bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of time-varying meshing stiffness of helical gear pair with tooth surface spalling
Authors: Zhang Yongjie , Liu Wen , Song Chen , Lin Tengjiao , Duan Mingxu 
Published: March 2021

Abstract
Time-varying meshing stiffness (TVMS) provides significant information about the health of gear systems. Tooth spalling will change the TVMS. Considering the contact state of gear pair, the purpose of this study is to investigate the influence of tooth surface spalling on the TVMS of helical gears. Firstly, the meshing stiffness analysis algorithm of helical gear pair with tooth surface spalling is proposed, based on the potential energy method, slicing method and numerical integration formula. Then the influence of different spalling parameters on the TVMS of helical gear pair is analysed by considering spalling length, width and the oblique cylinder bottom shape. Finally, the results show that the contact line falls into the defect area, the spalling has the most significant effect on the stiffness. The influence of oblique cylinder shape at the bottom of spalling is mainly affected after the contact line passes through the spalling area. This study improve the theoretical basis for spalling fault of helical geared mesh.
High precision auxiliary positioning system of drilling bit posture based on visual encoder
Authors: Haoyu-Zhang  , Fan-Zhang  , Pingyu-Zhu  
Published: March 2021

Abstract
In order to obtain the accurate spatial position reconstruction of the drilling bit wear surface, a drilling bit posture measurement system based on visual encoder is proposed in this paper. A tool clamping device with adjustable posture is designed for 3D digital microscope to hold the drilling bit with outer diameter within 6-8mm. The adjustment of the spatial posture of the drill bit is completed by the cooperation of two rotating discs whose axes are perpendicular to each other. The shaft of the vertical rotating disk is consistent with that of the encoder, and its rotation angle is used as the reference and measured accurately. The experimental results show that the angle detection accuracy is 0.1 degree. The accurate angle information provides the basis for the machine.
General uncertainty analysis and synthesis for a precision boomed centrifuge
Authors: Wang Shiming , Zhang Xiaolong , Li Yibin 
Published: March 2021

Abstract
The general precision uncertainty of a centrifuge is suggested here to determine whether a precision centrifuge is suitable to calibrate inertial sensors. The possible centrifuge errors that affect the accuracy of the specific input forces are analyzed with three kinds of centrifuge errors identified: displacement error, angular rate error, and attitude error. Taking an accelerometer’s calibration on a centrifuge as an example, the specific force components for the three axes of a tested accelerometer are acquired using a homogeneous transformation algorithm. The impact of the uncertainty for each kind of error source on the specific force is calculated, and their contributions to the general uncertainty of the centrifuge are acquired. Through simulation, by limitation of the tolerances towards the centrifuge errors, the general uncertainty of a precision centrifuge is designed to satisfy the requirements of the pre-design in 6×10−6. And the influence of the general uncertainty on calibration accuracy of error model coefficients of accelerometer is calculated, mostly on C

op
. The influence of the error which accounts for a large part of the general uncertainty on the calibration accuracy is mainly studied. Simulation results show with the increase of the general uncertainty of centrifuge, its influence on the calibration accuracy of error model coefficient is greater. And when the general uncertainty is constant, the influence of the general uncertainty on the calibration accuracy can be reduced by increasing the input specific force.
Research on model predictive peak current control algorithm for real-time inductance Identification
Authors: Zhang Dong , Zhang Yiming , Liang Biao , Wang He 
Published: March 2021

Abstract
Compared with the traditional PI control, the current loop based on model predictive control has better dynamic performance and is widely used in the field of power electronic control. Peak current control (PCC) can conveniently protect the peak current of inductance, avoid the saturation of inductance, and easily realize the over-current protection of converter. In this paper, a model predictive peak current control algorithm for real time inductance identification is proposed for BOOST converter, and a method of double sampling peak current and valley current is proposed to realize real time identification of inductance value in a sampling period, which improves the dynamic performance and robustness of the algorithm. Simulation and experiment show that the proposed method can identify the inductance value in real time and track the reference value of the inductance current in two control periods.
Identification and Control of Elongation System of Skin Passing Mill Based on Intelligent Algorithm
Authors: Ren Xin-Yi , Gao Hui-Min , Xu Hai-Wei , Huang Hua-Gui , Sun Jing-Na 
Published: March 2021

