bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved DSFD Algorithm for Balancing Speed and Accuracy
Authors: Wang Lihui , Hu Xichuan 
Published: May 2024

Abstract
Aiming at the mismatch between speed and accuracy of DSFD face detection algorithm, a lightweight network method based on improved DSFD is proposed. First, the backbone of the algorithm is replaced with EfficientNet B1, which greatly reduces parameters and computation. Then the feature pyramid network in feature enhance module is changed to BiFPN to further enhance the feature fusion. Finally, to improve the efficiency, dilated convolution layers in feature enhancement module are removed, and the convolution layers of BiFPN is replaced by ACBlock. The improved algorithm was evaluated on WIDER FACE and the effectiveness of the proposed methods are evaluated through ablation experiments. The detection speed of the improved algorithm is increased by twice. Compared with two popular algorithms, the improved algorithm achieves higher accuracy.
Building Edge Detection Technology From Remote Sensing Image Based On NSCT And Tensor Voting
Authors: Wu Yingbin , Zhao Peng , Zhou Mingquan , Geng Shengling , Zhang Dan 
Published: May 2024

Abstract
An edge detection technology based on the combination of non-downsampling contour wave transform (NSCT) and tensor voting is proposed, which aims to obtain more accurate and detailed edge information of buildings in remote sensing images. Firstly, NSCT is used for image decomposition to obtain the subband frequency information of different scales and angles. Then, position encoding is performed on these subband coefficients to obtain second-order symmetric tensors at the corresponding positions. Tensors of different scales and angles at the same position are weighted and summed to complete feature fusion. Finally, the edge features of the image are obtained based on tensor voting theory. The experimental results show that compared to common edge detection technologies, such as Canny, Fast Edge (Fast edge detection using structured forests) and HED (Holistically-Nested Edge Detection), our method can more accurately and intensively reflect the boundaries of buildings and the edge information of roofs, providing better support for the analysis of building types and architectural styles. Compared to the HED method, which is based on deep learning, our method improves PSNR and SSIM metrics by 0.98 and 0.03, respectively.
An illumination-robust edge detection method for nuclear fuel assembly deformation measurement
Authors: Chen Rui , Cheng Yuqi , Yang Wentao , Yang Bin , Li Wenlong 
Published: May 2024

Abstract
Fuel assemblies (FAs) are critical components of nuclear reactor cores. The excessive deformation of FAs can pose a serious risk of safety accidents in nuclear power plants. Hence, regular monitoring of the deformation of the FAs is necessary. To accomplish this task, the most critical challenge lies in accurately extracting the edges of FAs in the nuclear underwater environment with spatial variations in illumination. In this paper, a novel illumination-robust edge detection method is proposed. Firstly, tilt correction is applied to arrange the fuel rods vertically in the image. Then, the center of each fuel rod is located through grayscale projection and filtered by rod center spacing variance minimization. Subsequently, adaptive thresholding is employed to obtain a binary image. Finally, the edges are identified by searching in the vicinity of rod centers in the binary image. The effectiveness and robustness of the proposed method have been validated in experiments, and the approach has been successfully applied to the FA deformation measurement in nuclear power plants.
Distributed drive electric vehicle differential assistance particle swarm optimization PID control
Authors: Huang Zihao , Huang Kaiqi , Lan Haoqiang 
Published: May 2024

Abstract
To improve the vehicle’s handling stability and enhance the steering assist effect of distributed drive electric vehicles, a differential assist steering control strategy is proposed based on the Particle Swarm Optimization (PSO) algorithm to optimize PID control parameters. A dynamic model of steering torque considering the influence of tire force is established, based on the reference steering torque to calculate the required torque. The PSO algorithm with the Integral of Time-weighted Absolute Error (ITAE) is used to optimize the parameters in the PID steering control, and wheel torque allocation is performed by using the objective function constraint. Simulation results show that the differential assistance can meet the driving requirements of drivers at both high and low speeds, and the proposed control strategy is effective.
Finite element analysis of self-tensioning and bursting of 210 L carbon fiber fully wound hydrogen storage cylinders
Authors: Pan Yingbo , Xiao Jieli , Shen Wei , Wang Hehui 
Published: May 2024

