bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploration and Analysis on the Development Path of Smart Tourism in Shandong Province in the Era of Artificial Intelligence
Authors: Guo Ranran
Published: July 2020

In the new period, China’s tourism industry has entered a new stage of popularization and industrialization, the tourism mode has also changed from “scenic spot tourism” to “all-for-one tourism”, and the development of big data, Internet of Things, artificial intelligence, block chain and other new generation of information technology have promoted the transformation of traditional tourism into smart tourism. As a big tourist province, Shandong Province’s smart tourism developed earlier and achieved more; however, there are still some problems, such as insufficient attention, low popularity, and unbalanced development of cities. This paper takes the impact of artificial intelligence on the development of traditional tourism as the breakthrough point and explores the development path of smart tourism in the era of artificial intelligence in the next stage of Shandong Province through analyzing the internal need of smart tourism development in the era of artificial intelligence in Shandong Province.
An Empirical Study on the Digital Consumption of Luxury Goods Based on Outlets
Authors: Dai Hongyu , Xie Yutong
Published: July 2020

Outlets, a business model with a history of more than 100 years abroad, has shown a rapid increase in recent years. To explore the shopping needs of consumers in the luxury economy, at the same time, to improve the tourism competitiveness of outlets, this study uses quantitative research methods, taking outlets tourism industry and consumers as research objects, by using single-factor variance analysis, non-parametric test and variance homogeneity test, this paper analyzes the shopping willingness of consumers with different attributes and finds that the shopping willingness of consumers with low income is lower than that of consumers with high income; the shopping willingness of consumers with low educational level is lower than that of consumers with high educational level, and the shopping willingness of consumers with white-collar jobs is significantly higher than that of consumers with other jobs. In the light of the above conclusions, this study puts forward two suggestions for the flourishing tourism retail industry: it suggests that outlets managers should adopt targeted marketing strategies for high-income, high-educated and white-collar people and that outlets managers should pay attention to the important role of sales personnel in tourism retail consumer service.
Research on Side-Channel Attack Based on the Synergy between SNR and Convolutional Neural Networks
Authors: Liu Zixin , Zhu Ting
Published: July 2020

The data encryption process in the encryption chip will be leaked by means of timing operation time, probing data collecting operation power and electromagnetic signal interception, which makes side channel attack possible. Nowadays, the research on template creation of template attacks has shifted from Gaussian distribution to the use of machine learning algorithms to create templates. With many parameters, it is difficult to find a suitable network structure. Based on many experimental studies, the experience of a convolutional neural network structure suitable for side channel analysis is summarized and proposed.
Research on Side-channel Attack Based on Stochastic Model with Lower Leakage
Authors: Liu Zixin , Zhu Ting
Published: July 2020

Cryptographic algorithm as an important means to ensure information security transmission has been widely concerned, but as the carrier of cryptographic algorithm, the security of encryption equipment is easy to be ignored. With the wide application of encryption equipment in modern society, the existence of side channel analysis technology is a great threat to information security. Stochastic approach (SA) is a method to deal with side-channel attack, based on many experimental studies, combined with Chebyshev filter, it can improve the accurate for attacking and signal to ratio.
Four Arithmetic Operations on the Quantum Computer
Authors: Zhang Yong
Published: July 2020

Quantum computer has been proved to be superior to electronic computer in solving some NP problems. Based on the former quantum adder, this paper proposes a new quantum adder and quantum subtracter, and then designs a fixed-point quantum multiplier and a fixed-point quantum divider based on fixed-point number operations. Four arithmetic units are presented using quantum gate circuits. These researches lay a foundation for the quantum implementation of digital filters.
Research and Implementation of Mesh Model Deformation Simulation Technology
Authors: Wang Zhongmin , Cheng Jing
Published: July 2020

According to the requirement of deformation simulation of polygon mesh model which is easy to operate and control, the design idea of deformation simulation is given and realized. The deformation technique implemented in this paper is to calculate the deformation migration shape variables by acting on the local meshes to be deformed. During the deformation process, an adaptive subdivision mesh technology is proposed to solve the problems of insufficient mesh density and mesh vertices. A model cutting simulation technology is proposed, and a cover cutting simulation implementation is given for the polygon set without holes. This article uses the half-edge structure of the grid to reduce the time consumption of querying and editing the grid.
Aircraft Maintenance Training Simulation System Based on “CAN Bus + Ethernet” Architecture
Authors: Zhu Dan , Zhang Wei , Ren Zaiqing
Published: July 2020

