bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intensified Partial Least Squares Fault Diagnosis Method based on CARS
Authors: Wang Xuan , Zhang Liuqi 
Published: March 2023

AbstractPartial Least Squares (PLS) is a fault diagnosis model used in statistical process monitoring. PLS decomposes the data space into a quality correlated subspace and an uncorrelated subspace, and monitors the state of both subspaces by setting the appropriate statistics. However, post-processing methods such as modified-PLS (MPLS) and efficient projection to latent structures (EPLS) gradually lose their ability to detect faults when low-intensity faults that do not affect quality. In order to eliminate the drawbacks of post-processing methods and improve the stability of algorithms under different conditions, this paper introduces the competitive and adaptive reweighted sampling method (CARS) into the field of fault diagnosis and proposes a fault filtering method combining CARS and orthogonal signal correction algorithm (OSC). First, an initial screening of the variable space was completed using CARS method. OSC algorithm is then used to remove quality- uncorrelated components. Next, the variable space is completely decomposed into a quality-correlated and an irrelevant subspace using intensified partial least squares method (IPLS). Finally, the validity of the model was verified using the simulations.
A Representation Theorem for L-fuzzy Pseudo-metrics
Authors: Lin Shuang , Guo Mingxue , Zhong Yu 
Published: March 2023

Abstract
Through this article, the definitions of an L -fuzzy pseudo-metric and a family of pseudo-metric that satisfies the property of lower-semicontinuity (or named nests of pseudo-metrics) are introduced. After that, we indicated that an L -fuzzy pseudo-metric can be constructed from nests of pseudo-metrics and nests of pseudo-metrics can be constructed from an L -fuzzy pseudo-metric. Therefore, we establish a each to each association between L - fuzzy pseudo-metrics and nests of pseudo-metrics.
Estimation of A Partial Linear Model with Instrumental Variable for the Longitudinal Data
Authors: Sheng Lili , Kang Fangyuan , Zhao Jianxi , Liu Ruiping 
Published: March 2023

Abstract
A partial linear model with instrumental variables was developed for longitudinal data. In the partially linear model, the explanatory variable is an endogenous variable, which is correlated with the error term. The endogenous variable was expressed by an instrumental variable and an error item. The endogenous variable was estimated by the instrumental variable through the least square method. B-spline regression combined with QR decomposition was used to approximate the nonparametric function. For the estimation of parametric, the Quadratic inference function and Secant method were applied. Under some conditions, the estimator was consistent and asymptotic normality. Some simulation was conducted to prove the finite sample behavior of the estimator.
Application of Deep Wavelet Kernel Extreme Learning Machine in Fault Diagnosis of Tamping Vehicle
Authors: Wang Hairui , Li Dongwen , Zhu Guifu , Yang Xiuqi 
Published: March 2023

Abstract
Since it is easy to overfit due to the long training time of the fault diagnosis model for machinery. Introducing the idea of autoencoder (AE) into the wavelet extreme learning machine (WELM) and then stacking to form WELM-AE can convert the underlying fault features to more abstract and advanced ones. And then the adaptive boosting kernel extreme learning machine (Adaboost-KELM) is used as the top-level classifier for fault recognition. The experimental results verify the feasibility of the proposed algorithm in the fault diagnosis of tamping machine with the characteristics of the fast training speed of the extreme learning machine, and a higher accuracy rate than back propagation (BP), support vector machine (SVM), stacked autoencoder (SAE), and convolutional neural networks (CNN).
Single Intersection Traffic Light Control by Multi-agent Reinforcement Learning
Authors: Du Tongchun , Wang Bo , Hu Liangchen 
Published: March 2023

