bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of multiple injection optimization on combustion characteristics of low-pressure EGR system engine
Authors: Liu Yiqiang , Meng Yufei , Liu Qiyuan , Zhang Huijun , Chen Cheng , Li Xiaonan 
Published: August 2024

Abstract
In recent years, reducing oil consumption and pollutant discharge in transportation industry has become an important measure to alleviate energy crisis and environmental pollution. Therefore, the fuel economy and emission characteristics of the engine have become an important index to evaluate the quality of an engine, so EGR technology has gradually become the standard of gasoline engines. But EGR is not always good. In order to alleviate the adverse effects of EGR on combustion, further research on engine combustion is needed. In order to study the effect of multiple injection strategy on engine combustion characteristics, this paper takes a high pressure direct injection gasoline engine equipped with low pressure EGR system as the research object, and verifies the designed multiple injection strategy on special test equipment. A large amount of combustion data of the engine under the same load, different fuel injection stages and fuel injection ratios were collected. By comparing the evaluation indexes of main pollutant emission, fuel consumption and combustion stability, it is found that with the increase of injection times, the combustion characteristics and knock of the engine show a trend of improvement. After using multiple injection strategies at 2000r/min, it can be seen that the optimized injection strategy has better emission characteristics and lower fuel consumption lower knock intensity than that before the injection mode optimization without affecting the power performance.
Experimental research on the flow flied of the nacelle and inlet of a civil aircraft with wind-mounted engines at low speed
Authors: Hu Renyu , Zhang Dongyun , Sun Haoran 
Published: August 2024

Abstract
Modern civil transport aircraft with wing-mounted engines are exposed to the upwash induced by the wing, resulting in the local angle of attack at the nacelle leading edge, notably greater than that of aircraft. Large local angles of attack can be combined with different engine airflow conditions, causing adversary aerodynamic phenomena to occur at different locations. Total and static pressures obtained in a wind tunnel test using a full-span aircraft model and simulating required engine airflow with an ejector indicate that during the around with landing configuration, the high suction peak will form on the bottom of the inlet with local air flow being supersonic and could lead to inlet distortion. In contrast, the engine that fails during takeoff climb will have a significant suction peak form on the top of the nacelle and might experience external separation and additional drag. Test results also demonstrate that well-designed nacelles can postpone these adversary phenomena until the aircraft stalls first.
Simulation and optimization of fuze circuit housing structural strength under a high overload environment
Authors: Wu Qi , Yan Lide , Zhang Hang , Hou Lei , Liang Pengfei , Hao Yaoyun , Duan Xiaochang , Xue Yubo , Ding Jinshan 
Published: August 2024

Abstract
With the continuous improvement of modern military equipment protection systems, the demand for ammunition to penetrate hard targets has become increasingly apparent. The projectile is subjected to a high overload environment during launching and penetrating hard targets. Fuze as the detonation control system of the projectile, is prone to significant deformation or even damage to its circuit housing structure in a high overload environment. The dynamic response of fuze circuit housing under high overload conditions is analyzed, and the housing structure is further optimized according to the simulation results to ensure that the fuze circuit housing can meet the strength requirements under a high overload environment.
Prediction of packing performance of pneumatic control valve based on stem mechanism model and VMD-SSA-LSTM
Authors: Shi Limiao , Yang Bin , Wang Yujin , Wang Xinze 
Published: August 2024

Abstract
Packing performance is one of the most important parts affecting the control operation of pneumatic control valves. In order to avoid the failure caused by packing wear and aging of pneumatic control valves after long-term use, this paper constructs a valve stem mechanism model and combines the advantage of deep learning to predict the packing performance of pneumatic control valves based on Long Short-term Memory (LSTM) and Variable Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA). Firstly, the historical data of the actuator is calculated based on the mechanism model of the valve stem. The time-friction force obtained by the calculation is the parameter set. After the parameter set is subject to the variational mode decomposition, the parameter set is imported into the SSA-LSTM prediction model for prediction, and the prediction data is obtained.
Centerline measurement of rotary kilns based on the free station method
Authors: Niu Yuhan , Wu Jingbing , Zhang Yun 
Published: August 2024

