bluuebunny/crossref_metadata_2025_split huggingface dataset:
Moods in Classical Paintings: An AI based Classification Approach
Authors: Huo Shuqiao 
Published: October 2020

Abstract
According to WHO, globally there are at least 2.2 billion people who have a vision impairment or blindness. While they are known as the disabled in our society, rarely have they benefited from the development of technology and introduction of new products. That is how I developed this idea of constructing a medium that would transfer visual information to audio forms, which would help this disadvantaged group gain access to “visual experience”. In order to achieve this goal, several essential problems should be solved: how can one establish a technological formula to convert visual in-put to audio output? Extracting visual features from paintings, I could set up my own data collection based on some core features, which can capture the mood of a painting. I then use Convolutional Neural Network to train the AI to link visual and audio in-formation. Loss functions are introduced to minimize the error of the product. After I obtain the mood of my pictures, I will use it to formulate a definitive sound, in the process of which the “mood” gets preserved, such as joy, sorrow, or any artistic theme that is shared both in visual and audio experience. Towards the end of this paper, several experiments will be introduced to test the efficacy of my project. The first part is on the functional efficacy of my AI system. The second part is about my product hypothesis: I will give to two distinct participants one pair of visual input and audial output. Then I will check whether they can recognize the exact theme inherent in the corresponding media. Their successful identifying the same “mood” testifies the success of the project goal of transferring visual experience to audio.
Robust Optimization of Convolution Natural Network
Authors: Ye Chongjie 
Published: October 2020

Abstract
Deep learning has played a very important role in computer vision. However, most of the methods used in computer vision highly rely on human to adjust the hyperparameter. That takes researchers lots of time, but the results sometime could not be most optimized. Besides, many architectures cannot perform robustly in training with noised data. This essay aims to solve the hyperparameter optimization problem by adapting the fruit fly optimization algorithm and suppose a high robust Convolution Natural Network including a Gaussian filter. Compared with methods such as FaceNe, InceptionV3 and Resnet5, GauCNN perform higher efficiency and accuracy with noise data.
Multi-task Coordinate Algorithm Based on Improved PSO
Authors: Li Ziyang , Zhuang Yufeng , Hu Yanzhu , Tian Wenjia , Wang Song 
Published: October 2020

Abstract
This paper intends to use particle swarm algorithm as the research object of multi-tasking collaborative algorithm. Based on the specific task of multi-UAV distribution at the end of logistics as the background, the model in the task distribution process is constructed. The group modeling process was improved to solve the problem that the coefficient matrix in the task iteration process was discrete and the final result was not the optimal solution. And the task chain is introduced in the algorithm execution process to achieve conflict-free tasks.
Wave Soldering Structure Optimization Simulation of Multi-layer Ceramic Capacitors
Authors: Su Wei , Luo Wenhao , Li Junkui , Liu Renhuai 
Published: October 2020

Abstract
In this paper, the thermal analysis and structural analysis of MLCC are carried out by using the commercial finite element software ANSYS. By comparing the thermal-mechanical analysis results of the structural optimization models of different schemes, the optimal optimization scheme is found. The model calculated the time required for MLCC to reach temperature stability considering thermal convection and heat conduction, and obtained the thermal deformation and thermal stress distribution of MLCC element. Based on the analysis results of MLCC original model, the thermal analysis under different wave soldering temperatures was carried out. Based on the simulated transient results, the structure and materials of MLCC key components were optimized. The results provide a basis for the optimal design of MLCC.
Application Evaluation Research on the Airport Information System
Authors: Zhou Xingguang , Liu Yi 
Published: October 2020

Abstract
This paper puts forward an applicable evaluation research method for the information system satisfaction in the small and medium-sized airport. It constructs a multi-dimensional index system, and analyzes the influencing factors and weights based on the multiple liner regression function. The evaluation research method could provide quantitative decision support for optimizing the quality of information system and improving the comprehensive service ability.
Research on Airport Taxi Driver Decision Based on RBF Neural Network
Authors: Jin Xianxin , Gao Tianshu , Liu Xu , Kong Decheng 
Published: October 2020

Abstract
Affected by the combination of different factors, taxi drivers leaving the airport will have different decision-making schemes. This paper establishes mathematical models to optimize the decision of airport taxi drivers and the optimization of passenger-carrying problems between taxis and passengers. First take Shuangliu Airport as an example, and take the number of taxis and the number of passengers in the data into the RBF neural network decision model. The results obtained are compared with the real data. Finally, the cyclic test shows the changes in taxi driver decisions is more dependent on the travel season, number of flights, and holidays. Then a taxi passenger model based on M/M/S is established. There are two arrangements for the two parallel lanes of the airport “ride zone”: the final solution is concluded that when the length of the boarding area is 50 meters, setting three “boarding points” has the highest total boarding efficiency. By introducing the average mileage division index of taxi drivers, the short- and long-haul taxi drivers are finally obtained after the optimized scheme. The returns have basically reached equilibrium.
Research on a general approach to the balance coefficient of unloaded elevator
Authors: Tang Xiaozhou , Pan Guoqing , Chen Shu , Hu Dongdong , Wang Yu , Lin Huipin 
Published: October 2020

