bluuebunny/crossref_metadata_2025 huggingface dataset:
Measurements of neutron energy spectra of 9Be(d,n)10B reaction with a thick beryllium target *
Authors: Zhang Shuang-Jiao , Hu Zhi-Jie , Hu Zhi-Ming , Han Chao , Bai Xiao-Hou , Liu Chang-Qi , Huang Chang , Xie Qin , Huo Dong-Ying , Wu Kang , Nie Yang-Bo , Ding Yan-Yan , Zhang Kai , Zhang Yu , Deng Zhi-Yong , Guo Rui , Wei Zheng , Yao Ze-En
Published: November 2020

Novel measurements of the neutron energy spectra of the 9Be(d,n)10B reaction with a thick beryllium target are performed using a fast neutron time-of-flight (TOF) spectrometer for the neutron emission angles

and

, and the incident deuteron energies are 250 and 300 keV, respectively. The neutron contributions from the 9Be(d,n)10B reaction are distributed relatively independently for the ground state and the first, second, and third excited states of 10B. The branching ratios of the 9Be(d,n)10B reaction for the different excited states of 10B are obtained for the neutron emission angles

and

, and the incident deuteron energies are 250 and 300 keV, respectively. The branching ratio of the 9Be(d,n)10B reaction for the third excited state decreases with increase in the incident deuteron energy, and the branching ratios for the ground state and the second excited state increase with increase in the neutron emission angle.
Solutions for a hyperbolic diffusion equation with linear reaction terms
Authors: Lenzi E K , Lenzi M K , Zola R S , Evangelista L R
Published: November 2020

General diffusion processes involve one or more diffusing species and are usually modelled by Fick’s law, which assumes infinite propagation velocity. In this article, searching for the effect of finite propagation speeds in a system with two reacting species, we investigate diffusing and reacting particles governed by a hyperbolic diffusion equation, that is, the Cattaneo equation, which describes a diffusion process with finite propagation velocity, in the presence of a constant external field and reaction terms. These reaction terms are linear and may be related to irreversible and reversible processes, including memory effects, depending on the choices of the reaction rates. We obtain exact solutions for the equilibrium concentrations and explore the rich variety of behaviours exhibited by the species involved in reaction processes. Our results may shine new light into systems with more than one kind of diffusing and reacting particles, as is the case in several industrial and biological process, when finite speeds and memory effects are involved.
Intelligent Control of Threshing and Cleaning System Based on Big Data
Authors: Chen Xiusheng , Tang Huaizhuang , Xue Zhiyuan , Xing Xiaoming , Li Mengwei , Lv Guodong , Zhang Tongshuai
Published: November 2020

When the combine harvester works under different environmental conditions, its threshing and cleaning is a complicated process. To deal with changes in soil, climate, and crop conditions, it is extremely important to adjust and optimize the internal parameters of these machines. It is because the experience of agricultural machinery operators is different to cause uneven harvest quality, which cannot guarantee that all harvesters reach the best harvest state. Now, RS images of sensors and satellites are quite clear, and the error is controlled within the acceptable range. With the advent of the era of big data and AI, many fields have successfully applied them. In the agricultural field, innovation and transformation led by AI are also taking place. The article takes the intelligent control of the threshing and cleaning system as the object of study, which is specifically reflected in the impurity rate, crushing rate, and loss rate of cereals. The goal is to minimize the loss rate during harvest, and the lowest impurity rate and crushing rate in the grain after harvest. This subversive movement will completely rewrite the development status of China’s agricultural machinery.
A Summary of Research on Intelligent Dialogue Systems
Authors: Wang Bangjing , Cao Hui
Published: November 2020

Dialogue systems have always been an important research direction of natural language processing. In recent years, the potential and commercial value of dialogue systems have attracted people’s attention, and technologies such as intelligent customer service and chatbots have gradually become popular. First, it outlines the concept and classification of the dialog system; then introduces the constituent modules of the dialog system, and mainly studies the key technologies and mainstream ideas; finally, summarizes the problems encountered at this stage and looks forward to the future research direction.
Application of Artificial Intelligence Technology in College Physical Education
Authors: Zhang Li
Published: November 2020

With the rise of a new round of scientific and technological revolution, the vigorous development of artificial intelligence technology has gradually entered human daily life. Physical education, as one of the daily teaching activities of human beings, is an indispensable part of the current teaching evaluation and students’ quality assessment in Colleges and universities. With the development of the times, there are many shortcomings in the traditional teaching practice, and the unique advantages of artificial intelligence technology will largely make up for the shortcomings of traditional teaching. At present, the integration of physical education and artificial intelligence technology is still in the preliminary exploration stage. Therefore, this paper will first introduce the development history and main content of artificial intelligence technology. Then, starting from the reality of physical education in Colleges and universities, this paper analyzes the problems existing in traditional physical education teaching methods. Secondly, the paper puts forward the teaching methods of artificial intelligence technology such as virtual reality technology, big data analysis technology and intelligent recognition technology as the innovation points, in order to solve the shortcomings of traditional sports teaching method. The last part is the conclusion, analyzes the application prospect of artificial intelligence technology in college physical education.
Calculation and Application of Xin’an Painting School Art Style Model
Authors: Wang Decheng , Chen Yan
Published: November 2020

