bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Network Selection Algorithm Based on Vehicle Trajectory Prediction and AHP
Authors: Ding Qi , Zhang Dengyin , Zhang Zhen 
Published: May 2020

Abstract
This paper proposes a network selection algorithm based on Vehicle Trajectory Prediction and AHP (VMAP). The Markov chain is used to predict the running trajectory of the vehicle. The predicted trajectory is used to calculate the dwell time of the vehicle in the network. A network in which the dwell time is greater than a preset threshold is selected as a candidate network. Then, using the Analytic Hierarchy Process (AHP) to determine the weights of each attribute of the candidate network and obtain the comprehensive evaluation quality index of each candidate network to select the optimal network. This algorithm can not only minimize the ping-pong effect generated, but also enable the vehicle to access the optimal network stably.
Research on Time Triggered Ethernet Scheduling Planning Method
Authors: Wei Aodi , Zhang Guoqun , Zhang Tao 
Published: May 2020

Abstract
Time-triggered Ethernet adds a time-triggered traffic type based on traditional event-triggered best-effort traffic, which extends data traffic into three communication formats, namely time-triggered (TT) messages and rate-constrained (RC) messages and the best-effort (BE) messages. Aiming at the time-triggered Ethernet message scheduling planning problem, this paper first introduces the principle of the SMT solver and the idea of the solver algorithm, and designs constraints for the message scheduling problem. Based on this, the paper introduces a load balancing strategy and a step-by-step iterative conflict backtracking method, designs and completes a time-triggered Ethernet message scheduling algorithm, and then completes scheduling planning for time-triggered traffic and optimization of scheduling results. For the planning results, the paper proposes verification indicators for network load balancing and algorithm solution efficiency, designs a test set for algorithm verification, completes algorithm analysis and verification based on the test set solution results, and determines the algorithm optimization effect.
Research on Operational Risk Index of Distribution Network with Distributed Generation
Authors: Li Ping , Zhu Yu , Li Shenghui , Gu Cailian , Zhang Yizhong 
Published: May 2020

Abstract
This paper analyses the distributed generation’s influence on failure severity index, introduces the index which can represent the probability of islanding and derive the distribution networks’ failure severity index with the connection of distributed generation. This paper analyses how the distributed generation influence distribution networks when the wide-area distributed generation connect with networks, then deriving the distribution networks’ failure severity index with the connection of wide-area distributed generation. Through analysing the same IEEE 33 node network and calculating traditional distribution networks’ reliability index and failure severity index, we can obtain the system’s risk index. By comparing with every index in 3 different conditions, we can find that when the distributed generation connect with distribution networks, the networks’ risk tolerance has been improved, we can also find the significant improvement of distribution networks’ risk index when the wide-area distributed generation connect with distribution networks.
Face Attribute Manipulation Based on Self-Perception GAN
Authors: Tu X G , Luo Y , Zhang H S , Ai W J , Ma Z , Xie M 
Published: May 2020

Abstract
Manipulating human facial images between two domains is an important and interesting problem in computer vision. Most of the existing methods address this issue by applying two generators or one generator with extra conditional inputs to generate face images with manipulated attribute. In this paper, we proposed a novel self-perception method based on Generative Adversarial Networks (GANs) for automatic face attribute inverse, where giving a face image with an arbitrary facial attribute the model can generate a new face image with the reversed facial attribute. The proposed method takes face images as inputs and employs only one single generator without being conditioned on other inputs. Profiting from the multi-loss strategy and modified U-net structure, our model is quite stable in training and capable of preserving finer details of the original face images. The extensive experimental results have demonstrated the effectiveness of our method on generating high-quality and realistic attribute-reversed face images.
Research on Gait Recognition and Prediction of Exoskeleton Robot Based on Improved DTW Algorithm
Authors: Motao Wang , Zhijun Li , Qing Lei , Meng Wang , Rui Zhang 
Published: May 2020