Aiming at the problem of centralized data collection scheme, a distributed data acquisition and control scheme based on “CAN bus + Ethernet” is proposed. According to the functional structure design, the cockpit control equipment is defined as independent data acquisition and control nodes on the communication network. The architecture and related key technologies improve the reliability, maintainability and testability of the system. The application of this system to carry out training shortens the training period of aviation ground handling personnel and saves a large amount of maintenance and support funds.
Blind-Depth Light Field Super-Resolution
Authors: Zhang Lei , Fan Jianpeng , Yang Jungang
Published: July 2020

Light field cameras have drawn much attention due to the ability of post-capture adjustments such as refocusing and 3D reconstruction. However, the low resolution has been the bottleneck of them and limits their further application. To solve this problem, various methods have been proposed to increase the resolution, namely, to recover details and reconstruct a high resolution (HR) image of the scene from several low resolution (LR) observations. Most of the previous super-resolution (SR) frameworks depend on prior knowledge of depth information, which is an indispensable part in their SR approaches. However, it may be difficult to obtain precise depth information in some practical situations. In this paper, we propose a novel light field super-resolution (LFSR) framework independent of the prior depth information. The framework combines a variational regularization-based SR approach with light field refocusing process, which can super-resolve the focused region while preserve the unfocused region and generate a series of multi-focus super-resolved images. We then employ a multi-focus image fusion algorithm based on stationary wavelet transform (SWT) and finally obtain an all-in-focus HR image. Synthetic and real-world datasets are utilized to demonstrate the effectiveness of the proposed framework both quantitatively and visually.
Seawater Temperature Outlier Detection Strategy Based on Wavelet Analysis for Solving the Migration Problem of Fishery Companies
Authors: Liu Yuan , Qiu Zheyong , Wang Zhongyue , He Jiaqi
Published: July 2020

As the global ocean temperature rises, many marine organisms begin to find new habitats. The problem of fish migration has a profound impact on the development of fisheries. This paper uses wavelet anomaly analysis method to find the abnormal value of seawater temperature, uses discrete wavelet decomposition and reconstruction to de-noise the time series, and predicts the age of the fishing company. Firstly, we conducted continuous wavelet analysis of seawater temperature data to obtain the relationship between seawater temperature change rate and time. We performed Fourier transform to obtain its transform function, and then reconstructed the sequence to obtain wavelet coefficients of seawater temperature transformation speed. After that, we used wavelet coefficients to conduct simulation screening to obtain all outliers. In this way, the time for bankruptcy under the best and worst conditions of the fishing company can be obtained. Although the model comprehensively considers many factors, in order to establish a universal model, idealizing many influencing factors has certain limitations. The predicted bankruptcy time may have a certain error from the actual.
Morphological Enhancement for Remote Sensing Image Based on GAN Structuring Elements
Authors: Wang Xiaopeng , Wang Qingsheng , Yang Wenting , Yao Lijuan
Published: July 2020

A morphological image enhancement method based on the GAN (General Adaptive Neighborhood) structuring elements is proposed for some problems such as low overall contrast, noise and inter-target edge of some remote sensing images. Firstly, a pixel to be processed is selected as a seed point, and an adaptive neighborhood is defined according to the image pixel characteristic and the neighborhood constraint relation to obtain the self-adaptive structuring elements set. A corresponding self-adaptive morphological operation based on the self-adaptive structuring elements set is constructed with reference to the classical morphological operation definition, and the self-adaptive alternative filter is constructed by using the adaptive morphological operation combination so as to realize the enhancement of the remote sensing image. The experimental results show that the method can preserve the accurate positioning of the objects edge while enhancing the image, avoid the loss of information, has better noise removing performance. Compared with the classical morphological filter operator, the single-scale Retinex algorithm and bi-histogram equalization (BHE) algorithm, the contrast ratio and the signal-to-noise ratio of the enhanced image are improved.
Research on Application of Feature Analysis Method in DNS Tunnel Detection
Authors: Liu Yong , Gou Xiangnan
Published: July 2020

Traditional DNS tunnel detection methods based on load analysis and traffic monitoring have a high false positive rate and cannot effectively respond to new DNS tunnel attacks. To this end, a log-based statistical method is proposed to detect DNS tunnel attacks. Compare and analyse the differences between DNS tunnel attack behaviours and normal DNS parsing behaviour from the perspective of DNS sessions, extract the multi-dimensional features dominated by cache hit ratios, compose DNS session evaluation vectors, and use random forest classification algorithms to construct DNS session evaluation vector detection classifiers A DNS tunnel attack detection model based on characteristic statistical behaviours is established. The actual test results show that this method has a small false positive rate and low false negative rate, and it also has a high detection ability for unknown DNS tunnel attacks.
Design and Implementation of Rugby Tackle Technology Assisted Training System Based on Somatosensory Capture
Authors: Zhang Zhiqiang , Gao Li , Xiang Yangyang
Published: July 2020