Abstract
This paper focuses on a 210 L carbon fiber fully wound hydrogen storage cylinder. A 1/8 scale model is selected for analysis. Using the finite element software ANSYS APDL and secondary development language, an orthotropic material performance equation is formulated and applied to the composite material lay-up. Following DOT CFFC standards, the cylinder undergoes simulated loading and unloading analysis to determine the optimal self-tightening pressure. Subsequently, progressive failure theory and the Tsai-Wu failure criterion are employed to identify the failure modes of the composite material. Continual adjustments to the element stiffness are made during the loading process. Employing the element birth and death technique, failed elements are terminated and recorded, and based on increasing trends, the burst pressure of the cylinder can be predicted. The results closely align with experimental data, validating its applicability as the basis for burst prediction.
Research on temperature rise characteristics of grease lubricated ball screw pair
Authors: Yang Linhui , Fu Jie , Pei Xiaqing , Zhang Zixuan , Fan Yuanxun 
Published: May 2024

Abstract
Aerospace ball screws cannot be lubricated for maintenance and replacement, so they use grease that can run for a long time and keep the performance. Under high speed and high load conditions, the ball screw will be warmed up rapidly, and the temperature has a great effect on the capability of the grease. In this paper, we deduce the formula for calculating the screw temperature, establish a thermodynamic simulation of the ball screw model to get its temperature under different conditions, and set up a test bench to try out the screw temperature rise to investigate thermal properties in the grease lubrication conditions.
Research on the calculation method of grinding temperature field of spiral bevel gear based on generative machining
Authors: Gu Biao , Wang Yuhang , Tan Rulong , Zhang Weiqing 
Published: May 2024

Abstract
At present, the process parameter setting of spiral bevel gear grinding is mainly based on an empirical method, and the tooth surface temperature is difficult to estimate effectively during grinding, which may lead to excessive tooth surface temperature and surface burn caused by improper selection of process parameters. Aiming at the above problems, this paper presents a method for calculating the temperature field of spiral bevel gear forming based on a combination of analytical and numerical methods. The motion equation of spiral bevel gear grinding is derived based on the method of tooth surface development. The time-varying heat flow calculation method in tooth surface grinding was developed through the discrete and two-dimensional contact zone. The time-varying temperature field analysis method of spiral bevel gear forming is developed using the time-varying heat flow and finite element calculation method. The related research can realize the temperature prediction of grinding tooth surfaces and provide the basis for improving grinding technology.
Research and design of an online monitoring device for hydrogen peroxide concentration
Authors: Yu Guangshuai , Lin Weiguo , Feng Guodong , Chen Yongmei 
Published: May 2024

Abstract
To address the issues of manual measurement, inaccurate instrument measurement, and frequent manual intervention required for hydrogen efficiency, oxygen efficiency, and raffinate in the production of hydrogen peroxide using the anthraquinone process, a hydrogen peroxide concentration online monitoring device was designed. The device includes a sampling module, extraction module, temperature control module, detection module, and display module. The detection module is designed based on the amperometric analysis method, using a constant potential instrument with a three-electrode system for current detection, establishing a relationship model between current and hydrogen peroxide concentration. It also includes calibration and detection modes to reduce detection errors generated during long-term operation. The device enables online analysis of hydrogen peroxide concentration through online sampling, pretreatment, and testing analysis, with a detection range of 0.05-15 g/L. The device has demonstrated good accuracy and reliability and has been tested and applied in industrial settings.
An object detection system using array planar capacitive sensors
Authors: Ma Hanxu , Wang Gang , Xu Pengyuan , Wang Hao , Huang Yulin 
Published: May 2024

