bluuebunny/crossref_metadata_2025 huggingface dataset:
Simulation study on crowd evacuation based on Arnold emotion model
Authors: Gan Qiying
Published: June 2020

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

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

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

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

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

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

In 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

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

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

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

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

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

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

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

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

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

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

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

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.
Diabetic retinopathy identification system based on transfer learning
Authors: Li Lingling , Yan Xueqi , Peng Heng , Xiu Ying , Gao Yiyang , Wang Xin
Published: June 2020

Diabetic retinopathy, a complication of diabetes, is a significant cause of vision loss and blindness. Early detection of diabetic retinopathy can help reduce the risk of blinding. However, automatic diabetic retinopathy identification is a challenging task due to their different morphology during a different stage. Aiming at the problem of low efficiency for most of the existing methods, we developed a diabetic retinopathy recognition system based on transfer learning. The system utilizes transfer learning, which trains a neural network based on the DenseNet201 network model and Messidor Data Set, and can not only train an active network quickly, but also has a reasonable effect on the classification of diabetic retinopathy.
Object Detection and Localization Based on Binocular Vision for Autonomous Vehicles
Authors: Feng Mingchi , Liu Yibo , Jiang Panpan , Wang Jingshu
Published: June 2020

Environment perception based on vision plays an important role in autonomous driving technology. Although vision perception has achieved certain results in recent years, many methods can not solve the contradiction between speed and precision. In this paper, we propose a system for fast and accurate object detection and localization based on binocular vision. For object detection, a neural network model based on YOLOv3 is proposed. Specifically, MobileNet is employed in the backbone of YOLOv3 to improve the speed of feature extraction. Then the corresponding ORB feature points are extracted from continuous stereo images which take from the binocular cameras on the moving car. Thus, the disparity of each ORB feature point is calculated. After that, we use the result of object detection to screen the ORB feature points. Finally, the depth of the targets in the traffic scene can be estimated. Experiments on the KITTI dataset show the efficacy of our system, as well as the accuracy and robustness of our object localization relative to ground truth and prior works.
Research on the detection of curved ferromagnetic materials by flexible eddy current array sensor based on ldc1614
Authors: Li Cao , Zhang Weipeng , Zhang Qi , Chen Rui , Ma Wenqiang
Published: June 2020

Flexible eddy current array nondestructive testing technology has the advantages of large single scanning area and fast scanning speed in the defect detection of conductive materials, so it becomes a common research hotspot of sensor and nondestructive testing technology. The performance of the array probe on the sensor is an important factor to determine the performance of the sensor, and the detection circuit and excitation mode are another important factor. In this paper, the array coils with different parameters are simulated and analyzed, and the resonant detection circuit based on ldc1614c digital inductance conversion chip is used to detect the high permeability materials on curved surface. Through the digital imaging technology, the defect image of the tested material is obtained to verify the selection of the probe coil and the reliability of the detection circuit.
Cuffless Blood Prediction with Fingertip Pulse Wave
Authors: Song Weize , Sun Xiaoyan , Hu Chuhan
Published: June 2020

Cuffless method for blood pressure measurement is an important methods for continuous health status monitoring. A pulse wave is a periodic time-series signal that reflects a non-linear, non-stationary change in the pulse signal over time. Traditional ways of pulse wave based blood pressure assessment rely on feature extraction from pulse signals, which are usually signal quality dependent and lack of consistence among studies. In this paper, a method of blood pressure measurement of using continuous pulse waveform and long-term and short-term memory network is proposed, which avoids the process of manually extracting waveform features. Experiments were performed with both pulse wave signals and the arterial blood pressure signals form the MIMIC database. Empirical mode decomposition was applied for signal preprocessing, and the time series of the pulse wave was analyzed to establish a Long Short-Term Memory neural network for blood pressure assessment. An average prediction accuracy of 83.2% was achieved.
A novel CNN model for fine-grained classification with large spatial variants
Authors: Wang Junpeng , Lu Yanfeng
Published: June 2020

