bluuebunny/crossref_metadata_2025 huggingface dataset:
Coupled harmonic oscillator models for correlated plasmons in one-dimensional and quasi-one-dimensional systems
Authors: Khandelwal Aarushi , Mohammad Tashrif Shazed , Rusydi Andrivo
Published: August 2021

A new phenomenon of correlated plasmons was first observed in the insulating phase of the Sr1−x
Nb1−y
O3+δ
family (Asamara et al 2017 Nat. Commun.
8 15271). The correlated plasmons are tunable, have multiple plasmonic frequencies, and exhibit low loss—making them desirable in numerous plasmonic applications. However, their fundamental mechanism is yet to be explored. While conventional plasmons can be understood solely by considering long-range interactions, unconventional correlated plasmons arise in correlated electron systems and require consideration of the short-range interactions. Here, we report how the interplay of short-range and long-range interactions determines the correlated plasmon phenomena through a coupled harmonic oscillator model of both 1D and quasi-1D systems. In each system, the impact of various physical parameters like the number of oscillators, energy scale, free electron scattering parameter, quasi-particle concentration, charges, effective masses, and Coulomb interaction strengths are explored to gain an understanding of their impact on the complex dielectric function and loss function. We study both cases where the parameters are the same for all quasi-particles and where effective mass, Coulomb interaction strength, and charge are varied for individual quasi-particles. In an extended model of the quasi-1D system, we study both cases where the rung symmetry of all parameters is conserved and where it is broken. When rung symmetry is conserved, the overall trends in optical and plasmonic peaks are the same as the 1D model, though the peaks tend to shift to higher energies and amplitudes. When rung symmetry is broken, the quasi-1D behavior deviates significantly from the 1D model, including an increase in the maximum possible number of optical and plasmonic peaks. Overall, our results demonstrate the significance of the interplay of short-range and long-range interactions in determining the correlated plasmons and identifying how various parameters can be used to tune the resulting plasmons.
A reliable technique to fabricate superconducting joints between single grain, Y–Ba–Cu–O bulk superconductors
Authors: Congreve J V J , Dennis A R , Shi Y , Bumby C W , Druiff H , Cardwell D A , Durrell J H
Published: August 2021

The production of large (RE)Ba–Cu–O single grains ((RE)BCO), where RE = Y, Gd or Sm, of complex geometries is presently limited by the intrinsic slowness of the grain growth process. Additionally, the shapes achievable using established melt processing are constrained by the small number of post-processing techniques available. These materials are brittle and hard, which makes machining a difficult task and largely eliminates the possibility of producing highly intricate shapes. An alternative to this slow and inflexible growth process would be to join many small single grains to form one large composite grain, connected by high-performance superconducting joints. A reliable joining technique would also overcome the need for the careful and time-consuming post-growth machining processes. In this work we report on the use of single grain YBCO–Ag as an interface medium to achieve superconducting joints between (RE)BCO bulks. This joining technique is relatively quick and does not require tight process parameter control as there is no need to re-grow the interface joining material. We report on six joints produced from samples cut and joined in a variety of orientations. In addition, a joint was produced using bulk YBCO from two independent single grains. The trapped field properties of the resulting joined sample were measured and the microstructure at the joint was examined. We show that this simple but effective joining technique makes it possible to produce multiple composite grains with comparable superconducting properties to those of a single grain of the same size.
Probing inflation with precision bispectra
Authors: Clarke Philip , Shellard E.P.S.
Published: August 2021

Calculating the primordial bispectrum predicted by a model
of inflation and comparing it to what we see in the sky is very
computationally intensive, necessitating layers of approximations
and limiting the models which can be constrained. Exploiting the
inherent separability of the tree level in-in formalism using
expansions in separable basis functions provides a means by which to
obviate some of these difficulties. Here, we develop this approach
further into a practical and efficient numerical methodology which
can be applied to a much wider and more complicated range of
bispectrum phenomenology, making an important step forward towards
observational pipelines which can directly confront specific models
of inflation. We describe a simple augmented Legendre polynomial
basis and its advantages, then test the method on single-field
inflation models with non-trivial phenomenology, showing that our
calculation of these coefficients is fast and accurate to high
orders.
Quantitative evaluation of trunk function and the StartReact effect during reaching in patients with cervical and thoracic spinal cord injury
Authors: Castillo-Escario Yolanda , Kumru Hatice , Valls-Solé Josep , García-Alen Loreto , Jané Raimon , Vidal Joan
Published: August 2021

