bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study on 3D quantization and optimization of the acoustic package of vehicle powertrain by energy boundary element method
Authors: Wu Congbing , Yu Ping , Zhou Hongxia 
Published: June 2020

Abstract
Automobile noise has become the first source of environmental noise pollution, and its radiated power reaches 75% of the total environmental noise, while powertrain noise is the main source of automobile noise. With the problem of noise reduction of automobile powertrain, a 3D grid positioning technology, based on energy boundary element method, was proposed to quantify the noise source of automobile powertrain, and the generalized sound holography was applied to obtain the noise cloud map and analyze the noise propagation path, so as to determine the acoustic package material and the optimal location. Through the optimization experiment and simulation of 4 schemes, the acoustic package of automobile powertrain with the best effect was obtained, and the insertion loss of 6.7db could be obtained at 7m of the powertrain. Therefore, the results have important engineering application values for automobile noise reduction.
Design and Performance Simulation of Modern Automobile & Automatic Navigation Control System
Authors: Xiaoping Lei 
Published: June 2020

Abstract
This Study Focuses on the Composition, Performance, Application Field and Performance Simulation of Automotive & Automatic Navigation Control System, and Based on the Control Concept of the Specific Scheme, the Design Execution Leader is Used to Study the Redesign of the System Feedback. Finally, the Digital-Analog Simulation System is Applied to Improve the Control System, in Order to Provide a Feasible Reference for the Design and Performance and Improvement of the Modern Automobile Automatic Navigation Control System.
Research and Application of Mechanical Product Design Process Based on QFD and TRIZ Integration
Authors: Weijie Jia 
Published: June 2020

Abstract
with the Increasingly Fierce Competition in Science and Technology, the Competitiveness of Enterprises Depends on the Level of Products, and the Level of Products Depends on the Design of Products. Therefore, the Design Quality of Products Will Directly Affect Product Performance, Product Development Cycle and Economic Benefits, and Even the Development of National Economy. in Order to Achieve Product Innovation and Meet the Needs of Customers, This Paper Studies How to Integrate QFD and TRIZ to Achieve Mechanical Product Innovation Design. through the Research, It is Found That QFD Can Only Obtain Customer Demand, Not Create Demand, Only Find Conflict, Not Solve Conflict, and TRIZ Can Solve These Two Problems Well. Therefore, This Paper Takes HoQ as the Skeleton Framework, Integrates QFD and TRIZ in the Demand side of HoQ and the Roof to Guide the Product Design Process in Reality, Which Can Realize the Complementary Advantages, Overcome the Shortcomings of Only Relying on a Single Design Theory to Guide the Design Process, and Has Practical Significance.
Application of Wireless Positioning Technology in Risk Management and Control of Substation Operation Site
Authors: Feng Junjie , Shi Junfeng , Gao Lei , Huang Hongwei 
Published: June 2020

Abstract
the Construction of Smart Grid Puts Forward Higher Requirements for the Safe Operation of Substation, But There Are Still Some Hidden Dangers of Human Factors in the Current Operation of Substation. the Safety Management System of Substation Operation Based on Wireless Positioning Technology Can Avoid the Influence of Human Factors by Technical Means. Based on the Analysis of the Special Requirements of Operation Safety Management in Substation Site, It is Pointed out That Css Broadband Wireless Positioning Technology is Suitable for Substation Application Because of Its High Positioning Accuracy, Strong Anti-Interference Ability and Reasonable Price. the Work Flow and Application of the Substation Operation Safety Management System Are Described.
Simulation study on crowd evacuation based on Arnold emotion model
Authors: Gan Qiying 
Published: June 2020

Abstract
The computer simulation technology can reproduce the crowd movement trend under the emergency situation in a real all-round way, summarize the movement law, and formulate the emergency plan of public places. It can conduct scientific analysis and guidance on crowd evacuation in emergency situations through comprehensive evaluation of crowd evacuation channels in buildings and public places in combination with the emergency situations that can occur in the movement behavior simulation scenarios in emergency situations. In terms of security research hotspot, the emotion of personnel in emergency has a direct impact on crowd evacuation, so it is necessary to summarize Arnold’s emotion model. The occurrence of adverse events in emergency should be avoided to achieve more efficient evacuation of personnel.
Study on anti-scanning performance of WFRFT modulation signal
Authors: Zhuang Lei , Zhu JingFei , Cheng Jian 
Published: June 2020