Artists and critics generally make artistic analysis of paintings based on their experience and subjective consciousness. In order to calculate the art style of the Xin’an School of Painting objectively, this paper attempts to propose several digital features including redundancy, degree of order and complexity to describe the artistic style of paintings algorithm under the framework of computable aesthetics on the basis of Shannon entropy in information theory. Taking the five painters of different periods as the research object, the digital features of the selected paintings are calculated and analyzed. The experimental results show that the proposed digital features can reflect the artistic style and the differences between different artists of the Xin’an School of Painting in different periods to some extent, which are basically consistent with the subjective aesthetic perception of the School.
Research on influencing factors of coal price based on improved principal component analysis and grey correlation method
Authors: Duan Zhaobin , Meng Yanjun
Published: November 2020

As an important basic energy in China, the price fluctuation of coal is related to the operation of China’s coal market and the whole energy market and coal price analysis has always been the focus of the industry. By establishing an improved principal component-grey correlation analysis (PCA-GRA) model, the influencing factors of coal price were analyzed and tested one by one, and the influence degree of the influencing factors such as total coal production, total coal consumption, coal import and export volume, and world coal price on China’s coal price was studied. The results show that coal price fluctuation is greatly influenced by coal energy storage level and market environment level. In order to promote the development of coal industry, some suggestions are put forward.
Transformation Of Non-Commitment Decision Risk Of Three-Way Decision Based on Limited Time Observation
Authors: Pang Meng Yang , Suo Zhong Ying , Bao Zhuang Zhuang , Huang Lin
Published: November 2020

In order to avoid or reduce the loss of direct decision-making caused by insufficient information, on the basis of traditional two-way decision, the non-commitment decision is added in three-way decision. However, in most practical applications, the final decision needs to be made within a limited time, the risk of non-commitment decision will increase with time and even be equivalent to the risk of commitment decision. Therefore, from the perspective of multi-time decision points, the method of transformation of non-commitment decision risk of three-way decision based on limited time observation is proposed. First, the non-commitment decision risk is analyzed and the time risk coefficient is introduced to explain the law of non-commitment risk changing with time. Then, the subjective and objective factors in the decision process are analyzed so as to make the decision process more in line with human cognition and thinking logic. Finally, an example is given to verify the effectiveness and practicability of the method.
Modelling Dependence Structure of Exchange Rate and Energy Price by C-Vine Copula in China
Authors: Li Yangheling , Liao Ruofan , Sriboonchitta Songsak
Published: November 2020

This study aims to investigate the co-movement between the exchange rate and the different energy prices by using the canonical vine copula approach. China needs to import large amounts of oil, natural gas, and other energy sources every year, thus, the exchange rate plays a vital role in the energy market import in China. This study uses C-vine copula to estimate the interdependence between the exchange rate and the different energy prices including gasoline price, coal price, and liquified natural gas price. The result shows that the exchange rate has a positive dependency effect on all energy prices, especially the highest degree of interdependence between the exchange rate and gasoline prices.
Radio Frequency Temperature Measurement System Based on PDC Material
Authors: Tao Yongxiang , Li Tianli , Huang Dehang
Published: November 2020

PDC (Polymer Derived Ceramics, PDC) material sensor is a new type of temperature sensor, which has the characteristics of high temperature thermal stability, corrosion resistance, oxidation resistance and creep resistance. It is a high temperature polymer precursor ceramic temperature sensitive material. Using its characteristics, it can solve the problem of high temperature. There is a resonant cavity inside the sensor, and the resonant frequency of the internal resonant cavity will change accordingly when its temperature changes. In this study, a set of RF temperature measurement hardware system was developed using the perturbation method and the corresponding relationship between the resonant frequency and temperature of the PDC material temperature sensor.
Bivariate Oblique Decision Tree Algorithms Based on Linear Discriminant Analysis
Authors: Liu B , Song J , Jiao S R
Published: November 2020

To solve the problem of the low efficiency of the traditional decision tree algorithm in dividing decision boundaries that are not parallel to the coordinate axis as well as the problem of the difficulties and the limited interpretability of some oblique decision tree algorithms in solving high-dimensional data covariance matrix, the present study proposed a series of bivariate oblique decision tree algorithm based on LDA. In the algorithms, LDA was used to determine the division boundary, and impurity and variance analysis were used as a criterion for the selection of split variables, thus ensuring the interpretability of this method while improving the efficiency of defining the decision boundaries that were not parallel to the coordinate axis. Experiments conducted in multiple data sets show that algorithms has achieved satisfactory results.
Study on Multi-user Detection Technology of MIMO-OFDM System
Authors: Jiang Han , Zhou Xiaoli , Zhu Zhiyuan , Guo Jing
Published: November 2020