Abstract
Deep reinforcement learning is a data-driven method, which is very promising for alleviating traffic congestion through intelligent control of traffic lights. In this paper, the traffic signal of an intersection is divided into four independent phases and then controlled by deep Q-network (DQN) models respectively. Models can receive observations from their own angle of view, i.e., north-south straight, north-south left turn, east-west straight, east-west left turn, instead of extracting features from the whole scene. We suppose that it is beneficial for learning better policy if agents could sense the environment more precisely. DQN models are jointly trained under the revised QMIX framework to promote coordination capability. For decentralized execution, traffic lights of the phase with the highest Q-value will turn green. The experiments are done under SUMO, the results demonstrate that our method obtains higher reward and lower delay compared to controlling the holistic cycle by using a single DQN model.
Spatiotemporal Prediction Method for Vorticity of Three-dimensional Near-wall Cylindrical Flow Based on Convolutional Recurrent Neural Network
Authors: Mao Yuanxiang , Qiu Xiang , Li JiaHua , Liu YuLu 
Published: March 2023

AbstractDue to the existence of a wall surface, the flow around the near-wall cylinder is a very complex unsteady dynamic behavior. However, this kind of feature extraction and fast prediction method of complex flow is difficult to solve in engineering. Therefore, a data-driven model based on Convolutional Recurrent Neural Network (CRNN) is proposed to solve the feature extraction and fast prediction of complex flow, and to predict the vorticity of the near wake region of a three-dimensional near-wall cylinder with G/D=1. CRNN model can analyze the relationship between Spatiotemporal evolution. The three-dimensional flow around the near-wall cylinder with Reynolds number 1500 meshed, and the vorticity was calculated after grid calculation to establish the Spatiotemporal evolution data set. For machine learning problems with high dimensions and large amounts of data, the Adaptive Moment (Adam) estimation method served as an effectively optimize. According to the Spatiotemporal prediction results of vorticity, the prediction results of the model fit well with the numerical simulation results, and the vorticity of the future time step is successfully predicted on the space and time series, that is, the evolutionary relationship of the Spatiotemporal series of vorticity near the wake of the near-wall cylinder is established. After the above model is established, only the initial multi-time vorticity data need to be obtained. The model can serve as predicting the vorticity field in the near wake region of the near-wall cylinder in the future.
Analysis and Modelling of the Working Principle of the Deceptive Infrared Jammer
Authors: Zhang Kai , Chen Qiuju , Zhang Yi , Cheng Yubao 
Published: March 2023

Abstract
The deceptive infrared jammer is mainly used to jam the infrared point source-seeking guidance head of the modulated disc type. Through the mathematical modelling and simulation based on MATLAB, from the point of view that the essence of deceptive jamming is its signal should be recognized as the guidance signal, the jamming signal style output from the jammer has been analysed in detail. Then the parameter setting of the jammer has been determined, and the constraint relationship between the parameter setting and the jamming effect has been researched. The research work in this paper can effectively enhance the reader’s understanding of the deceptive infrared jammer and its jamming mechanism.
Review and evaluation of metal-hydride-based hydrogen sensors as safety devices for future sustainable aviation
Authors: Franke F , Kazula S , Enghardt L 
Published: March 2023

Abstract
This paper presents an overview of metal-hydride-based hydrogen sensors and evaluates their potential for utilization in aerospace safety applications in future hydrogen-powered aviation. The ‘electrical resistance’, ‘cantilever expansion’, ‘nanogap expansion’, ‘fiber optical’, ‘chemochromic optical’ and ‘acoustic’ sensing principles are being described. Requirements including specific performance parameters for hydrogen sensors in aerospace safety applications are identified. Evaluation criteria are derived from these requirements and finally the sensing mechanisms are evaluated by means of a weighted point rating. The results of this evaluation reveal the high potential of ‘electrical resistance’, ‘cantilever expansion’, ‘nanogap expansion’ and ‘fiber optical’ sensors, although none of these principles meets all the requirements yet. With the transition to hydrogen-based aviation, metal hydrides and its various applications will become more attractive. Synergies between these technologies may further drive the research and development progress, so that metal-hydride-based hydrogen sensors can overcome their current drawbacks and contribute to the transition to future hydrogen-powered sustainable aviation.
Modelling and complementary analysis of a small hybrid hydro-photovoltaic power system
Authors: Liu Junjie , Hua Yanke , Jin Ziyu , Li Huanhuan 
Published: March 2023