Abstract
The traction machine can suffer a motor power loss due to friction. Therefore the influence of the frictional force cannot be ignored in the measurement of the elevator balance coefficient. To overcome the shortcomings of the current method, a new general way of measuring the balance coefficient has undergone studies by following the definition of balance coefficient, and the fact that static friction is always against movement tendency. Here the feasibility of the method is demonstrated.
Chinese Emergency Event Recognition Based on BiGRU-AM Model
Authors: Yin Haoran , Cao Jinxuan , Wang Guodong , Zhang Houlu 
Published: October 2020

Abstract
A Chinese emergency event recognition based on bidirectional gated recurrent unit BiGRU-AM model with attention mechanism is proposed to resolve the limitation of traditional event recognition methods and the poor interpretability of general recurrent neural networks in respect of information features with different degrees of importance. Firstly, text corpus was trained to generate word vectors, and contextual information features were extracted through BiGRU, and then attention mechanism was introduced into BiGRU network to make feature extraction more selective. Finally, the learned features were activated by softmax function to output recognition results. Simulation results show that this method improves the accuracy and recall rate of emergency recognition, and the F value is superior to other methods.
Cloud computing resource elasticity scaling method based on neural network time series prediction
Authors: Wang Lingxiao , Zhang Jian 
Published: October 2020

Abstract
With the development of cloud computing, the requirements for dynamic distribution of cloud computing according to the real-time load are getting higher and higher. It is necessary to predict the load of cloud computing resources to achieve the goal of real-time elastic scaling. Cloud computing load will constantly change with time and actual demand. Therefore, lstm-attention model is used to collect data and model and predict cloud computing resource load for a long time, so as to achieve accurate prediction of cloud computing resource load in the future. It can be concluded that the lstm-attention model performs well in cloud computing resource load prediction.
Research on Image Super Resolution Reconstruction Model Based on Deep Learning
Authors: Zeng Yuhao , Peng Sijie , Ruan Shengyi 
Published: October 2020

Abstract
Artificial neural network has made the rapid development of artificial intelligence with its super-learning ability, making artificial neural network become a research hotspot again. At present, deep learning has been widely used in various fields such as computer vision, speech processing, and natural language processing, and has even played a leading role in some fields. The single image super-resolution reconstruction technique aims to reconstruct a low-resolution image through a series of algorithms to reconstruct a corresponding high-resolution image. This paper first briefly introduces the relevant theories of artificial neural networks, then studies the fast super-resolution reconstruction model, and improves the model layer and filter size to establish a new improved model.
Research on Robot Vision Servo Based on image big data
Authors: Su Shiyuan , Xu Zhijie , Yang Yihui 
Published: October 2020

Abstract
With the integration and development of information technology and control theory, industrial robot has become an indispensable automation equipment in modern industrial production, improving product quality and work efficiency. However, there is still a need to improve robot technology in terms of intelligence and flexibility. Robot vision servo control technology uses image sensors to obtain the information of the external field of vision, which can improve its environmental perception and response ability to cope with different production work. The important control method of robot system is visual servo control. Because of its good performance, wide application and convenience, visual servo control technology has become one of the hot spots in the field of mechatronics. In this paper, the problem of visual feedback and noise processing in the vision system is described. The six degree of freedom pose correction of the three focus tensor is made use of the big image data, and some tensor elements are selected to define the visual feedback, which effectively avoids the display estimation of the camera pose. Based on the extracted tensor features, an adaptive controller is designed to drive the camera to the desired attitude. The visual servo control system improves the real-time performance of big data image processing and reduces the calibration error, achieving the expected purpose. Finally, the future research direction is prospected, which is expected to become an important support technology for robot application in dynamic and variable environment.
LED Landscape Lighting Equipment Fault Diagnosis Research
Authors: Tian Zhihong , Zhao Yuwei , Zheng Zicheng , Meng Xiangang 
Published: October 2020

Abstract
Aiming at the fault diagnosis characteristics of LED landscape lighting equipment, a class of genetic algorithm improved particle swarm optimization optimized wavelet neural network model is constructed. This fusion algorithm introduces the idea of cross factors and inertia weights in the genetic algorithm to the basic particle swarm optimization algorithm, and adjusts for the traits of the standard wavelet neural network that has a slow convergence rate and might fall into local extreme values. The simulation results prove that this fusion algorithm can be efficaciously applied to the fault diagnosis of LED landscape lighting equipment and meet the needs of real-time monitoring of equipment.
Research and Development on Intelligent Cutting Tools with Capability of On-line Monitoring and Prediction
Authors: Zhu Hongyu , Ni Shouyong , Chen Weijin 
Published: October 2020