Abstract
In order to realize the follow-up control of the exoskeleton robot better, the gait phase of the human body should be accurately identified and the human body motion intention should be matched in real time. In this paper, a set of gait data measurement system is used to collect the gait information of the human body during the movement process. Then, the gait recognition of the six models is carried out by the support vector machine through the plantar pressure information. Then the human movement intention is divided into five kinds and the improved DTW algorithm was used to complete the work of matching human motion intentions. Ultimately, the BP neural network model was designed to accurately predict the gait data. The experimental results show that the exoskeleton robot can accurately realize the three functions of recognition, matching and prediction.
An Eye Gaze Tracking Method of Virtual Reality Headset Using A Single Camera and Multi-light Source
Authors: Lu Shengfu , Li Richeng , Jiao Jinan , Kang Jiaming , Zhao Nana , Li Mi 
Published: May 2020

Abstract
For the problems the hardware configuration of multi-camera and multi-light source is complicated and the eye image of little auxiliary light source is too dark in the eye gaze tracking system, A new eye gaze tracking system based on two-dimensional mapping model is proposed, which achieved the transformation from two-dimensional coordinates to three-dimensional coordinates combined attitude angle of virtual reality headset. In this paper, the mapping relationship between visual features and eye gaze points is established by improving the method of pupil image recognition and eye gaze estimation based pupil-corneal, which the accuracy of eye gaze estimation is further improved. The experimental results show that the error of eye gaze tracking system is less than 1.1 °, which achieved the good effect of eye gaze tracking.
Multi-modal Non-linear Continuous 3D Presentations
Authors: Rodin Daniil , Elber Gershon 
Published: May 2020

Abstract
Computer presentations are an integral part of our lives, yet the contemporary presentation tools have not changed for about three decades, still emulating old overhead slide projectors. Combined with the rapid evolution of computer graphics technologies, this results in contemporary tools being ill-suited for many modern needs where linear sequences of fixed-sized 2D slides consisting of text and images is not nearly sufficient. In this work, we propose a presentation system aimed at overcoming the limitations of the contemporary presentation software. To achieve this goal, we abandon the usual concept of a presentation as a 2D slide sequence, and instead treat them as continuous automatically created 3D scenes of non-linear structure with multi-modal content, all without requiring any professional skills from the end user.
End-To-End Rotational Motion Deblurring Method Combining with Motion Information
Authors: Qiu Yudan , Zhang Cheng 
Published: May 2020

Abstract
We propose an end-to-end rotational motion deblurring method based on conditional generation adversarial networks. The proposed method calculates the blur path value of each pixel on the rotational motion blurred image to provide a priori information of its blur degree, and then connects it to the blurred image as the input of the network. In addition, a rotational motion blurred image dataset is produced, which contains different degrees of rotational motion blurred images, as an evaluation dataset for the method to the effect of rotational motion deblurring. Experiments show that the proposed method is superior to existing end-to-end deblurring methods in both qualitative and quantitative analysis when dealing with different degrees of rotational motion blur.
Application of a Novel and Improved VGG-19 Network in the Detection of Workers Wearing Masks
Authors: Xiao Jian , Wang Jia , Cao Shaozhong , Li Bilong 
Published: May 2020

Abstract
In order to work and travel safely during the outbreak of COVID-19, a method of security detection based on deep learning is proposed by using machine vision instead of manual monitoring. To detect the illegal behaviors of workers without masks in workplaces and densely populated areas, an improved convolutional neural network VGG-19 algorithm is proposed under the framework of tensorflow, and more than 3000 images are collected for model training and testing. Using VGG-19 network model, three FC layers are optimized into one flat layer and two FC layers with reduced parameters. The softmax classification layer of the original model is replaced by a 2-label softmax classifier. The experimental results show that the precision of the model is 97.62% and the recall is 96.31%. The precision of identifying the workers without masks is 96.82%, the recall is 94.07%, and the data set provided has a high precision. For the future social health and safety to provide favorable test data.
Using Darts to Improve Mold ID Recognition Model Based on MASK R-CNN
Authors: He Wa , Wu Yuting , Liang Peng , Hao Gang 
Published: May 2020

