bluuebunny/crossref_metadata_2025 huggingface dataset:
Proximal sensing of Urochloa grasses increases selection accuracy
Authors: Jiménez Juan de la Cruz , Leiva Luisa , Cardoso Juan A. , French Andrew N. , Thorp Kelly R.
Published: April 2020

In the American tropics, livestock production is highly restricted by forage availability. In addition, the breeding and development of new forage varieties with outstanding yield and high nutritional quality is often limited by a lack of resources and poor technology. Non-destructive, high-throughput phenotyping offers a rapid and economical means of evaluating large numbers of genotypes. In this study, visual assessments, digital colour images, and spectral reflectance data were collected from 200 Urochloa hybrids in a field setting. Partial least-squares regression (PLSR) was applied to relate visual assessments, digital image analysis and spectral data to shoot dry weight, crude protein and chlorophyll concentrations. Visual evaluations of biomass and greenness were collected in 68 min, digital colour imaging data in 40 min, and hyperspectral canopy data in 80 min. Root-mean-squared errors of prediction for PLSR estimations of shoot dry weight, crude protein and chlorophyll were lowest for digital image analysis followed by hyperspectral analysis and visual assessments. This study showed that digital colour image and spectral analysis techniques have the potential to improve precision and reduce time for tropical forage grass phenotyping.
Continuity and change in the aspect systems of Vedic and Latin
Authors: Dahl Eystein
Published: April 2020

This paper examines the development of the aspect systems in the Indo-European languages Vedic and Latin. Even though aspectual distinctions are central in the verbal systems of both of these languages at the beginning of their attested traditions, they undergo quite different developments in the course of their history. The Vedic verbal system instantiates a classic case of the development from aspect to tense, whereas Latin maintains an aspect-based verbal system, which survives in the Romance languages. The paper explores the semantic properties of the Vedic and Latin past tenses in some detail from two distinct perspectives: a neo-Reichenbachian model, where aspect is regarded as a type of relation between reference time and event time (cf. e.g.,Dahl 2010,2015), and a model where aspect involves different types of partitive operators (cf.Altshuler 2013,2014). Although these two approaches may first appear to be in conflict, the paper attempts to show that they represent complementary perspectives highlighting different dimensions of aspectual meaning.
Historical change in the Japanese tense-aspect system
Authors: Narrog Heiko
Published: April 2020

Modern Japanese has a formally very simple tense-aspect system, which at its core has only three forms that are complemented by a number of peripheral markers and constructions. The core of the tense-aspect system was much more elaborate and complex in Classical Japanese. This paper discusses the systems of Modern and Classical Japanese, and then sketches the development from the latter to the former. This development involves the grammaticalization from aspect to tense, the recruitment of lexical means and constructions to renew aspectual categories, and category climbing. Two major paths of grammaticalization can be distinguished.
Capacitive coupling induced Kondo–Fano interference in side-coupled double quantum dots*
Authors: Sun Fu-Li , Wang Yuan-Dong , Wei Jian-Hua , Yan Yi-Jing
Published: April 2020

We report capacitive coupling induced Kondo–Fano (K–F) interference in a double quantum dot (DQD) by systematically investigating its low-temperature properties on the basis of hierarchical equations of motion evaluations. We show that the interdot capacitive coupling U
12 splits the singly-occupied (S-O) state in quantum dot 1 (QD1) into three quasi-particle substates: the unshifted S-O0 substate, and elevated S-O1 and S-O2. As U
12 increases, S-O2 and S-O1 successively cross through the Kondo resonance state at the Fermi level (ω = 0), resulting in the so-called Kondo-I (KI), K–F, and Kondo-II (KII) regimes. While both the KI and KII regimes have the conventional Kondo resonance properties, remarkable Kondo–Fano interference features are shown in the K–F regime. In the view of scattering, we propose that the phase shift η(ω) is suitable for analysis of the Kondo–Fano interference. We present a general approach for calculating η(ω) and applying it to the DQD in the K–F regime where the two maxima of η(ω = 0) characterize the interferences between the Kondo resonance state and S-O2 and S-O1 substates, respectively.
Pulse generation in Yb-doped polarization-maintaining fiber laser by nonlinear polarization evolution*
Authors: Liang Cheng-Bin , Song Yan-Rong , Dong Zi-Kai , Wu Yun-Feng , Tian Jin-Rong , Xu Run-Qin
Published: April 2020

We demonstrate a self-started, long-term stable polarization-maintaining mode-locked fiber laser based on the nonlinear polarization evolution technique. A polarized beam splitter is inserted into the cavity of the linear polarization-maintaining fiber laser to facilitate self-started mode-locking. Pulses with single pulse energy of 26.9 nJ and average output power of 73.9 mW are obtained at the pump power of 600 mW. The transmission characteristics of artificial saturable absorber used in this laser are analyzed theoretically, the influence of the half-wave plate state on mode-locking is discussed, and the mode-locking range is obtained, which is well consistent with the experimental results.
Analysis of the Informatization Construction of Library
Authors: Chen Liping
Published: April 2020

