bluuebunny/crossref_metadata_2025_split huggingface dataset:
Simulation Analysis of Detection Area for Flying Wing Layout UAV Using X-Band Bistatic Radar
Authors: Liu Wenjian , Xiong Jiajun , He Song , Lan Xuhui , Xia Liang , Chen Xin 
Published: December 2021

Abstract
The shape design of flying wing UAV can effectively reduce the detection and tracking probability of single-station radar. Bistatic radar has the advantage of anti-stealth for stealth targets due to the characteristics of multi-station distribution. However, there is no literature to study the detection area of flying wing UAV by bistatic radar. To solve this problem, FEKO electromagnetic simulation software is used to establish the X-band electromagnetic model of X-47B UAV, and the bistatic RCS of 0° pitch angle is simulated. According to the bistatic radar equation, the effective detection area of X-47B UAV lateral flight is simulated. The results show that the detection range of bistatic radar is mainly in the vicinity of 5 banded regions and bistatic radar. The five strip regions in the effective detection range correspond to the five bright lines of bistatic RCS one by one. When the baseline of bistatic radar is 20 km, the effective detection range is the largest. The research provides data support for the layout optimization of bistatic radar station and the trajectory optimization of flying wing UAV.
Research on temperature sensitivity of Single-event Transient pulse width in 28-nm bulk technology
Authors: Zhang Bohan , Liang Bin , Fang Yahao 
Published: December 2021

Abstract
The influence of temperature on single-event transient (SET) pulse width has always been a hot issue in the field of anti-irradiation. Based on 3D-TCAD simulation, the temperature sensitivity of the SET pulse width of 28-nm bulk devices has been studied. The simulation results show that the electrical characteristics of the device shows an anti-temperature effect, but the worst case of SET pulse width still occurs at high temperature rather than low temperature. The influence of the triple-well structure on the temperature sensitivity of the SET pulse width has also been studied. The N+ deep well can significantly increase the SET pulse width when hitting NMOS device and enhance the temperature sensitivity of the SET pulse width. The research content of this article will provide reference for the design of radiation resistant chip.
White Noise Tests on the LSTM Model Trained with Double Pendulum
Authors: Wang Xisen 
Published: December 2021

Abstract
This paper describes the intrinsic qualities of a simple double pendulum (DP), with a visual representation, a rigorous deduction of the Lagrangian equation, and a concrete factor analysis. LSTM model was utilized to simulate the double pendulum’s periodic and chaotic behaviors and evaluates the effectiveness of the model. The auto-correlation coefficients was calculated. Meanwhile, Box-Pierce test and Ljung-Box tests for various state-dependent time series were conducted to give various initial conditions to explore the DP system’s random characteristics. The research results are as follows: 1) Chaos did not lead to direct randomness; 2) seasonality could coexist with chaos; 3) the highly auto-regressive nature of DP’s time series data are found. Therefore, it can be concluded that the chaos in a double pendulum has particular patterns (such as the positive relationship with the likelihood of being a random white noise series) that could be further explored.
Kinematic analysis and simulation of 6-DOF vehicle driving simulator
Authors: Shu Wenbin , Yan Haonan , Meng Lijun 
Published: December 2021

Abstract
In order to reasonably design the six degrees of freedom turntable (hereinafter referred to as the transfer table), based on the structure size and motion index parameters of the turntable, The motion mechanics calculation model was established to simulate and analyze the mechanical variation curve of singing platform under different motion modes. The analysis shows that when the upper platform load and the component mass are 3000 kg, the maximum action force of the rotating platform support moving cylinder is 1376.4 kg.
Multi-sensor recognition of human pressure
Authors: Meng Xianyu , Wang Qi , Liu Hongsheng , Yu Xiquan 
Published: December 2021

Abstract
At present, skin pressure ulcers are a common problem in the care of bedridden patients. Solving this problem usually involves turning the patient over regularly, which requires a lot of manpower and material resources. This paper designs a human body pressure recognition model, which can solve the problem of human pressure ulcers very well in combination with intelligent nursing beds. This paper collects the bone data of the human body by using the Kinect sensor, and then processes the collected data. The film pressure sensor is used to collect the pressure information of the human body, and the pressure information is matched with the bone data of the human body, so as to obtain the pressure of the corresponding part of the human body, and judge the current posture of the human body through the pressure information. When the pressure of the compression part of the human body reaches the threshold, the intelligent nursing bed automatically turns over to reduce the pressure of the compression part of the human body.
Simulation Research on System Characteristics of Valve Opening Curve of Bidirectional Water Transfer System
Authors: Zhang Zheng , Yu Jian , Cai Biaohua , Xiao Longzhou , Zou Yujing 
Published: December 2021