Abstract
Cutting-tool wear would affect machining accuracy, machining efficiency and machining cost. The on-line monitoring for cutting-tool wear would help monitor running state of cutting-tool in real time, handle accidents in time, reduce scrap occurrence during machining and cut down the cost caused by cutting-tool breakage. By the way of experimental research, wavelet analysis, neural network and grey theory, this article established a prediction model based on grey theory and wavelet neural network, developed an intelligent cutting-tool system with on-line monitoring and prediction, and which realize the real-time monitoring of the cutting-tool wear state and could help predict the abnormal condition of the cutting-tool in time. This system has some certain promotion significance.
Dynamic Modeling and Analysis of a Freight Train Vertical Vibration Reduction System
Authors: Dong Shichang , Song Hao , Song Caiyun 
Published: October 2020

Abstract
Based on wheel-rail impact vibration and considering the body stiffness and natural damping, this paper builds a three-degree-of-freedom vibro-impact system model for freight train’s vertical vibration reduction system. The dynamic behavior of the system is analyzed. The Poincaré map of the system is determined by the analytic solution of the system derived from the motion differential equation of the multi-degree-of-freedom vibro-impact system combined with Newton’s second law. It is found that the fork bifurcation, Hopf bifurcation and other dynamical behavior leading to Chaos when the system parameters are changed. In the process diagram, fork bifurcation is easier to be observed by engineers than Hopf bifurcation and can be easily applied to the control strategy of semi-active suspension. The dynamic parameters of the train are optimized to avoid chaos in the train operation, reduce the vertical vibration of the train, improve the stability of the train operation, and provide the theoretical basis for the vibration reduction design of the train.
A Large Doppler Weak Direct Spread Signal Acquisition Algorithm and Optimization Method
Authors: Liu Zhicheng , Wang Ju 
Published: October 2020

Abstract
For satellite receivers, on the one hand, large Doppler frequency will cause accumulation loss for a long-term accumulation acquisition algorithm, which leads to the problem of correlation peak broadening; on the other hand, searching within a larger Doppler uncertainty range will consume more logic resources. Thus, this paper first analyses the impact of large Doppler frequency on coherent accumulation and non-coherent accumulation based on the traditional Double Block Zero Padding (DBZP) algorithm. Secondly, an improved DBZP algorithm is designed in combination with the Keystone transform of the transform domain, and simplify the variable-scale DFT to reduce the amount of calculation. Then, the key parameters of the algorithm are extracted to establish the optimization objective function, the algorithm parameters are optimized through genetic algorithm without reducing the performance of the algorithm. Finally, the simulation verifies that the optimized and improved DBZP algorithm can effectively solve the problem of code phase walking.
Research on Adaptive Comprehensive Learning Artificial Bee Colony Algorithm and Its Application in Constant Pressure Water Supply
Authors: Li Mingzhu , Feng Xi 
Published: October 2020

Abstract
The constant pressure water supply control system is nonlinear and hysteretic, which makes the control problem difficult to solve. In order to achieve accurate closed-loop control of water supply pressure, this paper designs ACLABC-PID controller based on improved Artificial Bee Colony algorithm. First of all, in view of the slow convergence speed and easy to fall into the local optimum of the ABC algorithm, an adaptive comprehensive learning strategy of the ABC algorithm (ACLABC) is proposed. The algorithm adopts the learning strategy of learning from other excellent individuals and global optimal individuals, enriches the diversity of the population, adaptively and gradually reduces the search area, and effectively eliminates the defects of easily falling into local extremum and slow convergence in the later stage. ACLABC algorithm takes both local development and global exploration into account, which improves the convergence speed and accuracy of the algorithm. Then, the ACLABC algorithm is applied to the PID control parameter optimization of constant pressure water supply, and compared with the ABC-PID controller and the critical proportion method PID controller, the simulation results show that the ACLABC-PID controller significantly improves the dynamic and steady performance of the system.
A design of machine tool for switchable multi-NC systems test
Authors: Zhang Yun , Lin Haifeng , Zeng Yang , Fei Ya 
Published: October 2020

Abstract
A machine tool with 4 kinds of NC systems have been designed. The systems can control the same transmission mechanism and switch freely. The machine tool mainly used for the test of NC system function and performance. At present, the machine tool has been tested. The results show that the functions and performances of different NC systems can be compared and tested under the same condition of transmission mechanism.
Contribution Rate Evaluation of Army Air Defence Brigade Equipment System Based on Vector Model
Authors: Du Min , Cheng Zhonghua , Shi Xiaowen 
Published: October 2020

