bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of Maize Leaf Diseases based on Convolutional Neural Network
Authors: Wu Yuhao 
Published: January 2021

Abstract
The identification and diagnosis of crop leaf disease is of great significance to improve the quality of crop cultivation. Compared with the traditional manual diagnosis method, the automatic identification of crop leaf disease based on computer vision technology has high efficiency and no subjective judgment error. But the traditional image processing technology is affected by different illumination conditions, cross shading. The algorithm’s robustness is affected. Because deep learning dose not need to set learning features manually, which greatly improves the recognition efficiency. In this paper, the two-channel Convolutional Neural Network was constructed based on VGG and ResNet. Taking the maize leaf diseases as research objects, the maize leaf disease data set has been constructed and preprocessed. And the structure and characteristics of AlexNet, VGG and ResNet are introduced respectively. By adjusting the parameters of the two-channel Convolutional Neural Network, the accuracy of identifying the maize leaf disease type in the validation set can reach 98.33%, while the VGG model can reach 93.33%. The classification results on three types of maize leaf diseases show that the two-channel Convolutional Neural Network has a better performance than the single AlexNet model.
Three kinds of leaf disease data sets (big spot, gray leaf spot and rust ) are downloaded from the “kaggle”platform.
Study on Data Management of Electric Power Distribution Dispatch and Cutting-through Based on Big Data
Authors: Cai Yingkai , Cao Shilong , Xu Jingjing , Mu Rong 
Published: January 2021

Abstract
With the development of technologies such as big data, Internet of Things, and cloud computing, power companies’ information management systems for dispatch, finance, projects, marketing, equipment, and materials have also made great progress. The construction of the data platform of the electric power company has realized the unified collection of data resources for the entire business of the power grid, which provides strong support for cross-professional, cross-departmental, internal and external data association analysis and in-depth data value mining. Based on the data management and management of power distribution dispatching, big data technology is adopted to optimize the coding and structure of power system distribution data to realize data cutting-through. Through experimental testing and analysis, it is proved that the research content of the paper can meet the current power operation needs. Compared with other data management methods, it can achieve higher management accuracy and coverage, and play a data guarantee effect for power distribution work.
Optimization of cutting parameters of microwave expanded stem in slim cigarette
Authors: Pan Wen , Feng Hongtao , Chen Ran , Li Xin , Hu Qun , Yang Lei , Wu Jiacan , Zou Juan , He Xufeng , Pu Qian , Zhao Meiwei , Qiu Ye , Zhang Tao 
Published: January 2021

Abstract
In order to explore the cutting optimization parameters of the processing technology of the expanded stem of slim cigarette, according to the quality characteristics of the microwave expanded stem, the samples with different combinations of cutting parameters were optimized. The results showed that under the combination of cutting parameters with a slicing thickness of 0.5mm and cutting width of 0.2mm, the high whole cut tobacco ratio, moderate small particle content, and minimum tobacco loss from cigarette end could be obtained. At the same time, the samples also had a lower hazard index and better sensory quality. This study provides a reference for technical research on the processing technology of the expanded stem of slim cigarettes.
Integrated scheduling algorithm for multiple complex products with due date constraints
Authors: Wang Xinkun , Song Yuchuan , Zou Yuji , Guo Weifei , Wang Yi 
Published: January 2021

Abstract
A new scheduling algorithm based on improved genetic algorithm is presented to solve integrated scheduling problems in small batches of multiple complex products with different manufacturing and due dates. This algorithm virtualizes the schedule of multiple complex products into a single scheduling of one product by establishing a relationship matrix of operation of multiple products to facilitate complete optimization. In addition, novel coding, crossover, mutation, and encoding are designed for the improved genetic algorithm. The proposed integrated scheduling algorithm is tested by existing examples. Experimental results validate the effectiveness and superiority of the proposed integrated scheduling algorithm.
Research on combat simulation system based on Multi-Agent
Authors: Wang Jianwei , Qiu Shaoming , Liang Ting , Du Xiuli 
Published: January 2021

Abstract
Combat simulation is an important part of simulation war. There is a big error between the analysis, processing and calculation of the battlefield situation of the combat simulation system developed by various scientific theoretical methods of the traditional linear machinery and the actual situation. Firstly, the method of combat modeling and simulation based on Multi-Agent and the process of combat model modeling based on multi-agent are studied, and the combat scenario is formulated. Secondly, the types of agents are extended, and the roles and relations of each agent are emphasized. Finally, based on the anylogic development platform, a new complex multi-agent combat simulation model is proposed, which expands the types of agents in the combat simulation model. The simulation results show that the agents in the new complex multi-agent combat simulation model are more comprehensive and more close to the real combat scene of modern war.
Research on the Application of Vocabulary Relation Extractio n Method of Demand Entity Based on Bi-GRU
Authors: Zhao Jiakun 
Published: January 2021

