bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vehicle Path Tracking Maneuver Based on Model Predictive Control Theory
Authors: Xu Qijiang , Wang Jian , Zhao Wenzheng , Ding Jinchuan , Liu Xinjie 
Published: October 2020

Abstract
Vehicle path tracking problem has been a hot research topic in the field of automotive safety. Therefore, combined with a 3-DOF vehicle model is established. The basic idea behind the work was to identify the optimal steering angle input during a vehicle travels along a prescribed path. Based on Model Predictive Control (MPC), the steering angle input was determined as the control variable, tracking the desired path was determined as control object. By using the Model Predictive Control, the optimal control problem was converted into a secondary plan problem which was then solved by the active set method. The results show that the minimum error of lateral position for the generated path-tracking trajectory can be good indicators of successful solving of the path-tracking problem in vehicle handling inverse dynamics for MPC. The study can help drivers identify safe lane-keeping trajectories and area easily as well as evaluating performance of emergency collision-avoidance for a vehicle.
Model Identification and Equivalence Method for Grid Optimal Operation
Authors: Liang Shuang , Zhang Yan , Wang Zeping , Zhang Xuan , Zhu Ning , Sun Shuo , Zhao Jie 
Published: October 2020

Abstract
In this paper, on the basis of simplification idea of external transfer consistence of internal power flow in power grid, the network identification method was used to find the local power grid with equivalent value, and WARD equivalent value was used to equalize the local power grid to the boundary node, because the power transfer relationship between internal nodes and boundary nodes of equivalent power grid is basically consistent, equivalent unit could reflect the overall output characteristics of equivalent power grid, there was no need to concern the power distribution of internal node of equivalent power grid, furthermore, the schedulable capacity and cost curve of schedulable resources of equivalent power grid could be calculated, on this basis, the dispatching optimization of power generation of large power grid were conducted. The example analysis showed that the equivalent method in this paper could maintain higher model equivalent accuracy, and the overall optimal solution of power generation dispatching of large-scale power grid was realized.
Study on An Improved Accuracy Algorithm Based on Channel Estimation in 5G Application
Authors: Wang Jingyu 
Published: October 2020

Abstract
5G has ultra-high spectrum utilization, energy efficiency and resource utilization. Orthogonal frequency division multiplexing modulation is a key technology in order to achieve the characteristics of 5G communication such as ultra-high spectrum utilization. In recent years, a pseudo-random sequence (PN) filled with cyclic prefix time-domain synchronous orthogonal frequency division multiplexing modulation technology has emerged as a modulation technology in high-performance 5G communication systems. And the technology has been maturely applied in the field of terrestrial digital multimedia wireless broadcasting because of its higher channel estimation and synchronization performance. At the same time, 5G has higher requirements in terms of transmission rate and so on, so it has stricter performance requirements for transmission delay control and timing recovery. However, the TDS-OFDM system in 5G application does not allocate pilot subcarriers, which are used for system channel estimation and various parameters. Since the autocorrelation function of the time-domain PN sequence filled in the TDS-OFDM system is not an ideal shock function, when the phase difference with the received sequence is not 0, the autocorrelation value is not 0 as well. When used as a channel estimation, it will cause mutual interference of various multipath components, reduce the accuracy of the estimation, and may also cause some weak multipath Loss of components. This paper proposes an improved channel estimation scheme for TDS-OFDM based systems in 5G application, which can effectively improve the accuracy of channel estimation in the case of high signal-to-noise ratio.
Research on Multiscale Wavelet Transform in Adaptive Filtering*
Authors: Liu Siyu , He Wei , Chen Xinmei 
Published: October 2020

Abstract
Convergence factor and steady-state error are a pair of inherent contradictions in fixed step size LMS algorithm. For a long time, how to balance the two has been the focus of research. This paper analyzed the fixed step LMS algorithm, proposed a variable step LMS algorithm, and combined them with the multiscale wavelet transform algorithm to propose a new algorithm to solve this problem. From the Maltab simulation results, it can be seen that the new algorithm solved the problem between them to a certain extent, and had stronger noise suppression ability and faster convergence speed.
Research on Driver Model of Public Transportation Energy Consumption System Based on Computer Cloud Computing Analysis
Authors: Liu Junhui , Wang Yaya , Shang Lisha 
Published: October 2020