Abstract
In view of the nonlinear, time-varying and time-delay characteristics of elongation control system of skin pass mill, according to analysis of the mechanism model of elongation control system of skin pass mill, BP neural network was used to identify the structural parameters of the model. With reference to the regulating function of biological immune system and the function of fuzzy reasoning logic which can approach nonlinear function, a fuzzy immune PID control strategy was proposed to improve the elongation control accuracy of skin pass mill combining fuzzy control and immune feedback mechanism with traditional PID control. The simulation results show that the control strategy has the advantages of small overshoot, fast response, strong anti-interference ability and robustness, and the control effect is better than the traditional control method.
Fast-axis collimating lens recognition algorithm based on machine vision
Authors: Wu Guodong , Zhu Ma , Jiang Qiuyue , Sun Xiang 
Published: March 2021

Abstract
In order to solve the problem of low efficiency and low accuracy of manually picked fast shaft collimation lens in actual production, the method of recognition of fast shaft collimation lens based on machine vision is studied. Using industrial camera to achieve image acquisition, using HALCON software to pre-process the image of fast-axis collimated lens, edge detection and other aspects of research, through the platform verification, the results show that the processed image is clear and easy to identify. In the lens placement is not accurate, the background is dirty can still ensure a better positioning effect, This study has some reference significance for visual recognition in the actual production of fast-axis collimation lens.
A Handwritten Chinese Character Recognition based on Convolutional Neural Network and Median Filtering
Authors: Zhuang Yuan , Liu Qiong , Qiu Chengjun , Wang Cong , Ya Fudong , Sabbir Ahamed , Yan Jiaqi 
Published: March 2021

Abstract
With the rapid growth of researches toward computer vision and pattern recognition, methods that based on convolutional neural network (CNN) have shown unique advantages on handwritten characters recognition, also provided impressive results. This paper proposes a model based on CNN to deal with matters of handwritten Chinese character recognition. Different with conventional recognition system, in this model, input images are preprocessed by median filtering to smooth and reduce noise. For testing the stability and performance of the model, two testes are managed respectively. In integral test, experimental results show that the accuracy rate of recognition approach to 90.91% after 5000 times training, mean square error is decreased to 0.0079 at last. Meanwhile, this system also has a good performance at real-time test.
Citrus recognition based on YOLOv4 neural network
Authors: Wang Changhong , Luo Qiang , Chen Xin , Yi Bingliang , Wang Haibao 
Published: March 2021

Abstract
In order to realize the accurate and rapid recognition of citrus fruit by autonomous picking robot in natural environment, a citrus recognition method based on YOLOv4 neural network is proposed. This algorithm improves the YOLOv4 model and uses the Kmeans++ algorithm to obtain the prior frame in the model to enhance the scale adaptability. Take citrus pictures independently and expand the data, use the LableImage platform to mark the data, and train the network model under the Darknet framework. According to the results, the citrus recognition model has good robustness and real-time performance to the common interference factors and their superposition in natural picking environment. The average recognition accuracy is 89.23 and the average detection speed is 60 ms. The average recognition accuracy is 89.23. It meets the requirements of real-time image recognition speed and accuracy of citrus picking robot.
Research on Visual Logistics Big Data Information Service System
Authors: Li Na , Yao Wei 
Published: March 2021