Abstract
Designing and mining the entity vocabulary relationship of requirements engineering to form a knowledge base of requirements engineering has a positive effect on providing the intelligent service of demand and software development in the future. But there are very few targeted extractions of the relationship between demand vocabulary in the current demand field. Therefore, this paper proposes a demand entity vocabulary relationship extraction model consists of the Bi-GRU, the attention mechanism and the labeling and filtering layer of the demand entity. This paper discribes how to use the data from actual projects to verify the methods above. The experimental results show that the method of relationship extraction combining the attention mechanism and the labeling and filtering layer of the demand entity in the Bi-GRU neural network has a significant effect in the demand vocabulary relationship extraction.
Air quality prediction method based on improved wavelet denoising and LSTM
Authors: Pu Teng , Lin Fang , Zhao Yuting , Fu Zhenxiang 
Published: January 2021

Abstract
in order to optimize the traditional wavelet de-noising algorithm threshold function in continuity and noise reduction effect, an improved soft hard threshold compromise method and deep learning model are proposed to analyze the prediction performance of different deep learning models and improved wavelet algorithm. RNN, GRU and LSTM neural networks are constructed to predict the original data, the traditional wavelet denoising data and the improved wavelet denoising data. The air quality data of Chengdu were used for simulation experiment. The results of quantitative analysis showed that: for AQI, PM2.5 and O3_ Oh denoising will cause the loss of useful signals. The improved wavelet threshold denoising for PM10, SO2, Co, NO2 data can improve the prediction effect of the model. The results show that LSTM model has good applicability in air quality prediction, the absolute percent error is 5.867%, and the mean square error is 4.870. The prediction performance of LSTM model is about 10%, the absolute percent error is 5.176%, and the mean square error is 5.314. The traditional threshold denoising will affect the prediction results of the model for the actual data due to the loss of high-frequency signal and pseudo Gibbs phenomenon. The results show that the improved soft hard threshold compromise method can better deal with air quality data, and the LSTM model based on improved wavelet denoising has strong applicability for air quality prediction.
RETRACTED: The construction of evaluation system for the cultivation of statistics professionals under the background of big data and artificial intelligence
Authors: Tian Xiujie , He Dongxia , Lu Chunyan 
Published: January 2021

Abstract
The high development of big data and artificial intelligence has put forward higher requirements for statistical talents, and the quality of statistical talents needs to be further improved. Based on the present situation of statistics teaching in our country, this paper takes the cross characteristics of big data and artificial intelligence as the research background, and aims to break through the evaluation model that regards the evaluation of teachers teaching work as the only evaluation content, to promote the four substantial transformations of the subject of judgment, the objective of judgment, the content of judgment and the object of judgment, meanwhile, to set up the corresponding index system.
Vehicle detection algorithm based on LW-SSD
Authors: Liu Yunxiang , Zhang Guoqing 
Published: January 2021

Abstract
Due to the complex structure of the traditional SSD network model and the large amount of parameters, it is not suitable for porting to mobile or embedded platforms, so the LW-SSD vehicle detection algorithm is proposed, this paper redesigns the basic feature extraction network of SSD, using MobileNet feature extraction network instead of VGG16, for the original MobileNet to improve, remove the original MobileNet network average pooling layer and Softmax layer, while reducing the MobileNet network from the original 28 layers to 13 layers. Then, on the basis of the subsequent network, feature maps of different scales are scaled, residual mapping and deep feature fusion operations, and the low-level position information and high-level semantic information are effectively combined to obtain new feature maps of different scales. The designed network model is trained using the Tensorflow framework, and the test effect of the network model is evaluated. The experimental results show that the accuracy of the network model designed in this paper is 78.76%, which is 9.27% lower than the original SSD network model. However, the amount of parameters is only 1/4 of the SSD network model. At the expense of partial accuracy, the design of a lightweight network model is exchanged, which lays the foundation for subsequent development on mobile or embedded platforms.
Research and application of data mining algorithm
Authors: Huang Cong , Yang Yao , Wang Huajun , Zhang Xueyu , Zhao Jinquan , Wan Jun 
Published: January 2021