In the era of the Internet, information technology has been widely used in various fields, to a large extent, it has greatly improved the quality and efficiency of work in various fields. Similarly, it is inevitable to develop towards the direction of informatization construction in the library management. The following is an in-depth analysis of the informatization construction of library management in the Internet era, hoping to effectively improve the quality and promote the long-term development of libraries.
Research on Mixed Learning Mode Based on MOOCs and Micro Class in Internet Background
Authors: Jiaoqingliang  , Sudan
Published: April 2020

In the era of education information and globalization, a new mode of teaching and micro class has emerged in the background of the Internet, which brings new challenges and opportunities to the teaching of the classroom. MOOC has been piloted and applied in many universities in the form of SPOC. As a new form of curriculum, micro-course has been applied to the teaching and learning process. The integration of Moor and micro class resources helps to turn the classroom into a mixed mode. This article will focus on this hot topic to analyse the characteristics of the class, the characteristics of the micro class and the influence on the students and teachers, to improve the quality of teaching and to realize the individualized and active study of the students. The article summarizes the results of blended teaching mode at home and abroad, and explores the development and application of MOOC and micro class resources.
Dynamic Service Composition in Semantic Internet of Things Based on QoS
Authors: Weiwei Zhang , Chengcheng Wang , Sa Zhang , Wanlu Zhu
Published: April 2020

There are some problems in the semantic internet of things, such as the limited resources of atomic services provided by physical devices and the dynamic changes of their environment. Based on this, this paper proposes a dynamic service composition method based on QoS in the semantic Internet of things. According to the QoS ontology of adding context, this method dynamically annotates the services in the semantic Internet of things, and dynamically selects and combines the service set to be selected. It can dynamically provide accurate services for service requesters according to service objects and service environment.
Analysis of technology evolution in 3D printing industry segmentation based on dynamic patent network
Authors: Li Shugang , Zheng Lingling
Published: April 2020

Technological evolution analysis is an important way to analyse the development of industry and the trend of technological change, which has important guiding significance for the government and enterprises to make the technological strategies. The patent network method can effectively identify the key technology of the industry, but it has some shortcomings in judging the technology group and other details. Therefore, this study proposes an identification model of the technology theme clustering based on IPC, which is helpful to fully reveal the evolution characteristics of the industry. This study further introduces the time dimension to build a patent network with multiple attribute nodes based on the patent of the 3D printing industry.
A Scalable Attention Mechanism Based Neural Network for Text Classification
Authors: Zheng Jianyun , Pang Jianmin , Zhang Xiaochuan , Sun Di , Zhou Xin , Zhang Kai , Wang Dong , Li MingLiang , Wang Jun
Published: April 2020

In general, deep learning based text classification methods are considered to be effective but tend to be relatively slow especially for model training. In this work, we present a powerful, so-called “scalable attention mechanism”, which performs better than conventional attention mechanism in terms of both effectiveness and the speed of model training. Based on the scalable attention mechanism, we propose a neural network for text classification. The experimental results on eight representative datasets show that our method can obtain similar accuracy to state-of-the-art methods with training in less than 4 minutes on an NVIDIA GTX 1080Ti GPU. To the best of our knowledge, our method is at least twice faster than all the published deep learning classifiers.
Research on Application Strategy of Deep Learning of Internet of Things Based on Edge Computing Optimization Method
Authors: Wu Huanyu , Guo Yating , Zhao Jingcheng
Published: April 2020

Due to the limitations of network performance IoT deep learning model, in order to optimize the performance of IoT network, this paper proposes an application strategy of IoT deep learning based on edge computing optimization method. Based on the multi-layered structure of learning, the edge calculation method is optimized, and the reduced intermediate data is uploaded at the edge node, thereby reducing the network traffic from the IoT device to the cloud server. On the basis of considering the limitation of edge node service capability in the edge computing process, the optimal strategy of task offload scheduling is formulated to improve the performance of the IoT deep learning model based on edge computing. The experimental results show that the IoT deep learning application strategy based on the edge computing optimization method can efficiently execute multiple deep learning tasks in the edge computing environment, which is superior to other algorithms.
Pooling Method On PCNN in Convolutional Neural Network
Authors: Yao Li-sha , Xu Guo-ming , Zhao Feng
Published: April 2020

The pooling method aggregates the points in the neighborhood in Convolutional Neural Network(CNN). It can not only reduce the dimension, but also improve the results, so that the results are not easy to over-fit. However, the common pooling methods have the problems of single feature and lack of self-adaptability. In order to solve this problem, the Pulse Coupled Neural Network (PCNN) is introduced and a pooling method based on PCNN is proposed. The algorithm learns the weights of each eigenvalue from the convoluted neighborhood sub-region by PCNN and fuses them to get the final pooling result. The experimental results on image recognition datasets MNIST, CIFAR-100 show that the proposed PCNN-based pooling method has better recognition effect and improves the performance of CNN compared with the existing pooling methods.
Research on Multimodal Transport Development in China under the Background of Internet Plus
Authors: Wenwen Jiao , Dongli Wei , Cui Cai
Published: April 2020