Objective. Impaired trunk stability is frequent in spinal cord injury (SCI), but there is a lack of quantitative measures for assessing trunk function. Our objectives were to: (a) evaluate trunk muscle activity and movement patterns during a reaching task in SCI patients, (b) compare the impact of cervical (cSCI) and thoracic (tSCI) injuries in trunk function, and (c) investigate the effects of a startling acoustic stimulus (SAS) in these patients. Approach. Electromyographic (EMG) and smartphone accelerometer data were recorded from 15 cSCI patients, nine tSCI patients, and 24 healthy controls, during a reaching task requiring trunk tilting. We calculated the response time (RespT) until pressing a target button, EMG onset latencies and amplitudes, and trunk tilt, lateral deviation, and other movement features from accelerometry. Statistical analysis was applied to analyze the effects of group (cSCI, tSCI, control) and condition (SAS, non-SAS) in each outcome measure. Main results. SCI patients, especially those with cSCI, presented significantly longer RespT and EMG onset latencies than controls. Moreover, in SCI patients, forward trunk tilt was accompanied by significant lateral deviation. RespT and EMG latencies were remarkably shortened by the SAS (the so-called StartReact effect) in tSCI patients and controls, but not in cSCI patients, who also showed higher variability. Significance. The combination of EMG and smartphone accelerometer data can provide quantitative measures for the assessment of trunk function in SCI. Our results show deficits in postural control and compensatory strategies employed by SCI patients, including delayed responses and higher lateral deviations, possibly to improve sitting balance. This is the first study investigating the StartReact responses in trunk muscles in SCI patients and shows that the SAS significantly accelerates RespT in tSCI, but not in cSCI, suggesting an increased cortical control exerted by these patients.
Research on Electrical Control System of CNC Machine Tool Based on Vector Control
Authors: Zhang Wei , Zhao Qingsong , Tuo Jianzhi
Published: August 2021

With the rapid development of science and technology, the society demands more and more diversification of products, which requires the cycle of product renewal to be shorter and shorter, which makes the proportion of multi-variety and small batch production increase obviously. CNC (Computer Numerical Control) machine tool is a very popular production machine in China’s industrial production, which is mainly composed of hardware circuit, mechanical part and lower computer software. CNC machine tools play an important role in solving the problem of precision or complex parts, and the data of CNC machine tools in mechatronics system are typical. Numerical control technology is related to a country’s position in international industry, so accelerating the development of advanced numerical control technology and strengthening the construction of numerical control equipment are important methods to improve a country’s position in international industry. Based on vector control technology, this paper discusses the design of electrical control system of CNC machine tools, and studies the methods to improve the safety, reliability, machining accuracy and productivity of CNC machine tools, in order to provide reference for the further application of vector control in CNC machine tools.
Regression analysis and prediction using LSTM model and machine learning methods
Authors: Tan Fangbi
Published: August 2021

In this paper, the LSTM model in deep learning is applied to regression analysis, and the LSTM model is used to solve the problems of nonlinearity and data interdependence in regression analysis, so as to improve the traditional regression analysis model. Through the actual modeling application experiment, on the one hand, the prediction accuracy of different model parameters is compared and analyzed, on the other hand, the effectiveness and practicability of LSTM model in multiple regression analysis and prediction are confirmed.
Overview of insulator state intelligent assessment in smart grid through fuzzy logic method
Authors: Luo Simin , Luan Le , Cui Yiping , Xu Shuo , Guo Quinten , Liu Tian
Published: August 2021

In the transmission system, the operating state of the line insulator is always a state quantity with both fuzziness and randomness. Fuzzy logic theory has certain advantages for evaluating such a system with both fuzziness and randomness. According to the theory of fuzzy logic in the field of insulator state evaluation development situation, this paper introduces four methods - weights fuzzy logic method, the multistage fuzzy logic judgment, variable weight fuzzy logic judgment and cloud theory. The paper also analyses the characteristics and advantages of four methods, the results show that the cloud theory and variable weight fuzzy logic method more engineering significance.
Generalized Control Algorithm and Artificial Intelligence Technology for Quadruped Walking Robot
Authors: Fei Guobiao , Liu Fenggang
Published: August 2021

Quadruped walking robot has superior adaptability and high adaptive mobility in various environments. These special advantages are particularly prominent in the mobile robot community. In this work, we introduce the omnidirectional gait and the rotational gait approach, which is a generalized control algorithm for executing any directional instruction. In addition, in order to improve the stability of the quadruped walking robot, we optimize the walking Angle and foot order. The proposed ideas were applied to the practical design of MRWallspect III, which was designed to examine large surfaces in industrial facilities. The effectiveness of the proposed method is verified by experiments on a robot.
Design and Implementation of Data Acquisition and Analysis System for Intelligent Meter in Smart Grid
Authors: Chen Jintao , Jiang Jianfeng , Shen Juan , Huang Liang , Sheng Qing , Zhou Qiang , Han Dongjun
Published: August 2021

