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Aiming at covering the shortage of single source sensor imaging and improving the contrast between the target and the background in image, this paper proposes an adaptive weight fusion algorithm of infrared and visible image based on a High-frequency Domain Convolutional Neural Network (HDCNN). Firstly, the high and low frequency components of the original image are obtained by using the Daubechies wavelet transform, and then a high-frequency domain convolutional neural network which can detect the frequency information ratio of infrared and visible light in the high-frequency subband is trained. Secondly, the network is used to perform adaptive weight fusion for the high frequency components and regional energy is used for fusion of the low frequency components. Finally, the fusion image is obtained by inverse wavelet transform. A large number of experiments have proved that the algorithm in this paper has a greater improvement over similar comparison algorithms in objective evaluation metrics such as standard deviation, spatial frequency and average gradient. The algorithm enhances the contrast between the target and the background in the fusion image, and enriches the characteristic information of the target itself.

In the fingertip intelligent blood collection robot, we need to move the blood collection needle to the designated position quickly and accurately, but it is difficult to meet the two indicators of speed and accuracy at the same time. This paper proposes a fast navigation method based on binocular vision. This method obtains the coordinates of the fingertip blood sampling point through three-dimensional reconstruction and contour extraction, and guides the robotic arm to quickly move the blood sampling needle to a position near the blood sampling point according to the coordinate position. The method in this paper is used for the rough navigation of blood collection robots, and it is verified by experiments that this method can meet the needs of blood collection robots.

According to the relevant provisions of the "Election Law of the People's Republic of China", the engineering software required for the implementation of large-scale conference elections can set voting regulations for "differential elections", "equal elections", and "alternative elections". This paper proposes an automatic ballot recognition system based on image processing, which is suitable for all levels of people’s congresses, party and government meetings or other meetings. It has complete functions and stable recognition. It can automatically count and summarize the voting results and display the voting results on the big screen. Show on the display system. Firstly, the paper ballot is scanned by a scanner to obtain the image of the ballot. Secondly, the system will preprocess the image of the ballot and segment the image into regions.Thirdly, according to the handwritten character recognition algorithm based on image processing and Shen filter proposed in this paper, the handwritten characters "√" and "×" in the subregion are recognized.The final experimental results show that the recognition accuracy of the system is as high as 99.82%, which has an absolute advantage over the traditional manual voting, and can meet the performance requirements of high efficiency, accuracy, objectivity and security of large-scale conference election.

In this paper, the electric field intensity of the electrode structure of the thermally emitted electron gun of UTEM is simulated, and the relation between the electric field intensity distribution and the electrode plate is analyzed. Using the electronic optics software, the optical system of the electron gun is simulated, the performance parameters of the electron gun are obtained, and the advantages and disadvantages of the whole optical system are analyzed. The research results of this paper can provide a reference for the design and simulation of similar electron gun.

The LDA model is a total probability generation model for analyzing a large number of documents. It extends PLSA, another text analysis model. In this model, each document is treated as a topic hybrid model, and the topic’s proportional prior distribution is a Dirichlet distribution. The LDA model does not reflect complex dependencies between underlying topics. Based on LDA, this paper introduces a new topic generation model, DMA (Dirichlet Mixture Allocation), which models document collections more accurately than LDA when documents are obtained in multiple classes. In this paper, we build a probabilistic topic model of DMA, use the method of variational inference to approximate each parameter of the model, study the model, and finally solve the estimation of each parameter.

This paper constructs a deep learning method for detecting network traffic anomalies to enhance the secure transmission of data in networks due to the complex, diverse and numerous types of anomalous traffic in current networks. The method combines multiple convolutional auto-encoders (Multi-CAE) with a long short-term memory network. The convolutional auto-encoders are obtained by combining stacked auto-encoders with convolutional layers, which can not only reduce feature loss but also effectively extract the spatial structure of samples. The use of Multi-CAE greatly improves the feature extraction capability, and combined with the long short-term memory network to extract temporal features, the effective features extracted in this paper are more comprehensive and less losses compared to the models used in other researches. A comparison of the loss values in the training of CAE (Convolutional Auto-Encoders) and SAE (Stacked Auto-Encoders) in the experiments shows that the loss values of CAE are about one-tenth lower than those of SAE, and the method consisting of Multi-CAE and LSTM for the USTC- TFC2016 dataset was trained with accuracy values up to 99.98%, and the precision, recall and f1-score parameters were also above 99%, outperforming other studies.