By brief introduction of the importance of multimodal transport, which has been paid great attention by Chinese government, this paper reviews the literature and sums up the present situation of China`s multimodal transport. Then, the evolutionary background ofinternet plus logistics in China has been summarized, and the influence on intermodal transport has been analysed. By critically analysing the problems, this paper finally gives some suggestions to solve problems from hardware side and software side.
Data model for smart electricity meter comprehensive verification based on BP neural network
Authors: Wang Qing , Dai Yanjie , Wu You , Liu Yanxi , Wang Tingting , Dong Xianguang
Published: April 2020

This paper proposes a BP neural network based model for smart electricity meter (SEM)comprehensive verification under variation conditions of temperature and humidity. Two-neuron BP neural network model and one-neuron BP neural network model are established, respectively. Theoretical results show that when temperature is fixed, the comprehensive verification error is rarely affected by humidity, whereas when humidity is fixed, the SEM comprehensive verification error has obvious variation with temperature which tends to linear correlation.
Modelling of 330MW Circulating Fluidized Bed Boiler System Based on Deep Belief Network
Authors: Xiong Zhonghao , Lin Yiwei , Li Xin , Yang Jian
Published: April 2020

Circulating Fluidized Bed Boiler (CFBB) is an important part of thermal power plants. The plant’s control system is a complex multivariable system with severe nonlinearities, uncertainty and strong coupling. It is not accurate to use traditional analysis methods to build mathematical models of the system. This paper introduces the modelling method of 330MW circulating fluidized bed boiler coordinated control system based on deep belief network (DBN), using the in-situ data of Inner Mongolia Jinghai Power Plant. Compared with the BP neural network modelling method, the effectiveness of the DBN method in the modelling of 330MW circulating fluidized coordinated control system is proved.
Research on Intelligent Measurement and Control System for Internet of Things in Greenhouse
Authors: Zhang Zhifang
Published: April 2020

With the continuous improvement of the level of agricultural informatization, China’s greenhouse intelligent control system has gained many new development opportunities in technology and scale. However, compared with developed countries, China’s greenhouse intelligent control technology is not stable. Based on past work experience, this paper summarizes the characteristics of intelligent measurement and control system for IoT greenhouses, and introduces the construction method of intelligent measurement and control system for greenhouse IOT through the actual design situation and content.
Design of UHF Tag Antenna Based on Internet of Things
Authors: Huajuan Cao
Published: April 2020

This paper presents a detailed study of UHF RFID system on tag antenna design and performance measure methods based on antenna design knowledge and electromagnetic theory. The paper analyzes the characteristics of UHF tag antenna. The paper provides the compact UHF antenna that covers 840MHz∼965MHz. The impedance of the antenna is easy to adjust. The tag antenna has a good performance which satisfies the design requirements. The paper studies the effect of the metallic ground on the tag antenna performance and designs a low-profile anti-metal UHF tag antenna. An antenna with a pair of F-slots is proposed. The antenna can achieve the good complex impedance matching between the antenna and chip. The simulation results show the anti-metal antenna has good working bandwidth from 840MHz to 965MHz and return loss S11<-10dB.
Search method for weld area in water-cooled wall pipe based on YOLO detection network
Authors: Xianbiao Yang , Yu Wan , Xubi Liu , Shutao Wang , Qiucheng Shen , Chengshuai Qin , Zhenyu Sun , Lihui Wang
Published: April 2020

Focused on the defects that the traditional target detection algorithm has low detection precision and is greatly affected by the industrial imaging environment, YOLO algorithm of computer vision is proposed, the features of the image through the deep convolutional neural network are extracted, and the automatic detection function of the weld area is realized. Firstly, the detected image is preprocessed, and the image inversion, k-nearest median filtering, CLAHE image enhancement, gamma image correction and other algorithms are used to improve the contrast of the weld area in the whole picture. Secondly, the data enhancement algorithm is used to increase the number of training samples and diversity. Finally, the YOLO target detection network is trained by using the training samples obtained from the data enhancement, and the performance of the network is evaluated through test samples. In experiments, 500 images of 50 ray-detected image data are used to train the YOLO target detection network, and the test results are tested in a test set consisting of 50 additional images. The experimental results demonstrate that the accuracy of weld inspection is 96%, it is much better than the accuracy of traditional target detection algorithms, and has guiding value for industrial application.
A Hardware Accelerator Based on Neural Network for Object Detection
Authors: Zhao Tong , Qiao Lufeng , Chen Qinghua , Zhang Qingsong , Li Na
Published: April 2020