Abstract
Based on the signal characteristics of the existing weighted fractional fourier transform (WFRFT) communication, this paper studies the equivalent signal to noise ratio (SNR) and bit error rate (BER) of the non-target receiver under the condition of the transformation parameter errors, with the purpose of measuring the anti-parameter scanning characteristics of the single-parameter WFRFT signal. The constellation splitting characteristics of multi-parameter WFRFT signal are further studied.Multi-parameter WFRFT modulation can not only improve the resistance of single-parameter system to parameter scanning detection, but also effectively hide the original modulation mode due to its characteristic of splitting modulation constellation.
Research on elevator hydraulic buffer detection system based on intelligent image recognition
Authors: Shuangchang Feng , Chen Wang , Xue Jiai 
Published: June 2020

Abstract
As a kind of vertical transportation device, elevator is widely used in the production field and daily lives. In order to detect the reset time of the elevator hydraulic buffer, this article first analyzes the shortcomings of the existing detection methods, then provides a design of detection system for the elevator hydraulic buffer based on intelligent image recognition. This system can detect the reset time and reset distance, and grasp the dynamic compression and reposition process of the elevator hydraulic buffer. Besides, this system can also bring lots of advantages, such as wide universality, easy installation, high degree intelligence, less influence of human factors and no risk for the inspectors in measurement process.
Airborne high-resolution image motion target detection combined with hyperspectral features
Authors: Wang Cailing , Zhang Yuchun , Guo Pu , Xu Jun 
Published: June 2020

Abstract
Aiming at moving object detection in Airborne Hyperspectral Remote Sensing Images under complex background, this paper proposes a background removal method based on Mixture Gauss Model. Firstly, in order to eliminate the influence of the dithering of airborne platform on remote sensing image, this paper uses affine transform-based image registration algorithm to achieve the image registration of 3D data. Then, this paper selects specific spectral images, establishes the Mixture Gauss background model, and subtracts the background to achieve the extraction of moving objects. The experimental results show that the method can extract moving objects in Airborne Hyperspectral Remote Sensing Images effectively.
Parameter estimation of LFM signal based on STFT and RANSAC
Authors: Fan Xiaolei , Li Bing 
Published: June 2020

Abstract
Aiming at parameter estimation of LFM signal in low SNR, a new method based on short time Fourier transform (STFT) and random sample consensus (RANSAC) is proposed in this paper. STFT is carried out to extract time-frequency curve firstly. Then median filter is used to eliminate glitches and disturbs. Finally, RANSAC process is applied to construct the model of the chirp line and obtain the parameters. In the proposal, the use of median filter and RANSAC iterations improve the adaptability to extremely low SNR. Simulation results confirmed the effectiveness of the proposed method.
GAN-based Sample Expansion for Underwater Acoustic Signal
Authors: Yang Hongbin , Gu Han , Yin Jinyong , Yang Jian 
Published: June 2020

Abstract
A deep learning model has a large number of free parameters, which need to be effectively trained on a large number of samples to calculate the depth parameters. However, many special applications like underwater acoustic signal recongnition cannot provide enough dataset to guarantee high performance. In addition, the original dataset adopts some formats, such as audio, which makes it difficult to capture features. To overcome these challenges, we propose a novel framework. Firstly, based on the evaluation of spectrum method, our framework selects the appropriate preprocessing method. Then it modifies GAN to generate samples, and establishes an independent classification network to ensure the quality of samples. Finally, our framework applies the existing classification network to evaluate performance and selects the best one for the LOFAR spectrum. The experimental results demonstrate that the proposed method can generate high-quality LOFAR spectrum and improve the prediction accuracy of the classification model significantly.
Non-motor vehicle illegal behavior discrimination and license plate detection based on real-time video
Authors: Feng Jihong , Wang Xiaoling , lv Hao 
Published: June 2020