Applying deep learning methods to achieve synthetic aperture radar (SAR) image ship target detection has become a current hot research topic. However, most of the existing detection networks are designed based on optical images, so when they are directly applied to SAR image target detection, there will be the following two problems: 1) There are big differences between SAR image and optical image in imaging mechanism, geometric characteristics and radiation characteristics, etc, which makes the detection network have more redundant imformation; 2) Real-time performance and accuracy are both very important in SAR image target detection task, and applying a network designed based on optical image cannot balance the performance of the two well. In response to the above problems, this paper proposes a lightweight network LSSNet specially designed for SAR image ship detection task. In earlier layers, we use depthwise separable convolution instead of conventional convolution to design dense block; in deeper layers, stacked dense blocks with shortcuts are used to enhance deep supervision, and finally achieve high-speed and high-accuracy detection. This paper uses SSDD dataset as baseline for expeiment and results show that LSSNet have higher accurracy and detecition speed. Its lightweight structure will also help transplant to hardware devices in the future.

The traditional swarm intelligence optimization K-means algorithm has some problems, such as poor global search ability and blind area selection of initial center points, which leads to the reduction of clustering availability. In order to avoid the above limitations, this paper proposed IABC k-means algorithm. Firstly, the employed bees stage in the traditional artificial bee colony ABC algorithm uses the current colony optimal solution information to guide its optimization search. Secondly, this paper wanted to solve the search ability of the employed bees and expand the information sharing range between various individuals, a random guidance mechanism is proposed in the onlookers bees stage. Finally, chaotic sequence is introduced in the scout bee stage to accelerate the convergence speed of the algorithm. IABC algorithm is proposed and applied to K-means clustering algorithm to improve the poor global search ability of K-means algorithm and the random selection of initial center points. Experiments show that the IABC and IABCK-means proposed in this paper effectively improves the clustering availability.

Targeting at the problem that the existing hybrid database solutions can not simultaneously realize the client’s verification and access function to the encrypted data in the public cloud database and the cross database query function in the heterogeneous database environment, this paper designs an access and integration middleware to realize the client’s verification and access function to the heterogeneous hybrid database in the public cloud and the cross database query function. Access and integration middleware is composed of verification access component and heterogeneous database data integration method. The verification and access component reconstructs the protocol interaction process based on MySQL protocol, and encrypts and decrypts the encrypted data in the public cloud database through the existing hybrid database scheme, so as to realize the client’s verification and access to the encrypted data in the cloud database. The heterogeneous database data integration method unifies the data from different types of databases, and then integrates them into the same database, and uses this database for query operation to realize cross database query. This paper first analyzes the hybrid database system model, then designs from the aspects of MySQL protocol analysis, verification access, data integration, and finally analyzes the access and integration middleware from the aspects of functionality and security.