Abstract
In view of the complexity of the evaluation process of the contribution rate of the current equipment system and the inconspicuous analysis of the system indicators, it is not possible to meet the special issues of contribution rate assessment and index analysis of the Army air defense brigade equipment system, and proposes a method for evaluating the contribution rate of the Army air defense brigade equipment system based on the vector model. This method analyzes the combination of various indicators and vector theory in the Army Air Defense Brigade system and derives the links between various equipment (elements) to achieve accurate positioning and quantification of various equipment (elements). This is an equipment for the Army air defense brigade. The analysis of system indicators and the evaluation of contribution rates provide reliable support. The case shows that this method is simple and convenient, with relatively high accuracy and strong practicality. It can provide an effective method for the analysis of the Army air defense brigade equipment system and the assessment of the contribution rate of the Army air defense brigade equipment system.
Forecast of Development Cost of Missile Equipment Based on Partial Least Squares
Authors: Li Qiaoyang , Chen Guiming , Li Ziqi , Fan Boxuan 
Published: October 2020

Abstract
Due to the complex and diverse influencing factors of the missile equipment development cost forecast and the lack of data, taking into account the partial least squares regression method (PLS) can perform main component analysis, canonical correlation analysis, and multiple regression on variables, and handle multiple linear correlations. The small sample multivariate data has the advantage of high accuracy, and a missile equipment development cost prediction model based on the partial least square method is established. Compared with multiple linear regression, least squares support vector machine and other methods, the results show the effectiveness of the model.
Road traffic security and maintenance system based on refined urban management
Authors: Dan Sheng 
Published: October 2020

Abstract
In the existing road maintenance and security data collection process, professionals have professional equipment to regularly collect and update, which is a large workload and time-consuming. Based on this, a smart mobile terminal and other equipment are used to improve data collection efficiency. It simplifies the spatial matching process of road auxiliary facilities on high-scoring images. This system improves the collection efficiency of road security and maintenance data through software development. At the same time, a multi-temporal high-scoring image database, roads and auxiliary facilities database are formed to form a complete set. The technical process and specifications of high-scoring images and road data collection, through the new data collection and management mode, effectively meet the processes and specifications of data production requirements of grass-roots workers, effectively reducing the high cost of manual data collection.
Research on road traffic situation awareness system based on image big data
Authors: Zhu Yinghao , Peng Yangyang , Wang Shangzheng , Shi Shuimeng , Ji Nan 
Published: October 2020

Abstract
to promote the construction of intelligent and information technology in the field of road traffic is conducive to the construction of smart cities and the formulation of macro strategies and construction plans for urban traffic development. In view of the shortcomings of the current road traffic system, based on convolution neural network, situation awareness technology, database and other technologies, using CNN, R-CNN, fast R-CNN and fast R-CNN, this paper constructs a deep convolution neural network model based on the big data of road traffic image, and carries out the system demand analysis and system framework design and implementation. Through the analysis of the actual case and the feedback of the trial, the application effect of the road traffic situation awareness system is illustrated, in order to provide scientific reference and basis for the establishment of modern intelligent transportation system
Electricity price decision-making method of electricity selling company based on multi-objective optimization and Min-Max Regret Theory
Authors: Zhao Ziyi , Lin Fen , Wang Xiuli , Liu Rui 
Published: October 2020

Abstract
In the electricity selling market, the long-term goal of electricity selling companies is to maximize the revenue, but the strategies adopted in the short term have been different. Some electricity selling companies pay more attention to the revenue, while some electricity selling companies tend to reduce the sales price so as to agent more electricity. Based on the mixed integer programming (MIP) formula, this paper proposes a multi-objective mathematical programming (MMP) model, which considers maximizing the retailer’s profit and minimizing the sales price to customers. The Pareto optimal solution is generated by the normal boundary intersection method. At the same time, considering the risk of electricity selling market faced by electricity selling companies, we use the theory of maximum and minimum regret to evaluate the risk of electricity selling companies in the decision-making of volume and price, and find the optimal solution of risk. Finally, through case analysis, it is proved that through reasonable electricity quantity and price decision, the seller can find the optimal decision to balance the two risks of market price fluctuation and user load fluctuation
Information System Operation and Maintenance Analysis Method Based on Five-dimensional Model
Authors: Liu Xusheng , Zhao Wei , Li Zhimin , Zhu Qing , Li Ziqian , Qiang Runhao 
Published: October 2020

Abstract
Considering the complexity of the current power industry information system and the severity of the existing data islands, a five-dimensional analysis method for business information system is proposed. By dividing the data collected by the system into different dimensions and then combining them for unified analysis, the index tolerance interval model is constructed to find hidden dangers and anomalies in the information system and assist the operation and maintenance personnel in root cause positioning and impact range assessment.