Abstract
This paper presents a target detection system using array-based planar capacitive sensors, demonstrating excellent detection performance in gesture recognition. The system achieves gesture target classification by collecting the capacitance values of the planar capacitive sensor array and optimizing the BP neural network through a genetic algorithm for model training. Additionally, the system employs visual detection for gesture targets, using transfer learning with the MobileNet V2 network in the Keras framework to achieve gesture target classification. The system utilizes a data fusion mechanism to merge visual recognition and planar capacitive sensor array recognition for decision-making. Experimental results demonstrate that the target detection system exhibits outstanding detection performance in gesture recognition, with an average accuracy of up to 98.92%.
Design of low-correlation sidelobe FSK-PSK waveforms with controllable frequency points
Authors: Lv Shurong , Liu Siqi , Shi Huili , Zhang Jindong 
Published: May 2024

Abstract
Frequency shift keying-phase shift keying (FSK-PSK) waveforms are recognized for their superior resolution and signal detectability. However, the optimization of FSK-PSK parameters, particularly frequency hopping control, remains challenging and can lead to increased sidelobe levels. This paper develops an optimization model to substantially enhance the controllability of hopping frequency in FSK-PSK waveforms for radar systems. By harnessing the Karush-Kuhn-Tucker (KKT) conditions, the model strategically minimizes the autocorrelation integrated sidelobe level (ISL), thereby sharpening radar target discrimination and bolstering anti-jamming capabilities. An iterative algorithm specifically addresses the model’s optimal subproblems, optimizing frequency agility and stability. Simulation results show that our FSK-PSK waveform outperforms the waveform optimized by the Multi-Objective Quantum Genetic Algorithm (MoQGA) with a 4.93 dB sidelobe suppression gain, enhancing detection accuracy and anti-jamming performance in disturbed environments.
Simulation and design optimization of embedded steam baking integrated machine bottom plate structure
Authors: Cheng Liangguo , Nie Qingfeng , Yu Keda , Chen Zhenlei , Tan Yongqiang 
Published: May 2024

Abstract
To address the challenges related to excessive deformation of the bottom plate during the opening and closing of the steam-baking integrated machine door, a comprehensive approach that incorporates finite element simulation and experimental testing was employed to investigate the plate’s deformation characteristics. Various structural parameters influencing the bottom plate were evaluated, and a series of enhancement strategies for the bottom plate structure were proposed. Following the optimization process, subsidence deformation of the bottom plate was reduced by 64%, and warpage deformation was reduced by 13.6%. Thus, a solid groundwork was established for subsequent structural stiffness analysis and optimization endeavors in the realm of embedded steam-baking integrated machines.
Reliability analysis of diesel engine cooling system based on improved Bayesian network
Authors: Liu Peng , Gu Yingkui , Ao Shancheng 
Published: May 2024

Abstract
For the benefit of solving the problem of absoluteness of conditional probability distribution and difficulty in obtaining basic event probability in Bayesian networks, this paper studies the reliability of diesel engine cooling systems through leakage noise or gate intuitionistic fuzzy Bayesian network. Firstly, the leakage noise or gate is introduced into the intuitionistic fuzzy Bayesian network, which makes the results more realistic. Secondly, prior probability in the Bayesian network is obtained based on the similarity aggregation method of triangular intuitionistic fuzzy numbers. The results show that the introduction of leakage noise or gate, intuitionistic fuzzy set, and Bayesian network can solve the problem that the probability of basic events in a Bayesian network is difficult to obtain. The conditional probability table corrected by leakage noise or gate is more realistic and greatly reduces the workload. It is determined that the two weakest components of the cooling system are the thermostat and the water pump. The results show that the way in this paper can provide support for the reliability analysis of the complex system.
Fiber-end control algorithm based on back-propagation neural network
Authors: Zhao Zhiqiang , Ouyang Xinyuan , Jia Mengzhao , Wang Yuanfei , Li Zhe 
Published: May 2024