Convolutional Neural Networks (CNN) have achieved great performance in many visual tasks. However, CNN models are sensitive to samples with large spatial variants, especially severe in fine-grained classification task. In this paper, we propose a novel CNN model called ST-BCNN to solve these problems. ST-BCNN contains two functional CNN modules: Spatial Transform Network (STN) and Bilinear CNN(BCNN). Firstly, STN module is used to select key region in input samples and get it spatially modified. Since the adoption of STN will cause an information loss phenomenon called boundary loss, we design a brand-new IOU loss method to solve it. We make a theoretical analysis of the IOU loss method. Secondly, to discover discriminative features for fine-grained classification task, BCNN module is applied. BCNN interacts CNN features from different channels to produce more discriminative bilinear features than fully connected features of CNN. ST-BCNN works by reducing irrelevant spatial states and producing fine-grained features. We evaluate our model on 3 public fine-grained classification datasets with large spatial variants: CUB200-2011, Fish100 and UAV43. Experiments show that the IOU loss method can reduce boundary loss and make STN module output spatial transformed image appropriately. Our proposed ST-BCNN model outperforms other advanced CNN models on all three datasets.
Feature Aware Dynamic Mesh Approximation with Local Area and Deformation Control
Authors: Zhang Shixue , Yang Qingyun , Lu Wenhao
Published: June 2020

In computer animation applications, simplifying the mesh is an important technique to reduce the workload of visualization processing. In this paper, we propose a new method to simplify the dynamic model using local area and deformation information control. Our method uses an improved quadratic error metric with the local distortion measurement as the basic level. In addition, the deformation parameters are defined to be embedded in the summarized quadratic error, so that the regions with large deformations during the animation can be successfully retained. The new method is efficient and easy to implement, and in average can generate high-quality animated models with well-preserved local and deformation features on eacn frame.
Anomaly Detection of Pantograph Based on Salient Segmentation and Generative Adversarial Networks
Authors: Wang Ye , Quan Wei , Lu Xuemin , Peng Yuchen , Zhou Ning , Zou Dong , Liu Yueping , Guo Shaopeng , Zheng Danyang
Published: June 2020

The pantograph is an important component of railway pantograph-catenary system, which can provide electric current for electrified railway electric locomotive. Since the pantograph is in an open roof environment, the defects of the pantograph are inevitable in the long run. In order to ensure the safe operation of trains, in this paper, we propose a new method for anomaly detection of pantograph based on salient segmentation and generative adversarial networks. First, an object location model is trained by U-Net which perform excellent properties for a small number of samples to accurately extract salient area of the pantograph. Second, a generative adversarial model is constructed to generate the reconstructed salient images of pantograph by vector mapping. Finally, the structural similarity algorithm is used to evaluate the similarity between the input salient image and the reconstructed salient image, so as to extract the image difference and realize the anomaly detection of the pantograph. Experimental results validate the effectiveness and accuracy of our approach.
Detection method of dense bridge disease targets based on SE-YOLOv3
Authors: Wang Yaoling , Zhang Zhi , Yin Hang
Published: June 2020

In this paper, we find that the overlapped detection frames of dense disease in the bridge disease data set, combined with the current research on target detection algorithms in dense scene. Based on YOLOv3, the feature pyramid is optimized to manually set thresholds for post-processing after extracting features. A bridge disease target detection model based on SE-YOLOv3 was proposed. In the post-processing process, IoU uses Soft-IoU to detect the accuracy of the prediction box, approves the value of IoU, and then uses the EM algorithm based on the Gaussian distribution to perform a function operation to delete the low-score prediction detection box to achieve the purpose of removing overlap. Experimental results show that the detection accuracy is 95.73%.
CG Image Recognition Algorithm Based on Convolutional Neural Network
Authors: Liu Chenyu
Published: June 2020