Rugby is a highly challenging and spectator contact team sport with intense physical confrontation, specific technique, and group tactics, which requires athletes have tenacious will, courage and teamwork. “Tackle” is an essential feature of rugby which is the smallest action unit of body contact and confrontation between the offensive and defensive players during match. Tackling ability is not only a demonstration of personal skills, courage, and experience, but also the base of tacitly cooperating with other teammates in helpful defense which a well-trained part of team needed. For rugby beginners’ tackle training, this study designs and implements rugby tackle technology assisted training system based on somatosensory capture to help beginners master rugby tackle correctly and effectively during single or double training so that beginners could complete the confrontation exercises from simple to difficult.
Evaluating Top-N Join Queries with Real-time Entity Resolution
Authors: Zhu Liang , Cheng Ye , Wang Yu , Ma Qin , Meng Weiyi
Published: July 2020

Using nonmonotone ranking functions in top-N queries is a challenge. Traditional techniques for top-N queries are based on clean data without entity resolution (ER). For dirty datasets with duplicate tuples referring to the same real-world entity, these techniques may yield top-N tuples duplicates for a query. Consequently, the effective size of the result set of the query is less than N, and some useful tuples may fail to be retrieved from the datasets, which leads to poor effectiveness. Using an ER-Index based on a divide-and-conquer mechanism and nonmonotone ranking functions, in this paper, we propose a method for processing top-N join queries with real-time ER. This method integrates ER with the processing of a top-N join query over dirty datasets on the fly. Extensive experiments are conducted to measure the effectiveness and efficiency of the method over dirty datasets.
Identification of Comprehensive Energy Consumption Feature Based on Rough Set Bayesian Classification Algorithm
Authors: Liu Jian , Ouyang Cengkai , Zhao Shuangshuang , Tian Zhengqi , Wang Lihui
Published: July 2020

Mining of valuable information from user energy consumption data efficiently and accurately has always been a research hotspot in the power industry. Bayesian classification method is one of the important data processing methods in the field of machine learning and data mining research. It has the advantages of simplicity, high efficiency and stable classification effect, and it provides an effective solution to the user’s comprehensive energy consumption feature identification. A model describing energy consumption data to predict trends is built, training data set is analyzed, a classification model is constructed, and the data records in the database to a given category are maped, which can be applied to data prediction. By studying incomplete information systems, expanding rough sets, constructing extended models, Bayesian classification algorithms based on rough sets theory is designed, and user comprehensive energy consumption feature identification is realized. Experiments show that the user comprehensive energy consumption feature identification algorithm based on rough set Bayesian classification can greatly improve the classification accuracy.
The Improved Equilibrium Optimization Algorithm with Best Candidates
Authors: Zhao Juan , Gao Zhengming
Published: July 2020

The best candidates play the important role during the exploration and exploitation of individuals in almost all of the swarm-based algorithms. More best candidates were involved in such procedure of the grey wolf optimization algorithm and the newly raised equilibrium optimization (EO) algorithm we called here. The EO algorithm introduced four best candidates besides their average and constructs an equilibrium pool. However, the best candidates would still perform the guiding role and a novel improvement was introduced. Experiments on some classical benchmark functions were carried out and results show the better performance than the original one. Consequently, the EO algorithm should be improved and focused more on the best candidates furthermore.
Research on Technology of Sea Emergency Rescue Based on Unmanned Surface Vehicle
Authors: Hu Xinming , Fu Huaichun , Wu Xiang
Published: July 2020

Based on the advantages of small size, easy to carry by large ship, high speed, flexible mobility, no need to drive by human, high autonomy, the Unmanned Surface Vehicle (USV) is very suitable for the sea emergency rescue. During rescue, the USV needs to track the castaway, and it needs to sail to the downstream position of the castaway in the shortest time to release the life-saving equipment. In this paper, the technology of sea emergency rescue of USV is researched, the motion of the castaway is established, and the downstream position and velocity of the castaway is predicted based on Kalman Filter (KF). In addition, the motion model of the USV is established, and the target tracking based on the Dynamic Window Approach (DWA) is carried out, which will guide the USV to reach the rescue position, and local obstacle avoidance is carried out near the castaway to avoid collision. The rescue strategy does not need to consider the influence of the weather on the position of rescue, so it has high adaptability. Finally, the rescue strategy is simulated, the simulation results validate it works effectively.
Design of Networked and Digital Teaching Platform based on Big Data
Authors: Xie Hui , Wei Li
Published: July 2020