Abstract
Renewable energy sources, especially solar power combined with pumped hydro storage generation (PHSG), have emerged as a remedy to conventional power generation, providing important opportunities for the safe operation of electricity. However, renewable energy is intermittent and random due to unreliable solar radiation. In view of these considerations, this paper focuses on the flexible complementary regulation of the PHSG responding to the fluctuation of solar power generation. To implement the analysis, a mathematical model of the small hybrid hydro-photovoltaic power system is established. Based on the established model, the complementary characteristic between these two renewable energies is simulated at the second scale. The results show that the PHSG has the ability to respond to the disturbance of solar power due to the flexible transition between generator mode and pump mode. The implemented model and obtained conclusions help promote the efficient use of renewable energy systems and reduce power failure accidents.
Boiling heat transfer on porous single layer brass meshes
Authors: Bialek A , Kargul M , Stokowiec K 
Published: March 2023

Abstract
Heat transfer enhancement has been a crucial issue in recently developed research. The article presents the possibility of increasing the heat flux values exchanged during boiling of water and ethanol on surfaces with meshes. Single layer brass meshes were tested and the results indicate a considerable potential of such structures to improve boiling heat transfer. The comparison of the experimental data with selected models indicate large differences that might be linked with different experimental conditions and sample treatment methods. Various experimental conditions found in literature concern different pressure values, various angles of heater surface inclination as well as different sources of heat (electrical heaters, laser beam, hot fluid).
Calorimetric methods and thermal management of lithium-ion batteries: A mini-review
Authors: Madani Seyed S , Ziebert Carlos 
Published: March 2023

Abstract
Lithium-ion batteries can be employed in various applications, including grid integration, electric vehicles, grid support, and consumer electronics. Lithium-ion batteries are currently one of the most important options for storing electrical energy. Therefore, modelling lithium-ion batteries and examining their temperature distribution and heat transfer using different calorimetric techniques is very important mostly for safety concerns. Thus, the study of battery heat transfer helps designers to propose and develop a suitable cooling or thermal management system. Different sources including overpotential contribute to heat generation. Different understandings were achieved from the previous modelling and experimental studies which involve the necessity for more accurate heat generation measurements of lithium-ion batteries, and improved modelling of the heat generation specifically comprehended at large discharge and charge rates for different applications including electric vehicles.
Estimation of integrated photovoltaics potential for solar city bus in different climate conditions in Europe
Authors: Karoui Fathia , Claudon Fabrice , Chambion Bertrand , Catellani Stephane , Commault Benjamin 
Published: March 2023

Abstract
In order to improve primary energy saving and reduce greenhouse emissions, vehicle integrated photovoltaics have an ongoing interest. Studies on the benefits from vehicle solar roof, which take into account all the losses and the monthly variation in different climate conditions, are required. Therefore, we developed a simulation tool of the mileage covered by VIPV. This tool takes into account various use profiles and different characteristics of the vehicles and of the PV system. Focusing on city bus, simulations show that many parameters influence the outputs of the model, mainly: the geographic location, the shading losses, the electric architecture and the battery saturation. With projections of the technology in 2030, VIPV cover up to 9739 km annual mileage. This represents up to 24 % of the total distance. For the best month, it can get up to 47 km/day. For average Europe case, with 30 % shading losses, the VIPV cover only 3711 km annual mileage. The upgrade of the technology from 2022 to 2030 improves the benefits of VIPV by approximately 34 %. Life cycle assessment of solar city bus shows neutral to high gains. The carbon footprint is up to 28 T CO2-equivalent avoided emissions on 20 years lifespan.
Research on fast solution method for vibration prediction of digital twin substation equipment based on neural network model
Authors: Wu Peng , Wu Yiming , Li Xiaohan , Li Jiansheng , Yao Tingli , Liang Jiabi 
Published: March 2023

Abstract
This study began by building a series of high-quality simulation modules of real space clones to obtain comparable data and conditions, and comparing them with data and situations obtained from the real state module to bring the system closer to reality. And the imaging model uses a number of numbers to generate the number of samples to train the neural network model of the plan. Finally, this paper takes the three-phase three column double winding transformer models of the module as an example to evaluate the proposed method of evaluating the design characteristics of the module.
Numerical study of spray cooling with flash evaporation
Authors: Chen Mengrong , Xie Yue , Gong Mengjun , Zhang Xinyu , Ren Yong 
Published: March 2023