Abstract
Due to long pipelines, complex hydraulic conditions, and high flow rate, Bidirectional Water Transfer System of ship is prone to producing Water Hammer Effect when the valve opens and closes, which has a great impact on the pipeline system. In this paper, Flowmaster software is used to simulate the Bidirectional Water Transfer System to study the system characteristics under different valve opening curves. The simulation results show that when the valve opens and closes, Bidirectional Water Transfer System will have a large pressure mutation. But the pressure changes of the stepped and the stepped curve opening curves are smaller and smoother than that of the linear opening curve. But the stepped and the stepped curve opening curves can’t eliminate the pressure and flow rate mutation when the valve opens and closes; In addition, the stepped and the stepped curve opening curves can effectively improve the phenomenon of water flowing out of the reservior, and effectively avoid the phenomenon of gas flowing into the reservior.
The Development of an Intelligent Zinc Oxide Lighting Arrester Tester
Authors: Liu Aihua , Ma Shuning 
Published: December 2021

Abstract
Zinc oxide arrester is often used as lightning protection device in 10KV distribution network. In order to check the reliable operation of the zinc oxide arrester, preventive tests are often carried out. In this project, the intelligent tester adopts high-precision clamp current mutual inductance technology as the front-end acquisition mode of current signals; Magnetic isolation technology is used to ensure the accuracy of current and voltage sampling and the anti-interference ability and testing accuracy of the instrument are improved by using fast Fourier transform for data processing. The integrated application of several technologies provides a basis for judging the operating state of the 10kV zinc oxide arrester. The charged measurement of operating parameters of 10kV zinc oxide arrester is realized and the measurement efficiency is improved.
Research of Light Field Technology
Authors: Yu Zhangsi , Yang Houcheng , Yan Yinxin , Zhang Ning 
Published: December 2021

Abstract
Light field technology is a relatively new theory and technical direction. At present, it has played a significant role in scientific research, production and life, national defense and military, but there are still many problems to be solved in terms of theoretical description, technical implementation, software and hardware processing capabilities, commercialization costs, and ease of use. Therefore, research the light field technology, explore the principle and application of light field imaging, and put forward some enlightenments for its application in industrial machine vision.
Strength analysis and method research of a light truck powertrain mounting system
Authors: Lai Yudong , Sun Shiping , Yu Xianzhong , Wu Hexing , Hui Huang , Qiu Xing 
Published: December 2021

Abstract
This paper presents an integral strength checking method to solve the problem that the step-by-step strength checking method cannot check the strength of metal parts in the rubber mounting system of light truck powertrain. By establishing an integral finite element model including powertrain and rubber mount system and considering the rubber bearing and force transfer mode, the motion coordination and overall analysis of mount system and powertrain are realized. The calculation results show that the proposed method can easily and accurately complete the strength check of metal parts of the mounting system, which provides a reference for the structural design of the mounting system.
The relationship between the single event upset and the order of sensitive transistors in flip-flop layout
Authors: Fang Yahao , Liang Bin , Zhang Bohan 
Published: December 2021

Abstract
Traditionally, it is believed that only reverse biased PN junctions can collect ionized electron-hole pairs. Therefore, only the drain of the transistor in the off-state can be considered as a sensitive node, which is easy to absorb charge and cause upset. This paper finds that on-state transistors can also become sensitive nodes. This paper studies the relationship between SEU and the order of transistors in the flip-flop layout. It is found that the adjacent placement of on-state sensitive transistors can promote the occurrence of SEU, and a targeted hardened plan is proposed. The results of this paper are helpful for the design of the radiation-resistant layout of the flip-flop.
An Integration Model for Text Classification using Graph Convolutional Network and BERT
Authors: Xue Bingxin , Zhu Cui , Wang Xuan , Zhu Wenjun 
Published: December 2021

Abstract
Recently, Graph Convolutional Neural Network (GCN) is widely used in text classification tasks, and has effectively completed tasks that are considered to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and cannot capture local information. The Bidirectional Encoder Representation from Transformers (BERT) has been shown to have the ability to capture the contextual information in a sentence or document, but its ability to capture global information about the vocabulary of a language is relatively limited. The latter is the advantage of GCN. Therefore, in this paper, Mutual Graph Convolution Networks (MGCN) is proposed to solve the above problems. It introduces semantic dictionary (WordNet), dependency and BERT. MGCN uses dependency to solve the problem of context dependence and WordNet to obtain more semantic information. Then the local information generated by BERT and the global information generated by GCN are interacted through the attention mechanism, so that they can influence each other and improve the classification effect of the model. The experimental results show that our model is more effective than previous research reports on three text classification data sets.
Harmonic detection algorithm based on Nuttall double-window all-phase Fast Fourier Transform trispectral interpolation
Authors: Su Yukun , Cao Rong , Hao Wenbo , Xu Mingyu , Hu Heng , Jiang Peng , Zhou Feng 
Published: December 2021