This article introduces the detection method of MIMO-OFDM, then introduces the V-Blast algorithm and compares the main interference cancellation algorithms. Finally, combining the advantages of the OBF algorithm and the V-BLAST signal detection algorithm, an improved OBF algorithm is proposed.
The interference effect and reason analysis of the three lasers to CCD detector
Authors: Peng Tian , Shao Zhiqiang , Xie Minyong
Published: November 2020

At present, the two lasers including Nd:YAG pulse laser and Nd:YAG high frequency laser, which can both produce laser with wavelength of 1064nm, are widely used in in medical, military, people’s livelihood and other fields. Their working material is Nd:YAG crystal. The appearance of passive Q Nd:YVO4 crystal of graphene quantum dots can improve the pumping efficiency of Nd:YAG high frequency laser and increase the peak power. In this experiment, the interference effect of the three lasers will be interfered with CCD, and the interference effect generated under different laser power and frequency will be measured. The interference mechanism of the three lasers will be analyzed to lay a foundation for their application.
A dynamic port isolation method
Authors: Tao Sun , Hai Wang , Yang Gao
Published: November 2020

With the rapid development of enterprise information construction, the enterprise internal network information interaction is frequent, which greatly improves the efficiency of enterprise work, but the problem of network security is increasingly serious. In view of the above problems, this paper proposes a port dynamic isolation method, which can complement the existing security tools, and provides an idea for solving the security of intranet.
Sentiment Analysis For Product Reviews Based on Deep Learning
Authors: Xiao Shaozhang , Wang Hexiang , Ling Zhi , Wang Lanfang , Tang Zhaoxia
Published: November 2020

With the popularity of the Internet and e-commerce, the sentiment analysis of text can help users to quickly and accurately obtain effective information they are interested in from massive product reviews to purchase satisfactory products. In this paper, a sentiment analysis system for product reviews was designed based on deep learning, and the digital and electronic products on Jingdong Mall with at least 100,000 reviews were crawled as the training data set. After data pre-processing operations such as word segmentation and removal of stop words for product reviews to remove useless features, the feature vectors were constructed based on the bag of words model and word2vec method, and then three classification algorithms, namely LSTM, Naive Bayes and logistic regression were used to model reviews. The LSTM algorithm is significantly superior to Naive Bayes and logistic regression algorithm in the training stage, and provides a reliable reference for the analysis of product reviews.
Investigating Back-Translation in Tibetan-Chinese Neural Machine Translation
Authors: Liu Ding , Li Yachao , Zhu Dengyun , Liu Xuan , Ma Ning , Zhu Ao
Published: November 2020

In recent years, the proposal of neural network has provided new idea for solving natural language processing, and at the same time, neural machine translation has become the frontier method of machine translation. In low-resource languages, due to the sparse bilingual data, the model needs more high-quality data, and the translation quality fails to achieve the desired effect. In this paper, experiments on neural network machine translation based on attention are conducted on Tibetan-Chinese language pairs, and transfer learning method combined with back translation method is used to alleviate the problem of insufficient Tibetan-Chinese parallel corpus. Experimental results show that the proposed transfer learning combined with back translation method is simple and effective. Compared with traditional translation methods, the translation effect is significantly improved. From the analysis of translation, it can be seen that the citation of Tibetan-Chinese neural machine translation is smoother, which is greatly improved compared to the translation without back translation. At the same time, there are common deficiencies in neural machine translation such as inadequate translation and low translation loyalty.
Prediction of stocks with high transfer based on ensemble learning
Authors: Ni Jie , Zhang Linghong , Tao Jiaming , Yang Xiaorong
Published: November 2020

The “high transfer” dividend policy implemented by listed companies is a special phenomenon in the Chinese stock market. The stocks with high transfer can generate significant positive excess returns before and after the announcement date, so it is important for investors to predict the high transfer stock as precisely as possible. Previous studies mainly focused on regression models to make predictions, such as logistic and probit models, the prediction results of which are average. This paper uses the data of the Chinese A-share market for the last eight consecutive years and screens out 13 important factors through stock dividend theory. We construct three ensemble models for prediction, namely XGBoost, LightGBM, and CatBoost. The results show that the three ensemble models are far superior to the traditional Logistic model. And the prediction effect of the three ensemble models have been improved after Bayesian parameter tuning. Furthermore, three models are fused by simple voting and rank-score, it turns out that almost all metrics, including accuracy, precision, F1 value and AUC value, perform better than the single model. In addition, the top 10 and top 20 hit rate reach 100%, suggesting that the fusion model can predict the top 20 stocks with high transfer accurately, which has certain practical significance for strengthening the security of investment.
Hierarchical Convolutional Features Fusion for Visual Tracking
Authors: Zhang Fan , Chang Shuo
Published: November 2020