Design a smart meter data acquisition and analysis system to collect and process electrical energy data, report remote data and collect electrical energy. The data collector of the smart meter supports multiple protocols and the collinear communication of the electric meters, and the collinear communication of the electric meters with different protocols, different models and different communication speeds is connected on the same communication line. The data is sent to the data collector through the RS-485 bus, and the data collector collects and processes the data and uploads it to the server through the WAN. The design of the smart meter data acquisition and analysis system effectively improves the scientific validity of electric energy billing.
RETRACTED: Analysis of the Impact of the Internet on Farmers’ Intelligent Selection under the Era of Big Data and Cloud Computing
Authors: Lu Youlan
Published: August 2021

With the advent of the era of big data, many industries have become deeply integrated with Internet technologies. Farmers using the Internet for entrepreneurship can promote the reform of agricultural development, optimize the entrepreneurial mode of farmers, drive farmers to increase their income, and inject new strength into the construction of the new socialist countryside. By reviewing relevant literature and data, this paper reviews the development trend and opportunities of farmers’ Internet entrepreneurship in the era of big data, analyzes the problems in the process of entrepreneurship and the corresponding solution strategies, in order to provide some reference value for the development of farmers’ Internet entrepreneurship.
Analysis and Construction of EEMD Smart Model and Fuzzy Forecasting through Improved Bayesian Estimation
Authors: Tang Liyao , Cui Zijian , Liu Kaishuo
Published: August 2021

Different portfolio models will have a great impact on the model rate of return. In order to meet the data needs of portfolio investment forecasting, a big data generation method based on incremental Bayesian network model is proposed. Firstly, the fuzzy interval number is constructed by using the global GM (1pare 1) prediction model, then the fuzzy Mmurv model is established based on the interval fuzzy number, and the model is optimized based on the midpoint, radius and acceptability of the interval number, thus a single objective programming model with parameters is obtained. The future data is partially generated by using the time series generation algorithm, and the Bayesian network model trained by historical data is updated with the newly generated data. So that the updated Bayesian network can reflect the relationship between the variables and the laws contained in the new and old financial data in this period of time. And use the model to predict the amount of foreign direct investment absorbed by China in 2020, test the robustness and analyze the impact of major random events to show the reliability of the prediction. The analysis is carried out on multiple scales. The results show that the integration of IMF1 and IMF2 components through the white noise test can be regarded as the random impact of external factors; the cycle length of IMF3 component is about 3 years, which can be regarded as the inventory cycle caused by the change of enterprise inventory; the cycle length of IMF4 component is 5 years, which can be regarded as political cycle; the cycle length of IMF5 component is about 20 years, which can be regarded as construction cycle. The set of portfolio paths is generated by path search algorithm in Bayesian network, and the big data set with real data characteristics is generated according to the probability distribution of each path. The experimental results show that the method is feasible and ensures a certain accuracy. The forecast results show that the amount of foreign direct investment in China will continue to fluctuate seasonally and in a trend in recent years.
Research on the Assessment of Qingdao Residents through Artificial Intelligence Algorithms and Grey Prediction Model
Authors: Shi Zhenqi , Lv Luyu
Published: August 2021

The model of health level system was constructed based on medical treatment, environment, policy, life and culture in Qingdao, the weight of each index was determined based on entropy method, and the impact of each index on residents’ health level was analyzed. Then the comprehensive score of health level was calculated according to the weight of each index. Finally, according to the grey prediction model, the comprehensive score is fitted and predicted, and relevant suggestions are provided according to the fitting and prediction conclusions.
Research and Application of Computer Artificial Intelligence Technology in Machine Pronunciation
Authors: Zhang Yisha
Published: August 2021

The traditional oral English training system uses a layered training strategy, which cannot meet the system performance design requirements. For this reason, the thesis designs a computer artificial intelligence system for oral English training based on human-computer interaction. The system hardware structure is composed of C8051F021 single chip microcomputer, LM317 chip, LM337 chip, A/D chip and IN5822 diode, which is used to reliably control the sampling frequency of the chip. At the same time, the paper introduces Sugeno points to realize the correct evaluation of spoken pronunciation. First, the randomness of natural language pronunciation and the instability of the speech processing system are modeled by fuzzy measure and credibility, and then they are integrated into the Sugeno integration framework to evaluate the language score instead of the specific score. The experimental results prove the credibility and stability of the evaluation model in closed and open tests.
The Application of Cloud Computing and Big Data Correlation Analysis to the Intelligent Evaluation Modeling
Authors: Xiao Yunxuan , Yang Leize
Published: August 2021