Abstract
Big data visualization is a new technology, from the core functions of data collection, storage, calculation, scheduling, advanced search, and other core functions, and based on cloud computing to scientifically manage data to ensure data security and stability. How to use big data visualization in logistics management is a problem that needs to be solved now. The paper introduces the main functional modules of the urban logistics big data platform and its system design and implementation. The platform is an information service platform with “big data” as the core and “data analysis” as the center, using cloud computing, significant data tooling, and other information technologies are used to establish an urban logistics big data platform to realize comprehensive functions such as credit evaluation of logistics and warehousing enterprises, display of warehousing space distribution and analysis of warehousing resource usage, and improve the public service capabilities of logistics institutes.
Research on Fault Diagnosis Method of Train Rolling Bearing Based on Variational Modal Decomposition and Bat Algorithm-Support Vector Machine
Authors: Jin Zhenzhen , He Deqiang , Chen Yanjun , Liu Chenyu , Shan Sheng 
Published: March 2021

Abstract
Aiming at the problem that the vibration signal of train rolling bearing presents nonlinear and non-stationary characteristics, which leads to the difficulty of fault feature extraction, a fault diagnosis method of train bogie rolling bearing based on variational mode decomposition (VMD) and bat algorithm optimization support vector machine (BA-SVM) is proposed. Firstly, the center frequency method is used to determine the K value of VMD algorithm. Then, the original signal is decomposed into a series of intrinsic mode components and the distribution entropy of each component is calculated as the feature vector, and the bat algorithm is used to optimize the model parameters of support vector machine. Finally, the BA-SVM model is used for fault pattern recognition of train rolling bearing. The experimental results show that this method can effectively extract the fault characteristics of train rolling bearings and realize fault diagnosis, and the recognition rate is better than that of the comparison method.
Research on load simulation control system based on optimal H∞ method
Authors: Yang Wenlin , Lv Haoliang , Zhou Xiaoqi , Li Gen , Li Yafeng 
Published: March 2021

Abstract
In order to optimize the accuracy of the vehicle load simulation system, the control framework of the system is established based on the speed tracking method. Based on H∞ mixed sensitivity method, a disturbance observer is established. In order to solve the problem that the order of disturbance observer is too high to restrain disturbance directly, a design constraint framework of disturbance observer is proposed, and the optimization of disturbance observer is realized in the constraint framework. The simulation and experimental results show that the optimized disturbance observer has stronger disturbance rejection ability.
An explicit and implicit hybrid method for structural topology optimization
Authors: Zhang Junfeng , Liu Enhai , Liao Jingyu 
Published: March 2021

Abstract
In order to improve the manufacturability of topology optimization results, this paper proposes a hybrid method based on explicit description of Moving Morphable Components (MMC) and implicit description of Solid Isotropic Material with Penalization (SIMP). The method uses the global convergence characteristics of SIMP to quickly obtain the main force transfer path of structure, and threshold processing is used to eliminate the problem of gray-scale elements generated by SIMP. Furthermore, a morphological idea is proposed to simplify the structure. Then, components are used to fit the structure and extract its geometric parameters, and transition to the MMC is further optimized. Finally, this paper studies several typical examples, and compares them with the single MMC method from three aspects of design domain components layout, structural compliance values, and structural uniformity. The results show that the structure obtained by hybrid method has smaller structural compliance value and significant increase in the uniformity of the structure size.
Cooling prediction of motorized spindle based on multivariate linear regression
Authors: Zhou Huimin , Wang Zhan 
Published: March 2021

Abstract
In order to reduce the influence of heat produced by motorized spindle on the machining performance, cooling of motorized spindle need to be studied. Based on the multiple linear regression theory, the motorized spindle cooling model was established according to the temperature conditions of different cooling water temperatures and flow rates for the cooling process of the motorized spindle temperature. The validity of the model was verified by three verification methods. Based on the experimental data, the numerical simulation model of the motorized spindle cooling is established. It is effective and reliable to verify the mathematical model of the motorized spindle cooling with three test methods. The results showed the cooling prediction model of motorized spindle was relatively stable.
Hierarchical multi-layered scaffolds based on electrofluidodynamic processes for tissue engineering
Authors: Piegat Agnieszka , Niemczyk Agata , Boccaccini Aldo R , El Fray Miroslawa , Liverani Liliana 
Published: March 2021