Abstract
In harmonic detection of power grid, spectrum leakage of traditional FFT algorithm affects the accuracy of detection results. In this paper, a Nuttall double-window all-phase FFT trispectral interpolation algorithm is proposed. The phase of the spectral line with the maximum amplitude at the sampling point is taken as the initial phase of the signal, and the position of the main harmonic line is determined by using the amplitudes of the three spectral lines near the harmonic peak frequency point. Simulation results show that this algorithm can reduce the error of harmonic parameter detection more effectively and reduce the influence of white noise.
Image recognition algorithms based on deep learning
Authors: Ma Hongli , Xie Fang , Chen Tao , Liang Lei , Lu Jie 
Published: December 2021

Abstract
Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.
Classification of Magnetic Tile Surface Defects Based on Efficientnet Network with Attention
Authors: Fan Chengshuai 
Published: December 2021

Abstract
The magnetic tile image has the characteristics of uneven illumination, complex surface texture, and low contrast. Aiming at the problem that the traditional defect detection algorithm is difficult to accurately identify the defects, and the deep learning algorithm is difficult to balance the classification accuracy and the size of the speed model, a defect classification algorithm based on attention-based EfficientNet is proposed. The algorithm first enhances the network’s spatial and location information for image features by integrating the Convolutional Block Attention Module, and improves the network’s ability to identify defects. Then, on this basis, Criss-Cross Attention is added to the network, so that the network can better the context information of the horizontal and vertical cross of image features, so that each pixel can finally capture the full image dependency of all pixels. Experimental results show that the algorithm has higher classification accuracy than EfficientNet-B0, reached 99.11%, and has a better balance between accuracy, speed and model size than other classification models.
Optimistic locating method of transformer voiceprint based on beam and delay algorithm
Authors: Xu Honghua , Yi Laibing , Li Yong , Chen Shoulong , Zhu Lei , Xin Shuaikui 
Published: December 2021

Abstract
In view of the on-line inspection mode of transformer voiceprint information during operation, an optimistic location method is proposed to assist the acoustic print diagnosis for fault identification, location and further evaluation work. Firstly, according to the directional location algorithm of antenna array (Beamforming), traditional directional location is extended to general spatial location. Then on the basis of TDOA(Time Difference Of Arrival) algorithm of time domain signal characteristic analysis, integrated weighted processing and data centralization, on the one hand, reduce the edge value, on the other hand, correct the trend of bad data, and improve the existing cross-correlation operation. The simulation results show that the optimized beam-based and weighted TDOA algorithm has excellent performance in 2D localization of transformer voiceprint. These research results can provide a basis for transformer online monitoring and voice print diagnosis.
Contrasting biophysical and societal impacts of hydro-meteorological extremes
Authors: Orth Rene , O Sungmin , Zscheischler Jakob , Mahecha Miguel D , Reichstein Markus 
Published: December 2021

Abstract
Extreme hydrological and meteorological conditions can severely affect ecosystems, parts of the economy, and consequently society. These impacts are expected to be aggravated by climate change. Here we analyze and compare the impacts of multiple types of extreme events across several domains in Europe, to reveal corresponding impact signatures. We characterize the distinct impacts of droughts, floods, heat waves, frosts and storms on a variety of biophysical and social variables at national level and half-monthly time scale. We find strong biophysical impacts of droughts, floods, heat waves and frosts, while public attention and property damage are more affected by storms and floods. We show unexpected impact patterns such as reduced human mortality during floods and storms. Comparing public attention anomalies with impacts across all other considered domains we find that attention on droughts is comparatively low despite the significant overall impacts. Resolving these impact patterns highlights large-scale vulnerability and supports regional extreme event management to consequently reduce disaster risks.
Drivers of global nitrogen emissions
Authors: Malik Arunima , Oita Azusa , Shaw Emily , Li Mengyu , Ninpanit Panittra , Nandel Vibhuti , Lan Jun , Lenzen Manfred 
Published: December 2021