This passage gave an analyse big data collected from UNESCO and report from governments then give health index to countries and focus on Brazil to give some possible solutions to the problem. First, we aim to develop EHSIS model to assess the health of any national higher education system. We define the weight of the indexes by subjective weighting and objective weighting by using Analytic Hierarchy Process and Principal Component Analysis (AHP) to compare the importance of different weight and Entropy Method to get the weight from comparing the degree of dispersion of indicators. Then combine the weight and the normalized data to get the model to give scores to sample countries relying on the eight indexes. Then, we screened out several indicators with greater influence through Principal Component Analysis and correlation analysis of factors and extracted the recommended adjustable indicators to 3. We found that the proportion of foreign students has the greatest impact on the health and sustainability of higher education in the country, and it has a high correlation with QS excellent schools and UN News excellent schools, which are difficult to control. Other indicators that have a greater impact include the teacher-student ratio, employment rate, etc. Last, we forecast the changes of indexes without the policy by Grey prediction and use the memory-less of Markov Chain to predict the change after introducing new policies. In order to evaluate the effectiveness of our policy, we utilize EHSIS model to give the score and compare the score with and without the policy and find a significant increase in score. What’s more, we discuss the real-world influence and the hardness of achieving the goal.
Intelligent Promotion Mechanism of the Sustainable Aquaculture from the Perspective of Game Theory
Authors: Chang Zhao
Published: August 2021

China has always attached great attention to aquaculture development, but some inappropriate aquaculture modes have brought a series of marine resources waste and environment problems. In order to solve these problems more effectively and lead to a sustainable aquaculture mode, based on stakeholder analysis, this paper constructs evolutionary game models on both supply side and demand side of aquaculture, and puts forward some suggestions for sustainable aquaculture development. The results indicate that in initial stage of promoting sustainable aquaculture, mandatory control measures and economic subsidies of the government are needed to the promotion. After gradually forming a mature management mode, the market should play a decisive role in resource allocation, so as to realize the sustainable development of aquaculture with the tripartite win-win interests in the game.
Development and Research of Intelligent Testing Information System for Full Performance Test of Metrology Equipment
Authors: Ma Xiaofang , Ding Haili , Zhou Yuanfeng , Liu Pengyuan , Hu Tingting , Yan Shaokui
Published: August 2021

Aiming at the measurement laboratory with many types of testing equipment, many test items, low automation of instruments and equipment, inconsistent data interface standards, ineffective coordination of testing services, low level of laboratory testing resources and data sharing, and difficulty in achieving effective data and resources connection, etc. Problem, design and develop an intelligent detection system for full performance test of measurement equipment. The research and development of the full-performance test intelligent detection system replaces manual operation with standardized automatic operation, which improves the consistency, standardization and reliability of the full-performance test of the measurement equipment, and realizes the reproducibility and traceability of the test. The automatic collection of full performance test data has been realized, laying a solid foundation for the construction of the national industrial basic database.
Research on breach prediction for big data through hybrid ensemble learning and logistic regression
Authors: Zhang Xiaohan , Chen Xu
Published: August 2021

The fast-growing online lending sector faces the risk of borrowers defaulting. Network lending data are often unbalanced. On the basis of EasyEnsemble algorithm, we proposed a hybrid ensemble algorithm, which combines the advantages of Bagging and Boosting. By comparing the performance of different learning models, the experimental results show that the hybrid ensemble algorithm has good classification accuracy and generalization ability. Further, we find that social capital has a negative effect on borrower default rate. This paper provides a relatively reliable method for the credit risk assessment of online borrowers, and also provides a new idea for risk monitoring in the field of online lending.
Application of XML and TCP communication on Dynamic customization of the smart home system
Authors: Li Zhihao , Sun Jie , Danzengouzhu  , Lan Hai , Lu Wenbo
Published: August 2021

Aiming at the problem of personalized customization of a smart home system, a scheme of personalized customization of the smart home system based on parsing XML authorization files is proposed. First, develop an XML authorization file generator, and use it to generate personalized XML authorization files; secondly, use the developed app to parse the XML authorization file to realize the legal authorization of the app and the dynamic generation of the home control interface; finally, click buttons on the control interface to trigger the TCP communication between the app and ESP8266, and then with the help of esp8266 to realize the switch of electrical appliances. Experimental results show that dynamic customization of the smart home system based on XML can implement dynamic customization of control interfaces for various home layouts, and can effectively control the home furnishings.
Group Intelligence Perception Applied in the Electromechanical Equipment Fault Intelligent Diagnosis Support System
Authors: Zhou GenRu
Published: August 2021