Abstract
The rotary kiln is an important production equipment in the building materials and metallurgy industry, and its operating conditions, safety, and efficiency largely determine the economic benefits of its users and the safety of workers on site. At present, the testing methods for each rotary kiln part are relatively mature and can be used to realize accurate measurements in a dynamic environment. However, the main means of measurement is limited by the site conditions, and the actual detection speed is slow; the preparation work is cumbersome and will be subject to the measurement of the site obstacles and other environmental factors constraints. In this paper, we propose and design an optimized rotary kiln centerline measurement method using the free station method and verify its feasibility with respect to the optimization of the centerline offset, which is an important measurement item in rotary kiln measurement.
Bushing fault diagnosis based on SVM and the improved sparrow search algorithm
Authors: Li Shengrong , Wang Yefan , He Zixi 
Published: August 2024

Abstract
In order to address the issue of low precision in traditional bushing fault diagnosis, a bushing fault diagnosis method based on an improved sparrow search algorithm (ISSA) and support vector machine (SVM) is proposed in this paper. Firstly, the bushing vibration signals are extracted by wavelet packet, and the feature vectors are used as inputs for the SVM. In view of the impact of support vector machine parameters on the model, a sparrow search algorithm is proposed for intelligent optimization. To prevent reaching a local optimum, adaptive inertia weight is added based on the original approach. The final bushing fault diagnosis model is established by training. Comparison experiments with three fault diagnosis models, SSA-SVM, PSO-SVM, and SVM, found that the proposed method achieves complete diagnosis in a shorter time, and the diagnostic accuracy rate is 96.5%, which verifies the feasibility and effectiveness of the model.
Estimation of ultrasonic flight time based on SHADE algorithm and extended kalman filtering
Authors: Mao Peng , Xiong Jinglin , Li Xisheng 
Published: August 2024

Abstract
Traditional methods of using ultrasound to measure oxygen concentration have the issue of inaccuracy and susceptibility to noise interference when measuring the time of flight (TOF) of ultrasonic echo signals. A proposed algorithm for ultrasonic parameter estimation, utilizing an asymmetric Gaussian model, aims to enhance the precision and reliability of TOF estimation, aiming to reduce the influence of noise interference. The algorithm combines an improved Self-Adaptive Differential Evolution (SHADE) algorithm based on successful historical records with an Extended Kalman Filter (EKF). The improved SHADE algorithm controls the degree of mutation strategy greediness during the mutation operation based on the number of iterations, thereby enhancing the algorithm’s iteration speed. Then, the obtained solution is used as the initial value for the EKF to estimate the model parameters, thus addressing the issue of EKF’s dependency on accurate initial values to obtain precise outputs. Through simulation and experimental measurements of ultrasonic oxygen concentration, several commonly used parameter estimation algorithms are compared with the proposed SHADE-EKF algorithm. Results demonstrate that the SHADE-EKF algorithm exhibits superior performance in estimating the TOF of ultrasonic waves.
Consistency and repeatability verification for vehicle in the loop test platform based on digital twin of proving ground
Authors: Huang Zhichen , Liu Qiangsheng , Chen Liang , Ke Yingjie , Huang Xiongdong , Zhang Xinghui 
Published: August 2024