Abstract
With the breakthrough development of a series of technologies such as machine learning and internet of things, data plays an increasingly important role in everyone’s daily affairs and work. With the explosive exponential growth of data volume, data types are also quietly changing, from the traditional single, structured data to today’s diverse, semi-structured data. In the face of emerging new massive data, data mining technology has gradually become the focus of attention. Data mining was also known as knowledge discovery (KDD) at that time. It was mainly defined as a pattern hidden in massive data, which must be understood by people and bring potential benefits. In this paper, we mainly studies the basic principle and algorithm knowledge of data mining, and applies ridge regression and random forest algorithm model in real estate price forecasting. Finally, through the stacking thinking of ensemble learning in ensemble learning, we propose a fusion method of ridge regression and random forest model, and obtain a more accurate and stable prediction model.
Effectiveness of handshake strategy in 3D underwater acoustic networks
Authors: Zhao Danfeng , Lun Guiyang 
Published: January 2021

Abstract
Underwater acoustic networks (UWANs) have emerged as a promising technology to accomplish Internet of Underwater Thing (IoUT). Handshake-based protocols have been used in UWANs to resolve various problems for different protocol layers. However, the effectiveness of handshake is seriously influenced by interference as the underwater channel has lower spreading loss factor and absorption attenuation. In this paper, we derive effectiveness of handshake strategy for 3D UWAN and analyse it with various parameters.
Text sentiment analysis based on Glove model and United Network
Authors: Li Duojiao , He Chenwan , Chen Ming 
Published: January 2021

Abstract
Traditional convolutional neural network has been proved to have good classification effect in text sentiment analysis experiments. However, CNN ignores the context dependent information of sentences, which affects the classification effect. Therefore, this paper proposes a text sentiment analysis model based on CapsuleNets and Attention mechanism. Firstly, the word vector is trained by using Glove model; secondly, the local features are extracted by using the features of double-layer capsule network; then, attention mechanism is added to assign the corresponding weight to the extracted text information; finally, we complete classification. The experimental results show that the proposed model can effectively improve the accuracy of emotion classification.
Research on Target Tracking Algorithm in occlusion scene
Authors: Zhao Jianguang , Cao Xinying 
Published: January 2021

Abstract
Target tracking is widely used in video image, intelligent transportation, behavior analysis and other fields. At the same time, target tracking technology faces more and more challenges. In this paper, the target tracking technology in occlusion scene is studied, and the target tracking method based on spatiotemporal context and the target tracking method combined with depth network model are introduced respectively. The main ideas and principles of the two methods are analyzed, and the improvements based on these two methods are summarized. Finally, the problems existing in the future development of occluded target tracking algorithm are prospected.
Research on syntactic dependency tree and Ontology constraint in remote Supervising relation extraction
Authors: Zhao Shuxu , Zhang Yiguang , Wang Xiaolong 
Published: January 2021

AbstractAt present, research on remote-supervised relationship extraction fail to fully consider the position information among words in a sentence, and the extracted relationship may be inconsistent with the actual relationship. Therefore, this paper combines syntactic dependency tree and ontological constraint to carry out remote-supervised relationship extraction. The syntactic dependency tree is used to obtain the position weight of each word in the sentence and the domain ontology constraint is introduced to improve the accuracy of the extraction relationship. Experiments on Freebase+NYT data set show that this model can effecttively reduce the noise interference of wrong labels, and the model improves the accuracy by 2% compared with other reference models, thus better realizing relation extraction and laying a relevant foundation for the construction of high-precision knowledge map.
Sentiment analysis based on BiGRU information enhancement
Authors: Yin Xing , Liu Changhui , Fang Xiaodong 
Published: January 2021

Abstract
In view of the existing sentiment analysis technology, model design is often a combination of different network models, which may not give full play to the advantages of the network. This paper proposes a data pre-training based on BERT, and input the obtained data into BiGRU neural network layer to enhance the features, and achieve the feature enhancement by stacking and repeated use. The input of the second layer is the result of the output of the first layer, and the input of the third layer is the result of the output of the second layer, and then it is calculated multiple times in different subspaces to learn relevant information. The experiment was conducted on the Twitter data set, and the results showed that in this idea, The two-layer BiGRU classification effect is better, reaching an accuracy of 82.63%, the convergence rate is faster, and it is better than other classification models, and the classification work can be completed well.
Research on Network Data Security Based on RS-PS Support Vector Machine (SVM)
Authors: Su Li , Yu Linlin , Li Ting , Liu Xinxin 
Published: January 2021