AbstractIn recent years, automatic detection technology of motor vehicles has developed rapidly. However, due to some interference problems, there is little research on the automatic detection technology of non-motor vehicle. This paper proposes a method which based on the experience and results of actual projects for discriminating non-motor vehicles in real-time video, detecting and recognizing license plates. The algorithm and steps will be described in detail. The image difference method and fractional differential method are combined to extract the contours of dynamic non-motor vehicles in the video, and the methods are used to filter the contours of non-motor vehicles using skin color filter detection and geometric feature discrimination to assist the training of cascade neural networks. Non-motor vehicle license plate image clustering is detected in the extracted contour by clustering the background color of the license plate and determining the ratio of the rectangle surrounding the license plate. The boundary expansion method is used in combination with the Faster R-CNN (Faster-Convolutional Neural Networks) to train the model, and then BP (Back propagation) neural network is used to identify characters in the target area. And a trajectory tracking method is proposed to automatically determine whether non-motorized vehicles have violations.
Transferring Deep Belief Networks for the Classification of LANDSAT8 Remote Sensing Imagery
Authors: Zou Qingqing , Chen Chen 
Published: June 2020

Abstract
This paper studies the structure and characteristics of LANDSAT8 OLI data, and transfers Deep Belief Networks model to OLI datasets. Firstly, the sub-sampling window is used to filter the pretraining data, which reduces the calculation cost of the hidden layer feature representation of the model. The parameters of each layer of DBN are trained forward, optimization parameters are used to make the error converge to a low value in back propagation. Finally, the activation function is used to reduce the dimension of the hidden layer of the model, and the output vector is used as the global feature representation of the image. Running DBN to complete the classification of LANDSAT8 remote sensing image in the study area, the experimental results show that the model has completed the classification and achieved high user accuracy and reliability in the case of that all types of the wetlands have similar spectral characteristics. The conclusion is that the pretrained features can be well applied to the LANDSAT8 remote sensing datasets. The accuracy is improved within the scope of cost calculation, and the local minina problem will also occur in deepening model.
Design of Function Signal Generator Based on DDFS
Authors: Lv Meimei , Zhang Bin , Li Mengnan , Shen Dapeng 
Published: June 2020

Abstract
The system is designed to construct a Function Signal Generator based on Direct Digital Frequency Synthesis Technology (DDFS), in which MCU and FPGA are the key components and necessary peripheral circuits are provided. Its main modules are responsible for the formation of oscillogram, the frequency synthesis, the digital-to-analog transformation, the control of range, and back-stage treatment, the user interface and other necessary modules. MCU controls FPGA to generate the oscillogram and other control words, and the FPGA controls output waveform and frequency synthesis. Amplitude control is realized by 2 DA. Low-pass filter and Transistor push-pull power amplification circuit are used to increase the quality of the output waveform and its loading capacity, and display the signal on the LCD. The system constitutes of 2 DAC and the FPGA, so that realizes the expanded requirements of 1 Hz-1MHz of the four waveform output.
An ECG arrhythmia image classification system based on convolutional neural network
Authors: Yu Ximing 
Published: June 2020

Abstract
The automatic classification of ECG images has great significance for doctors to diagnose cardiac diseases. In order to improve the accuracy and efficiency of disease diagnosis, this paper presents an automatic classification system for ECG arrhythmia images. The database of MIT-BIH is processed visually and a waveform detection method is proposed for detecting the QRS waveform. Wavelet transform is used to detect the R-R interval and locate the R point peak. A convolutional neural network(CNN) model was built to train and classify the ECG images. Experimental results show that according to the ANSI/AAMI EC57 evaluation criteria, the accuracy(Acc) rate of ventricular ectopic beat(VEB) can reach 95.9%. The sensitivity(Se) evaluation index is 93.0%. The specificity(Spe) evaluation index is 91.9%. For the supraventricular ectopic beat(SVEB) class, the accuracy rate is 93.2%, the sensitivity evaluation index is 81.3%, and the specificity evaluation index is 90.5%.
Implementation of Accelerating Video Preprocessing based on ZYNQ Platform Resource Management
Authors: Sheng Mengxue , Hou Wanwan , Jiang Juchao 
Published: June 2020