At present, deep learning algorithms such as neural networks are widely used in all aspects of artificial intelligence. The computing performance of the CPU is low, and the power consumption of the GPU is large. This topic uses FPGA to study and implement the target detection algorithm. The proposed related technology can meet the accuracy requirements of the unmanned system for target detection, as well as the real-time requirements of the video stream. The frame rate of this design is 23.1FPS, and the mAP value is basically the same as that of CPU and GPU. The delay is 47.2% of Intel CPU, and the energy efficiency is 26% of Titan X GPU.
Message Queue Optimization Model Based on Periodic Execution and Category Priority
Authors: Li Kunying , Li Xiaolian , Qiao Dexin , Ding Yu , Wang Liling
Published: April 2020

In recent years, the state has invested tremendous energy in the development of enterprise informatization construction and has also derived numerous high-quality enterprise informatization forms. At the same time, when the enterprise develops to a certain extent, the demand for change management, innovation mode and strategic transformation of information technology is gradually highlighted. Among them, the rapid development of information technology brings about many business systems, weak structure, difficult to adapt to system integration, information sharing and business collaboration. It is also the next important goal of enterprise information construction solutions. In order to solve the huge throughput problem caused by the integration platform of large enterprises in the face of tens of business system information data integration, the aim is to achieve three kinds of requirements for data integration, business integration and process integration of upstream business systems. Based on execution in the period range, this paper proposes a double-execution optimization algorithm for message queue with periodic execution and priority definition. The algorithm is implemented by analysing the operation behaviour of user roles, systems and time, and combining with the defined message importance tag. The experimental results show that this method can effectively filter, sort and execute a large number of message throughput accurately in a distributed working environment, and reduce the performance and delay risk caused by the complexity of information in the integrated system.
The Development Trend of Artificial Intelligence in Cyberspace Security: A Brief Survey
Authors: Li Ziyuan , Xian Ming , Liu Jian , Wang Huimei
Published: April 2020

In recent years, artificial intelligence and network security have attracted much attention. After decades of practice, artificial intelligence and relevant theoretical methods have provided new ideas for the traditional field of cyberspace security. Knowledge mapping is a tool to show the relationship between the development process and structure of science and technology. At present, there is no knowledge mapping that can describe the development trend of artificial intelligence in the field of cyberspace security. So we used Bibliometric, Citespace and VOSviewer to analyze more than 1, 000 quality articles downloaded from Web of Science (WoS), include literature quantity analysis, co-citation analysis, keyword cooccurrence analysis and Research hotspot analysis.
Application of Autonomous Monitoring Method Based on Distributed Environment Deployment in Network Fault
Authors: Li Xiaolian , Li Kunying , Ding Yu , Wei Daiming , Ma Xianying
Published: April 2020

With the rapid development of information technology and the increasing demand for computing, the scale of cloud environment and distributed deployment is becoming larger and larger. Often, due to the outage of a node, a series of chain reaction problems occur, which makes the production environment suffer unpredictable losses. All kinds of uncertainties, randomness, concurrency and diversity exist in the above-mentioned problems. Because of these factors, it brings great troubles to the relevant staff to locate the causes of network failure. In order to solve the problem of network failure in large-scale data network, it is difficult to locate the fault location accurately, and how to give users accurate feedback on the causes of network failure. In this paper, we construct a random forest algorithm, which is based on agent distributed in each device. The system is trained continuously by machine learning, and the samples are classified by decision tree classifier. Each decision tree represents a judgment of the cause of network failure. When an error occurs, the agent collects and pre-processes the device data information according to the algorithm and feeds it back to the controller for aggregation. Then, the analyzer is used to match, judge and find out the corresponding network faults that meet the requirements. The practical application results show that the design model is feasible, which significantly improves the efficiency of problem tracking and the accuracy of problem feedback screening, but also provides strong support for the follow-up intelligent operation and maintenance.
Real-time Vehicle Pose Measurement Technology based on Multi-sensor Information Fusion
Authors: Zhu Changjian , Zhou Lin , Li Wen , Bi Qiushi , Yang Ruipeng
Published: April 2020

To achieve vehicle unmanned driving, it is necessary to obtain the exact position and posture of the vehicle in real time. To get accuracy position and attitude information, multiple different sensors will be used.However, it’s difficult to deal with the collected information and to integrate the relevant data.This article first introduces the method of obtaining measurement data when using encoder, inertial navigation system and GPS alone.Secondly, using the INS/GPS loose combination system to design a multi-sensor information fusion method.In order to verify the accuracy of the method, a multi-body dynamics simulation model was established and a test bench was built.The simulation and experimental results show that the proposed method can well fuse multi-sensor data and accurately obtain the real-time position and attitude of the vehicle body.
A BIA-NOMA Scheme for Downlink of Two-Cell SISO Network
Authors: Chen Kaiping , Zhou Qingfeng , Wang Juan , Wu Jianjian
Published: April 2020