Abstract
This work aims to apply Computational Fluid Dynamic (CFD) method to establish a flash evaporation spray cooling (FESC) model to simulate the heating process and find the optimum cooling performance. The heat transfer process during FESC is studied through numerical simulation using commercial code ANSYS FLUENT. The species transport model and the discrete phase model are applied to simulate the multiphase flow and heat transfer process. The turbulence effect is included. The effects of flow rate, nozzle pressure, nozzle angle, and the nozzle orifice size on spray cooling are investigated through analyzing the final surface temperature distributions. This work revealed the mechanism of the heat transfer process in FESC by means of particle tracks and velocity magnitude distribution. The simulated results for the effect of flow rate were compared with other researchers’ previous published experimental results. The comparison shows same trend, which verified the model and the simulation result. The optimum cooling performance is found by analyzing various working conditions. The results show that high flow rate, high nozzle pressure, small nozzle angle and small nozzle orifice can improve the FESC characteristics. The detailed mechanisms of these effects under various working conditions are also discussed. Under giving working conditions, the optimum cooling performance is obtained for the condition where mass flow rate of working fluid is 2L/min, the nozzle pressure is 100MPa, the nozzle angle is 15 degrees and the orifice size of the nozzle is 1mm.
Safety Evaluation Modelling of Hydrogen Energy Industrial Chain for Transportation-energy Integration System in China
Authors: Du Lianming , Liao Guangrong , Wu Yangwei , Deng Qian , Li Aijun , Mao Ning 
Published: March 2023

Abstract
This paper divided the safety evaluation indexes of hydrogen energy industrial chain for transportation-energy integration in China into four target tiers, such as hydrogen production safety, storage & transmission safety, supply safety and hydrogen utilization safety. Then the influential factors of security risks under different target tiers were analyzed, and the evaluation index system of security risks were established. Furthermore, according to the characteristics of different target tiers of hydrogen energy industrial chain, the safety evaluation model was constructed by combining analytic hierarchy process with risk assessment matrix. This safety evaluation model can assess the safety status of hydrogen energy chain quantitatively and qualitatively, including calculating the weight of each index and analyzing the risk level of whole system. Moreover, such modeling method could provide a core assessment framework for enterprises and industries related to hydrogen energy to realize real-time security monitoring and alarm.
Hot compression deformation behaviour and microstructure evolution of Ti-3.5Al-3.5V-0.5Fe-0.6Mo-0.5Cr alloy
Authors: Zhu Mingting , Liu Rui , Song Xiaoyun 
Published: March 2023

Abstract
Ti-3.5Al-3.5V-0.5Fe-0.6Mo-0.5Cr alloy is a new superplastic titanium alloy The alloy was deformed by plane strain compression at 820°C~880°C, 0.16 s−1~8 s−1, which can provide reference for obtaining homogeneous microstructure during the rolling process. The results show that the flow stress of the alloy has a negative correlation with the deformation temperature and a positive correlation with the strain rate. The flow softening behavior of alloy is greatly influenced by deformation temperature. The fraction and the length of α lamellar increase with the decrease of temperature, and the flow-softening phenomenon of the alloy is more obvious. The homogeneity of microstructure and the recrystallization ratio of α lamellae increase with the increase of deformation temperature and the decrease of strain rate. The alloy can obtain homogeneous microstructure at 850°C, 0.16 s−1, and 880°C, 0.16 s−1~1.6 s−1 deformation conditions.
Investigation of corrosion inhibitor used in cement
Authors: Yan Wei , Hao Wang , Xu Zhongying 
Published: March 2023