Abstract
The aim of this study was to obtain hierarchical scaffolds combining 3D printing and two electrofluidodynamic methods. The multi-layered scaffold is composed by 3D printed struts, electrospun fibers obtained from poly(ϵ-caprolactone) and electrosprayed spheres produced from hydrophobically modified chitosan, namely chitosan grafted with linoleic acid (CHLA). Since CHLA has been used for the first time in the electrospraying (electro dynamic spraying, EDS) process, the formation of spheres needed an optimization process. The EDS process was strongly affected by the solvent mixture composition, concentration of acid used for CHLA dissolution and solution flow rate. By using the optimized electrospraying conditions, uniformly distributed spheres have been obtained, decorating struts and nanofibers. Preliminary biological tests with mouse preosteoblasts (MC3T3-E1) were performed to investigate the effect of the hierarchical scaffold on cell seeding efficacy. Results showed that the hierarchical structure enhances cell seeding efficacy, respect to the 3D printed struts alone, preventing that the cells passed through the struts during the seeding. Moreover, the addition of the electrosprayed nanoparticles does not affect the cell seeding efficiency. The versatility of the proposed structure, with the added value of CHLA nanoparticles decoration could be suitable for several applications in tissue engineering, mainly related to drug delivery systems.
Projected changes in wind speed and wind energy potential over West Africa in CMIP6 models
Authors: Akinsanola Akintomide Afolayan , Ogunjobi Kehinde O , Abolude Akintayo T , Salack Seyni 
Published: March 2021

Abstract
The development of wind energy in West Africa is essential to meet the rising energy needs due to population growth and societal development. However, only few studies have investigated the changes in turbine hub-height wind characteristics over the region under changing climate. This study aims at assessing the impact of climate change on wind power density (WPD) over West Africa using the simulations from the newly developed Coupled Model Intercomparison Project version 6 (CMIP6) models. The CMIP6 near-surface wind speed and directions simulations for the historical climate (1985–2014) were compared with ERA5 reanalysis data using multiple descriptive statistics. Relative to ERA5 reanalysis, the CMIP6 models alongside their multimodel ensemble mean (EnsMean) realistically reproduce the near-surface wind characteristics (i.e. wind speed and directions) across most subregions of West Africa, although noticeable biases still exist. Overall, the CMIP6 EnsMean performs better than most individual models at capturing the near-surface wind speed over the region. Under global warming, we find a robust projected increase (about 70%) in WPD over the Guinea coast subregion of West Africa, especially in June–July–August season. The December–January–February and March–April–May seasons show alternating projected WPD increase and decrease, with predominantly robust projected decrease over the Sahel subregion. The projected increase over the Guinea coast has strong temporal qualities, with the end of the century (2070–2099) changes showing stronger magnitude compared to the mid-century (2040–2069) changes, and thus may provide a commercially viable renewable energy source.
Discriminating qubit amplitude damping channels
Authors: Rexiti Milajiguli , Mancini Stefano 
Published: March 2021

Abstract
We address the issue of the discrimination between two-qubit amplitude damping channels by exploring several strategies. For the single-shot, we show that the excited state does not always give the optimal input, and that side entanglement assistance has limited benefit. On the contrary, feedback assistance from the environment is more beneficial. For the two-shot, we prove the in-utility of entangled inputs. Then focusing on individual (local) measurements, we find the optimal adaptive strategy.
Effect of Styrene-Butadiene-Styrene on the Properties and Grading of Local Asphalt Binder in the UAE
Authors: Alnaqbi Ali , Zeiada Waleed , Al-Khateeb Ghazi , Ezzat Helal , Shanableh Abdallah 
Published: March 2021