Internet of Things (IoT) is the most considerable medium for all smart applications, in which it provides a huge support to agricultural industry in fine manner. In literature, there are lots of smart devices are available for monitoring the crops and agricultural field, but all are strucked under certain limitations such as power problem, cost expensiveness and so on. This paper is intended to design a new machine learning enabled Smart Internet of Things medium to support agricultural field in proper way. In this paper, a Intelligent Crop Monitoring Device (ICMD) is introduced to monitor the crops over the agricultural field in 24x7 manner. This kind of monitoring devices enhances the production and quality-of-service of the agriculture as well as related products. This paper associates an innovative technology to the Smart Device called Machine Learning, but instead of using the classical learning schemes, this approach introduced a new scheme called Modified Learning based Field Analysis Strategy (MLFAS). This approach is inspired from the classical machine learning scheme called Convolutional Neural Network (CNN), in which the proposed Smart Device called ICMD accumulates the real-time agricultural field details and pass it to the monitoring unit for manipulation. The manipulation end maintains the data into the server unit, in which the machine learning model called MLFAS acquires the received field data and process it based on the training samples. The training samples are nothing but data collected from the agriculture field, the collection of received data are maintained into the server end for processing, the proposed MLFAS model manipulates the data and created as a model for further testing. The newly arrived data from the field is considered as a testing data and cross-validate that data into the trained model. The data acquired from the agriculture fields are temperature, humidity and soil moisture level, in which these records are passed to the server unit by using IoT module associated with the ICMD. The data available into the server can easily be monitored by the farmer from anywhere at any time. The learning model predicts the status of the crop in the field by means of analyzing the input acquired from the real-time testing input and report that to the respective farmer for taking an appropriate action. For all this system is useful to the agricultural field and provides good support to farmers to monitor the crops over the agricultural field from the remote place even. By using this proposed scheme, the farmers can make accurate and efficient crop management decisions with the use of results obtained by using the Smart Device called ICMD.

For the purpose of faster and more accurate anomalous traffic detection with increasing classes of data traffic in the network, this paper proposes a new anomalous traffic detection method based on stacked auto-encoders and a long short-term memory network model. The method uses Multi-SAE to extract the effective features of sequential traffic, which is obtained by concatenating multiple stacked auto-encoders, and a long short-term memory network to extract the temporal structure of the effective features, with the Multi-SAE and the long short-term memory network in a back-and-forth tandem structure. To further improve the efficiency of the detection, redundant MAC addresses are also removed in the pre-processing. From the experimental results, this paper achieves effective detection of twenty types of data traffic with an accuracy rate of 98.25%, which is higher than that of the same category of research by nearly 2 percentage points, and the parameters of precision, recall and f1-score also reach over 96%, improving the detection results.

The Quasi-Newton method (QN) is widely used in solving large-scale optimization problems due to its high efficiency. However, this algorithm requires a large number of iterative calculations, which is more time-consuming to compute in software. The Davidon-Fletcher-Powell (DFP) algorithm has been implemented on field-programmable gate array (FPGA) in order to speed up the computation. The number of iterations in the online search part of the algorithm is excessive, which affects the speed of the calculation. In order to solve this problem, this paper proposes a hardware implementation of DFP algorithm using inexact linear search. The results show that the calculation speed of the DFP algorithm implemented in this paper on the software side and FPGA platform is increased by 7.59 times and 3.08 times, respectively. And resource consumption and accuracy have little effect on the results.

For distance education, the application of computer network and software technology is very important to open teaching and management. This paper mainly studies the application of blockchain technology in the field of Open University distance education. Blockchain technology, as a new type of decentralized trust structure technology, provides necessary support for distance open education in teaching resource utilization, teaching process control, teaching effect evaluation and teaching management, and provides an effective foundation for teachers and students to participate in distance teaching practice together. This paper adopts a hierarchical network architecture and hierarchical storage structure, which has good scalability and node transparency, provides support for user service strategy in large-scale applications.

In this paper, based on the research focus of the current logistics field, a deep learning based express single-side bar code extraction algorithm is designed. This algorithm can recognize the original image and read out the information of the package. The algorithm is divided into two parts: bar code localization algorithm and bar code recognition algorithm. To avoid high delay, we use the YOLOV5S network to complete the barcode localization algorithm. First, the YOLOV5S algorithm is used to frame out the barcode, and the bounding Box is cut off. Then, OpenCV's method is used to obtain the deflection Angle of the image. Finally, affine transformation is used to correct the images with deflection. The bar code recognition algorithm is to use ZBAR algorithm to decode the bar code and output the decoded content in the form of string, and finally realize the location and recognition of the bar code on the single side of the express. Finally, through the test of the data set, it can be seen from the experimental results that this paper has accomplished the goal well.