Abstract
Steel bar corrosion inhibitor can be incorporated into concrete (or mortar) with a small amount, or coated on the cement surface to protect the steel materials. It can prevent or delay the corrosion of metal bars by directly acting on the steel bars in concrete (or mortar), but it does not affect the structure and properties of concrete. In this paper, the single component corrosion inhibitor was screened and evaluated at room temperature by salt water immersion test and static coupon weight loss method. The results show that hydrazine hydrate and KNO3 can effectively prevent the corrosion of reinforcement after being placed in simulated corrosion solution for 7 days. When the concentration of hydrazine hydrate and KNO3 is greater than 0.5%, the corrosion inhibition rate of KNO3 is 95.44% (100mg·L−1).
Effect of annealing process on microstructure and properties of 980MPa grade hot-dip galvanized dual-phase steels
Authors: Wang Ke-qiang , Liu Ren-dong , Guo Jin-yu , Yan Ping-yuan , Jin You-peng , Zhang Feng 
Published: March 2023

Abstract
A hot-dip galvanizing simulator studied the effects of soaking temperature and rapid cooling end temperature on the microstructure and mechanical properties of 980MPa grade hot galvanizing dual-phase steel. The results show that at the end of a rapid cooling temperature of 290°C, with the increase of soaking temperature, the tensile strength of the test steel increases, the yield strength decreases at first and then increases, and the elongation decreases as a whole. The microstructure is composed of ferrite and martensite. At the soaking temperature of 800°C, the strength decreases, and the elongation increases with the increase of the end temperature of rapid cooling. When the end temperature of rapid cooling is lower than 320°C, the martensite tempering is not apparent, and there are many high-density dislocations in the ferrite near the neighbor. When the cooling end temperature is 350°C, some martensites are tempered. The twins in martensite are widened, carbides are unevenly precipitated along the twin planes, and the dislocation density decreases. When the soaking temperature is between 800°C and 820°C, the end temperature of rapid cooling is between 260°C and 290°C. Annealing and zinc plating are beneficial to obtain dual-phase steel with excellent mechanical properties.
Microstructure and mechanical properties of Selective Laser Melted Ti-6.5Al-2Zr-1Mo-1V alloy
Authors: Nie Xuan , Song Xiaoyun 
Published: March 2023

Abstract
Selective laser melting (SLM) offers a new solution to the production of complex-shaped Ti-6.5Al-2Zr-1Mo-1V(TA15) alloy components with the advantage of low weight and high performance in the aerospace industry. In this paper, Ti-6.5Al-2Zr-1Mo-1V samples with relative density > 99.5% were fabricated by SLM. The microstructure of SLMed samples is composed of numerous prior-β grains and interior acicular α′ martensite, exhibiting extremely high strength but poor plasticity. After heat treatment, the ultimate tensile strength(UTS) and yield strength(YS) are reduced by various degrees, while the plasticity is obviously improved owing to the decomposition and transformation of α′ martensite.
Study on the dynamic deformation behavior and microstructure evolution of Ti-5Al-2.5Sn utilized Quasi-In-Situ method
Authors: Wei Rui , Li Anmi , Wang Boya , Ye Wenjun , Luo Yumeng 
Published: March 2023

Abstract
In this paper, the microstructural evolution process and deformation mechanism of annealed Ti-5Al-2.5Sn alloy under a high strain rate of 2700 s−1 were investigated by quasi-in-situ method. The main deformation mechanism is found to be dislocation slip in the strain range from 0 to 0.05, and about 96% of the whole grain is deformed by dislocation slip, and

{
1

1
¯

00
}
<
11

2
¯

0
>

prismatic slip dominates the plastic deformation. When the strain increased from 0.05 to 0.10, the original slip system continued to move, and 13% of the whole grain activates the new slip system. In the strain range from 0.10-0.20, the number of slip traces does not increase significantly, and the new slip system is difficult to be activated. In the whole deformation process, about 42% of whole the grain activates a variety of slip systems including

{
1

1
¯

00
}
<
11

2
¯

0
>

prismatic slip and

{

1
¯

101
}
<
11

2
¯

0
>

pyramidal <a> slip which activated at the same time. Results show that

{
11

2
¯

1
}

twin only appeared in a few grains in the whole observed region, indicating that twinning is not the dominant deformation mechanism of annealed Ti-5Al-2.5Sn alloy at a high strain rate.