Abstract
The evenness of the trajectory of the optical fiber grinding track has a substantial effect on the quality of the transmission of optical signals. To boost this transmission quality, we present a blueprint for the trajectory of the optical fiber grinding track, together with the recently introduced equipment for shaping the end-face of the optical fiber. This model establishes the correlation between the grinding trajectory and the rotational speed of the grinding motor, enabling control of the grinding trajectory of the optical fiber connector by manipulating the motor’s rotation speed. Three-speed control methods, namely the Proportional-Integral-Derivative (PID) controller, fuzzy control algorithm, and Back-Propagation (BP) neural network PID control algorithm, were compared for effectiveness using Matlab/Simulink simulation software. The BP neural network displayed exceptional performance, leading us to implement the PID control algorithm to govern the speed of the grinding motor. Our experimental findings validate the superior efficacy of the BP neural network PID control algorithm in governing the speed of the grinding motor, thereby ensuring a consistent grinding trajectory for optical fibers.
FPGA implementation of joint synchronization algorithm based on short training sequence
Authors: Liu Yuanhang , Zhang Yanmin , Zhu Bowei , Valentinovich Volvenko Sergey , Xu Yidong 
Published: May 2024

Abstract
Synchronization processing is a vital component of the Orthogonal Frequency Division Multiplexing (OFDM) receiver, playing a decisive role in determining the performance of the OFDM receiver. Based on the repetitive structure of the Short Training Sequence (STS) in the IEEE 802.11 physical layer protocol data unit, this paper proposes and implements a joint synchronization algorithm to address issues related to data packet detection and coarse frequency offset correction during the synchronization process. Through software simulation and Field-Programmable Gate Array (FPGA) board-level validation, it is concluded that the algorithm ensures performance while, to some extent, conserving FPGA resources.
Coverage optimization of wireless sensor networks based on bottle sea sheath optimization algorithm
Authors: Ou Xiaopeng , Yang Xu 
Published: May 2024

Abstract
To tackle the problem of inadequate placement of wireless sensor networks’ sensor nodes, a proposed algorithm aims to enhance network coverage through a more optimal approach. Recognizing limitations in the Salp Swarm Algorithm (SSA) optimization, specifically its insufficient local search capability and susceptibility to local extreme values, a hybrid strategy is introduced in the search step. This strategy involves adaptive weight factor adjustments and incorporates Levy flight, achieving a better balance between global search, convergence speed, and computational accuracy. Consequently, this refinement contributes to improved effective network coverage, enhancing the overall network coverage in wireless sensor networks and facilitating faster coverage of the nodes.
A point cloud semantic segmentation method with augmented local geometry
Authors: Wang Zhiyuan , Wang Haifeng , Han Cong , Ma Yuhui , Wang Jiaxu 
Published: May 2024

Abstract
With the development of 3D sensors, complex spatial structures can be established from point cloud data. Accurate semantic segmentation of 3D point clouds is beneficial for tasks such as robotic scene understanding. To extract the geometric features of the point cloud, most prior works use the xyz coordinates to potentially learn the shapes of 3D objects, which is not insufficient to handle complex scenes. In this paper, a Local Geometry Encoding (LGE) module is proposed to provide local normal information and express 3D shapes explicitly. Furthermore, considering that most prior methods use a single decoder to recover from abstract features, a Multi-Decoder (MD) ensemble module is proposed to restore multiple segmentation results from various scale feature maps in the encoder, and then a multi-attention fusion mechanism is utilized to produce single segmentation results. Benefiting from the local geometry encoding module and Multi-Decoder ensemble module, our network performs 6-fold cross-validation on the S3DIS dataset and achieves 75% mIoU with fewer parameters.
Study of the energy calibration of the DEAP-3600 detector using Na-22 source data and simulations
Authors: Luzzi L. ,   the DEAP-3600 collaboration
Published: May 2024

Abstract
DEAP-3600 is a single-phase liquid argon (LAr) direct-detection dark matter experiment, operating 2 km underground at SNOLAB (Sudbury, Canada). The detector consists of 3.3 tons of LAr contained in a spherical acrylic vessel. At WIMP mass of 100 GeV, DEAP-3600 has a projected sensitivity of 10-46 cm2 for the spin independent elastic scattering cross section of WIMPs. Radioactive sources have been used for the energy calibration and to test the detector performance. One of the most effective calibration run has been taken with a 22Na source deployed in a tube located around the DEAP-3600 steel shell. The simultaneous emission of three γs by the source provides an excellent tagging for the 22Na decay. The results concerning the energy response of the detector and the agreement between data and Monte Carlo simulations in DEAP-3600 are investigated in this study.
Secondary estuarine circulation and the related vertical mixing at the sill of Ambon Bay, eastern Indonesia
Authors: Giliant Salamena Gerry 
Published: May 2024