In the traditional network-learning, we have produced the massive data, but they do not play a corresponding role in the actual teaching, therefore, large data technology is used for improving the existing teaching platform, so that it can dig the hidden knowledge in teaching, this issue has become a hot topic that many educators continue to explore. We deeply study the big data technology, take MapReduce as the data analysis model, introduce clustering technology and association rule as the main models of data analysis, which are used for building the teaching platform based on big data. In the process of designing the large data teaching platform, we have built the digital teaching platform and the networked teaching platform respectively, the emphasis of the two platforms is different, among them, the networked teaching platform is used to analyze the students’ academic performance, that is, we take a certain student as the research object, analyze the students’ different academic achievements, adopt association rules technology, analyze the relationship of different disciplines, to develop a set of knowledge points to improve the performance of students’ weak disciplines, and generate a networked large data teaching platform; digital teaching platform can be used for the recommendation of students’ suitable curriculum combination, clustering technology will be introduced, analyze the effective combination of disciplines, which can help students improve academic performance, at the same time, students are willing to spend more time on learning, and take the subject combination module as a main function to generate digital large data teaching platform. By the design of digital and network large data teaching platform, they help educators make personalized learning plans for students, therefor, it help more students to achieve their learning goals.
Application of Computer Virtual Reality Technology in Design
Authors: Xie Hui , Wei Li , Li Peng
Published: July 2020

With the continuous development of technology, the design of the environment can be simulated by computer technology. The virtual reality (VR) technology can be used to take the real world as a three-dimensional space, three-dimensional space can be designed to simulate the actual environment. In this paper, virtual reality technology is applied to design, and design is divided into two fields, namely landscape design and scene lighting rendering. We study characteristics of virtual reality technology and framework of virtual reality technology, at the same time, we define the concept of light and the concept of landscape in design, and analyze the problem of lighting rendering and the problem of landscape design. From the perspective of landscape design, we discuss the difficulties of virtual reality-landscape design, and further analyze the landscape design level and multi environment elements display based on virtual reality technology, and propose a feasible virtual reality-landscape design method; from the perspective of light rendering, the principle of lighting tracking algorithm can be analysised, the realized method is used for lighting rendering, the game engine is used as the rendering engine, MAYA and Photoshop software are used as the main rendering techniques of virtual reality, therefore, the real-time rendering of landscape’s surrounding environment is realized.
Research On Key Technologies Of Equipment Support Intelligent System
Authors: Chen Jian , Wang Yan , Jiang Xuepeng
Published: July 2020

In view of the rapid development of information technology, the construction requirements of intelligent equipment support system are proposed based on the results of intelligent construction. On the basis of putting forward the logical framework of the intelligent equipment support system, this paper analyzes its technical framework. To provide theoretical support for the construction of the intelligent support system, analyzes its key technologies, especially the concepts of GIS visualization technology, Internet of things technology deep learning technology and big data technology, and their functions in the intelligent equipment support system.
An Intelligent Learning Method and System for Cybersecurity Threat Detection
Authors: Tao Yuan , Hu Wei , Li Moyan
Published: July 2020

To protect information system and data effectively is the core of cybersecurity. At present, most technologies are based on known static protection strategies. Through the cybersecurity threat is dynamic, so it is necessary to detect the cybersecurity in advance, so as to update the protection strategy in time, which means the dynamic and active defence. The intelligent learning method and system for cybersecurity threat detection is proposed. And the immune selection of cybersecurity threat detection is proposed to select the appropriate threshold, and the knowledge map of cybersecurity threat detection are constructed from suspicious time, security management centre, security computing environment, security area boundary and security communication network. The threshold is built by immune selection, and the cybersecurity intelligent detection can be achieved by knowledge map. The architecture of intelligent detection system is proposed. So that the cybersecurity threat detection can be dynamic and intelligent, and the performance of cybersecurity threat detection and analysis can be greatly improved.
Cooperative Training Video Surveillance Technology under the Edge Computing
Authors: Lian Dong , Xu Aidong , Chen Songlin , Xu Xinchen , Jiang Yixin , Hong Wen
Published: July 2020

Edge computing offsets the computing power devices to close the terminals, which avoids delay caused by long-distance transmission and network congestion under the cloud computing support scenarios. This paper presents the video surveillance model under the edge computing. A novel cooperative training edge computing system for the image processing is proposed to realize the shortest training and processing time. The simulation of the novel system is performed under Tensorflow software and the results verify the advantages of the new system.
Analysis of the Topological Structure of the Convolution Neural Network Model RESNET
Authors: Shang Tao , Song Ying
Published: July 2020