Aiming at the problem that the existing computer generated images (CG) and real photograph (PG) have low recognition rates, this paper proposes an improved convolutional neural network method to achieve accurate recognition of the two photos. Firstly, a convolutional neural network classification model is established, then different models are established based on the VGG-19 network structure, and transfer learning is introduced to reduce training time and save computing resources. Finally, a softmax classifier is used for classification. The experimental results show that the accuracy of the proposed method for the recognition of PG images is about 93%. Compared with other methods, the method has the highest recognition accuracy, which indicates that the method is feasible and effective.
Failure Prediction of Circular Sawing Machines Based on Condition Evaluation and ARIMA Model
Authors: Chen Yao , Yu Changhong , Liu MengLi
Published: June 2020

As the starting point of cutting, sawing is an important link in the production process. Circular saws have high precision and high sawing efficiency. Once a failure occurs, it will bring considerable losses. This paper presents a fault prediction method for circular saw based on state evaluation and ARIMA model. According to the mechanism of failure analysis, considering different parameter indicators for different failures, we first find out the components of the equipment involved in these parameters, and adopt the entropy weight method and trapezoid-triangular membership function model to determine the health status. The key mechanism of the components in poor condition is predicted using the ARIMA model, and the weighted method is used to predict the failure time.
Research on Automobile Rear-end System Based on Parameter Self-tuning Fuzzy Control
Authors: Xiong Yuhang , Li Yunlu , Tan Meifang , Lv Xiaotian , Li Jiashun
Published: June 2020

With the development of China’s industry and the “Made in China” and “Created in China” proposals, the automotive industry has developed rapidly. Nowadays, China’s car ownership and sales volume has ranked first in the world. The rapid growth of vehicle ownership has also brought problems such as traffic congestion and frequent accidents. Traffic accidents often bring double losses of human and financial resources. Rear-end collisions should be given a lot of attention as high-incidence traffic accidents. This paper is based on the research of auto-tuning fuzzy control of vehicle auto-tuning system. Based on the vehicle’s sensors to obtain the optimal road surface slip rate and road peak adhesion coefficient, the driver’s input signal is synthesized, and the parameter auto-tuning fuzzy control is applied to multiple. After the fuzzy input is inferred from the complex input, the fuzzy output is determined to obtain the determined output signal effect and speed limit on the motor. At the same time, the safety distance of the actual road is predicted based on the peak adhesion coefficient of the road surface to remind the driver to drive safely and maintain the distance. This system can not only remind the driver to drive safely with a safe distance, but also limit the speed of the vehicle itself to ensure the safe driving of small cars.
Face Recognition System for Complex Surveillance Scenarios
Authors: Guo Xu , Nie Jisheng
Published: June 2020

In recent years, with the continuous development of the Internet and artificial intelligence, face recognition technology has also been widely used in many application scenarios. Facing complex surveillance scenarios, face recognition technology still faces great challenges. This paper focuses on implementing real-time and efficient face recognition systems in complex surveillance scenarios, such as insufficient lighting, small faces, dense crowds, and sides at 45 ° environment. The system is mainly based on RetinaFace for face detection and face alignment, and uses lightweight mobilenet (0.25) as the backbone network of RetinaFace. Facial feature extraction is based on deep residual neural network combined with ArcFace loss, and feature matching is performed by Euclidean distance. The experimental results show that the face recognition system has good real-time performance, accuracy and robustness.
Online Multiple Object Tracking with Reid Feature Extraction Network and Similarity Matrix Network
Authors: Ji Qingge , Yu Haoqiang
Published: June 2020