Abstract
Nitrogen is crucial for sustaining life. However, excessive reactive nitrogen (Nr) in the form of ammonia, nitrates, nitrogen oxides or nitrous oxides affects the quality of water, air and soil, resulting in human health risks. This study aims to assess the drivers of Nr emissions by analysing six determinants: nitrogen efficiency (Nr emissions per unit of production), production recipe (inter-sectoral dependencies), final demand composition (consumption baskets of households), final demand destination (consumption vs. investment balance), affluence (final consumption per capita) and population. To this end, we construct a detailed multi-regional input-output database featuring data on international trade between 186 countries to undertake a global structural decomposition analysis of a change in global Nr emissions from 1997 to 2017. Our analysis shows that nitrogen efficiency has improved over the assessed time-period, however affluence, final demand destination and population growth have resulted in an overall increase in Nr emissions. We provide a global perspective of the drivers of nitrogen emissions at a detailed country level, and breakdown the change in emissions into contribution from domestic footprint and rest-of-world footprint. We highlight that food production coupled with growing international trade is increasing Nr emissions worldwide.
An encounter-based approach for restricted diffusion with a gradient drift
Authors: Grebenkov Denis S 
Published: December 2021

Abstract
We develop an encounter-based approach for describing restricted diffusion with a gradient drift toward a partially reactive boundary. For this purpose, we introduce an extension of the Dirichlet-to-Neumann operator and use its eigenbasis to derive a spectral decomposition for the full propagator, i.e. the joint probability density function for the particle position and its boundary local time. This is the central quantity that determines various characteristics of diffusion-influenced reactions such as conventional propagators, survival probability, first-passage time distribution, boundary local time distribution, and reaction rate. As an illustration, we investigate the impact of a constant drift onto the boundary local time for restricted diffusion on an interval. More generally, this approach accesses how external forces may influence the statistics of encounters of a diffusing particle with the reactive boundary.
Align, then memorise: the dynamics of learning with feedback alignment*
Authors: Refinetti Maria , d’Ascoli Stéphane , Ohana Ruben , Goldt Sebastian 
Published: December 2021

Abstract
Direct feedback alignment (DFA) is emerging as an efficient and biologically plausible alternative to backpropagation for training deep neural networks. Despite relying on random feedback weights for the backward pass, DFA successfully trains state-of-the-art models such as transformers. On the other hand, it notoriously fails to train convolutional networks. An understanding of the inner workings of DFA to explain these diverging results remains elusive. Here, we propose a theory of feedback alignment algorithms. We first show that learning in shallow networks proceeds in two steps: an alignment phase, where the model adapts its weights to align the approximate gradient with the true gradient of the loss function, is followed by a memorisation phase, where the model focuses on fitting the data. This two-step process has a degeneracy breaking effect: out of all the low-loss solutions in the landscape, a network trained with DFA naturally converges to the solution which maximises gradient alignment. We also identify a key quantity underlying alignment in deep linear networks: the conditioning of the alignment matrices. The latter enables a detailed understanding of the impact of data structure on alignment, and suggests a simple explanation for the well-known failure of DFA to train convolutional neural networks. Numerical experiments on MNIST and CIFAR10 clearly demonstrate degeneracy breaking in deep non-linear networks and show that the align-then-memorize process occurs sequentially from the bottom layers of the network to the top.
Comparison of Invasive and Non-Invasive Methods in Site Response, Case Study: Soil Deposits of La Estrella
Authors: Largo Daniel , Hidalgo César , Olarte Juan 
Published: December 2021

Abstract
A great part of the Colombian territory is under medium to high seismic hazard due to the complex tectonic condition, which in turn affects, particularly, areas where the population density is highest. A response spectrum analysis of the ground is currently required by seismic design codes for site response analysis. For this, the shear wave velocity (Vs) profile must be established. The use of seismic invasive methods such as Down Hole or Cross Hole for the determination of the shear wave velocity (Vs), has been typically recommended. In recent years, significant progress has been made in non-invasive seismic methods such as MasW (Multichannel Analysis of Surface Waves) and ReMi (Refraction Microtremor), in order to estimate the Vs profile from surface waves analysis. Due to the accessibility and low cost, these methods represent a viable alternative to determine the profile of Vs. In this project, the seismic response of soil deposits was evaluated in the La Estrella municipality located in the south of The Aburrá Valley. One-dimensional (1D) models were simulated by characterizing the soil profile through the shear wave velocity with MasW and ReMi seismic tests. The results were compared with models based on shear wave characterization through Down Hole methods. The 1D response spectrums were determined with an equivalent linear model in DEEPSOIL and GTS NX software. The resulting spectra were compared through relative difference and correlation coefficient. Final results demonstrated that the spectra present low relative differences for long periods, moderate relative differences for moderate periods, and low to moderate relative differences for short periods. The general correlation coefficients were 0.6. This was evidence that non-invasive seismic methods allow an appropriate response spectrum analysis.