Abstract
Embedded AI rised in recent years, AI algorithm deployment platforms need strong computing power, but embedded systems focus on the balance between performance and cost. The contradiction has become a great challenge for the development of embedded AI. This paper analyzes the “ARM+FPGA” architecture and NEON register resources of the ZYNQ series chips introduced by Xilinx, and proposes a method to reasonably schedule the above resources to accelerate video preprocessing when it is used as an edge processing platform for intelligent monitoring terminals. This method makes the video intelligent analysis process of the monitoring terminal achieve real-time.
Research on Medical Image Storage and Retrieval System Based on Hadoop
Authors: Yaya Xie , Bi-Geng Zheng 
Published: June 2020

Abstract
With the Continuous Improvement of Medical Information, the Continuous Development of Image Processing Technology and the Continuous Upgrading of Hospital Image Acquisition Equipment, Medical Image Detection Has Become More and More Reliable and Effective, Becoming an Important Basis for Doctors’ Diagnosis. Now Large Hospitals Use the Combination of Radiology Information Management System and Image Archiving and Communication System to Store and Access Patients’ Images. with the Establishment of Regional Image Sharing and Cooperation Platform, the Online - Near Line - Offline Storage Strategy Adopted by the Traditional Image Archiving and Communication System Can No Longer Meet the Needs of Mass Image Data Storage. in the Retrieval of Mass Image Data, the Relational Database Used by the Image Archiving and Communication System is Inefficient in Reading the Image on the Scale of Mass Data, and the Existing Image the Archive and Communication System Lacks Complete Data Backup and Disaster Recovery Scheme. This Paper Analyses the Feasibility, Advantages and Problems to Be Solved of Using Hdfs to Store Massive Medical Image Files, and Studies How to Realize the Function of Precise Access and Range Access Provided by Pacs Efficiently in Hadoop.
Pedestrians Detection of Offline Retail Stores Based on Computer Vision
Authors: Wang Mingxu 
Published: June 2020

Abstract
Pedestrian Detection is a sub-task in the object detection due to it plays an important role in video surveillance. This project train model algorithm by using offline retail data and intelligent identification based on the offline retail brand store scene and focuses on the two core personnel of the retail scene-shopping guides and customers. This project detects pedestrian’s localization and their key points by using deep learning and computer vision technologies.
Study on the Mixed Teaching Mode of the Program Language Courses in the New Situation
Authors: Ma Lei , Zhang Zhongqiu , Zhang Nana 
Published: June 2020

Abstract
With the spread of new coronavirus, new forms of online teaching are widely used in colleges and universities in China. Teachers are the main parts relying on the existing online teaching platform, which can make up for the defect of single online platform, simulate off-line teaching better, complement the advantage of off-line teaching, and improve the quality of online teaching effectively by using the new hybrid teaching mode of program language courses with other network tools software.
Parallel Cascade R-CNN for object detection in remote sensing imagery
Authors: Hou Jingyou , Ma Hongbing , Wang Shengjin 
Published: June 2020

Abstract
Object detection in remote sensing image is a challenging task in computer vision. Remote sensing images have different characteristics compared with conventional images. Especially object detection in remote sensing image needs to focus on small targets with different ratio and orientation. In this paper, we propose a novel detection architecture for remote sensing imagery to solve the problem of scale diversity. On the basis of Cascade R-CNN, we have developed Parallel Cascade R-CNN. In the second stage, parallel detection heads are used for separate detection, and their RoIAlign modules have different output sizes. In addition, different preprocessing methods are applied according to the shape and quantity characteristics of different classes. We evaluated our algorithm on DOTA dataset. Experiments have shown that our algorithm can achieve performance improvement on a high baseline. And the detection performance of those categories with smaller objects has been improved. In the detection task of horizontal bounding box, we obtained the mAP of 78.96%, which reached the state of the art. Our algorithm is simple and has good performance, easy to be migrated to various network structures.
Ultrasound-guided Needle nerve contact positions detection in Regional Anaesthesia using Needle tip force and Opening injection pressure
Authors: Zhang Shengli , Graeme  , George  , Qiao Panpan , Huang Zhihong , Xia Chunming 
Published: June 2020