In multiple object tracking(MOT), data association is a crucial part. By constructing similarity loss matrix for trajectories and detections, they can be matched correspondingly by using Hungarian matching algorithm. However, the similarity loss is often obtained by calculating the Euclidean distance or other handcraft distance metrics of features extracted between objects, which may not be robust enough, resulting in matching inaccuracy. In this paper, we propose a novel MOT method with applying deep learning to feature extraction and data association. We firstly design the appearance feature extraction network(AFN) to learn effective features by training it on a large-scale person re-identification dataset(reid). Then, we propose the similarity matrix estimation network (SMN) to obtain reliable similarity by training it on the public MOT Challenge dataset, MOT17. Additionally, the similarity matrix output of SMN includes the dummy objects, which are used to deal with the association problems of object missing and object appearance between frames. In the end, our proposed MOT method is evaluated on MOT15, MOT17 and ablation study is carried out.
Alongshore Ship Detection Based On Multiscale Feature Fusion of Rotation Region Proposal Networks
Authors: Dou Hao , Mao Jiaxing , Pan Zhihong
Published: June 2020

In this paper, we propose an alongshore ship detection method based on multiscale feature fusion of rotation region proposal networks, the method provides an end-to-end ship detection framework, which is divided into three modules: multiscale feature module, rotation region proposal network module and context rotation region pooling module. The multiscale feature module integrates multiscale features, the rotation region proposal module can generate rotating bounding boxes in any direction, the context rotating region pooling module fuses the context information to pool the rotating region and complete the classification and position regression of bounding boxes. Experimental results show that our method can achieve good performance in remote sensing data sets.
GA-APEXNET: GENETIC ALGORITHM IN APEX FRAME NETWORK FOR MICRO-EXPRESSION RECOGNITION SYSTEM
Authors: Jin Qiu-Shi , Xu Huang-Chao , Liu Kun-Hong , Liong Sze-Teng , Gan Y. S. , Su Shu-Wen
Published: June 2020

This paper introduces a novel method to recognize the facial micro-expressions by directly adopting state-of-the-arts deep learning algorithms. Specifically, genetic algorithm (GA) is applied based on the principles of evolution in searching an optimal solution to generate discriminant features. Prior to that, the feature vectors of each video are first encoded using existing apex-based feature descriptors, viz, Bi-WOOF, OFF-ApexNet and STSTNet. Then, GA is utilized to enrich the features by eliminating irrelevant information that do not contribute to the expression prediction. It is acknowledged that the overfitting phenomena can be avoided in the feature selection process, as GA employs tournament selection and deterministic mutation procedures during the evolution procedure. As a result, Genetic Algorithm in Apex Frame Network (GA-ApexNet) is introduced and significant improvement of the recognition performance has been achieved. A standard experimental configuration is designed to evaluate the robustness of the proposed framework. Following the suggestion in Facial Micro-Expressions Grand Challenge (MEGC 2019), the UF1 and UAR obtained are 78.85% and 78.28%, for the composite dataset that comprises CASME II, SMIC and SAMM. We also note that this is the first work that fuses GA in micro-expression recognition system.
Research of UAV Flight Control Algorithm Based on Improved Fuzzy Neuron
Authors: Liu Haibin , Fang Shengyu , Yang Chenyu
Published: June 2020

UAV flight control systems have the characteristics of non-linear, multi-variable, and strong coupling. Traditional PID control algorithms have many problems in complex flight environments, such as large adjustable parameters, poor robustness, and slow convergencein. Due to the defects of traditional PID control algorithm, this paper proposes an improved PID control algorithm based on fuzzy neurons. The modified fuzzy neuron is used to modify the traditional PID control algorithm. Through Matlab experimental simulation, it is proved that the algorithm has a significant improvement in response speed, robustness, accuracy and anti-interference compared with the traditional PID algorithm.
Application Of Improved Hopfield Neural Network In Path Planning
Authors: Liu Yunxiang , Xu Wei
Published: June 2020