Abstract
Mask R-CNN model is an excellent image segmentation model, but cannot perform well on the task of mold ID recognition. To address the aboved problem, this paper improves the classification branch of Mask R-CNN model by applying DARTs, making the improved classification branch more suitable for mold ID recognition task. Firstly, DARTs is employed to obtain network cell structure. Secondly, the network cell structure is used to improve the classification branch. Moreover, we make the model converge under 2GPU days based on transfer learning. Finally, the experimental results show that the improved model can achieve higher accuracy with IOU of 0.5 (increased by 1.02%) and 0.75 (increased by 2.42%).
Research on Intelligent Vehicle Damage Assessment System Based on Computer Vision
Authors: Qianqian Zhu , Weiming Guo , Ying Shen , Zihao Zhao 
Published: May 2020

Abstract
At present, under the guidance of the new generation of information technology, the rapid accumulation of data, the continuous improvement of computing power, the continuous optimization of algorithm models, and the rapid rise of multi-scene applications have made profound changes in the development environment of artificial intelligence. In this paper, based on the demand of automobile insurance claims and intelligent transportation, combined with abundant basic data and advanced machine vision algorithm, an intelligent damage determination system of ‘Artificial Intelligence + Vehicle Insurance’ is constructed. This paper first introduces the functions of the intelligent damage assessment system. Secondly, it discusses the realization path of each functional module in detail, and finally puts forward the vision for the future.
Non-Global Shared Recursive Network for Image Super-Resolution
Authors: Zheng Bing , Ma Lihong 
Published: May 2020

Abstract
In Super-Resolution(SR) reconstruction, some recursive networks use shared parameters to keep lightweight. But these methods lack flexibility while use shared parameters to process different feature maps. We propose a non-global shared recursive network that consists of recursive units and channel attention modules. Firstly, we propose multi-scale channel attention module to learn the inter-channel interdependence and spatial information within the channel. The multi-scale channel attention module is composed of a multi-scale pooling layer, fully connected layers and activation functions. In order to flexibly process different feature maps in different layers, the attention module parameters are not shared, but only a small number of parameters are used. Secondly, residual dense recursive unit is used to accelerate convergence and feature reuse. In order to further reduce parameters, bottleneck layer is added to the dense connection. Based on the above mechanism, we propose a lightweight and efficient network structure. Experiments show that the proposed method has better performance than other lightweight networks in the standard dataset.
Improved IMM Algorithm based on RNNs
Authors: Deng Lichuan , Li Da , Li Ruifang 
Published: May 2020

Abstract
The Interactive Multi-Model (IMM) algorithm uses multiple motion models to simultaneously track the target, which effectively solves the problem of model mismatch when a single model tracks the maneuvering target, and is widely used in maneuvering target tracking tasks. However, the Interactive Multi-Model recognition motion model is not accurate enough, and there is a certain delay in the maneuver recognition of the target, which leads to a reduction in tracking accuracy. To solve this problem, considering that deep neural networks are very good at processing classification tasks, we introduce it into target tracking tasks, combining the respective of deep neural networks and traditional tracking filtering methods for maneuvering target tracking. we use the Recurrent Neural Networks to identify the motion model of the target and propose an improved LSTM-IMM model algorithm based on the interactive multi-model algorithm. Finally, we compare the traditional interactive multi-model algorithm and verify the algorithm using Monte Carlo simulation. The results show that the proposed algorithm has improved the recognition accuracy and recognition speed of the model, and the tracking accuracy has been improved.
X-ray Power Fault Detection Method Based on Feature Spectrum Reconstruction and Convolutional Neural Network
Authors: Zhang Jianlong , Cui Mengying , Wang Bin 
Published: May 2020