With the continuous innovation of artificial intelligence technology, the teaching level of mechanical education courses in colleges and universities is also constantly improving. In the context of the era of big data, the teaching of mechanical education courses in colleges and universities has become more intelligent and informatized. This article mainly introduces BP neural network method and hill climbing algorithm. This paper uses BP neural network method to analyze the application of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, and establishes a potential mathematical model of BP neural network method. The model is solved by BP neural network method, and the current situation analysis and application status of the teaching mode of mechanical education courses in colleges and universities are analyzed, and the model is revised using historical data to improve the application research of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities Accuracy. The experimental results of this paper show that the BP neural network method has increased the effect of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities by 33%. Finally, by comparing the value analysis of the application of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, and the data analysis of artificial intelligence technology in the teaching of mechanical education courses in colleges and universities, the system shows that artificial intelligence technology is used in mechanical education courses in universities. Application in teaching.

In the past few decades, Cognitive Radio network (CRN) has been regarded in the literature as the most promising technology for performing dynamic spectrum management. One of the major aspects of spectrum management is referred to as the spectrum-reconfiguration decision-making ability of CR users. Dynamic spectrum reconfiguration has previously been reported to improve the spectrum utilisation in CRN. In exploiting spectrum reconfiguration to improve spectrum utilisation, various approaches have been adopted to develop models. However, none of these research works has been evaluated in the urban and rural settlement context. In a CRN environment, the frequency availability is not static like that of traditional networks. There are resource-rich regions such as in rural areas, where many frequencies are available for the Secondary Users (SUs) and resource-poor regions such as urban areas, where there are only a few frequencies available for SUs. Hence, this paper proposed a novel Foraging Inspired Spectrum Selection and Reconfiguration (FISSER) model. The performance of the FISSER model has been analysed through computer simulations both in the rural and urban CRN, using high and low perceptual radii. A high perceptual radius has been shown in the literature to improve energy efficiency and data communication performance in ad hoc networks. However, the simulation results reported in this paper show that even though the high perceptual radius improves the nodes’ energy efficiency it does not achieve optimal data communication performance compared to when the nodes use a low perceptual radius. Also, the efficacy of FISSER model was tested by comparing it against the reinforcement learning model. Based on the obtained results, it was shown that the FISSER model achieved better results in both the network throughput and the channel switching time.

The fourth-order dispersive nonlinear Schrödinger (FODNLS) equation can simulate the nonlinear propagation and interaction of ultrashort pulses in high-speed optical fiber transmission systems in optics. In this paper, all traveling wave solutions of the envelope are obtained by means of the polynomial complete discriminant system and the elementary integral method. Under specific parameters, the propagation modes of all solutions are given. In this paper, by using the direct integration method, all traveling wave solutions of the envelope of the equation are given, including the singular rational solution, the singular periodic solution, the solitary wave solution and the double periodic solution.

With the rapid growth of IoT devices, machine communication is becoming more and more popular in 5G wireless communication systems. This paper proposes a novel MAC protocol for the Internet of Things (IoT) in a scenario with a gateway and multiple IoT devices. The IoT devices with packets (active IoT devices) send independent double-pilot(IDP). The gateway takes the number and ID of IoT devices and allocates appropriate power to IoT devices. These IoT devices adjust the power and transmit packets using the Power domain Non-Orthogonal Multiple Access Protocol (PD-NOMA). Compared with the traditional single pilot scheme (TSP), the independent double-pilot can dramatically reduce the pilot collision probability, although it will probably bring a moderate increase in detection workload. Furthermore, the IDP-NOMA protocol proposed in this paper dramatically improves the system throughput and the probability of transmission success.