The paper designs a fault diagnosis and monitoring system for electromechanical equipment based on group intelligence perception technology. The system uses the Internet of Things technology to establish a fault feature selection and fault diagnosis model for electromechanical equipment. At the same time, the system uses micro-electromechanical system sensors to realize real-time monitoring of electromechanical equipment, and realizes the collection and upload of status data and fault data of base electromechanical equipment. The design of this system allows all parties involved in electromechanical equipment such as users, manufacturers, maintainers, and industry experts to break through the constraints of time and space. All relevant parties can communicate in real time on the problems of the equipment, so that the electromechanical equipment can be more efficiently automated and intelligently maintained.
Single Image Dehazing Method Based on Semi-Training Color Stripping
Authors: Wang Ye , Ji Dahai
Published: August 2021

In this paper, a semi training color stripping dehaze-net (STCSDN) is proposed which is a new method of dehazing with strong adaptability. It mainly depends on two important properties of convolution neural network in the process of dehazing. One is that the learning speed of convolutional neural network for contour and shadow information is faster than that for color information. The other is that network is not sensitive to haze concentration. Based on property one, STCSDN extracts the haze free gray image from the hazy color image by using the semi trained generator as the sketch extraction module through the CycleGAN. The gray image only contains the image contour and shadow information, and discards the original color information interfered by haze information. This method has strong adaptability, visibility and authenticity, and can be applied to any scene. Based on property two, STCSDN can process hazy images with different concentrations and get better results. Through the simulation experiments on different types of dehazing data sets, it is proved that the STCSDN proposed in this paper can remove the influence of haze, restore the image details and enhance the visual effect.
Design of On-line Simulation System for Signal Control of Urban Intersections Based on Visual Sensing Technology
Authors: Wang Juanjuan , Wang Yanan , Zhou Hongfang
Published: August 2021

Intersection area is the area with the most serious conflicts and accidents in the whole urban traffic system. Therefore, it is of great significance to combine modern computer technology, sensing technology and communication technology to intelligently control intersections, improve the efficiency of traffic signal control and reduce the delay time in conflict areas. Starting with video detection and using deep learning in the detection module, a video detection mechanism based on restricted Boltzmann machine network is proposed. Based on C # programming language, the intersection traffic signal simulation module is designed through COM interface provided by traffic simulation software VISSIM, which realizes road network import, parameter setting, signal timing change, simulation operation and simulation result output. Numerical experiments show that fuzzy control method with self-correcting factor is superior to timing control method and classical fuzzy control method in average delay time of vehicles.
Solving TSP based on an Improved Ant Colony Optimization Algorithm
Authors: Zhang Hao , Gao Yunfeng
Published: August 2021

Traveling Salesman Problem (TSP) is a typical Problem in combinatorial optimization field in modern times. Most of the problems in reality can be transformed into TSP problems for solving. Such as postal problems, communication network design, etc. Ant colony algorithm, as a heuristic algorithm, has been successfully applied to solving TSP problems. Based on the improved ant colony algorithm, this paper solves the travel agent problem, evaluates the population according to the membership degree, and updates the pheromone in turn, so as to achieve a good balance in solving speed and quality.
Design scheme of ship risk prediction model from the perspective of artificial intelligence
Authors: Ruan Qunsheng , Luo Fang , Li Zhiliang , Shi Manying , Zhang Zhiling
Published: August 2021

Vessels sailing at sea are full of risks, accidents occur frequently, and it is easy to cause heavy casualties and property losses. To this end, this article focuses on how to build a high-precision and rapid ship risk prediction model, and proposes a design plan for a ship risk prediction model based on big data and artificial intelligence. The plan includes a basic data layer, a technical business layer, and an application layer, with a focus on The technical research scheme of the graph representation model construction of the ship risk big data, the dynamic graph matching algorithm and the ship risk prediction is elaborated in detail, and finally the experimental design method of the scheme is given. The design scheme proposed in this paper can provide reference and inspiration for the research and development of marine risk system.
Research on Fatigue Detection Method of Equipment Operators Based on Multi-Source Physiological Signals
Authors: Guo Wen , Di Changan , Long Li
Published: August 2021