Hierarchical convolutional features have different impact on the tracking performance, as the higher convolutional layers encode the semantic information of targets and earlier convolutional layers are more precise to localize targets. In this paper, we propose a novel scheme for hierarchical convolutional features fusion for visual tracking. In the proposed scheme, hierarchical convolutional features are first concatenated to form the cascading feature at the feature level, and then a convolutional layer is added to reduce the feature dimension. Discriminative correlation filter (DCF) is finally utilized to obtain the target location, which is treated as a differentiable layer in the neural network. The experimental results demonstrate that our proposed scheme achieves superior performances on the visual tracking benchmark.
Real-time indoor semantic map construction combined with the lightweight object detection network
Authors: Gao Xumin , Jiang Lin , Guang Xingyu , Nie Wenkang
Published: November 2020

Aiming at the lack of semantic information of indoor objects in the grid map commonly used in the indoor mobile robot, this paper proposes a real-time indoor semantic map construction method combined with the lightweight object detection network: firstly, we proposes a lightweight object detection network which is called S-SSD (ShuffleNet-SSD) by improving the SSD (Single Shot MultiBox Detector) network, it can be used to extract the semantic information of indoor objects in real time; then the semantic information of indoor objects is transformed into the grid map which is created by mobile robot, so the indoor semantic map is constructed. The effectiveness and superiority of S-SSD in the indoor object detection of mobile robot are verified by the comparative experiment, and the effectiveness of the real-time indoor semantic map construction proposed in this paper is verified by the experiment of semantic map construction.
Building extraction from remote sensing image based on improved segnet neural network and image pyramid
Authors: Yu Shisen , Xie Yuehui , Liu Congna
Published: November 2020

Aiming at the problems such as the insufficient use of context information in high-resolution remote sensing image by convolutional neural network and the time-consuming training model, a building extraction method based on the combination of improved Segnet neural network and image pyramid is proposed. First, the Gaussian and Laplacian pyramid algorithms are used to obtain the multi-scale and multi-resolution detailed feature images of the original image respectively, and the network is trained together with the original image, so that the model can learn and extract more abundant image features. Secondly, in order to reduce the complexity of the network, reduce the encoder and decoder structure in the original Segnet neural network, save the calculation cost and effectively avoid the occurrence of over-fitting and other phenomena. The experiment uses Massachusetts buildings as the dataset. Compared with the original segnet neural network extraction method, the proposed method has significantly improved the extraction accuracy and extraction efficiency.
Generative Pedestrian Trajectory Prediction with Graph Representation
Authors: Zhang Haoyu
Published: November 2020

Multi-agent trajectory prediction is one of the core modules of unmanned driving and intelligent robots. The traditional method is difficult to measure the relationship between multiple agents, and the modeling ability is rigid. Nowadays, most of the methods make less use of geographic information, and social relationship modeling is not sufficient. Our model uses Graph Neural Network (GNN) to measure social relationships to improve its usability. The model is built by the basic Conditional Auto-encoder (CVAE) framework, using Gaussian Mixture Model to mix multiple Gaussian distributions and the possibility of obtaining more potential space through dynamic integration to make the model achieve better results. Our model has achieved excellent results on the Stanford Drone Dataset. The evaluation by average displacement error (ADE) and final displacement error (FDE) metrics has exceeded the majority of existing models.
Design and application of learner emotion recognition for classroom
Authors: Su Chao , Wang Guozhong
Published: November 2020

The emotion recognition of learners in the classroom plays an important role in improving classroom efficiency. At present, the recognition methods based on traditional image processing generally have problems such as low recognition accuracy, difficulty in feature extraction. In order to effectively solve the above problems, a deep learning-based method is proposed to perform emotion recognition for learners. This method first replaces the DarkNet-53 of YOLO v3 by introducing the convolution structure of MobileNet, which makes the model lighter and reduces the parameters. Then improve the loss function of the model by using GIoU loss. Finally, K-means clustering algorithm is used to obtain an anchor suitable for the emotion recognition scene on the self-made dataset. Thus, a high-accuracy and lightweight learner emotion recognition model ER_YOLO is obtained. Experimental results show that the improved model mAP increased by 4%, F1 score increased by 3.2%, detection time reduced by 1/3, and parameters reduced by 1/10.
A Deep Composite Predict Network for Eye Disease
Authors: Kong Weiyi , Yang Menglong , Huang Qinzhen
Published: November 2020