A modified Hopfield neural network algorithm is proposed and applied to the path planning to solve some problems in the traditional Hopfield neural network. Firstly, the traditional A* algorithm is used to select the nodes in the search area that meet the criteria. Then, the nodes conforming to the standard are converted into neurons in the Hopfield neural network, and the stability of the network is used to iteratively select an optimal path. Experiments show that the improved Hopfield neural network algorithm can reduce the search time of path planning and improve efficiency.
Research on target detection technology of MIMO technology in vehicle collision avoidance
Authors: Zeng Jieqiong , Zhong Cuilin
Published: June 2020

MIMO (multiple input multiple output) vehicle anti-collision radar refers to the application of MIMO system radar in vehicle anti-collision. MIMO radar is a new type of radar which has been put forward in recent years and attracted great attention. It is a kind of radar with multi transmitting and multi receiving. The characteristics of wave form diversity make it have many performance advantages, such as strong anti-jamming ability and strong resolution, compared with traditional radar. In recent years, many researchers have studied the application of MIMO Radar in the field of aviation and land transportation and made remarkable achievements. Compared with the traditional radar, the advantage of MIMO radar is due to the orthogonal performance of the transmitting waveform. The related problems of the pattern are deeply related to the resolution. As an important reference index in radar, the resolution is worthy of further study. In view of the above problems, the main work of this paper is as follows.
Research on Artificial Intelligence Machine Learning Character Recognition Method Based on Feature Fusion
Authors: Huang Wen , Zhang Haibiao
Published: June 2020

With the continuous development of artificial intelligence technology, people’s research on it has become more in-depth and machine learning has become a new research core issue in artificial intelligence. Before artificial intelligence technology was mature, it was a very difficult task to realize automatic character recognition and input. At present, human beings have made some progress in intelligent robots, voice recognition and network search. The text recognition method based on machine learning is of great significance to the promotion of information technology. Text recognition based on machine learning, although it has formed relatively rich theoretical support in principle and skill, but still faces various factors during actual practice, making its recognition effect difficult to be reflected. Based on the feature fusion perspective, this paper combines the background and main identification methods of text recognition in artificial intelligence machine learning to analyze the purpose, which lays a foundation for future specific applications.
The Impact of Big Data Analysis on Consumer Behavior
Authors: Zhang Caihua , Tan Tongxin
Published: June 2020

Based on the new environment of big data, this paper expounds the connotation and characteristics of big data, and analyzes the characteristics of consumer behavior under the application background of big data analysis technology. With the help of AISAS model, which is used to analyze consumer behavior in the network economy, and in combination with the influence mechanism of big data analysis on consumer behavior decision-making process, we have constructed a consumer behavior model under the background of big data and tested it by means of questionnaire survey. The results show that the factors that affect consumer decision-making include external factors and internal perception, while big data affects consumer internal perception through the impact of external factors, thus affecting consumer decision-making. At the same time, consumer information sharing is also conducive to improving the accuracy of big data analysis.
Performance Assessment of Single Frequency GNSS RTK/MEMS-IMU Combined Positioning
Authors: Yuan Jing , Luo Hao , Yu Ling , Luo Ming , Ban Ya , Shi JunJiong
Published: June 2020

With the increasing demand for high-precision positioning of civilian positioning terminals, the combination of single-frequency global navigation satellite system(GNSS) Real-Time Kinematics (RTK) and Inertial Measurement Unit of the Micro Electronic Mechanical System(MEMS-IMU) has been selected for high-precision positioning in the industry with its advantages of low cost, miniaturization and high precision. Based on the extended Kalman filter single-frequency RTK and Ultra low-cost MEMS-IMU loose combination positioning algorithm, the data is collected and tested in static scenes, and dynamic scenes. The experimental results show that: (1) In the static scenes, the combined positioning accuracy is better than 1.5cm, which is equivalent to the accuracy of the dual-frequency commercial receiver; (2) In the dynamic scenes, the single-frequency GNSS RTK positioning accuracy is better than 3cm, and the combined positioning accuracy is better than 5cm. In general, the combined positioning in static and dynamic modes can provide reliable high-frequency centimeter-level positioning.
RETRACTED: Preliminary Study on Credit Risk Management under Internet Finance Based on Big Data Age
Authors: Ma LiYun
Published: June 2020