With the advent of the era of big data and the development of artificial neural network, a large number of different neural network models have come out. In the field of image recognition, the application proportion of convolutional neural network model is very high, and the different structure levels of the same convolutional neural network model are also very different, and the recognition effect in solving different problems is not the same. Taking the application of image recognition as an example, this paper analyzes and compares the influence of different topological structures in RESNET model on recognition accuracy. Through the experimental comparison and analysis, it is concluded that the structure of shortcut_6 in RESNET convolutional neural network is the turning point. When the length of shortcut is less than 6, the recognition efficiency is on the rising trend with the deepening of the network. When it is equal to 6, the recognition efficiency is basically fixed, and when it exceeds 6, the recognition accuracy is reduced.
The Exploration of Progressive Experimental Training Teaching Mode Based on O2O
Authors: Hu Yahui , Lv Shujun , Fang Qiqing
Published: July 2020

In view of the current situation that the experimental teaching hours are few and the task is heavy, the offline progressive experimental teaching, online micro class teaching and online progressive practical training breakthrough teaching mode are adopted, the experimental teaching contents with progressive logical relationship are designed, the difficult and in-depth practical training tasks are allocated, and the trainees’ practical training results are mastered openly, fairly and fairly in real time on the training platform It is proved that this method can improve the quality of practical teaching.
Intelligent Decision Algorithm of Nutrients based on Big Data
Authors: Chen Yungui , Fan Hongbin
Published: July 2020

With the improvement of living standard, People’s requirements are also improving for children’s dietary nutrition, which fully illustrates the attention of people for healthy development of children. Children’s dietary nutrition should not be solely based on advice of expert, because every child needs different dietary nutrients. In this paper, a large data computing framework MapReduce is studied, which fully considers the eating habits of children, the balance of dietary types and the interaction of food. A intelligent decision algorithm based on large data is proposed for the best children’s dietary nutritional components. The algorithm is divided into two steps, the fuzzy GK algorithm is realized by the MapReduce framework, and the main nutrients are selected effectively; these nutrients are taken as parameters and used for multivariate linear regression model, so as to realize the rational allocation of nutrients. We design the algorithm, introduce the details of the algorithm in detail, and continuously optimize relevant parameters to produce a better dietary nutrition composition scheme. In the process of experiment, we analyze the performance of decision algorithm by testing examples, and make a lot of comparisons with the existing decision analysis algorithms. After a large number of arguments, it is proved that the algorithm is effective.
Research on Plant Lighting Driver Control Strategy Based on LED Spectral Characteristics
Authors: Fei Jingwei , Xu Yuzhen
Published: July 2020

To realize the continuous adjustment of the light quality ratio of LED plant lighting power supply, a plant lighting driver control strategy based on the spectral characteristics is studied in this paper. First of all, the spectral mathematical model of monochromatic LED chip is established. Secondly, the mathematical model of the number of photons generated by LED the driving current is further studied and then, a new driver control strategy about the pant lighting is proposed based on the model of the number of LED photons. At last, a step-down circuit built by RT8471 chip is used as the LED driver power supply to verify the correctness of the new driver control strategy in this paper. Different color LED chips of Cree and Philips Lumileds companies are used as the circuit loads. It shows that the errors values between the results from the experiments and the theoretical model calculations of the number of LED photons are all less than 5%. So, the new plant lighting driver control strategy proposed in this paper is feasibility and has certain theoretical guiding significance and engineering value.
Main Content and Implementation Path of College Sports Informatization Construction
Authors: Yu Qiaoyan , Pan Xiaofei
Published: July 2020

Information technology not only has a revolutionary influence on the development of physical education, but also plays a strategic role in the development of physical education. Based on the current situation and significance of sports informatization development in colleges and universities in China, the author makes an in-depth analysis of informatization innovation of sports teaching and research, optimization and management of sports training, physical fitness monitoring and evaluation, cloud platform construction of extracurricular sports activities and competitions, scientific material selection and talent cultivation of young athletes, and intelligent management of campus competitions. From the aspects of the development goal of college sports informatization, the establishment of information platform, the building of characteristic team, the utilization ability of sports teachers’ information and the utilization of information achievements, this paper puts forward the implementation path of college sports informatization construction, aim to provide theoretical support and practical guidance for college sports informatization construction and development in China.
Quantitative Analysis of Protein and Polysaccharide in Lilium Lanzhou Based on Near Infrared Spectroscopy
Authors: Lian Xiaoqin , Tang Shenmiao , Wu Jingzhu , Wu Yelan , Chen Qun
Published: July 2020

In order to quickly detect the nutritional components of Lilium Lanzhou, a national geographical indication product, a quantitative analysis model of protein and polysaccharide was established by near infrared spectroscopy and chemometrics. A total of 81 samples of Lilium Lanzhou were collected. SG smoothing + first derivative + MSc spectral preprocessing method was selected to establish the spectral model of protein and polysaccharide quantitative detection of Lilium Lanzhou Based on partial least square method. The correlation coefficient (R2) of protein model is 0.844, RMSEP is 0.268, the correlation coefficient (R2) of polysaccharide model is 0.715, RMSEP is 0.273. The experimental results show that NIRS technology is feasible for the rapid detection of key quality of Lilium Lanzhou, which is expected to provide a new method for the rapid evaluation of key nutritional quality of Lilium Lanzhou.
A Low-Complexity 2D-DOA Estimation Algorithm with Massive URA
Authors: Lu Zhou , Li Baobao , Lai Xin , Zeng Haowei
Published: July 2020