Different areas of eyes reflect different states of internal organs of human body. That can assist to detect the disease internal organs, facilitate the prevention and cure by analyzing the image of human eyes. In this paper, we propose a new algorithm, Deep Composite Predict Network (DCP-Net), combined deep learning technique with traditional Chinese medicine theory, which can segment interesting areas of the human eyes, detect the disease eyes texture and then predict internal lesion organ. Aimed at helping doctors to diagnose quickly. The proposed algorithm consists three sub-modules, i.e., multiple segmentation sub-module, fast detection sub-module and predict eye disease module. The multiple segmentation module is solved by the improved retraining U-network, which can obtain an accurate eye to segment interesting areas. The fast detection module is based on enhanced Yolo framework. The predication disease method combined Chinese traditional theory with computer vision technology. Finally, our network can accurately detect the disease of human eyes and predict lesions of internal organs, which can help doctors quickly diagnose lesion organs by human eyes.
TSFnet: a new two-stream fusion framework for scene text detection
Authors: Zhou Ziheng , Zhao Xuezhuan , Pei Lishen , Li Lao , Pan Jiahao
Published: November 2020

In order to solve the problem of algorithm robustness caused by scale change and imbalanced distribution of classes in the scene text detection task, we propose a new two-stream fusion framework TSFnet. It is constructed by the Detection Stream, the Judge Stream and the Merge output algorithm. In the Detection Stream, we propose a loss balance factor (LBF), which is used to optimize region proposal network. Then, the Regression-net and the Segmentation-net are used to predict text global segmentation map and its corresponding coordinates probability score. In Judge Stream, we use the feature pyramid network to extract the Judge map. In the process, the LBF is calculated to support the Detection Stream. Finally, we design a novel algorithm to fuse the outputs of the two-stream, and the precise position of the text is localized. Extensive experiments are conducted on the ICDAR 2015 and the ICDAR2017-MLT standard datasets. The results demonstrate that the framework performance is comparable with the sate-of-the-art approaches.
Vision Positioning method for Autonomous Precise Landing of UAV Based on Square Landing Mark
Authors: Chen Jiaju , Wang Rui , Wang Rumo
Published: November 2020

Rotor-craft is a kind of VTOL UAV and is widely used in multiple fields. Among relative researches, vision guided autonomous landing of rotor-craft has been a hot spot, where vision positioning is the most crucial. The core of the algorithm is to calculate the position and attitude information according to the change of the visual image of the same object at different time or frame. Based on the real-time self calibration technology of airborne monocular vision, this paper puts forward a method that takes the designed landing mark composed of black and white squares as the cooperative target, solving the relative pose of camera and cooperative target to carry out the landing positioning of UAV, and realizes the full-automatic sub-pixel precision linear edge detection to ensure the accuracy of visual positioning. A series of simulation experiments show that for 768 * 576 pixel image, when the camera is about 12m away from the target and the noise deviation reaches 3 pixels, the total time of edge extraction and calibration is 0.511s, and the position and attitude estimation are still satisfactory, which shows that the proposed algorithm can effectively realize the autonomous landing of UAV.
Research on Image Feature Point Matching Based on ORB and RANSAC Algorithm
Authors: Zhang Hua , Zheng Guoxun , Fu Haohai
Published: November 2020

Image matching is one of the basic problems in the field of machine vision research. In order to improve the accuracy of image feature point matching and enhance the anti-interference ability of the algorithm, for the shortcomings of traditional ORB algorithm, an image feature point matching algorithm based on ORB and RANSAC algorithm is proposed. The algorithm can filter out false matches on the basis of coarse matching results, and filter out exact matching points through the homography matrix. Experiments show that this method can effectively improve the accuracy of image matching and shorten the execution time, and it is very robust to image matching with different scales, ambiguities, brightness and rotation.
Travel time prediction of urban road based on BP neural network
Authors: Deng Fangyi , Su Pei , Luo Bingxue , Wu Peng , Guo Yan
Published: November 2020

In view of the diversity and complexity of the existing travel time prediction methods, travel time is an important indicator to quantify the state of road traffic congestion. In this paper, based on the analysis of various travel time prediction methods, a network model based on the idea of BP algorithm is constructed by SPSS software, and the method of travel time prediction of urban road is studied by combining BP neural network with three indexes affecting vehicle speed. Firstly, taking Jinshui road of Zhengzhou as the research object, this paper obtains the data of three factors that affect the vehicle speed, namely, vehicle spacing, flow and density; then, it constructs the neural network model and analyzes the data with the help of SPSS; finally, the experimental results are obtained, and the analysis shows that the error between the predicted speed and the real speed is very small, the average predicted error value is 0.3, and the average error rate is 2.35%, the result has better accuracy and feasibility, which can further realize the effective prediction of the travel time of the car. The model established in this paper has better practicability for travel time prediction, and the data can be used as reference value for travel time prediction of intelligent transportation system and other systems.
Extraction of Blast Furnace Burden Line Based on Radar Spectrum Restructured by Entropy Weight
Authors: Shi Qiudong , Ye Fenxian , Dang Wenzhi , He Yanan , Hou Qingwen , Chen Xianzhong
Published: November 2020