Abstract
Secondary estuarine circulation at the sill of Ambon Bay during wet season (July 2019) was investigated to add important insights into the existing knowledge on primary estuarine circulation done by a recent study. The datasets from the previous study were employed to calculate tidal-mean vertical advection as secondary estuarine circulation at the sill. Vertical mixing at the sill was also quantified using the continuity equation. The vertical profiles of vertical advection at the sill of Ambon Bay formed bow-like shapes with zero value at the surface that increased to reach maximum at the mid-depth of the sill (∼6 m depth), subsequently decreasing to zero at the seabed. Vertical advection at the sill was found to be larger during spring tide (∼2.5 × 10−3 m/s) than during neap tide (∼1 × 10−3 m/s). Vertical diffusivity, indicating the magnitude of vertical mixing at the sill of Ambon Bay, showed similar characteristics to vertical advection in terms of vertical profiles (bow-like shapes) and spring/neap tidal variation (spring tide value: 8.5 × 10−3 m2/s; neap tide value: ∼5 × 10−3 m2/s). The intense vertical mixing at the sill of Ambon Bay has the potential to create turbid water linked to resuspension of seabed sediment in the location.
The Effect of Different Concentration of Liquid Organic Fertilizer on the Growth of Sea Grape (Caulerpa lentillifera)
Authors: Kesaulya Gloria Hillary , Pattiasina Bethsy Jane , Loupatty Joice Wenny , Kesaulya Taufiniringsi 
Published: May 2024

Abstract
Sea grapes (Caulerpa lentillifera) have potential economic value and need to be conserved in their natural environment. The growth and distribution of C. lentillifera depend on physical (such as substrate) and chemical (nutrient) factors of the marine environment. The purpose of this study is to analyze the effect of different concentrations of liquid organic fertilizers on the growth of C. lentillifera in a closed culture system. This research was done from April to June 2022. The culture of C. lentillifera was done under a closed culture system using a fiber cage (50 x 50 x 60 cm) inside a (plastic) greenhouse. The experimental design used a complete randomized design of 5 treatments with three replications each. Variables observed during this study were length, weight, and sea grape ramuli quantity. The data was analyzed by using one-way ANOVA. The results show that adding liquid organic fertilizer with different concentrations has an impact on the sea grape weight (F
value
3.764 > F
tab
3.48) and an increase in the quantity of sea grape ramuli (F
value
3.608 > F
tab
3.48). The optimum concentration of liquid organic fertilizer for the growth of sea grapes was 20 ml.L−1. The culture of C. lentillifera can be done in a closed system and give good results in its growth. Further study still needs to be done by applying different treatments such as salinity and temperature in order to get exactly good conditions for C. lentillifera to grow in culture conditions and give a good quality that can be consumed and a good economic value.
Oceanic turbulence parameters recognition based on convolutional neural network
Authors: Gao Siyu , Liu Xiaoyun , Chen Yonghao , Jiang Jinyang , Liu Ying , Chai Tengfei , Jiang Yueqiu 
Published: May 2024

Abstract
The distortion induced by ocean turbulence has a substantial impact on the propagation of light in water, posing challenges for applications including underwater wireless optical communications and submarine surveys. Obtaining accurate information about the properties of oceanic turbulence (OT), particularly the parameters describing OT, is crucial for addressing these challenges and enhancing the performance of such applications. In this paper, we propose a convolutional neural network (CNN) and validate its ability to recognize OT parameters. The physical quantities of oceanic turbulence collectively influence the formation and strength of turbulence. We recognize the dissipation rate of temperature variance χ

T
and the turbulent kinetic energy dissipation rate ɛ, taking into account various balance parameter ω, transmission distance z. Furthermore, in order to simultaneously recognize χ

T
and ɛ, we enhanced the existing network by modifying the output structure, resulting in a dual-output architecture that facilitates concurrent classification of both χ

T
and ɛ. Our method for classifying turbulence parameters will contribute to the field of underwater wireless optical communication and promote its further development.