With the development of computer technology and network technology, various digital learning methods have seriously impacted the traditional education and teaching mode, among which gameplay learning represented by teaching games has attracted wide attention of educators. It is considered to be the most effective way to stimulate learners’ learning motivation. Mobile game is an important part of mobile phone value-added service. The research of mobile game engine has extremely important application value for the development of mobile phone game. This paper mainly studies how to realize a high-performance mobile game engine. Through the analysis of mobile phone operating system, mobile game market, mobile phone game user needs, the technology selection and main function modules of the engine are determined. Through the research of java technology, the technical architecture of engine server and client is determined. Performance and algorithm optimization are the focus of this paper, because performance issues are the biggest bottleneck in developing games on java phones.

Recover the high-resolution image from a single low-resolution image is known as single image super-resolution reconstruction (SISR). SSIR is currently a research hotspot in computer vision. In this paper, we study the feature learning module of the magnification-arbitrary network for Super-Resolution(Meta-SR), and propose an improved method of image feature learning. Its idea is multi-scale residual dense block (MRDB). We integrate it into the existing structure of the Meta-SR network. As a result, the same network has increased the receptive field, and it can obtain more detailed features of the image. This structure can be extended to other existing SISR network models. In the published and standard test set Set14, compared with the interpolation method, the average PSNR value of the algorithm in this paper increases by 3.79dB, 3.01dB and 2.85dB at amplification factors 2, 3 and 4, respectively. Through the analysis of SISR results, it can be obtained that the improved method proposed in this paper has a significant improvement in all indicators, and can more fully extract the image detail features, thereby improving the quality of the reconstructed image.

The link of traditional architectural design is very complicated. It costs a lot of human and financial resources. The traditional architectural design drawing error is also very big. Engineers can only measure some errors with their own experience. On this basis, experts believe that computer-aided systems can help architectural design. In recent years, the pollution caused by building materials is becoming more and more serious [1]. This situation has attracted the attention of environmental protection departments. Experts put forward the theoretical research of green building. This paper briefly summarizes the characteristics of computer-aided architectural design. This paper describes the application of computer technology in green building. Finally, this paper puts forward the research on the combination of the two.

This paper starts with how college students’ consumers accept the new technology model. Based on the technology acceptance model of e-commerce industry and the unique factors, this paper constructs the influencing factors model of consumers’ acceptance behavior under the mode of O2O, and obtains samples for scientific analysis through data survey for structural equation analysis. Finally, this paper gets the influencing factors of college students’ consumer buying behavior under the mode of O2O.

Communication of the human brain with the surroundings became reality by using Brain- Computer Interface (BCI) based mechanism. Electroencephalography (EEG) being the non-invasive method has become popular for interaction with the brain. Traditionally, the devices were used for clinical applications to detect various brain diseases but with the advancement in technologies, companies like Emotiv, NeuoSky are coming up with low cost, easily portable EEG based consumer graded devices that can be used in various application domains like gaming, education etc as these devices are comfortable to wear also. This paper reviews the fields where the EEG has shown its impact and the way it has proved useful for individuals with severe motor disorder, rehabilitation and has become a means of communication to the real world. This paper investigates the use of Cubic SVM algorithm In the EEG classification. EEG feature extraction is Implemented by maximum overlap discrete wavelet transform (MODWT) to reduce the dimensionality of data. The Sliding Window Technique is used to calculate the mean within each window samples. The feature vectors are loaded into the support vector machine (SVM) and optimize tree.

s This paper presents algorithm for draping 2D textile fabric on curved surface. Using computer analyze to predicting strength and stiffness of textile reinforced composite details allows to achieve better characteristics of received parts. Part of this task is stacking initial cloth on the surface. Modeling of draped fabrics on surface produced by representation initial cloth by pin-jointed net model, which are describes position of each thread. This algorithm also provides to analyses angles between each pair of threads in considered object. It is used to find areas with critical tension, or places of formation of folds of fabrics. In this paper threads of fabrics are inextensibility.

Embedded system is a development trend of computer technology, which is a low-power, specialized computer technology. Embedded system is widely used in industrial production and daily life, which is a very cheap computer system. Data collection is a kind of commercial behavior, which can provide basis for product decision-making, management and control. Therefore, data acquisition through embedded system will reduce the cost of the whole system, which can also guarantee the expansibility and flexibility of the whole system according to specific needs. Therefore, the intelligent collector based on embedded technology is an important application. Firstly, this paper analyzes the importance of intelligent collector based on embedded technology. Then, this paper puts forward the main technology of system development. Finally, an example is given.