In order to effectively detect the fatigue status of equipment operators, this paper proposes a fatigue detection model that integrates multi-source physiological signals to evaluate the fatigue level of equipment operators in order to improve the efficiency of military operations and training. This method selects various physiological signals such as human heart rate, heart rate variability, body temperature, respiratory rate, blood oxygen concentration, etc. according to the working environment and working characteristics of the equipment operator, and establishes the relationship between the characteristic values of various physiological parameters and fatigue levels, And then use the optimized support vector machine to perform fusion judgment on the feature values of multi-source physiological parameters, and obtain the fatigue state of the equipment operator. Experimental results show that this method can make a more accurate judgment on the fatigue state of equipment operators, and is of great significance for ensuring the efficiency of equipment operators’ work and training.
A fusion algorithm model based on KNN-SVM to classify and recognize spam
Authors: Ge Zengle
Published: August 2021

Spam usually has the characteristics of large data dimension and large numbers of samples. At present, the common classification learning algorithms have their own advantages and disadvantages. Aiming at the classification tendency of K nearest neighbor algorithm (KNN) in the face of unbalanced sample data and the long time consuming of support vector machine (SVM) training model, a fusion algorithm of KNN and SVM is proposed. Firstly, the content of spam is extracted, and then the text preprocessing is transformed into sparse matrix. The k nearest neighbor samples of the test sample set are screened out by KNN algorithm, and then the SVM algorithm is used to train these screened samples, establish the separation hyperplane, identify the mail, and judge whether it is spam or not. The results show that the KNN-SVM fusion algorithm can effectively reduce the impact of sample imbalance while maintaining the high classification accuracy of SVM algorithm, and its classification efficiency is slightly lower than that of KNN, algorithm but much higher than that of SVM algorithm.
Summary of Metal Fracture Image Recognition Method
Authors: Lu Yufei , Wang Lin , Pan Dongyu , Chen Xiaoxia
Published: August 2021

Fracture is the metal component in the test or use of the fracture surface after the formation of a matching section. In fracture analysis, the macroscopic and microscopic morphology of fracture provides the most direct information for failure analysis and the most direct evidence for fracture analysis. How to effectively process a series of feature information contained in fracture image and make reasonable use of these feature information is of great significance in engineering practice. At present, metal fatigue failure detection in industrial production mainly relies on human eyes, experience and simple auxiliary tools. However, it is difficult to guarantee the accuracy of detection because of individual differences. To solve this problem, this paper lists three metal fracture image recognition methods, which are Grouplet-RVM recognition method, the empirical Ridgelet-2DPCA method and the empirical Ridgelet-KPCA method. All the three methods aim to extract higher recognition effect so as to compare and optimize the recognition of metal fracture images. The advantages and disadvantages of these methods are discussed in detail in this article for ease of use.
A reversible information hiding algorithm in AMBTC domain based on human vision system
Authors: Zhang Tao , Ding Jiantao , Tian Zhewen
Published: August 2021

Compression is a common processing method of digital image, and the compression of carrier image usually affects the performance of hidden information. Therefore, it is necessary to research on the preprocessing method to improve some performance of hidden information in the compressed domain. Aiming at the problem of tampering attack on compressed domain image and the security of secret information preprocessing in the previous information hiding algorithms, we propose a reversible information hiding algorithm in AMBTC domain based on human vision system. Firstly, RGB color image is converted to YCbCr image, AMBTC coding is used to generate high and low average value sequence in brightness component. Secondly, the secret information is compressed by run length coding and scrambled. Thirdly, the histogram shift technique is used to embed the information into the mean sequence and generate the reserved information at the same time. Fourth, the color component is divided into normal block and absolute smooth fast, the specific position of the bit plane of the normal block is deleted, and the reserved information is embedded. The experimental results show that, compared with other algorithms, the proposed algorithm has better invisibility and robustness, and after a certain attack, the extracted secret information still has high recognizability.
Greenhouse Intelligent Monitoring System Based on MCU
Authors: Zhu Yingying , Fang Yueyun
Published: August 2021

Modern greenhouse cultivation of crops yield and quality is affected by temperature and humidity is very big, this paper designed a greenhouse intelligent monitoring system based on single chip microcomputer, STC89C52 as control core, using composite was carried out on the temperature and humidity sensor DHT11 real-time monitor and record, use buzzer and LED lights to alert users to temperature and humidity changes and respond in a timely manner.
Research on the method of underwater image enhancement
Authors: Li Tiansong , Yang Jian , Zhou Haiyan , Lian Tao
Published: August 2021

A multi-angle image enhancement method based on underwater imaging was proposed to solve the problems of color bias, geometric distortion and low contrast of the observed object in the complex and changeable underwater environment. Firstly, the gray world algorithm is used to preprocess the underwater image. Then an image optimization denoising algorithm based on K-SVD dictionary learning is used to keep the structure and texture information of the original image. Finally, combining with the underwater optical imaging model, the blue-green channel enhancement algorithm is used to realize the underwater imaging defog processing, which makes the restored image closer to the reality. The results show that this method can solve the problems of noise interference, fuzzy underwater image and low contrast.
RETRACTED: Research on Algorithms of University Course Multimedia Assistant System Based on Scientific Human-Computer Interaction
Authors: Luo Yang
Published: August 2021

