of the litter carbon in agarwood small scale forest in Asahan Regency, North Sumatra, Indonesia
Authors: Muhdi  , Htb K W , Hanafiah D S
Published: December 2024

One of the sources of forest carbon is the forest floor, such as dead organic material and litter. The aim of this research is to analyze estimates of litter and soil carbon stocks in small-scale forest agarwood in Asahan, North Sumatra. Laboratory analysis was carried out at the Agricultural Laboratory of the Universitas Sumatera Utara. The analysis of litter biomass is carried out in the laboratory. The average litter carbon on land in Hessa Perlompongan Village is 0.36 tons/ha, which is greater than the average litter carbon on land in Bunut Village, which is 0.19 tons/ha. There is a significant difference between the average carbon biomass of litter carbon in Hessa Perlompongan Village and Bunut Village.
Assessment of land suitability for palmarosa cultivation (Cymbopogon martinii) as an atsiri essential oil plant in Karangturi Sub-District, Karanganyar, Indonesia
Authors: Muhammad D M , Samanhudi  , Rahayu M , Setyawati A
Published: December 2024

Palmarosa (Cymbopogon martinii) cultivation has gained significant interest because of the increasing demand for its essential oil (atsiri) in many industries. This research set out to thoroughly evaluate the land’s potential for palmarosa agriculture in the particular Karangturi Sub-District, located within the Gondangrejo District of Karanganyar Regency. This research employs a comprehensive approach to remote sensing, soil sampling, and geographic information system (GIS) analysis. It thoroughly examines and evaluates important factors including soil texture, pH levels, nutrient composition, topographical features, and climatic nuances, comparing scientific sources. According to the study’s findings, Karangturi Sub-District’s land area is at the marginal level (N; NW), which means it is not suitable enough for growing palmarosa due to the limiting factor for water availability. However, on other parameters, the status was suitable (S1-S3). Instead, some areas may require soil management to make them more suitable for cultivation. Through a framework based on scientific research, this study not only offers information on land suitability but also provides a basis for Palmarosa cultivation, which in turn can develop local agriculture and increase palmarosa essential oil production in the region.
Yield And Quality of Several Banyuwangi Sliced-Tobacco Lines for Sustainable Kretek Cigarette Production
Authors: Rochman F , Heliyanto B
Published: December 2024

Kretek cigarettes, which use sliced tobacco along with clove and sauces, are among the most popular tobacco products globally. Banyuwangi tobacco is one type of sliced tobacco used in these cigarettes. The primary challenge in developing Banyuwangi tobacco has been its low productivity. This study aimed to assess the yield and quality of ten promising tobacco lines from previous selections. The research was conducted over three years at two locations using a randomised block design with three replications. Parameters observed include dried sliced, quality index, crop index, nicotine, and stability. The results revealed that the Semarang Jahe 1 line produced the highest dried sliced tobacco, averaging 941.37 kg/ha, which is 31.44% above the average. Its crop index was 27.80% higher than the average, and it demonstrated high stability. The Jepril 1 line yielded 726.26 kg/ha of dry slices, 1.41% above the average, with a crop index 9.90% higher than the average, also showing stability. The Semarang Jahe 2 line produced 875.81 kg/ha of dry slices, 22.29% above the average, with a crop index 17.30% higher than the average, but it was location-specific. These three lines are promising for increasing farmers’ incomes. Special measures are recommended to promote sustainable organic cultivation.
Optimization of sterilization Ebony (Diospyros celebica Bakh.) leaf explants in suppressing phenolic compounds and contamination of in vitro culture
Authors: Tanjung S P , Siregar L A M , Hanafiah D S
Published: December 2024

This research aims to obtain the most optimal method of sterilization of ebony leaf explants and to know the types of contaminants that appear to improve the success of ebony regeneration initiation in vitro. This research was compiled using a non-factorial, completely randomized design (CRD) with five types of treatments using various sterilization methods by observing the percentage of sterile and non-browning explants, the percentage of explant browning, and the percentage of explant contamination. The research results showed that the D3 treatment (Fungicide + Tween 20, Bactericide + Tween 20, 10% NaOCl, 20% NaOCl, 30% NaOCl, and 70% Alcohol) gave the best results on the percentage of sterile and non-browning leaf explants at 100%.
Evaluation of agronomic performance of local rice from West Sumatra
Authors: Alfi R
Published: December 2024

Exploration and identification activities are the first steps in plant breeding activities in managing genetic resources. The use of new superior varieties will usually shift the use of local varieties. This study aims to identify the agronomic behavior of local rice cultivars from West Sumatra. The research was conducted in Padang City, West Sumatra, from September 2023 to January 2024. The genotypes used were five national superior varieties, 29 local cultivars, and adaptive varieties originating from five cities and districts in West Sumatra. The design was conducted using field experimental random block design with three replications for each genotype. The results showed that almost all observed results were significantly different except for the observation of flowering age. It is suspected that the environmental conditions are not a threat to the plants, so the cultivar can complete its life cycle until harvest.