This paper studies a transmission problem based on a two-cell downlink SISO network. We propose a blind interference alignment transmission scheme based on TDMA by using interference alignment technology, which can improve the DoF of users. In order to improve the average achievable sum-rate of the system, we propose the BIA-NOMA scheme by combining NOMA with BIA technology. Theoretical analysis shows that the BIA-NOMA scheme can effectively improve the achievable rate of the center users, and its sum-rate is higher than that of the BIA-TDMA scheme. At the end of this paper, we give the simulation results to verify the performance of the scheme.
Research on Trust-Based Secure Routing in Wireless Sensor Networks
Authors: He Junyao , Xu Feng
Published: April 2020

With the increasing scale of wireless sensor networks, the limited resource of sensor nodes and the uncertainty of the layout environment, sensor nodes are very vulnerable to malicious attacks, but the traditional security mechanism is not suitable for wireless sensor networks. This paper summarizes the common attack forms, security technologies and the security mechanisms of trust-based routing protocols in wireless sensor networks proposed in recent years, then analyzes and compares these secure routes.
Application research of mixed teaching mode based on Wechat applet
Authors: He Fen
Published: April 2020

in the era of rapid development of mobile Internet, Wechat applet because it does not need to install and uninstall characteristics, become a very popular application. Wechat applet applied to teaching is a new teaching auxiliary means. Firstly, the feasibility of WeChat is analyzed from the perspective of function and students’ willingness to use it. Then it discusses the application strategy of Wechat applet in the course of database application technology teaching. Finally, through the analysis of the effect of Wechat applet auxiliary course teaching, it is shown that Wechat applet is an effective auxiliary means of classroom teaching, which can be promoted and applied in other courses.
RFID network scheduling using an improved bat algorithm
Authors: Lyu Shilei , Wei Zhiwei , Li Zhen
Published: April 2020

The performance of RFID networks can be optimized by RFID reader scheduling. This paper proposed a novel approach using an improved bat algorithm (IBA), which can optimize RFID networks. The IBA algorithm includes 2 improved mechanisms. In the proposed approach, all RFID readers are scheduled to work in appropriate sequence, which can greatly reduce RFID collisions. Experiments on two different RFID networks have been carried out to evaluate the effectiveness. Simulation results show that the proposed approach using IBA has better optimization precision than control algorithms.
The Research Status and Hotspots in the Domain of Smart Education from 2003-2019
Authors: Donghua Wang , Nanning Yu , Jinfei Wu , Yuyan Shi , Rui Hu , Yuanyuan Guan , Haibin Wang
Published: April 2020

To explore the status and hotspots in the domain of smart education, 183 related articles form the Web of Science (WOS) were analyzed by the visualization software CiteSpace. The results show that: (1) the area of smart education stared in 2003 while there was no research in the next six years. There was an upward trend year by year form 2011 and the number of articles reached the historical peak in 2016. Bakken, Lee and Shi, China and Smart Innovation Systems and Technologies topped the lists of contributing authors, country and publication respectively. (2) “engineering education”, “smart city”, “pedagogical affordance”, “ICT” and “big data” were the top five largest clusters on the area of smart education from 2003-2019.
Students’ Classification Model Based on Stacking Algorithm
Authors: Pan Fei , Yuan Yingchun , Song Yufei
Published: April 2020

A model for Students’ Classification which aims to help college teachers teach in accordance with students’ aptitude is proposed based on the daily practice data of a course. Based on the Stacking method, this classifier model ensembles eXtreme Gradient Boosting algorithm, Random Forest algorithm and the Logistic Regression algorithm. The experimental results show that this model can divide effectively students into different classes and the accuracy can be significantly improved by comparing with other classifier models. And it can serve as reference and guidance for college students’ classifier on other courses.
Naive Bayesian Algorithm for Spam Classification Based on Random Forest Method
Authors: Wang Lu , Zeng Guohui , Huang Bo
Published: April 2020

Aiming at the problem of dimensionality disasters in the spam filtering system, a spam filtering model based on content feature selection random forest algorithm is proposed. The method combines the Naïve Bayesian algorithm and effectively reduces the influence of redundant information on classification performance through feature selection filtering of Gini impurity. The improved algorithm takes advantage of decision tree integration and naive Bayesian algorithm. The experimental results show that compared with the naive Bayesian algorithm, the improved algorithm has been improved in the classification performance indicators of spam filtering, which is more feasible and effective in practical applications.
Comparative and Analysis of spatial domain and frequency domaindigital image watermarking algorithm
Authors: Ma Yongqiang , Wang Shunli , Song Jinping , Yu Yandong , Sun Wei , Bian Jing
Published: April 2020

Digital image watermarking belongs to the branch of information hiding discipline, the study of the digital watermarking will play an invaluable role in the integrity of business transactions and the protection of personal privacy.This paper introduce the embedding process of digital watermarking technology and the extraction process of digital watermarking technology. We analyze the the characteristics of digital image watermarking and juxtapose the basic principle of the Lease Significant Bits algorithm and the basic principle of the Quaternion Fourier Transform algorithm. Finally, According to analysis and comparison, we summarize the advantages of the frequency domain method.
Multi-level feature filtering algorithm based on MapReduce
Authors: Zhang Zhenling , Wang Chao , Li Zhiyuan
Published: April 2020