Abstract
A high-confidence vehicle-in-the-loop test method based on the digital twin of proving ground (DTPG-VIL) is proposed for the test of autonomous driving vehicles. The overall architectural design of the DTPG-VIL and the corresponding components are introduced, with a focus on describing the scene simulation system based on the digital twin of the proving ground. A King Long intelligent driving sightseeing bus for test verification is introduced, and its positioning performance is verified by the high-definition positioning base station and GPS/INS combined navigation system to verify the accuracy of its positioning precision and meet the requirements of the digital twin test so that the DTPG-VIL can obtain more information from the bus. Taking the automatic emergency avoidance function as an example, the method, process, and experimental results of the control test between the DTPG-VIL test and the real vehicle test in a proving ground are designed. Through the correlation coefficient analysis and transient response analysis, consistency and repeatability comparisons are carried out to verify the high confidence of the digital twin test.
Dynamic time warping based instantaneous frequency estimation for rotating machineries under non-stationary operating conditions
Authors: Tao Xiyue 
Published: August 2024

Abstract
For tacho-less order tracking, it is crucial to achieve an accurate estimation of instantaneous phase and frequency under non-stationary conditions for fault diagnosis of mechanical systems. Therefore, an instantaneous frequency estimation method based on dynamic time warping for rotating machinery was proposed. Firstly, a mono-component signal is obtained by variational mode decomposition of multi-harmonic vibration signal generated in practical engineering. Then, a stationary reference signal is constructed by the mean frequency of the vibration signal, which is obtained by the signal spectrum. Aligning the vibration signal with the reference signal to obtain the rough phase estimation and warping path. Finally, a polynomial model is constructed to calculate the analytic expression of the smooth warping path to obtain the instantaneous frequency result. The simulation and experiment present that the dynamic time warping-based method can estimate the instantaneous frequency of the signal under non-stationary conditions. Compared with the classical Hilbert transform method, the proposed dynamic time-warping algorithm has stronger noise resistance.
Localization and detection of underwater acoustic communication signals using convolutional recursive neural networks
Authors: Ouyang Junjie , Zhang Leilei , Zhang Youwen 
Published: August 2024

Abstract
This paper employs a multi-task neural network (UWAELD) to simultaneously perform the Underwater Acoustic Communication Event Detection (UACED) and direction-of-arrival (DOA) mission. Typically, the UACED and DOA tasks are executed separately, as the UACED task is identified through time-frequency analysis, while the DOA task is identified through the amplitude and phase differences of the signal, making it very difficult to jointly perform both tasks. To overcome this, we utilize a logarithmic spectrogram for time-frequency analysis and stack the normalized eigenvectors obtained by processing the signal covariance matrix. We ultimately evaluate the network’s feasibility using simulation data and lake test data, utilizing acoustical signals measured in actual experiments. Finally, for the UADCED task, the error rate is only 6%, with an F1 score of 0.83; for the DOA task, the localization error is only 1.72°, with a localization recall score of 0.87. Additionally, for the DOA task, we employ the classical MUSIC algorithm to demonstrate the network’s superiority.
Ship accident re-enactment based on AIS data and virtual reality: a case study of “Xiang Xiang Yin Huo 0410” ship accident
Authors: Zhang Shuntao , Zhou Shibo , Shao Jinxing , Ren Luzhen 
Published: August 2024

Abstract
The recurrence of ship accidents was an effective method of maritime investigation. To analyze the cause of the sinking of “Xiang Xiang Yin Huo 0410”, a vessel accident recurrence simulation system was constructed by integrating an automatic identification system and virtual reality technology to replicate the navigation and accident process of the accident vessel. The voyaging process of the accident vessel was reduplicated in the simulation system according to the AIS information, such as speed, course, and turning speed. The mathematical model of accident vessel maneuvering was established by using MMG to reproduce the ship maneuvering scheme based on the maneuvering data before the accident obtained through on-the-spot investigation. The navigation and sinking process of the accident vessel were reproduced visually, and the cause of the accident was judged and analyzed intuitively by constructing a three-dimensional model of the accident vessel and a mathematical model of ocean waves. The actual application indicates that the ship’s navigation and accident process under external force, combined with the ship’s maneuvering data, automatic identification data, and hydrometeorological data, was consistent with the actual accident process of the accident. The method proposed in this paper provided a new idea for the investigation and analysis of maritime accidents.
Research on digital twin architecture of ship maneuverability simulation for system model
Authors: Yang Yunfan , Liu Xu , Yin Wenhui , Guo Yu , Bao Jiawei 
Published: August 2024