Under the support of modern technology, Internet finance as a new financial model gradually improves the work structure and changes the traditional forms of financial work, so that the work becomes more convenient. After the emergence of Internet finance, due to the short time of its development, we should also introduce modern technology in the process of carrying out related work according to financial management, and use big data to analyze working data, and find that many work will be difficult to carry out because of lacking of scientific management mechanisms during the period of Internet financial application so that it cannot achieve the desired effect. There are great security risks in the Internet financial market because of lacking of credit risk management. In order to improve our financial system, we need to pay more attention to the credit risk management. According to the problems existing in the operation of Internet finance, we put forward some solutions.
Detection of Malicious Domain Name Based on DNS Data Analysis
Authors: Zhang Kunsan , Ji Wen , Li Nan , Wang Yiting , Liao Shengyang
Published: June 2020

This paper describes the research background of malicious domain name detection based on DNS data analysis, obtains DNS data through active domain name data or passive domain name data, then obtains DNS data through knowledge-based methods, detection methods based on machine learning and hybrid methods, and finally puts forward research suggestions.
Research on Data Acquisition System of EMU Structure Health Monitoring Based on DSP and FPGA
Authors: Li Chao , Zhang Bin
Published: June 2020

According to the characteristic that structural health monitoring have many monitoring sensors and monitoring signal types, with DSP and FPGA used as the core controller, a data collection system was designed. The designed system can realize multi-channel parallel data collection, flexible port configuration and good scalability, which meets the requirements of high-speed collection and processing and real-time online monitoring. The designed data acquisition system has the advantages of high performance, low cost and convenient application. In addition, the system has a wide sampling frequency range, which can adapt to a variety of sensor signal sampling requirements, is easy to install, easy to achieve network configuration, and can better meet the structural health monitoring of railway vehicles.
Attribute Reduction of Coal Mine Fire Incidents Based on Finding Maximum Mutual Information
Authors: Zhang Manting , Feng Weibing
Published: June 2020

There are many redundant attributes for the original data of the emergency system with uncertainty and data loss, and the existing incomplete information system attribute reduction algorithms have the disadvantages of high time complexity and completeness of the reduction result, an attribute reduction method for incomplete emergencies based on finding the maximum mutual information is proposed. Firstly, the original data of the emergency was preprocessed, and the situation and the consequences were regarded respectively as conditional attributes and decision attributes, thereby an incomplete decision information table was constructed; then, according to the characteristic that the mutual information of the conditional attribute set and the decision attribute is equal to the information entropy of the decision attribute in the consistency incomplete information system, the attribute reduction algorithm based on finding the maximum mutual information was obtained; finally, this method was used to reduce the attribute of coal mine fire data and compared it with other three methods. The results indicate that this method can effectively reduce the redundant attributes of coal mine fire emergencies, and can ensure that the reduction results have strong completeness when the algorithm time is low, thus decision-making for the emergency department is provided.
Research on Precision Planting Management System Based on Agricultural Big Data
Authors: Li Yu
Published: June 2020

Through the use of agricultural intelligent sensing equipment and intelligent control equipment, the precision planting management system builds the Internet of Things monitoring network in the agricultural production site to realize real-time data collection, monitoring of the climate environment, soil conditions, crop growth, pests and diseases, and remote automation control of various agricultural facilities and equipment on the site. The collected agricultural big data is uploaded to the planting management cloud platform in real time, and statistical analysis is performed instantly, which addresses the staff’s demand for data collection and analysis. Through data mining and analysis, the rules and expertise of high-quality and high-yield crops are obtained, which further guides the regulation of agricultural varieties and production environment, so as to achieve intelligent planting.