In engineering practice, we need to consult a large number of professional data to support theoretical innovation. If we rely on manual classification and screening one by one, it will take a lot of time and energy, and the classification accuracy can not meet the requirements. In order to improve the work efficiency, a multi-level feature selection algorithm based on MapReduce is proposed. The improved Chi feature selection algorithm can be used for the initial screening, and then the noise words and pre quality features can be filtered by mutual information method. The experimental results show that the algorithm not only ensures the low time complexity of processing big data, but also improves the accuracy of text classification.
Parameter Identification of Electro-Hydrostatic Actuator Based on Particle Swarm Optimization
Authors: Zhou Guozhe , Feng Tieshan , Lv Xin , Liu Lihui
Published: April 2020

The Electro-hydrostatic actuator with high power/weight ratio is compact and efficient. In order to obtain an accurate linear model of the actuator, particle swarm optimization is adopted to identify the parameters of the model. A group of parameters can be seen as the position coordinates of a particle. The real frequency response data of the system is used to evaluate the quality of the particle position and a fitness function is established. The evolution equation with constraint conditions is employed. The particle swarm gradually evolves to search for the optimal solution. Finally, the identification result is presented to prove the efficacy of this method.
Optimization of PSO Algorithm Based on Adaptive Inertia Weight and Escape Strategy
Authors: Gu Lili , Liu Yong , Zhen Jiaqi
Published: April 2020

For Particle Swarm Algorithm uses fixed inertia weights, weaken too slow and extremely easy to fall into local optimum so I propose an adaptive inertia weight method, this inertia weight has a larger weight in the early stage, can increase the global search capability and avoid algorithm falling into local optimum, in late period, this inertia weight has a smaller weight and can increase algorithm local search ability and overall increase convergence rate. But during the optimization process, There are some more complicated functions that will still fall into the local minimum even using this adaptive inertia weight. In order to avoid the occurrence of premature phenomenon so use reverse escape strategy, apply new optimization methods to the basic particle swarm algorithm, the application of several basic functions proves that the proposed optimization method has higher precision and convergence speed.
Product Parameter Calculation Based On Configurable Bom Model
Authors: Lin Xia , Jiang Ri Nian , Tao Zhu Han
Published: April 2020

In order to meet the customer ‘s demand for product customization and to solve the problem of complicated BOM configuration and product parameter calculation in manufacturing enterprises, we design a configurable BOM model and realize a dynamic calculation engine of product parameters based on the model. The configurable BOM model abstracts key entities such as product, product attribute, material, material attribute, material brand, and so on. It realizes flexible expansion of customized product and its materials. In order to get the key parameters of the product, such as raw material cost, sale price, etc., the key parameter’s expressions of the product are dynamically configured, the values of product attribute and material attribute are used as input parameters and the real-time dynamic calculation is realized by using the algorithm based on the JavaScript engine-Mozilla Rhino, which makes the parameter calculation logic decoupled from the business logic and achieves the scalability of the product parameter calculation. The validity of the configurable BOM model and the calculating engine of product parameters are illustrated by an example of BOM structure and raw material cost calculation of a flat box in carton manufacturing industry.
Hepatic vessel segmentation based on animproved 3D region growing algorithm
Authors: Zhang Huahai , Bai Peirui , Min Xiaolin , Liu Qingyi , Ren Yande , Li Hui , Li Yixuan
Published: April 2020

Hepatic vessel segmentation of CT image is of great importance in the computer aided diagnosis. This paper proposes an automatic segmentation method of 3D vessel CT images to obtain better segmentation results. First, the single Gaussian kernel of Hessian matrix in the Jerman’s algorithm is replaced by bi-Gaussian kernel. Then, a histogram-based method is adopted to adaptively estimate the threshold value of the region growing. Finally, a new scheme is proposed forautomatically searching seed points of the region growing. The experimental results show that the proposed method achieves a significant enhancement of hepatic vessels segmentation with an average accuracy 98.1%.
Research and Practice of Neural Network PID Process Control Algorithm Based on Offshore Oil and Gas Platform
Authors: Yang Qing , Liu Zhiyao , Zhang Yu
Published: April 2020

Since the exploitation of offshore oil and gas, after decades of development, it has gradually played an important role in world economic development. The central control system of offshore platforms is the core equipment of offshore oil and gas fields. Offshore platform industrial process control requires PID control most of the time. However, in practical applications, the tuning of PID parameters has not been well resolved. This paper combines neural network and PID control to build a neural network PID controller. At the same time, using OPC communication technology, the intelligent control PID algorithm is perfectly combined with the central control system, and the optimized model and parameters are applied to the actual control object of the oil level control of the offshore oil production platform, and the control effect is obtained satisfactorily.
Research on the Development of Smart Home Based on Multi-Perspectives
Authors: Wang Yujie , Liao Zhipeng , Shen Yongluo
Published: April 2020