Abstract
Ultrasound imaging guidance has been widely used in peripheral nerve block to guide the anaesthetist to insert the needle and observe injection fluid diffusion. However, needle-nerve contact(NNC) positions visualization is difficult to achieve because of requirement of high image solution and expert. To better guide the anaesthetist to predict the distance from needle to nerve and reduce the needle trauma, we attempted to use Needle tip force(NTF) and Opening injection pressure (OIP) to identify the NNC positions under ultrasound guidance. The relationship between OIP and NNC, NTF and NNC, NTF and OIP is unclear now, and we conducted a prospective, observational study to define the relationship. In this study, we designed a multi-function block needle and built a real-time NTF and OIP measurement which could trace and record NTF and OIP data in the simulator sciatic nerve. NNC positions were divided into three distinct NNC positions: Pre-NNC, First-NNC, Forceful-NNC using ultrasound guidance image. We statistically analysis NTF, OIP in the three NNC positions, and the results indicate the NTF differentiate in three NNC positions(p<0.001), OIP differentiate as well (p<0.001) and the NTF has a highly positive with the OIP in the same NNC positions with Pearson correlation 0.62.
A Gesture Recognition Approach Using Multimodal Neural Network
Authors: Song Xiaoyu , Chen Hong , Wang Qing 
Published: June 2020

Abstract
Gesture recognition based on visual modal often encounters the problem of reduced recognition rate in some extreme environments such as in a dim or near-skinned background. When human beings make judgments, they will integrate various modal information. There should also be some connections between human gestures and speech. Based on this, we propose a multimodal gesture recognition network. We use 3D CNN to extract visual features, GRU to extract speech features, and fuse them at late stage to make the final judgment. At the same time, we use a two-stage structure, a shallow network as detector and a deep network as classifier to reduce the memory usage and energy consumption. We make a gesture dataset recorded in a dim environment, named DarkGesture. In this dataset, people say the gesture’s name when they make a gesture. Then, the network proposed in this paper is compared with the single-modal recognition network based on DarkGesture. The results show that the multi-modal recognition network proposed in this paper has better recognition effect.
A signal detection method based on matrix information geometric dimensionality reduction
Authors: Feng Sheng , Hua Xiaoqiang , Wang Jiangyi , Zhu Xiaoqian 
Published: June 2020

Abstract
Traditional signal detection methods have achieved satisfactory performance in many contexts of signal processing. However, these methods based on mathematical statistics show a drawback in dealing with the low SNR cases, which limits their practicability. To this end, inspired by image processing techniques, we first make use of the short time Fourier transform to generate sufficient 2-D spectrograms of the received data. Then we extract high dimensional features of these spectrogram to construct high dimensional covariance matrices, transforming into a binary classification problem lying on a symmetric positive definite (SPD) manifold. In addition, by reducing the dimensionality directly on the SPD manifold, these spectrograms are mapped into a more discriminative SPD manifold, which improves the separability between the two classes. The simulation experiment results demonstrate that our method achieve satisfactory signal detection performance in the task of signal detection under K distribution data, even in the case of SNR = -10.
A New Method for Parameter Estimation of Cross-Channel Ultra-Wideband LFM Signal
Authors: Fan Xiaolei , Li Bing 
Published: June 2020

Abstract
Aiming at the reconnaissance of ultra-widaband (UWB) LFM signal, a method using dual-channel joint working mode is introduced and a parameter estimation algorithm of cross-channel signal is proposed in this paper. Short time Fourier transform (STFT) is carried out to extract time-frequency curve firstly. Then least-square (LS) algorithm is used to roughly estimate the parameters of signal in single channel and whether it is a cross-channel signal is determined. Finally, random sample consensus (RANSAC) iteration is applied to construct the model of the chirp line and obtain the accurate parameters. Simulation results confirmed the effectiveness of the proposed method.