The main aim of this research paper is to provide multi security for the pedestrians/older using smart stick using IOT and GPS. This stick having IOT, voice module and attached with multiple sensors to provide high security to the blind peoples while walking. Now a day’s safety is main important cause to the peoples while walking or driving and many more places. This smart stick provides the high security and show way to walk, by using this system we can monitor the blind person position using web application/mobile application and also we can get emergency alert message along with exact location. This system has obstacle sensor, water sensor and MEMS sensors, so that it can detect obstacles sizes automatically and gives voice alert. By using water sensors, it can detect the floor condition is dry or wet and shows the way to walk. In case of any emergency person suddenly fall at floor/any place then this system immediately detects using MEMS tilt sensor and automatically send the message to respective numbers with location and also gives buzzer alarm continuously to alert the neighbours. So that this smart stick can be very useful to peoples to show correct path while walking on the floor or steps and many more places. In this paper, the system can be interconnected with the microcontroller and alert the respective persons when any emergency occurs. This tracking system is composed of a GPS receiver, Microcontroller. The Microcontroller processes this information and this processed information is sent to the respective numbers using web page.

Artificial intelligence is an effective way to promote the intelligent development of education and teaching, and also an important concept of the development of quality education. This paper mainly studies the new teaching method of free combat based on artificial intelligence technology. This paper analyzes the innovation brought to education by intelligent image recognition, speech recognition, big data analysis, deep learning, intelligent teaching robot and other related technologies in the era of artificial intelligence. Meanwhile, it analyzes and discusses the influence of teaching reform in the era of artificial intelligence development. From the experimental results of this paper, it can be seen that from the perspective of students’ free combat sports skills, using artificial intelligence technology can more effectively improve students’ free combat sports skills than using conventional teaching methods to conduct elective free combat teaching. In the elective teaching of free combat in colleges and universities, the application of artificial intelligence skills can effectively improve the excellent rate and good rate, control the pass rate and reduce the failure rate of students’ examination results of free combat course.

With the progress of science and technology, big data technology has penetrated into all aspects of life. The topic of this paper is how to develop students’ concept innovation and learn to learn in Chinese painting teaching under the background of big data, mainly focusing on how to develop and guide students’ concept change in their activity process. This paper uses the research method of case analysis. The research object is the students of a university in our city. I get the necessary data through the questionnaire survey data collected before the course, the communication data with students after class and the creation of works of two parallel classes. Then the questionnaire answers to the data list, after class talk statistics, comparative description and analysis of students’ works. The purpose of this paper is that through the practice of Chinese painting, students can learn to learn, use their brains spontaneously, improve their ability of autonomous learning and innovative ideas, and make different works. Teachers can use their own knowledge and creative experience and Research on the implementation strategy of Chinese painting class to improve their ability to better guide students’ concept innovation.

With the continuous and stable growth of China’s national economy, the demand of the whole society for the total power supply is higher and higher, which requires the power enterprises to continuously improve the power supply. However, if the total power supply is increased, the power enterprises need to invest a lot of power transmission facilities, which requires a huge investment in the distribution network. Therefore, different economic development levels among regions will lead to the difference of distribution network investment, which is also an important content of distribution network investment decision. Therefore, we must control the whole process through big data, which will provide a better evaluation system of distribution network investment benefits. Through the whole process management and control mode, the State Grid can build a systematic and complete distribution network investment decision-making system, which will improve the accuracy of distribution network infrastructure project investment. By analyzing the income ratio, the power grid can get scientific economic investment analysis report. Through big data technology, the power grid can realize a variety of data of distribution network operation, such as maintenance plan optimization, distribution network operation process optimization, project investment income, equipment health status analysis, etc., which can provide auxiliary decision support. Through the three aspects of technical benefit, social benefit and economic benefit, we can evaluate the operation efficiency of equipment, which can construct the index system of project investment benefit. Firstly, this paper constructs the whole process management and control system of distribution network based on big data. Then, this paper constructs the accurate investment index system of distribution network. Finally, some suggestions are put forward.