Abstract
The urban public transportation subsystem, that is, the electric vehicle public transportation system on the ground, is an important part of the urban transportation system, and its energy consumption is increasing. However, the research on the energy consumption structure of urban public transportation subsystems is not yet mature. From a development perspective, there is an urgent need to study the energy consumption of urban public transportation subsystems. On this basis, according to actual needs, a typical public transportation energy-saving technology quantified driver model for energy-saving benefits was established, data requirements and processing methods were clarified, and relevant case analysis was carried out, which not only provided intelligence for city managers to choose transportation stand by. It can effectively analyze the energy consumption of the driver during the driving of the vehicle, On the basis of cloud computing analysis combined with public transportation energy consumption system can provide effective data support.
An Improved Artificial Bee Colony Algorithm and Its Application in Machine Learning
Authors: Ge Liang , Ji Enhui 
Published: October 2020

Abstract
In order to effectively overcome the problems and shortcomings of the basic improved artificial bee colony algorithm in solving the optimization accuracy of complex standard test functions, the low accuracy of the solution function and the large blindness of the local search strategy, a more basic improved artificial bee colony is proposed. Parameter optimization algorithm. The above artificial bee colony algorithm introduces the conjugate gradient method with strong local optimization search strategy performance in the follow bee stage of the basic improved artificial bee colony algorithm to change the optimization search strategy, and replaces the blind search strategy with a local deterministic optimization search strategy. The sexual optimization search reduces the randomness, enhances the local certainty of the food source of the follower bee and the optimization search ability, and ensures that each update of the follower bee’s food source will quickly be substantially improved. This improved artificial bee colony algorithm is widely used in parameter optimization of traditional dengue virus propagation model. The simulation results of the improved standard test function on the problem show that the improved artificial bee colony parameter optimization algorithm has higher optimization and solution function accuracy during simulation than the basic improved artificial bee colony parameter optimization algorithm. The obtained standard test the parameter output corresponding to the dengue virus model parameter output is in good agreement with the actual situation when the data is simulated.
Supernode selection mechanism based on location information
Authors: Zhu Yongqiong 
Published: October 2020

Abstract
In a hierarchical p2p network such as Gnutella0.6, supernodes are selected high-capacity nodes. As the central server of leaf nodes, they process queries more efficiently than a single leaf node. So how to effectively select supernodes and construct a supernode network topology is a key issue in hierarchical P2P networks. Existing research shows that this mechanism of peers randomly selecting logical neighbors without any underlying physical connection information causes a mismatch between the P2P logical overlay network and the underlying physical network. The same message traverses the same physical connection multiple times. A lot of unnecessary network traffic is generated. Therefore, this paper proposes a super node selection mechanism based on distance information. When selecting a super node, the actual physical distance between the nodes must be detected, so that the selected super nodes are logically adjacent, and their physical locations are as close as possible, thereby improving the processing capacity of the network.
Analog Gauge Reader based on Image Recognition
Authors: Li Xingxing , Yin Panpan , Duan Chao , Zhi Yan 
Published: October 2020

Abstract
Analog dial are widely used in modern production and life. In the fields of meter detection and meter scale value recording, the demand for automatic reading of analog dial is increasing. This paper proposes an improved method of fast reading of images, which refines the scale circle and uses the branch point extraction algorithm to extract the intersection of the scale line and the scale circle to express the scale line indirectly. Finally, the pointer reading is calculated based on the ratio of the deflection angle of the pointer to the total angle of the range.
Research on the Construction of Construction Engineering Cost construction Information System Based on BIM Technology
Authors: Wu Bei , Liang Huimin , Shi Yansiyi , Zhu Junhui 
Published: October 2020

Abstract
This subject is based on cost construction and BIM technology, researches the project cost construction system based on BIM technology, analyses the system’s related business processes and system processes; through analysis of the existing cost construction system, finds the existing system Existing problems, it is concluded that applying BIM technology to the project cost construction system can directly solve many problems faced by traditional systems. It analyses that the two systems can exchange data through the data interface. Finally, a cost construction model based on BIM technology is constructioned. Through the research in this article, I hope to apply it to the actual production and life of the enterprise, bring benefits to the enterprise, promote the informationization and systemization of the cost construction of the enterprise, and provide accurate data support for the project decision makers of the enterprise in the future.
A Robust and Low-cost Computer Method Based on Deep Residual Learning
Authors: Chung Ka Po 
Published: October 2020

Abstract
At this exact moment on our planet, there are more than 65.2 million people suffering from visual illnesses caused by cataract. It is the cause of a third of all blindness in the world, and 99% of these patients live in developing countries. However the recovery surgery is one of the most cost-effective ones in the field. Lack of medical care is the real cause of such skyrocketing number of patients in developing country. Out of this reason, I developed an integrated diagnosis system based on deep learning methods, which could diagnose cataract with the accuracy of 91.7%. The high accuracy and the low-cost features of this diagnosis method make it an excellent auxiliary tool of preliminary diagnosis in developing countries. The best performance is held by the 50-layer deep residual neural network trained with Adam optimizer, which could adjust learning rate according to the training status and specific weights. The further experiments with the quantity of data as a variable indicated that the best performance of deeper model is limited by the insufficient data.
Image defogging algorithm based on dark channel prior of adaptive weight
Authors: Li Xingxing 
Published: October 2020