With the rapid advancement of artificial intelligence theory, this paper adopts the human-computer interaction method and derives the mathematical XXX. In combination with the optimization of the university course content, the performance is improved through the use of multimedia auxiliary systems and the use of B/S network model experience Formula, it consists of a client layer, a Web service layer and a database service layer. The physical architecture of the system is deployed. Finally, the statistical comparative analysis of mathematical science proves that the multimedia auxiliary algorithm has significant effects, and the artificial intelligence technology plays an active role in the multimedia auxiliary system.
Predictive Classification Model Based on Vespa Comprehensive Detection System
Authors: Gong Ci , Deng Gefeng , Shan Haicheng , Deng Changxin
Published: August 2021

Aiming at the problem that it is difficult to distinguish the authenticity of a large number of eyewitness reports when Asian giant hornet invaded, a multi-scale comprehensive detection model of Asian giant hornet was designed. The model uses cellular automata to process geographical information, YOLOv3 and improved Resnet101 to process image information, TF-IDF and Linear SVC to process textual information, and fuses the three kinds of information with a certain weight to obtain the final detection probability of Asian giant hornet. Compared with a single detection algorithm, this model makes use of multi-scale and multi-class information, and has higher classification accuracy and better robustness.
Object detection based on self feature distillation
Authors: Wu Zhuguanyu , Hu Zixiao
Published: August 2021

For object detection, traditional feature distillation methods need to select features from the teacher model to supervise student model training. But what kind of teacher model to choose and what kind of feature transformation to do are two significant problems. In this work, we put forward a self-supervised target detection approach, a simple and effective teacher-free feature distillation method. Specifically, we utilize more semantic features in-depth to distill shallow features, and in the channel dimension, we utilize salient features to monitor the remaining features. We use L2 loss to achieve feature minimization, with complex feature transformation and loss definition, because there is a smaller semantic gap between features from the same model and features from the same model. We prove theoretically that such self feature distillation is equivalent to adding feature perturbation and self feature of different subnetworks. For the first time, Self Feature Distillation (SFD) extends feature distillation from teacher model avatar into a general feature regularization method. Our experiments show that SFD is superior to the current feature regularization method. At the same time, self achieves 1.5 times of the training acceleration of SOTA’s teacher-based distillation method because it does not need additional training parameters as the teacher model.
ACDNet with ASPP for Camouflaged Object Detection
Authors: Zhu Qihui
Published: August 2021

This paper proposes a camouflage identification network ACDNet, which can perform end-to-end search and identification simply and efficiently. Use ASPP (Atrous Spatial Pyramid Pooling) to enhance the image features of camouflaged object, and then connect the Parallel Decoder (PD) module to get the global map, and then use the Group-Reversal Block (GRB) module to connect sequentially, refine the details, and finally perform deep supervision learning got result. Compared with the current 13 methods, ACDNet has a significant performance improvement in accuracy and similarity, and it is one of the potential solutions for camouflage identification.
Research on the Theory and Application of Deep Interactive Learning
Authors: Wang Ziyuan , Guo Fan
Published: August 2021

Knowledge distillation (KD), in which a small network (students) is trained to mimic a larger one(teachers), with high precision, has been widely used in various fields. However, the interaction between teachers and students is still weak. It is found in this study that most existing methods, such as Deep Mutual Learning (DML), mainly construct loss function through soft weight indexes. Few researchers pay attention to the sharing of hard and heavy ones. As an improvement of DML, a new online learning distillation method, namely, Deep Interactive Learning (hereinafter DIL), was proposed in this research, which has deeper interaction than DML. We not only output the features of layers, but also disclose the features of hidden layers. We transfer the features to other models to obtain the corresponding softer distribution or features for distillation. Extensive experiments on various data sets show that the accuracy of our method is improved by almost 3% in CIFAR and 2% in ImageNet, which proves the validity of our method.
Brand Loyalty Measurement Model Based on Machine Learning Clustering Algorithm
Authors: Li Yueqiu , You Chunming
Published: August 2021