Abstract
Flexible pavements in the UAE are subjected to heavy traffic loading repetitions and severe weather conditions which are the main causes of local pavement deteriorate. Fatigue cracking and rutting are most common deterioration types. One effective solution to mitigate both pavement distresses is to use asphalt modifier technologies to improve the properties of asphalt binders used locally in the UAE. The main goal of this study is investigating the effects of Styrene-Butadiene-Styrene (SBS) on the rheological properties of local asphalt binder as well as its grade based on both penetration and Superpave grading systems. Three different SBS contents of 2.5%, 4.5%, and 6.5% by weight of asphalt binder were utilized. It was found that increasing SBS content increased the asphalt binder softening point and rotational viscosity and decreased the penetration value. SBS-modified asphalt binders were less susceptible to temperature changes. Superpave performance results showed that SBS-modified asphalt binders exhibited higher rutting parameter G*/Sin δ values as the SBS content increased for both unaged and short-term-aged conditions. Adding 2.5% SBS increased the asphalt penetration grade from 60/70 to 40/50 and the performance grade (PG) from 64 to 70. For the 4.5% and 6.5% SBS cases, the asphalt binder PG increased to 76 and 88 respectively. SBS-modified asphalt binders were found to possess significantly higher mixing and compaction temperature ranges compared to the control asphalt binder.
Accuracy of RSA Design Demands for Multi-Tower Buildings
Authors: Youssef Tarek , Chintanapakdee Chatpan 
Published: March 2021

AbstractResponse spectrum analysis (RSA) procedure commonly used in design practice has been found to underestimate design forces in tall buildings. This study aims to check the accuracy of the RSA procedure when applied to multi-tower buildings sharing a common podium. Two hypothetical multi-tower buildings, the first consists of two towers with the same height (building SH), and the second consists of two towers with the different height (building DH), were designed for Chiang Mai site of Thailand using the conventional RSA procedure, and then the nonlinear response history analysis (NLRHA) is carried out. Results from NLRHA, which is the most accurate method, were used as reference values to evaluate the accuracy of RSA procedure. The results show that seismic shear demand from nonlinear analysis is about 3 times larger than the demand obtained from the RSA procedure. This could lead to shear failure in the shear walls designed by RSA procedure. Since the elastic method is preferred in practice, a modified response spectrum analysis (MRSA) procedure previously proposed for computing shear demand in regular tall buildings, was tried to apply to irregular tall buildings in this study. It is found that MRSA provides good estimates of shear force in vertical elements. MRSA can significantly enhance the design of tall buildings by avoiding brittle shear failure.
Optimizing Knitted Fiber-Reinforced Composite Carabiners
Authors: Fu William , Ahmadikia Behnam 
Published: March 2021

Abstract
Due to the looping geometry of the knitted fibers, composites reinforced by knitted fabrics excel particularly in their light weight, high impact strength, and adaptable contourability for complex shapes. Currently, knitted fiber-reinforced composites are mainly used in applications involving substantial impact stress, such as helmets and car crash guards, but the applications of knitted fiber-reinforced composites can be expanded if its weak tensile strength is improved. Carabiners are one possible application with high potential and a lack of extensive previous research. Optimizing knitted fiber-reinforced composites with improved tensile modulus can help create lighter, but still structurally strong, carabiners. Climbing-grade composite carabiners are not currently available, but these lighter carabiners can help stem the many issues caused by the excessive weight on climbers’ harnesses. To achieve this goal, we used computer-aided design and micro-scale simulations to optimize the design of the knitted-fiber reinforced composites to improve the tensile modulus to be comparable to standard carabiner materials.
Upgrading and Renovating Practice of the Schoolhouse in the High-density Old Town
Authors: Chen Zhiyuan , Wang Zhiyao , Qin Haiyang 
Published: March 2021

Abstract
In view of the degradation of the existing structure of the school buildings in the old city and the need to update the education model in the new period, whether to upgrade the function of the school building has become a key issue for the education department to improve the quality of teaching. In the process of renovation, there are many challenges in the design and construction of how to upgrade and renovate the school buildings with excellent historical buildings with a long history in the old city with dense buildings and public facilities. This paper takes the renovation of excellent historical buildings at No. 95 Huqiu Road, Huangpu District, Shanghai as an example. Through the description of the buildings and implementation process, it highlights the important role played by technological innovation in the upgrading and renovation of school buildings.