bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Impact of Data Statistics on the Execution Plan of the Oracle Database
Authors: Li Aiwu
Published: October 2020

The data in the database is constantly changing. In order to make the statistical information truly reflect the current situation of the database, it is necessary to update the statistical information when needed. This paper uses column statistics as an example to analyze the impact of statistics on the execution plan of a single-table query.
A Wind Speed Prediction Model Based on ARIMA and Improved Kalman Filter Algorithm
Authors: Yuan Shengyuan , Shen Yanxia
Published: October 2020

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.
Moods in Classical Paintings: An AI based Classification Approach
Authors: Huo Shuqiao
Published: October 2020

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Talking about the Management and Analysis of Intelligent Wiring in Data Center
Authors: Deng Zhidong , Sun Lintan , Zhang Yue , Tang Zhenying , Liu Hongwei
Published: October 2020

For the application scenario of data center, the wiring management problem of data center equipment room is effectively solved through four purposes: visual wiring operation display, intelligent wiring selection construction scheme, automatic calculation of jumper length, and topology deduction of wiring link integrity. Eliminate the traditional operation and maintenance excel wiring ledger management method, eliminate the management pain points such as complicated wiring management, huge number of wiring, and inconsistent wiring ledger. In a visual way, make the wiring management intelligent, regular and efficient in a visual way. Select the start port and end port of the wiring, intelligently calculate all the connection paths of the wiring, and intelligently select the best wiring path for operation and maintenance personnel for reference. At present, the calculation method of the best path is the minimum number of device nodes. The jumper length of the route selected by the user is calculated automatically, and the wiring distance is calculated to facilitate the operation and maintenance personnel to select the suitable wiring length for construction. Support operation and maintenance personnel to quickly import construction information to generate construction orders. The intelligent wiring module supports the display and topology deduction verification of the global wiring in the equipment room, so as to check the integrity of the connection information of the upstream and downstream physical wiring of the current equipment in the form of topology, and supports the functions of hiding the distribution frame, changing the topology shape and saving. It makes the operation and maintenance personnel’s management of wiring in the equipment room simple and intuitive. The human-computer interaction mode can visually check the status of the equipment port occupation and all the device relationship sets passed by the entire wiring link.