Hostile environment inside blast furnace (BF) and nonuniform fluidization characteristics of burden surface bring challenges to the extraction of burden line, which is an important factor affecting the smelting efficiency. In this study, based on the imaging principle of Synthetic Aperture Radar (SAR), a mechanical swing radar was designed to capture the high-density radar echo signals. By analyzing the characteristics of radar spectrum, the entropy weight method was used to complete the coordinate transformation from the nonuniform coordinate point cloud map to the grayscale image in real space to visualize the smelting states. Then, the burden surface features were enhanced by gamma correction, and the adaptive threshold segmentation was used to extract the surface transitional belt in the image. Finally, the burden line points were extracted by the energy centrobaric correction method to fit the burden line. Compared with traditional algorithm, experiments on industrial data indicate its feasibility and effectiveness.
Research on the Fast Solution Method of Pilots’ Head Attitude
Authors: Zhang Limin , Jiang Jie , Fang Wei , Liu Kai
Published: November 2020

Aiming at the problem of poor accuracy and poor real-time performance in the process of head pose calculation in pilot cabin simulation, a high accuracy and fast algorithm for head pose calculation is proposed. On the basis of traditional EPNP algorithm was improved, and combining with POSIT algorithm’s speed advantage, select face five key feature points as virtual control points, according to the mapping relationship between space and virtual control point coordinates, establish a system of linear equations, then the internal camera parameters is calculated using Zhengyou Zhang camera calibration method, finally using POSIT algorithm of calculating to get the head rotation matrix and translation vector, by conversion from the head of Euler angle in real time. The experimental results show that compared with DLT, EPNP, POSIT and other traditional methods, the proposed method has higher accuracy, better real-time performance and better robustness in solving pilots’ head attitude in continuous video.
Stiffness Enhancement Method for Complex Thin-walled Parts Driven by the Principal Stress Field
Authors: Zhang Weinan , Dai Ning , Guo Ce , Yu Yi , Gong Sai
Published: November 2020

Aiming at the problem that the stiffness enhancement effect of traditional reinforcing methods such as Tic-tac-toe type stiffener is not always optimal in the face of complex thin-walled parts, a stiffness enhancement method for complex thin-walled parts is proposed. In this paper, the bionic principle of Victoria vein layout based on the principal stress field model is explored, and an optimization method for the lightweight stiffness enhancement of complex thin-walled parts driven by the principal stress field is proposed. The experimental results show that the specific stiffness of the complex thin-walled parts generated by this method increases by 28% when the quality of the structure is basically the same as that of the traditional Tic-tac-toe type stiffeners.
Foreign object damage simulation of aero-engine blade
Authors: Xie Zhenbo , Ma Zhongyuan , Chen Qinggui , Li Bianjiang , Zhao Zhenhua
Published: November 2020

To study the anti-foreign object damage (FOD) ability of aero-engine compressor blades, finite element model of compressor blades was established. Blades damage was evaluated with different steel ball diameters and shock angles. Results show that the damage width and depth becomes bigger with the steel ball’s diameter increases. The damage width doesn’t change apparently with the shock angles, and the damage width nearly equals to the steel ball diameter. The damage depth mostly becomes smaller with the increase of shock angle.
Speed Optimization Design of Stacker in Automatic Stereoscopic Warehouse Based on PLC
Authors: Ding Li , Chen Yongsheng
Published: November 2020

The S-type speed control is applied to the motion control of the stacker, and the S-type speed curve control is simulated by MATLAB to verify the feasibility of the optimal design. In practice, Siemens S7-300 is used for PLC programming to realize S-type speed curve control technology of stacker. The high efficiency, real-time and accuracy of the design realized the effective optimization of the speed of the stacker in the automatic stereoscopic warehouse.
Research on the Damping Device with Adjustable Inertia and Damping
Authors: Tao Xiangyuan , Dong Xiaomin , Yu Jianqiang
Published: November 2020

In this paper, a new type of composite structure is proposed based on the original semi-active suspension of magnetorheological shock absorber and automobile suspension with inertia container, and the structure is analysed theoretically. The proposed new composite device is composed of a hydraulic cylinder, a magnetorheological damper and a hydraulic motor. The magnetorheological damper is compounded in the hydraulic cylinder and connected to the hydraulic motor through a hydraulic pipeline. The magnetorheological damper has the function of adjusting the damping, and the hydraulic motor achieves the purpose of adjusting the inertia by changing the mass. In order to analyse the device, the mechanical characteristics of magnetorheological valve and hydraulic motor are modelled and analysed. A quarter-vehicle suspension simulation analysis was performed on the overall operating characteristics of the system. The analysis results showed that the compound device has good variable damping inertia capacity characteristics, which is of great significance for improving the semi-active control suspension system.
Innovative design of eight-bar linkage motorcycle suspension based on mechanism regeneration method
Authors: Fan Yi , Deng Rukui
Published: November 2020