Abstract
At present, the selection of parameters based on the dark channel defogging algorithm is relatively simple, and it is difficult to adapt to the fog images in different environments. To solve this problem, this paper proposes an adaptive weight parameter defogging algorithm. In this paper, it is found that the weight parameter ω of demisting has a certain relationship with the atmospheric light value, and based on this relationship, the optimization of demisting effect is realized. In this paper, after the improvement of the original algorithm, the improved method is compared with other methods. The experiment shows that the visual effect of the image after defogging is more real and natural.
Denoising method of motion capture data based on neural network
Authors: Zhu Yongqiong 
Published: October 2020

Abstract
Optical motion capture systems are prone to data loss or errors during the capture process, which can cause noise in the data. In order to suppress the influence of noise, it is necessary to denoise the captured data to improve the quality of the data and make the denoised motion data reflect the actual motion of the actor as accurately and completely as possible. Aiming at how to effectively denoising motion capture data, researchers have proposed many effective approaches. These approaches are mainly divided into the following three categories: based on filtering strategies, algorithms based on matrix low-rank theory, and data-driven algorithms. Among them, the data-driven denoising method has gradually become the mainstream technology. At present, many studies in this area have achieved certain results, but there is still space for further improvement. Based on the data-driven method, this paper proposes a neural network-based machine learning algorithm to implement denoising processing for motion capture data. Experiments show that this algorithm can significantly recover noisy data.
Design and Application of a Test System for Ship Platform
Authors: Wang Dongyang , Ma Chao , Zhang Wei , Fan Wei , Gui Canzhi 
Published: October 2020

Abstract
Aiming at the problems of single function, poor integration and low expansion performance of the traditional test and test tools for ship platforms, an integrated test and test system based on an integrated and distributed architecture is designed and studied. Through the index system construction, system (equipment) test evaluation, data modeling and simulation, we develop an integrated simulation test platform, support the software and hardware functional test and test of ship platform system equipment, support the generation of test data, and comprehensive evaluation of equipment capabilities.
Movies and Pop Songs Recommendation System by Emotion Detection through Facial Recognition
Authors: Zhang Jingye 
Published: October 2020

Abstract
In this paper, a convenient, accurate, and widely applicable recommendation system of films and pop songs, using deep learning as an efficacious tool, is propounded. We devised a refined Deep Residual Network (ResNet-38) for the emotion detection of the users, which achieves an accuracy of 64.02% for the testing set of Kaggle-fer2013. Other traditional methods including the use of Classifier SVM or four-layer CNN produce the average accuracies of 40.7% and 60.7% respectively. Thus, it is cogent to conclude that our model outperform other traditional models. The usual emotion-based movies or songs Recommendation systems include web scrawling of the user’s personal information, like his or her recent comments, based on which researchers construct “interest models” to achieve an understanding of the user’s emotion. However, this type of method, though comprehensive, infringes the users’ privacy, presents biased results, and lacks instantaneity and interactivity. Thus, this research paper serves to introduce a novel mode of Recommendation system so as to counter theses drawbacks.
Fast single-shot multi-frame aerial image traffic density analyzer Based on deep learning
Authors: Zhou Xuchao 
Published: October 2020

Abstract
This paper studies a real-time traffic density analyzer based on target detection. Based on the development of deep learning technology in recent years and taking full advantage of big data strength, the intelligent and real-time traffic density analyzer is constructed. It collects the road condition in the current status in real time, and estimates the current number of driving vehicles and the specific positions of the corresponding vehicles through the real-time calculation and analysis of efficient target detection algorithms such as faster rcnn [1], SSD [2] and so on. It can easily be carried on unmanned aerial vehicles or fixed road stations, and communicate with other digital devices in intelligent cities in real time to reflect traffic conditions.
Detection technology of malicious code family based on BiLSTM-CNN
Authors: Wang Guodong , Lu Tianliang , Yin Haoran 
Published: October 2020