In the “new economy” represented by the Internet economy background, online consumption as the representative of the new forms of consumption is gradually changing people’s consumption idea and way, online brand loyalty has the extremely important status in the field of online consumption, to stimulate consumption and achieve accurate enterprise marketing, risk control and decision support, improve efficiency and product design business model, business forms, and even change of business thinking, improve enterprise competitiveness is of great significance in the field of online. It is urgent and necessary to apply scientific and effective machine learning method to systematically analyse and study online brand loyalty. In the big data environment, facing the massive data information provided by online consumption, traditional technology methods have gradually failed to meet the competitive needs of enterprises to create and maintain brand loyalty. The traditional random sampling method is difficult to locate the consumer groups with high brand loyalty. At the same time, the traditional data processing technology can not deal with the online consumption behavior with the characteristics of massive, mixed and unstructured data. Traditional approaches have limitations when it comes to the sheer volume of online data and how to use it in real time to target the needs of a brand’s consumer group. The purpose of this study is to build an online consumption of the era of big data model of artificial intelligence, machine learning model, by machine learning method, branded goods purchase behavior of consumers online clustering, building an online brand loyalty measurement model, achieve similar loyalty user clustering, at the same time realize the online measurement of brand loyalty. Among them, focus on machine learning path, machine learning algorithm, model construction methods, and model verification and optimization methods.
Analysis of executive transaction characteristics based on machine learning cross-validation
Authors: Li Yueqiu , You Chunming
Published: August 2021

Executive trading is a kind of investment strategy that analyses and models the financial market with mathematical methods. Machine learning requires computer programs to improve their performance at specific tasks by learning on data sets. Both require the extraction of information from data, so with the success of machine learning in recent years, there has been a strong interest in the combination of executive trading and machine learning methods in both industry and academia. In this paper, based on machine learning is complete, a more complete model of data processing - build - generated forecast - trading strategy - back-test analysis “research framework, and discusses the structures of branching and merging model structure set up, based on the value relevance of the output value and supervision model selection criteria, independently of the back and trading strategy model assessment method and a series of important issues. This paper focuses on the innovative exploration of the application of the loss function, and puts forward several loss functions based on the portfolio point of view, among which the negative cosine loss function shows significant advantages and strong practicability in stability and gain. Corresponding empirical research also reveals the application value and limitation of other loss functions.
Research on Feature Extraction Technology of Japanese Corpus Resources Based on Rule Matching
Authors: Zhang Lele
Published: August 2021

Existing methods of relation extraction can be divided into pattern matching-based methods, dictionary-driven methods and machine learning-based methods, among which machine learning-based methods are the mainstream methods of relation extraction at present. Based on the analysis of Snort’s new features and existing rule matching methods, this paper effectively combines Snort algorithm with Japanese language features, and corrects keyword deviation caused by word frequency dependence by introducing multiple eigenvalues such as position factor and part-of-speech factor. Experimental results show that the introduction of part-of-speech factor and position factor is simple and efficient, and can effectively improve the extraction effect of Japanese keywords, which is suitable for short article keywords.
Path Planning for Mobile Robot in 3D Environment Based on Ant Colony Algorithm
Authors: Wenrui Fan
Published: August 2021

This paper introduces the ant colony algorithm to the path planning of mobile robots, and explores the optimal path solution. The definition of path planning was studied firstly. Then modelling method of path planning was discussed. Also, current research status of three-dimensional path planning at home and abroad he existing problems of 3D path planning was analyzed. These basic theoretical knowledge have laid the foundation for the research work of this paper, and we then modelled the three-dimensional terrain environment, and used MATLAB software for simulation experiments to implement the ant colony algorithm to plan the robot’s three-dimensional terrain path, and finally performed fuzzy reinforcement learning method based on ant colony algorithm to find the optimal path for the mobile robot.
Digital Media Image Recognition Method Based on Improved Fuzzy C-means Clustering Analysis
Authors: Yang Yanyan , Wang Zhiping , Liu Kecheng , Zhu Huijun
Published: August 2021

Digital media plays a more and more important role in people’s life. In recent years, as an important part of digital media, image is widely used. It has become a hot spot in image information processing to determine the similarity between images by calculating the perceptual hash. This paper mainly studies the intelligent traceability of digital image based on improved fuzzy c-means clustering analysis. This method can improve the information truth degree identification of data image, which can assist the development of data image information detection and recognition system based on improved fuzzy c-means clustering analysis. Firstly, this paper analyzes the value of image recognition technology in streaming media environment. For the current streaming media image recognition technology, the main application areas and demand state are described. Then, from the perspective of technology, several main technologies in this field are discussed, and the digital image technology of improving fuzzy c-means clustering analysis is analyzed and constructed. Experimental results show that the method can improve the efficiency and accuracy of digital media image recognition. This method has a certain reference value to promote the development of digital image analysis technology.