Abstract
The high frequency of X-ray high-voltage power supply (XHPS) leads to conspicuous parasitic effect of power components. And this will transform the equipment into a time-varying and nonlinear complex system. By applying the combination of convolutional neural network (CNN) and traditional methods, this paper proposes a fault detection method based on 2-D feature spectrum reconstruction and CNN. Firstly, the multi-wavelet transform is utilized to decompose the 1-D high-voltage power signal to obtain the coefficients of each frequency band. Secondly, the inverse Zigzag scan reconstructs the multi-wavelet coefficients into a feature spectrum that satisfies the input form of VGG-16, and then cascades the deep features obtained by VGG-16 with the multi-wavelet features. Finally, the final fault detection result is obtained by the support vector machine (SVM). The simulation results show that the proposed method has better fault detection performance and could provide a workable idea for fault prediction and avoidance.
Defect Detection Algorithm of Printing Roller Based on Visual Salience
Authors: Li Bilong , Cao Shaozhong , Wang Jia , Xiao Jian 
Published: May 2020

Abstract
Aiming at the complex surface condition of the printing roller and the requirement of high precision and high efficiency of the surface defect detection of the printing roller, a detection algorithm based on visual salience is proposed. Firstly, the dodging processing is used to eliminate the uneven illumination of the printing roller image; secondly, the non local means denoising algorithm is used to weaken the surface texture based on the redundant information commonly existing in the printing roller image; secondly, the spectral residual salience algorithm is used to calculate the salience of defects in the image and obtain the saliency map; finally, Sobel is used to detect the saliency map of defects and the manually labeled The defect images were compared and analyzed. Experimental results show that the algorithm has high recognition rate and accuracy, and can meet the needs of surface defect detection of printing roller.
Predicting the Rise and Fall of Stock Prices based on the modified BP_AdaBoost
Authors: Li Lu , Xu Shizhan , Liu Yanhong , Yang Xiaorong 
Published: May 2020

Abstract
In the big data era, the studies of the quantitative stock selection strategy based on machine learning are becoming more and more popular. Most of existing studies focus on short-term strategies, and few on the medium-term or long-term strategies. Moreover, many scholars tend to transform the problem of predicting changes of stock prices into the binary classification problem, which makes it difficult to earn steady abnormal returns. Therefore, it is extraordinary meaningful to study effective quantitative investment strategies. In this article, we propose the modified BP neural network combining AdaBoost algorithm (the modified BP_AdaBoost) and apply it into the quantitative stock selection. We carry out empirical studies about medium-term and long-term price changes in the A share market of our country, construct the factor pool and check the performances of the modified BP_AdaBoost.
Noise Removal from Medical Images Using Hybrid Filters of Technique
Authors: Aslam Muhammad Aqeel , Munir Muhammad Asif , Cui Daxiang 
Published: May 2020

Abstract
In this paper noise removal from the medical images using the hybrid filter of technique is presented. From the last couple of decades, medical image processing and analysis techniques based on computing algorithms acquired prominence as an alternate skillset for medical experts in disease diagnosis and prevention. As the number of patients are increasing yearly, doctors don’t have enough time to calculate the actual information from the medical images, as most of the medical images are affected by the noise. Medical images contain a different kind of noises because several machines are operating for data acquisition and transmission, so in order to reduce the complexity from the radiologist point of view we were very much interested in the design of an algorithm which can be beneficial and useful at the convenient level. Image Processing has become a very prominent technique in medical image analysis and medical image processing. The proposed architecture is the amalgamation of morphological operations, A modified form of Median Filters and Wiener Filters. The boundary and the shape of the image is extracted through Morphological operation. For noise removal and enhancement purpose modified median filter and wiener filters were used. The parameters like Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Root Mean Square Error (RMSE) are determined through proposed algorithm. Overall results indicate that the enhancement quality is performing well in proposed technique.
Feature Extraction of Radar Echo Image Based on Improved Convolutional Neural Network
Authors: Chenhao Zhang , Hongquan Li , Xun Feng , Jingmian Tang 
Published: May 2020