Motorcycles have high requirements on operation agility, steering accuracy and braking stability when running at high speed, and the core structure that determines the above performance lies in the suspension system. Therefore, this study uses the mechanism regeneration method to analyze the Duolever multi-bar linkage scissor front suspension of BMW and the eight-bar linkage rear suspension of Honda, and the topology matrix and generalized kinematic chain of the mechanism are obtained. Under the condition of meeting the design constraints, 12 kinds of eight-bar ten-pair combined kinematic chains can be gotten. After specifying the link types in turn, 17 feasible new eight-bar linkage motorcycle suspensions are finally realized. This study provides an efficient method for the independently design of the motorcycle suspension topology.
Research on the Topology of Wireless V2G for Electric Vehicle
Authors: Hao Zhonghan
Published: November 2020

Based on the circuit topology for the electric vehicle wireless V2G power transmission system, the boost circuit, wireless resonance circuit and grid-connected inverter circuit of the system were studied. According to the characteristics of the boost converter, a voltage feedback closed-loop control model based on PID and PWM was established. Since the excitation and magnetic leakage of the wireless transmission coil could not be ignored, the maximum transmission power model of the wireless resonance circuit was derived. Starting from the operation mode of the three-phase grid-connected inverter circuit, the control rules of the three-phase bridge arm that sent electrical energy back to the grid in a steady state were obtained. The simulation results showed that the maximum transmission power of the electric vehicle wireless V2G power transmission system was affected by the leakage magnetic field of the wireless transmission coil, and the maximum transmission power was inversely proportional to the leakage magnetic field. In addition, the boost circuit and grid-connected inverter circuit in the system were sources of harmonics, and a large amount of harmonic energy was sent to the grid during the operation process of the electric vehicle wireless V2G circuit.
Design of temperature humidity and illumination control system for indoor flowers
Authors: Liu Haidong , Wang Xiaosong
Published: November 2020

In order to improve the automation of indoor flower environment monitoring, enable the flowers grow in the best environment, this paper designs an automatic control system of temperature, humidity and illumination based on STC89C52 single chip microcomputer. Temperature sensor DS18B20 is applied to sense indoor air temperature, meantime, YL-69 soil moisture sensor and photosensitive resistance are used to sense soil relative humidity and light intensity respectively. Temperature, humidity and light intensity are transmitted to single chip microcomputer for analysis and processing, and the results are displayed by OLED display module. The upper and lower limits of environmental parameters are manually preset through the keyboard. When the temperature and light intensity exceed the upper limit, the system can send out alarm signals and output control signals to drive the corresponding actuator to work; when the soil moisture is lower than the lower limit, the system will automatically alarm and water automatically; when the humidity reaches the upper limit, watering will be stopped to ensure the indoor environmental parameters in the best value suitable for flower growth. The system has been tested and debugged. The results show that the control system can effectively process and display the flower environment information, achieving the purpose of automatically adjusting and controlling the temperature, humidity and illumination of flowers, has strong promotion value.
Parameter Change in Engine Simulation and Performance Optimization of Diesel Engine
Authors: Lyu Haomin
Published: November 2020

Diesel internal combustion engines have been widely used in automobiles, ships, power generation, and other fields, laying a foundation for modern science and technology. Due to the wide application of internal combustion engines, the working efficiency and exhaust gas generated by internal combustion engines have become issues of common concern. In order to optimize the working efficiency and emissions, simulations are carried out under different conditions by using Cantera IC Engine code. Through analysis, the relationship between similar factors and their effects on emissions and thermal efficiency are shown, thus obtaining the most effective method to improve efficiency and reduce emissions. On the one hand, it is found that factors, such as the compression ratio, have a very intuitive impact on the efficiency of the engine. On the other hand, the ambient temperature and air pressure have less influence on the engine operating within the controllable range. These results provide a general idea for diesel engine optimization under certain environments.
Discussion on Application Verification Method of Xinyan Electronic Components
Authors: Qiu Xiuling , Xie Yang
Published: November 2020

This article first analyzes the verification of Xinyan electronic components, including functional verification, performance verification, and process verification. Secondly, the author elaborated on the current status of use of the newly developed electronic component verification methods, such as low applicability of domestic components, poor authority of verification specifications, and low integration of development and application. This paper studies the overall verification process analysis, application verification method analysis, verification effect evaluation analysis, verification information sharing evaluation, etc., in order to improve the accuracy of verification results and the rationality of electronic components distribution.
Multi-Candidate Electronic Voting Scheme Based on Fully Homomorphic Encryption
Authors: Liao Guopeng
Published: November 2020