Abstract
In the era of rapid development of digital information, resource sharing under the Internet environment, convenient access to information has brought us great convenience. However, as a new independent variable, the Internet itself has many unknown vulnerabilities, which provide an attack entrance for attackers and cause serious security risks. In this paper, intrusion detection research based on rough vector set and particle swarm optimization mainly involves particle swarm optimization (PSO), learning of rough function, etc. Based on some shortcomings of the algorithm optimization in the field of intrusion detection, a rough set(RS)-particle swarm(PS) SVM network data security detection method is proposed to provide security guarantee for information services.
Research on Scientific Research Data Platform Based on Big Data
Authors: Yuxiang Ji 
Published: January 2021

Abstract
In order to realize the fault tolerance of real-time tasks in scientific research data platform information platform, based on adaptive feedback equalization and symbol modulation technology, a fault-tolerant scheduling model of real-time tasks in scientific research data platform information is proposed in the paper. What is more, the platform transmission channel model of the scientific research data platform information under routing conflicts is first constructed to optimize the information transmission protocol of the scientific research data platform. Secondly, the fuzzy C-means clustering method is applied to perform the inspection robot information fusion. Meanwhile, the symbol modulation method is used for fault-tolerant scheduling of real-time tasks in the information platform. Finally, a simulation experiment is carried out. The results show that the method proposed in the paper has better fault tolerance for platform real-time task scheduling of scientific research data platform information, and the channel balance of the information platform is stronger, which improves the platform real-time task scheduling capability of scientific research data platform information.
Research on Optimization of Big Data Fusion QM Model Based on Block-chain Distribution
Authors: Zeng Weijia , Qin Fang , Guan Sheng , Jiang Jing Jing 
Published: January 2021

Abstract
In terms of poor classification and detection effect of big data fusion QM model as well as weak feature mining ability, a big data fusion QM model based on block-chain distribution optimization is proposed in the paper. Moreover, a block storage structure model for the distribution of big data is first established on the block-chain to perform shared scheduling according to the attribute distribution of the distributed big data on the block-chain, and then the big data of the distributed big data on the block-chain is mined. On this basis, the differentiated feature distributed retrieval method is used to cluster the distributed big data information on the block-chain, and the rough set feature quantity is extracted from the fuzzy cluster center. Meanwhile, combining the fuzzy correlation feature detection method, the information of the block-chain distributed big data is reorganized to extract the statistical features of each module. In addition, the quantitative recursion in the process of block-chain distributed big data fusion is analyzed, so that the optimization design of the QM model of block-chain distributed big data fusion can be realized. The simulation results show that the feature resolution ability of block-chain distributed big data fusion with the method proposed in the paper is better, and the classification detection and feature mining capabilities of block-chain distributed big data are improved, which has high application value in the block-chain distributed big data information retrieval and scheduling.
A method to remove image noise based on 6-tap Filter Interpolation
Authors: Wang Nan , Zhang Ya-Di 
Published: January 2021

Abstract
A method to remove the noise of image based on 6-tap filter interpolation is investigated in this work. To this goal, the algorithm principle of 6-tap filter interpolation including 1 / 2 pixel interpolation process, 1 / 4 pixel interpolation process and 1 / 8 pixel interpolation process are firstly analyzed. Then, in order to validate the effectiveness of 6-tap filter interpolation for the image denoising, two images added with Gaussian noise are processed by 6-tap filter interpolation and the SNR is used to evaluate the post-processing image. The obtained results show the SNR of post-image is larger than that of pre-processing image, which proves that the 6-tap filter interpolation can be acted as an effective method to remove the noise of image.
Tradeoff analysis of SWIPT-Based regenerative relay communication system
Authors: Shi Xiaoye , Sun Jin , Zhang Zhaowei 
Published: January 2021

Abstract
It has been brought to attention that the energy-carrying relay system can not only increase the energy harvesting of the system but also reduce the energy loss of the system at the same time. In this paper, a trade-off metric is incorporated in the power allocation of the energy-carrying relay system, which can substantially meet the energy harvesting and data transmission requirements of various application scenarios in a more flexible manner. Subsequently, a trade-off metric is proposed, and an optimization problem for maximizing the trade-off metric value is constructed. Eventually, the optimal solution for optimizing the trade-off metric is obtained by applying Lagrangian multiplier method. It has been shown in experimental simulations that the trade-off metric has the minimum value of 0.4073 when ω=0.54, which represents the intersection of the three curves. It implies that the proposed trade-off metric can precisely describe the relationship between the probability of successful information transmission and the energy harvesting power. Most importantly, the trade-off metric proposed in this paper can well illustrate the relationship between energy harvesting and information transmission. Whether it is a symmetrical channel or an asymmetrical channel, where the optimal solution derived through theoretical calculations is consistent with the experimental result.