Conventional Direction of Arrival (DOA) estimation algorithms, such as Multiple Signal Classification (MUSIC), Root-MUSIC, and Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithms, are difficult to apply to large-scale antenna arrays. Because these algorithms not only require a lot of snapshots of the received data, but also the hardware equipment cannot meet the algorithm complexity requirements. This paper presents a low-complexity DOA estimation algorithm for large-scale Uniform Rectangle Array (URA). We first obtain coarse initial DOA estimates via the two-dimensional fast Fourier transform and then search for the accurate estimates within a very small region. The proposed algorithm only needs one snapshot of the received data to obtain the estimation accuracy of the traditional algorithm. The algorithm also has good robustness, and it can estimate DOA accurately even under extremely low signal-to-noise ratio and many sources. Theoretical results and numerical simulations are provided to assess the superiority of the proposed algorithm.
Extracting and Reconstructing Human Behavior Video Feature in High Voltage Exclusion Zone
Authors: Chen Jun , Sun Jian , Rao Yuanliang , Rao Xiaoyong
Published: July 2020

A method is presented to the discrimination, extraction and reconstruction of human behavior in the area forbidden by high voltage, in particular, a method of scene reproduction of the accident scene when a high-voltage area endangers the power equipment injury or causes a personal property accident due to human behavior. To validate this method, an experiment was conducted on a small lake with high voltage transmission line over through. The Server video after the proposed method can be used to recognize human behavior, and transmission efficiency has been improved up to 35%.
Development and Allometry Patterns of Fine Scale Fish Larvae at Low Temperature
Authors: Yang Guiqiang , Wang Zhanquan , He Jie , Li Wentong , Yuan Ding
Published: July 2020

Ecological experiment was conducted to study the allometry of fine scale fish yolk-sac larvae and its significance in adaptation in early life stage. The results indicated organs such as feeding, breathing and swimming of larvae in the yolk sac stage are rapidly differentiated, and all key organs are allomorphic. In all parts of the body, the head length and tail fin length are growing at an allometric speed, the trunk is growing at a slow speed, and the body height is isokinetic growth; in the head organs, the rostrum length and head length are growing at a positive allometric speed; in the swimming organs, tail fin length is positive allometric growth; yolk sac width and yolk sac height are positive allometry consumption. The fast development of feeding, breathing, swimming and other organs in yolk-sac larvae of fine scale fish, increases larval survival ability at the early stage in the shortest possible time, and there were important ecological significance to adapt the complex and changing external environment.
Innovative Applications and Development of AI for Emergency Services
Authors: Kong Depeng , Lv Shangyi , Gu Weisi , Li Jiusheng , Dai Shaoqing
Published: July 2020

With the outbreak of 2019-ncov (NCP) virus, many AI technologies, equipment and platforms are emerging in the main battlefield of anti-epidemic, such as patrol UVA, 360 disinfection robot, infrared intelligent temperature measuring equipment, throat swab robot, whole lung quantitative analysis technology, medical robot, etc. In the future, AI will continue to play an important role not only in virus type identification, abnormal pathology detection, optimization of treatment methods, prediction of the flow of people, etc., but also in emergency services such as plugging loopholes, making up for shortcomings, strengthening weaknesses. It is suggested to integrate AI into the construction of the emergency system, strengthen the institutional guarantee and make a top-level design, build a coordination platform for AI emergency response, promote the research of key technologies in the field of emergency services and solve the problem of cross-platform coupling in order to build an efficient, scientific and intelligent emergency management system as soon as possible.
A Markov Chain Model of Air Quality Index: Modelling and Simulation
Authors: Gao Yannan
Published: July 2020

This paper discusses a Markov chain model of discrete-time and discrete state and applies this model to detect the stochastic performance of AQI time series. First, it covers the set-up of the model, makes useful definitions like Markov property, transition matrix, steady-state distribution vector and present the maximum likelihood estimation method. Then an empirical research is performed using true data of some place with transition matrix estimated. At last, simulated data are generated to be a comparison of true data.
Modeling of Lighting Load in Office and Living Area Based on Multi-region
Authors: Jin Feng , Kang Li , Hu Biwu
Published: July 2020