Abstract
With the development of civil and military requirements, ship maneuverability simulation systems tend to be complicated, and the traditional simulation methods are difficult to meet in terms of the development needs of digitalization. The joint simulation technology based on the FMI standard can realize the digital twin of complex system models, which can solve the difficult problem of multidisciplinary and multi-sub-system integration simulation. This paper analyzes the current domestic and foreign research content on joint simulation technology, digital twin technology, and complex system modeling, takes a typical Z-type maneuverability test as an example, establishes a multidisciplinary and cross-platform subsystem model, and designs and develops a ship maneuverability joint simulation algorithm and system software for the complex system model. The simulation results verify the effectiveness of the system modeling technique and the joint simulation technique based on FMI. Based on this system, the whole framework of the ship maneuverability simulation system, which is based on digital twins, is designed. According to the development requirements of ship maneuverability simulation, the main design framework, key technology, and implementation approach of this research method are given, which provides basic methods and ideas for the digital research of ship maneuverability simulation.
Haze generation and feature fusion network aiming at real-world single image dehazing
Authors: Pan Hao , Tang Huanle 
Published: August 2024

Abstract
Because most dehazing methods use synthesized haze images for training, they may perform well on synthesized datasets. However, when these methods are applied to real-world scenes, their performance may significantly decrease due to domain shift. Therefore, we propose a dehazing network for real-world hazy scenes. This network includes a haze generation network that can utilize the hazy information of real haze images to generate images that are closer to real hazy scenes, generating training pairs to address the domain shift problem. The network also includes a dehazing network that integrates feature fusion attention mechanisms, which can achieve better dehazing performance.
Research on thermal runaway early warning algorithm of lithium battery based on improved particle swarm algorithm optimized BP neural network
Authors: Yan Peng , Li Shaopeng , Wang Xiao , Cao Zhenkun 
Published: August 2024

Abstract
In response to the issues of false alarms and missed alarms in traditional fire alarm systems for energy storage station fire prevention and control, this study proposes a fire alarm approach employing improved PSO-BP neural networks. Firstly, we set adaptive inertial weights and asymmetric learning factors on the PSO algorithm that are automatically optimized as fitness changes to improve the optimization accuracy and convergence speed of the algorithm. Secondly, the thresholds or weights are mapped to random particles for training to obtain the optimal value. Finally, utilizing the Pyrosim software, a model of the battery cabinet was established, and the thermal runaway data samples were input into the neural network model for training. The test consequence indicates that the improved PSO-BP model achieved a 5% and 3.75% rise in precision relative to the neural network and PSO-BP model, respectively, while the frequency of iterations declined by 55% relative to the PSO-BP.
Active disturbance rejection control of a bearingless permanent magnet slice motor
Authors: Jiang Dikai , Meng Shuping , Xu Wenbo , Wei Zeyu , Li Chao , Wang Helong , Huang Shuxian 
Published: August 2024

Abstract
Bearingless permanent magnet slice motor has the advantages of easy stator-rotor isolation, no contact, simple structure, and high integration, which is a significant feature in the ultra-pure field of biology, chemistry, medical treatment, semiconductor manufacturing, aerospace, and other applications, as well as high-speed drive field. To realize the control of a bearingless permanent magnet slice motor, the mechanism of torque control, as well as suspension control, is systematically analyzed. On this basis, a simplified control parameter design method for decoupling torque and suspension is proposed, and the parameter tuning rules are given. For the problem of insufficient anti-disturbance performance of the suspension control under the traditional linear active disturbances rejection controller (LADRC), an improved LADRC strategy based on known disturbances is proposed, and an expansion state observer (ESO) with partially known disturbances is designed. The suspension accuracy and robustness of the system are verified by simulating the motor control with a Matlab/Simulink module. Finally, the improved LADRC control is used in a BPMSM prototype. The simulation and experimental results show that the improved LADRC control with the addition of known perturbations effectively improves the suspension control stability and accuracy compared to the traditional LADRC control.
Welding defect detection based on YOLOv8
Authors: Zhou Jianhang , Zhang Wang , Sui Xuejun , Chen Yuandong 
Published: August 2024