In present time, meta-heuristic algorithms have been widely adopted for solving diverse classes of optimization problems. These procedures/algorithms have advantage over classical algorithms due to strong exploration and exploitation capabilities. In recent time, a Bat algorithm is developed on the basis of echolocation characteristics of bat. This algorithm has applicability to solve number of constrained and unconstrained optimization problems. The bat algorithm gives state of art results as compared to same class of metaheuristics. The objective of this paper is to explore bat algorithm in terms of modifications, improvements, search strategies and nature of problems being solved. Hence, this paper presents the mini review on bat algorithm. Further, the applicability of bat algorithm in diverse filed has been discussed.

Paper considers educational digital tools development results for university level within geo-information management paradigm during Industry 4.0 era under climate change and COVID-19. While research, authors used modern web-technologies for educational platforms design. Recently, the ways of geo-information support for environmental economics have distinct features of digitalization with new concepts in data obtaining and presenting. In paper, preference is given to the use of open online platforms, which integrate heterogeneous hardware and software resources with the use of web-technologies in distributed networks and wide application of cloud services. There are considered examples of presented digital tools using. The presented research results have significant scientific novelty can be used in training and educational purposes at university level, including the preparation of Master’s programs.

With the popularization of the Internet and the rapid development of information technology, the types and quantities of data are increasing at an unprecedented rate. We are in an era of ever-increasing data penetration, and “big data” has arrived. Nowadays, all walks of life are using big data, and human production and life can no longer leave big data. It has been widely used in many fields, including large-scale hydropower and ceramic glaze. This article mainly introduces the literature reference research method, analytic hierarchy process and investigation research method. This paper uses the analytic hierarchy process to analyze the application of big data in ceramic glaze, and establishes a potential mathematical model. The model is solved by the analytic hierarchy process, and the application status of big data in ceramic glazes is studied, and the model is revised using historical data to improve the accuracy of the application status of big data in ceramic glazes. The experimental results of this paper show that the analytic hierarchy process increases the efficiency of the application research of big data in ceramic glazes by 13%, and reduces the rate of false alarms and false alarms. Finally, by comparing the application value analysis of big data in ceramic art glaze and the modernization demand analysis of ceramic art glaze, the influence of the application of big data in ceramic art glaze is systematically explained.

To solve the problem of low ratio success delivery, long latency caused by the rapid dynamic change of network topology, limited node buffer, large amount of data transmission and large user population density in delay-tolerant network (DTN). This paper propose a routing algorithm in DTN based on nodes’ sociality. In this paper, contact history was used to determine the node link quality; Markov model was used to predict the node position based on the nodes’ movement track; according to nodes’ social similarity to divide community, then calculated node betweenness centrality. Finally, the best forwarding nodes for message transmission were determined. Besides, to increase the ratio of successful delivery, message delivery probability in the relay node’s buffer was calculated. Experimental results show that compared with traditional routing algorithm, the proposed algorithm performs better on the delivery ratio and average end-to-end delay.

The paper designs a square hole model and uses a delta3D printer to carry out printing experiments with different process parameters. It mainly conducts experimental research on four process parameters, including printing speed, layer thickness, filling density and platform temperature. The paper carries out the analysis of the influence of process parameters on the external dimensions of the hole, the inner depth of the hole, etc., and graspes the law of the influence of different process parameters on various sizes in order to provide a certain reference for the research of the 3D printing process of special parts.