Driven by the third round of industrial revolution, the Power supply-distribution network-load connection has become more and more tight, and the development of microgrid has become an inevitable trend. It can supplement the shortage of large power grids in response to fragmentation load. The load characteristics under the power grid also play an extremely important role in the distributed system. Studying the load in the microgrid not only has a positive effect on the construction and planning of the microgrid, but also has a guiding role on the economic dispatch of the microgrid. The paper investigates the lighting situation in the office and living area, and divides the office and living area into multiple areas for detailed modeling. Firstly, the working time, building condition and lighting equipment in different areas were studied. Secondly, the probability model of the lighting load was established. Finally, Monte Carlo was used to establish the multi-area office and living park lighting load model.
A Five-Dimensional Grid Multi-wing Butterfly Chaotic System and Its Circuit Simulation
Authors: Huang Yun
Published: July 2020

Based on the two-wing butterfly chaotic system, through adding two first-order differential equations with the piecewise linear function, a five-dimensional grid multi-wing butterfly chaotic system is presented. Through numerical simulation, it is studied for the gird multi-wing butterfly chaotic attractors, the Lyapunov exponent spectrum and the bifurcation diagram of the system. At the end, an electronic circuit is designed to carry out the system. The circuit experimental results are consistent with the results of numerical simulation, which confirm the availability and feasibility of this approach.
Explore on Application of Two-Dimensional Hydraulics Model in Flood Impact Assessment of the New Century Bridge
Authors: Liu Yanhong , Jin Zhiwei , Sun Xiaowei
Published: July 2020

The New Century Bridge will be built across Xiliao River. The construction of the bridge increases the complexity of water flow condition of Xiliao River. By establishing the mathematical model and adopting the form of finite element mesh dissection, the distribution of the maximum depth of flood water, the maximum flow velocity and the highest water level for flood in front of the dam and bridge are simulated before and after the bridge built to judge flow pattern effectively. By establishing a numerical model of flow pattern, which plays a decisive role for bridge abutment protective, depth of the pile foundation and pile caps, and has the greatest impact for next structure optimization of the bridge.
Research on the Evolutionary Path of COVID-19
Authors: Dong Yunyuan , Guo Wei , Ren Qiong
Published: July 2020

World Health Organization reported that a novel coronavirus (COVID-19) was identified as the causative virus of pneumonia of unknown etiology first reported by Chinese authorities on 7 January, 2020. Our objectives were to discover the evolutionary path and evolutionary characteristics of COVID-19. As time goes on, the evolution of COVID-19 has shown a variety of trends. In this paper, we proposed a method of constructing the evolutionary network of COVID-19 in chronological order, and analysed the evolutionary tree and haplotype of COVID-19.
Revisiting Cloud Migration: Strategies and Methods
Authors: Wang Zhixiao , Yan Wenyao , Wang Wendong
Published: July 2020

Cloud computing attracts users with its advantage of unlimited resource supply where resources can be elastically expanded on demand and balanced-load at the same time. This means that the application in the cloud environment should run in the way of elastic expansion cluster. At present, most of the elastic expansion and load balancing technologies provided by IaaS level are oriented to virtual machines (VM), with little consideration of application level, which does not fundamentally meet the needs of users. Based on this, starting from the motivation of cloud migration, we first analyse the cloud migration technology from three aspects: migration object, migration means and migration objectives, then explore the cloud migration criteria and four cloud migration strategies at the application level in the cloud environment, and point out that cloud migration should be optimized and maintained in terms of elastic expansion, load balancing, security, etc.
The Thermal Error Modeling with Deep Transfer Learning
Authors: Li Peiwen , Lou Ping , Yan Junwei , Liu Nianyun
Published: July 2020

Thermal error of CNC machine tools is one of the main factors affecting the machining accuracy. The data-driven method for thermal error modeling is an effective and efficient, but they have some flaws, such as poor accuracy, bad robustness, and etc. because of having no quite enough data set and imbalanced data set. In this paper, a new method based on transfer learning for thermal error modeling is presented for solving the issue of imbalanced data set. The dataset of monitoring the temperature field of the machine tools includes monitoring data of three kinds of operating conditions, namely stopping, idling, and machining. When the fewer idling data is used to train a model, the larger stopping data are introduced as train aids. Transfer learning is adopted to fully learn the common characteristics of the two different working conditions, which can effectively solve the problem of imbalanced dataset. The experimental results prove that our method have better performance than other methods trained only with limited idling data.
Research on Methods for Complex Chinese Entity Recognition
Authors: Mengxing Ren , Lei Ma , Ye Tian , Hongchen Qin
Published: July 2020