With the continuous growth of China’s emerging industries, the digital life of Chinesehas ushered in a new era, and smart home has a huge market and user value. In order to provide a reference for the theoretical research of smart home, and help the application and development of the important concept of “smart+” wasfirst proposed in the 2019 government work report.Through systematic review of domestic and foreign literature on smart home, the reasons for the rapid development are summarized from the functional and social technology perspective. The challenges faced in the development are summarized from the user’s point of view. The trend of smart home development is summarized from the perspective of industry and society.
Research on state prediction of secondary device in smart substation based on dynamic interval weighted fuzzy c-means algorithm
Authors: Zhongyang Chen , Pengfei Zhang , Mahuan
Published: April 2020

A weighted fuzzy clustering algorithm based on dynamic interval is proposed to solve the problem of inaccuracy of missing attributes in clustering. By selecting the index that can reflect the secondary equipment state of intelligent substation comprehensively and combining with the weighted fuzzy clustering algorithm based on dynamic interval, the clustering classification result of the whole secondary equipment state of intelligent substation can be obtained accurately. According to the result, the corresponding maintenance strategy is adopted. The experimental results show that the weighted fuzzy clustering algorithm based on dynamic interval can effectively improve the clustering accuracy and the stability of the convergence, which can be very good to adapt themselves to the secondary device state overhaul.
Method for Detecting Visible Impurity in Oral Liquid Based on Improved SURF Algorithm
Authors: Liu Xiongfei , Liu Huimin
Published: April 2020

In order to solve the problems that the visible foreign objects in the large bottle oral liquid are small, the detection speed is low and the single feature would be easy to cause the mis-tracking. In this paper, the speed-up robust features (SURF) algorithm is improved and applied to the detection of visible foreign objects. Firstly, features from accelerated segment test (FAST) detection algorithm is used instead of the Hessian matrix for feature point detection to avoid the extraction of numerous and useless feature points in the edge region. Secondly, two-way fast library for approximate nearest neighbours (Flann) algorithm is adopted for the feature matching to accelerate the matching rate and improve the accuracy of matching. The related experiment shows that the proposed algorithm can accurately match the target and effectively improve the detection speed, which meets the requirements of online detection.
A novel numerical function optimization framework: campaign based optimization framework
Authors: Liang Yuntao , Xiao Jiangwen , Huang Zhengyi , Liu Tiqian
Published: April 2020

This paper proposes a new optimization framework: campaign optimization framework (COF), which is based on the historical results of traditional swarm intelligence algorithms and uses two different campaign rules to get better solutions. Firstly, the candidate solution set is generated by running the swarm optimization algorithm. Secondly, cross campaign rule (CCR) or increment campaign rule (ICR) is used to update the candidate solution set. The campaign rule describes the steps to get better solutions. In CCR, the better solution is obtained by combining the variable bits of two randomly selected solutions. But in ICR, it is generated by every variable bits’ optimal pools. Then, the best candidate solution is optimized by swarm optimization algorithm again. Finally, the proposed framework is tested on two well-known benchmark functions. Through the analysis of the experimental results, the proposed algorithm is compared with traditional swarm optimization algorithm. And two campaign rules are compared in terms of results and optimization time dimensions.
Design and Implementation of an Adaptive Assembly Algorithm for Edge Nodes of Satellite OBS Networks
Authors: Li Lisiyang , Qiao Lufeng , Chen Qinghua
Published: April 2020

The edge nodes of the satellite optical burst switching network are the interface devices of the ground network and the satellite optical burst network. Ground network data needs to be classified, assembled, and generated bursts at the edge nodes. The burst will be transmitted on different wavelength channels. Reasonable selection of the assembly algorithm of the edge nodes can reduce the design complexity and improve the forwarding performance and QoS capability of the satellite optical burst switching network. In this paper, an adaptive assembly algorithm is proposed to adjust the length threshold and time threshold dynamically. The basic principle is to evaluate current network traffic depend on the actual burst parameters of previous bursts, and then determine the specific value of length threshold and time threshold. The algorithm is implemented on a Xilinx’s XC7VX690T-2FFG1927I FPGA, and verified by Modelsim SE 10.6d.
An Improvement of Remote Sensing Images Edge Extraction Algorithm Based on Quantum Derivation
Authors: Rao Siwei , Chen Shi
Published: April 2020

Aiming at the problem that the edge connectivity is poor when the original quantum derived edge extraction algorithm detects the edge of remote sensing image, it is necessary to improve the original algorithm in this study. The remote sensing image is initially multi-threshold segmented globally by quantum genetic algorithm, and then the method of quantum probability statistic is used to extract the edge of images. The experiment results show that the proposed algorithm improves the edge connectivity under the premise of preserving the precise edge of the image edge.
The Efficient Erasure code with Low Recovery-Overhead and degraded reads optimization
Authors: Zhang Hang , Zheng Liu Shan , Tan Dang
Published: April 2020