Abstract
Feature extraction of radar echo image is an important part of identification and recognition of air targets. In recent years, with the rapid development of deep learning, new solutions are provided for it. In this paper, the convolution neural network (CNN) is applied to feature extraction. Based on the classic CNN model, Adam is used to update the model parameters, dropout is used to prevent over fitting, and an improved CNN model is constructed. Then, the radar echo image data set is used to train the model, so as to extract target features and classify them. Simulation results show that the accuracy of the improved model is 99%, and the training speed is greatly improved. Different from the traditional extraction method which relies on manual experience, the improved CNN can improve the efficiency of feature extraction of radar echo image and lay a solid foundation for further research and identification of air targets.
High-Precision Portrait Classification Based on MTCNN and Its Application on Similarity Judgement
Authors: Du Juan 
Published: May 2020

Abstract
Portrait classification is a complex course including at least face detection, recognization and compare each of which contains multi-tasks, facing plenty of various challenging questions due to askew poses, illuminations, occlusions, image blurring and small scale face in the pictures. Though deep learning methods, such as Convolutional Neural Network (CNN) family and You Only Look Once (YOLO) series, had boomed a large number of areas on object detection and accelerated the solving of these difficulties on image processing, they are not specially designed for the image classification and may require a great deal of resource, expensive computation and taxing annotation. In 2016, an innovative face detection model named Multi-task convolutional neural network (MTCNN) arose and triggered viral and wide spread. Its high efficient and accurate performance on both face detection and face alignment tasks, real time effect based on lightweight CNN as well as effective conducting online hard sample mining, all contribute to significant improvement to the challenges above. This paper introduces the MTCNN algorithm and applies it to the similarity judgement with two industrial real problems together with FaceNet model. In addition, some effective practical methods on increase precision of classification are also proposed to gain better effect.
A Blind Watermarking Algorithm for Digital Image Based on DWT
Authors: Tan Linglong , He Yihong , Wu Fengzhi , Zhang Dong 
Published: May 2020

Abstract
In order to improve the robustness of digital image watermarking algorithms, the digital image blind watermarking algorithm proposed in this paper embeds watermark information in a reasonable position. First, scramble the watermark information and embed the random signal into the low-frequency domain of the image. On the one hand, it can effectively prevent attacks on the watermark system and enhance the security of the system, disperse as much as possible. In the watermark embedding process, a method based on the wavelet transform domain is used. The original image is not required to participate in the watermark extraction process, and the blind watermark function is implemented. In order to verify the effectiveness of the algorithm, the watermarked image is subjected to filtering, scaling, noise, cropping and rotation attacks, and the peak signal-to-noise ratio and normalized correlation coefficient are used to quantitatively evaluate the watermarked image. Experimental results show that the algorithm can resist a variety of attacks, and has good imperceptibility and robustness.
Dynamic Scene Deblurring Based on Semantic Information Supplement
Authors: Liu Yiming , Li Junhui , Huang Wenzhuo , Tang Kang , Xu Dahong 
Published: May 2020

Abstract
To solve the problem of semantic information dilution in network propagation, a semantic information supplement mechanism (SIS) is proposed to improve the performance of dynamic scene deblurring algorithm. Based on GANs structure, our generator is to recycle the semantic information and features spanning across multiple receptive scales to restore a sharp image, when a blur image is given. What’s more, in order to better integrate semantic information with the latent-feature and solve the problem of training difficulty in very-deep network, we put forward a long and short skip-connection method. Extensive experiments show that our Semantic Information Supplement network (SIS-net) achieves both qualitative and quantitative improvements against state-of-the-art methods.