Named entity recognition(NER), as a key task of natural language processing, is of great significance in relational capture, information retrieval, knowledge mapping, machine translation, and question and answer systems. In the neural network-based Chinese named entity recognition method, the vectorized word representation only maps words into one vector, which cannot represent the above problem, In order to solve these problems, this paper proposes a Cascading model based on the character vectorization, This method uses BERT to represent the ambiguity of the word. In the agile BIGRU model, the initial entity recall is completed to increase the entity recall rate. Then the output of this layer is input into the high-level bilstm model of self attention mechanism to further filter and improve the accuracy of the final recognition. Finally, the CRF is used to complete the output of the final recognition result. Experiments show that the model can effectively improve the accuracy, accuracy and recall rate of complex Chinese entity recognition. The f1 value has increased by about 2% overall.
Improved MSER Pedestrian Detection Algorithm based on TOF Camera
Authors: Wang ZiJin , Zhao YiQin , Zhao CuiLian
Published: July 2020

To solve the problems existing in the traditional pedestrian detection methods, such as the complexity of pedestrians wearing clothes and the change of light leading to the decrease of detection accuracy, this paper proposes an MSER pedestrian detection algorithm combined with D-NP (Double Nine-Grid). The algorithm utilizes the characteristics of high pixels of TOF (Time Of Flight) cameras and the adaptability of MSER (Maximally Stable Extreme Regions). Firstly, the depth image is subjected to hole patching, and median filtering and morphological operations are used for preprocessing. Then the MSER algorithm combined with D-NP is used to segment the image to extract candidate head regions. Finally, We perform experiments on our dataset, and the detection recognition rate of this algorithm reaches 97.26%, which can effectively reduce the impact of light, and has a good detection performance for pedestrians carrying backpacks, hats and other complex scenes.
Research on Pipeline Video Defect Detection Based on Improved Convolution Neural Network
Authors: Huang Qing Hao , Li Bao An , Lv Xue Qiang , Jin Zhang Zi , Hui Liu Ke
Published: July 2020

At present, the existing drainage pipeline defect detection methods cannot meet the use standards. This paper proposes an image classification method based on improved convolution neural network. By adopting multi-scale convolution kernel and splitting convolution kernel, pipeline image features can be fully extracted and accurate image classification can be realized. The data set used in this method is 6 kinds of pipeline defects collected under real scenes, including residual wall, deposition, root invasion, foreign body penetration, obstacles and hidden connection of branch pipes. Through a large number of comparative experiments, the accuracy rate of the method proposed in this paper is as high as 90.2%, which can effectively solve the complicated problem of pipeline defect classification. The method proposed in this paper has greatly improved its accuracy and efficiency, which has laid a solid foundation for efficient and accurate detection of pipeline defects.
Research on Buckling Load Prediction of Composite Stiffened Plates Based on BP Neural Network
Authors: Zhao Chengcheng , Yin Junqing , Chen Yongdang , Gu Jinyu , Du Haotian , Cheng Yunfei
Published: July 2020

In view of the complexity of the composite stiffened plate structure, a prediction model between the properties and geometric parameters of the composite stiffened material and the buckling load is established based on the BP neural network. Firstly, the finite element model of composite stiffened plates was established by using Abaqus software, and the buckling load under axial load was studied. Secondly, 500 experimental samples were drawn using the Latin hypercube experiment method, and the corresponding values were obtained based on the software Buckling load value. Finally, 450 experimental samples are selected as the training set and the remaining 50 samples are used as the test set to establish a BP neural network prediction model. The results show that the method of using BP neural network to predict buckling load is effective and correct.
Research on the Practical Classification and Privacy Protection of CT Images of Parotid Tumors based on ResNet50 Model
Authors: Yuan Jiantin , Fan Yangyang , Lv Xiaoyi , Chen Chen , Li Debao , Hong Yue , Wang Yan
Published: July 2020

Parotid gland disease is one of the main causes of facial paralysis, and parotid gland tumor is a great threat to the life of patients. The main diagnostic way of parotid diseases is imaging examination, so it is of great significance for the rapid classification of parotid image. In conclusion, 51 CT images of parotid malignant tumors and 101 CT images of parotid pleomorphic adenomas are selected as the research data set, and an intelligent and efficient machine learning algorithm is proposed for the practical classification of parotid images. At the same time, this paper also explores the privacy protection of medical images. Based on the advantages of deep learning, such as no feature engineering, strong adaptability and easy conversion, ResNet50 model in deep learning is selected as the basic network framework to achieve the purpose of rapid classification of parotid CT images. This is the first time that ResNet50 classification algorithm is applied to the practical classification of parotid tumor CT images. The results show that the accuracy of the test set converges to 90% when the model is iterated 1000 times, which also proves that this study has certain practical significance and application value for the auxiliary diagnosis of parotid gland tumor and other head and neck tumors. Simultaneously, this paper also explores the application of desensitization strategy in CT images of parotid tumors, which improves the performance of the model and also greatly protects the privacy of patients, and has a good application prospect in medical big data.