At present, the distributed storage systems are being adopted by more and more Internet companies because of their reliability and efficiency. In the distributed storage systems, some fault-tolerant technologies are often used to ensure the data reliability. Since the erasure code technology can store more data with lower storage space, it is often used as one of fault-tolerant technologies by some distributed storage systems. However, the existing erasure code technology has many problems. The large amount of repair overhead and I/O overhead consumed to recover data after data loss has become the biggest obstacle in actual use. This paper starts from the RS code, and constructs a new packet repair SLRC code by using the coding idea of stripe set, which can effectively reduce the repair cost required for single-node data recovery. At the same time, for the structure of SLRC code, a new data storage method, Curve storage, can effectively reduce the extra I/O overhead required for SLRC code degraded read operation. The experimental horizontal comparison with the LRC code and the RS code shows that the new method can effectively reduce the single node repair overhead and the additional I/O overhead required for the degraded read operation.
Construction of Experiential Learning Space Model Based on Virtual Reality Technology
Authors: Gan Baiqiang , Zhang Chi , Meng Hainie
Published: April 2020

In order to better help learners to improve their learning experience and learning efficiency, this paper starts from the interaction, immersion and imagination of virtual reality technology, and sorts out the evolutionary process of formal learning space, informal learning space and virtual learning space. It also proposes the conception of experiential learning space with the virtuality and reality combination. Aiming at the application of virtual reality technology in the knowledge presentation, simulation training and experience environment of experiential learning space, an experiential learning space model based on the virtuality and reality combination is constructed to enrich the learning environment and information presentation of learners, enhance learners’ situational experience and improve their learning efficiency.
Research onC/SProtocol and P2PProtocol in Wireless Communication
Authors: Yue Geng
Published: April 2020

This paper mainly studies how the two communication protocols c/s protocol and p2p protocol are implemented and applied in the communication system. The traditional c/s protocol and p2p protocol are two widely used communication protocols. This paper studies how these two communication systems are implemented, and imagines how the two protocols work together. This paper first studies the development and characteristics of the two protocols, and then studies the structure of the two communication systems separately, how to implement the two communication systems separately and explore how to apply them in practice.
Research and Implementation on OFDM Technology for Machine Communication
Authors: Li Zhihao
Published: April 2020

Machine communication provides the means of information interaction for Internet of Everything (IoE) and unattended operation, promoting the development on the related areas such as Internet of Things, automatic drive, robot, and unmanned aerial vehicle (UAV) which are on this basis. In machine communication on a large scale, mass user multiplexing will give rise to high peak-to-average ratio (PAR) of OFDM and redundant control information will lower the utilization efficiency of OFDM’s effective frequency. In the light of the problems above, OFDM optimization technique used in machine communication is put forward in this thesis. Under the circumstances of more user access currently, more complex application context, high PAR and low utilization efficiency, the thesis proposes computer emulating by OFDM and optimization of access mode. Additionally, OFDM optimization technique, applying partial transmit sequence and carrier to noise ratio (CTNR) to realize the redundant information control of short frame carrier, optimize PAR and enhance the transmission efficiency. Finally, simulation experiment and hardware design verify the feasibility of OFDM optimization technique in machine communication.
Research on interactive design of multilingual e-commerce platform based on eye tracker
Authors: Zhu Shengyin , He Xiangzhen , Yu Hongzhi
Published: April 2020

In “interactive design”, designers usually rely on personal experience and understanding to work. The design interface has strong subjectivity, and it is difficult to match the intention of users. Based on the basic experiments of eye movements, this study collected and analyzed the pages of the search engine for a multilingual e-commerce platform through the indicators such as the duration of the objects being watched, the number of objects to be followed, and the sequence of objects to be looked at. After the experiment, we found that the first movement of the eyeball was still focused on the upper left corner of the page. During the browsing process, the subjects were used to the order from top to bottom and from left to right. So when we design the page, we should keep this style and respect the user’s habits. After analyzing the data, we found that the simple design made a good impression on the subjects, so we should keep the simple style in the page design, avoid filling every inch of space, otherwise it will cause the burden of visual memory. The data analysis also showed that the use of controls, prompt wording, color settings and page style are consistent, which can make users easily speculate about various functions and increase the user’s preference.
The Feasibility Study of The Application of Virtual Machine Technology in OPC of GDX1/2 Cigarette Packing Machine
Authors: Jibo Li , Yuanzheng Huang
Published: April 2020

OPC is the operation interface of GDX1/2 packaging machine. Its application software and related drivers are developed in Windows XP operating system. This paper introduces the virtualization of the OPC system and finally realizes the function of human-computer interaction of OPC in win7 operating system. It can improve the maintainability of GDX1/2 cigarette packaging machine.