Abstract
In this paper, we propose an improvement scheme for the unclear boundary capture issues of the YOLOv8 algorithm in weld defect detection. We replace the Conv module with the CG black module in Backbone to enhance the algorithm’s ability to extract image features so as to capture weld defects more accurately. Meanwhile, the CBAM attention mechanism is introduced in the tail of the Backbone to improve the model’s capability to learn the correlation of different regions of the image and improve the generalization performance. The experimental findings substantiate that the enhanced algorithm exhibits superior accuracy in comparison to the original algorithm, recall rate, Map@0.5, and other indicators of the improved algorithm. The enhanced algorithm demonstrates a 3.6% increase in accuracy when contrasted with the original algorithm.
Enhancing safety in undersea exploration: a novel rescue strategy using genetic simulated annealing
Authors: Wu Yi , Tang Rui , Yan Boqiang 
Published: August 2024

Abstract
The increasing interest in undersea exploration necessitates the development of advanced safety and rescue strategies. This paper introduces a novel rescue strategy for manned submersible expeditions, emphasizing the use of a genetically simulated annealing algorithm to optimize rescue operations. By integrating this algorithm with multi-unit cooperative rescue tactics and optimal search path modeling, we propose a comprehensive approach to improve the efficiency and effectiveness of rescue operations. Our simulations in a maritime area of 10, 000 m × 10, 000 m demonstrate the algorithm’s ability to efficiently coordinate the rescue of multiple distressed submersibles, ensuring a rapid and strategic response to emergencies.
Spatio-temporal action detector with cross-stream knowledge transfer
Authors: Yang Xianyu 
Published: August 2024

Abstract
Relying on human labor for uninterrupted monitoring through surveillance systems is highly inefficient and prone to oversight, making intelligent surveillance have great application prospects and value. To implement this technology, spatio-temporal action detectors are typically used, which are neural networks capable of locating action instances simultaneously in both time and space dimensions. To address the issue of high computational demand commonly associated with 3D backbone architectures, we propose a Dynamic Cross-Stream MovingCenter Detector (DCMOC) based on a 2D backbone network, an improved version of the MOC detector. In the DCMOC detector, the movement branch and the bounding box branch correspond to the motion and appearance features of action instances, respectively. Through a designed cross-stream knowledge transfer module, feature knowledge is transferred between the two branches to enhance feature representation. To compare with existing spatio-temporal action detectors, experiments were conducted on two datasets, JHMDB and UCF101-24. The experiments demonstrate that the DCMOC detector achieves competitive performance with significantly lower computational requirements than existing 3D architecture models and achieves optimal performance on the video-map metric at high thresholds.
Design of multi-component gas measurement system based on STM32
Authors: Qi Xinyi , Zhang Shiqian , Yang Yanchao 
Published: August 2024

Abstract
To detect the concentration of toxic and harmful gases in the environment in a timely and effective manner, this article designs a multi-component gas measurement system based on the STM32 processor, which can effectively detect three toxic and harmful gases (CO, NO2, and SO2), a gas detection terminal for the concentration of two greenhouse gases (CO2 and CH4) and an inhalable particulate matter (PM2.5), and a handheld display terminal that can display the measured gas concentration in real-time. A practical application circuit for electrochemical sensors has been designed, simplifying the design of driving amplification circuits. Finally, the feasibility of the system was verified through automobile exhaust experiments.