Abstract
The explosive growth of the number of malicious code makes it one of the important threats to network security. Among them, a new type of malicious code family accounts for a small part, and most of them are generated by mutation on the basis of the original family. Based on batch processing of newly added malicious code, this paper proposes a malicious code family detection technology combining malicious code visualization and deep learning. The malicious code executable file is directly converted into a grayscale image, and then the BiLSTM-CNN deep learning algorithm is used to detect the malicious code family. Experiments prove that the model has higher accuracy.
Simulation and Optimization of Emergency Evacuation in Gold Museum Based on AnyLogic
Authors: Feng Xiwen , Gao Mengying , Zhou Yu , Li Jing 
Published: October 2020

Abstract
This article combines the characteristics of the gold museum building structure, facilities distribution and personnel distribution characteristics, uses AnyLogic to complete the museum’s environmental modeling and visitor behavior modeling, simulates the completed tourist evacuation model, analyzes the evacuation process of personnel, and responds to potential safety hazards. The evacuation process was simulated and optimized, and an improvement plan was proposed. Finally, reference suggestions were made for the tourist evacuation plan of the Gold Museum. It provides a way of thinking and method for safe evacuation in scenic spots, and has certain practical application value.
Application of MOG Background Subtraction Algorithm in Automatic Measurement of Contact Angle
Authors: Ren Xi , Xiang Guoyuan , Zhang Chengyang , Duan Yu 
Published: October 2020

Abstract
Contact angle measurement is very important in determining the liquid-solid affinity properties of materials. In recent years, many advances have been made in the research of contact angle automatic measurement algorithms, but most of the existing image processing and analysis algorithms are very sensitive to changes in the characteristics of the target, including the light source illumination angle, brightness, and the tangent of the camera lens axis to the target point Factors such as error will affect the measured contact angle. This paper proposes a key improvement technique, using the MOG background subtraction technology to realize that only the edge of the morphology after the final stabilization of the droplet is reduced, which reduces the serious interference of the mirror image of the droplet on the smooth material surface on the measured value. This method has achieved accurate fitting of the droplet contour circle, avoiding the use of measurement parameters with large errors to calculate. The algorithm has many advantages, in particular, it has noise resistance to changes in the shape, size and position of the sample, has good robustness, and has low calculation cost and fast speed. The measurement results are very similar to the manual marking of manual optical measurement High consistency.
Realization and optimization of face recognition system based on MATLAB
Authors: Yan Xi , Liu Gaqiong 
Published: October 2020

Abstract
In this dissertation, several techniques used in face recognition have been investigated. Besides, a critical problem in face recognition which is illumination variation has been considered. In this dissertation, a face recognition system has been built using MATLAB. In this system, after the Eigenface has been achieved, two techniques to deal with illumination changing have been implemented, shadow compensation and local binary pattern (LBP) respectively. After the experiment, LBP shows a better performance than shadow compensation on Yale B cropped database.
Research on Dynamic Reconfiguration Technology of Neural Network Accelerator Based on Zynq
Authors: Lv Hao , Zhang Shengbing , Liu Xiaojian , Liu Shuo , Liu Yongqiang , Han Wei , Xu Shaowei 
Published: October 2020

Abstract
Target detection based on convolutional neural network is a research hotspot in the field of computer vision. Conventional neural network (CNN) accelerators use the time division multiplexing method, and different network layers use the same accelerator, and their adaptability and resource utilization are not high. How to combine the dynamic reconfigurable characteristics of FPGA so that the calculation of each layer can be matched with the corresponding accelerator architecture at the cost of a certain configuration delay, and to improve the utilization efficiency of computing resources is a research hotspot. This article takes the YOLOv2 target detection algorithm widely used in the industry as an example, and uses Xilinx’s Zynq as the platform to describe the process of mapping the CNN model to the FPGA. Combined with the dynamic reconfigurability of FPGA, the calculation of each layer can be matched with the reconstructed accelerator architecture at the cost of a certain configuration delay, and the reconstruction delay can be shared by batch data multiplexing accelerator architecture, which effectively improves In order to improve the accelerator performance, the convolutional layer and the cascaded maximum pooling layer are merged to reduce the memory access delay. Experiments and evaluations were carried out on the accelerator architecture combined with dynamic reconfigurable characteristics, and the performance of 30.35GOP/s was obtained on the Zynq platform. Provide a reference for the application and optimization of CNN on embedded platforms.
A Wind Speed Prediction Model Based on ARIMA and Improved Kalman Filter Algorithm
Authors: Yuan Shengyuan , Shen Yanxia 
Published: October 2020

Abstract
This article is dedicated to solving the problem of wind speed prediction. A time series analysis method based on the establishment of a differential autoregressive sliding model for simple training and a prediction model to obtain the state is proposed. The Kalman filter method with adaptive weighting coefficients is used to predict the equation, and the experimental results show that the composite algorithm can effectively reduce the prediction error.