In recent years, multi-candidate electronic voting has attracted tremendous attention and been put into lots of practice because of its outstanding effectiveness and convenience. As a result, it has become an urgent task to achieve the anonymity and public verifiability of electronic voting. Several schemes have been proposed so far, but most of them are either inefficient or too demanding of the security of the entities involved. Focusing on this problem, a multi-candidate electronic voting scheme based on fully homomorphic encryption and digital signature algorithm is proposed in the paper. To encrypt votes efficiently and reduce the dependence of different entities, the Identity Based Full Homomorphic Encryption(IBFHE) scheme is used in the paper. A homomorphic addition ticket counter is then used to realize the homomorphism calculation of encrypted votes. To settle the problem of identity authentication in electronic voting with high efficiency and security, Elliptic Curve Digital Signature Algorithm(ECDSA) is utilized. The capability of batching votes is achieved by making use of Single Instruction Multiple Data(SIMD) technology, which is designed for improving the efficiency of the voting system. Finally, the paper makes an analysis based on six security features of e-voting, which reveals the scheme’s safety and feasibility.
Research on Practice of Electrical Control Simulation System in Motor and Electrical Control Technology Course Teaching in Higher Vocational Education
Authors: Chen Long
Published: November 2020

In the fierce competition environment, students’ practical ability is more important. In order to adapt to the social development, constantly promote the curriculum reform of higher vocational colleges, enhance the professional practicality of electrical control technology course, enable students to master the relevant technology and improve the teaching quality, considering the actual situation of the school, the practical course of electrical control simulation system is proposed. The control group and the experimental group are set for teaching comparative analysis. While learning theoretical knowledge, students can practice through the simulation experiment system to further improve their practical ability. The results show that adding electrical control simulation system to the teaching of motor and electrical control technology course in higher vocational colleges can greatly improve students’ practical ability, and give full play to students’ subjective initiative, so that they can actively learn. Therefore, the application of electrical control simulation system in the teaching of motor and electrical control technology course in higher vocational colleges is of great significance for promoting teaching reform and improving students’ practical ability.
Sequence optimization of machining elements for process model based on the genetic algorithm of matrix constrained
Authors: Bin Cheng , Dingjie Guan , Jiyan QI , Bingxue Jing
Published: November 2020

Process sequence planning is the permutation and combination orienting to product manufacturing process, which plays an important role in improving the manufacturing produce efficiency and reducing the manufacturing cost. In this paper, an improved constraint matrix method is proposed to transform the relation between manufacturing features into the constraint relation between machining elements, and to determine the sequence coefficient of machining elements according to the correlation relation of manufacturing features. Through constructing the interactive associative relationship of sequence constraint matrix and combined constraint matrix, the initial population with high adaptability is selected out, and can improves the adaptability of initial population of genetic algorithm by using the process sequencing algorithm of improved constraint matrix. In order to improve the convergence speed, the accuracy and efficiency of process sequencing, the genetic algorithm of elite preserving is used.
Short-text intention recognition based on multi-dimensional dynamic word vectors
Authors: Xiao Mei , Yao Miao , Li Ying
Published: November 2020

For short-texts with few words and sparse features, it becomes very important to use text context semantic information to enrich its text representation. At present, the text representation method of static word vectors is the same for the same word in different sentences. From a certain perspective, contextual semantic information is not fully utilized. Therefore, this paper proposes to use the combination of BERT and ELMo to mine more comprehensive contextual semantic information from different dimensions to enrich the text representation. The BERT model fully describes the characteristics of word level, sentence level and relationship between sentences. The ELMo model can solve the problem of polysemy. LSTM and Attention Mechanism are used to extract high-dimensional text features, and CRF is used to recognize intentions. Experiments show that the multi-dimensional dynamic word vector intention recognition model (MD-Intention) proposed in this paper has achieved good performance on both the ATIS-2 dataset and the SNIPS dataset.
Classification of Customer Reviews on E-commerce Platforms Based on Naive Bayesian Algorithm and Support Vector Machine
Authors: Zhao Wenyuan
Published: November 2020

Customer reviews on e-commerce platforms contain valuable information, while sifting through them manually tends to dismay people because of the huge amount of data. Ideally, the identification classifier would analyze the emotional disposition of product reviews (positive, negative) and aggregate opinions about each of them. Previous literature has demonstrated extensive research conclusions on opinion extraction and semantic classification of product reviews on websites. However, analysis on the universality of machine learning algorithms in identifying the emotional tendency of different Chinese e-commerce reviews is not yet studied. This study uses a method, which is based on general machine learning algorithms, to classify feedbacks. Our classifier extracts Chinese word segmentation and text frequency for feature extraction and scoring, and implements the classification with methods of Naive Bayesian and Support Vector Machines. Experimental results on the Alibaba product review sentiment datasets show that our model based on two machine learning algorithms, though results in different performances, can provide suggestions on the selection of the identification classifier using a trade-off strategy and helps obtain fast and accurate classification on reviews of different categories.