bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diabetic retinopathy identification system based on transfer learning
Authors: Li Lingling , Yan Xueqi , Peng Heng , Xiu Ying , Gao Yiyang , Wang Xin 
Published: June 2020

Abstract
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

Abstract
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

Abstract
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

Abstract
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

AbstractConvolutional 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

Abstract
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

AbstractThe 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.