Chinese traditional manual embroidery has a long history, which is one of the important intangible cultural heritage in China. However, with the increasing social demand for embroidery, the traditional manual embroidery production capacity is too small and the price is high, computer embroidery and embroidery technology has an important market. With the optimization of artificial intelligence algorithms such as deep learning, computer embroidery skills have been gradually optimized, which has replaced manual embroidery in some aspects. In the clothing industry, computer embroidery skills have been widely developed. Through computer technology, designers can carry out three-dimensional modeling design, which is not achieved by traditional plane decoration. Therefore, computer embroidery technology can be gradually applied to the behavior and social factors of modern society, which will encourage the inheritance of national costume culture. First of all, this paper analyzes the current computer embroidery design. Then, this paper analyzes the steps of computer embroidery. Finally, this paper puts forward the application of computer embroidery techniques in modern Chinese fashion design.

The construction of local high-level universities is an important part of the comprehensive reform of higher education in China. With the coming of the computer “Internet +” era, major strategic tasks such as cloud computing, big data and new generation of artificial intelligence have been gradually laid out, which will further achieve the goal of educational modernization and the development of a powerful educational country. Promoting the development of education informatization in the Internet era has become the top priority in the development of national education. Based on the background of high-level universities, this paper first analyses the necessity of the educational informatization development. Then, this paper puts forward some problems. Finally, some suggestions are put forward [1].

In recent years, big data is used in finance, education, biotechnology, and other fields. In the field of education, thanks to the overall characteristics of big data, it can be applied to understand the objective learning rules of students, to provide effective decision-making suggestions for educators. With the development of electronic communication and media technology, Massive Open Online Courses (MOOC), which contains a large number of user behaviors and information, has gradually become one of the mainstream ways of education. However, MOOC have also been criticized for their high dropout rate. Through the analysis of big data contained in MOOC real data set, it can help teachers master students’ learning rules and learning behaviors in time, which is of great significance to improve the level of education and the dropout rate of MOOC. First, it is helpful for teachers to adjust the course content in time according to the learning status. Second, teachers can carry out benign interventions on students’ learning behavior. In this paper, based on the analysis of the real MOOC data of students’ learning behaviors, we find that different groups of students have the same learning behavior at the same learning period, and students’ learning situations can be divided into three stages. Then ARIMA model is used to predict the learning behaviors of students, to further analyze the factors causing the difference in prediction accuracy, we propose an error analysis method based on MSE. Through the experimental observation, the method proposed in this paper can effectively find and predict the characteristics of students’ learning behavior, to give suggestions for improving the quality of teaching.

With the rapid development of computer technology (hereinafter referred to as CT), China’s e-commerce has achieved great success, which also applies a variety of modern computer, electronic, control and other aspects of technology. Therefore, e-commerce is in urgent need of professional talents, which requires colleges and universities to cultivate more logistics management (hereinafter referred to as LM) talents. Through specialized division and cooperation and collaborative education, colleges and universities can cultivate professional talents more suitable for modern LM. Through computer software, we can control the sorting system of logistics. At the same time, we can optimize the distribution network and routing. Therefore, LM has become a hot emerging specialty, which has become an indispensable specialty in E-Commerce teaching. Therefore, colleges and universities can improve the teaching effect of e-commerce through multimedia, simulation software and other modern technologies, which can cultivate the practical ability and innovative spirit of college students. First of all, this paper analyzes the important role of CT in LM teaching. Then, this paper puts forward some problems in the teaching process. Finally, some suggestions are put forward.

In recent trends, digital images are commonly used for communication, storage, medical imaging, etc. These images may contain confidential data. Image encryption and steganography are popular techniques to provide information security. In image encryption, a secret image is encoded using an encryption key, and in image steganography, a secret image is embedded in some cover media. Encrypted images are visually recognizable as these are noise-like, but stego images are visually unrecognizable. In this concern, some visually secure encryption techniques are developed, known as Meaningful Image Encryption (MIE). It includes the advantages of both image encryption and steganography. This paper provides a thorough review of existing MIE techniques. A comparative assessment based on various performance parameters is presented. Further, this paper provides the main application areas of MIE techniques. Moreover, the future research perspective to enhance the existing MIE techniques are discussed in detail.

The 21st century is the era of multimedia, which has been applied to all walks of life, especially the education industry. With the rapid development of economy, the process of global economic integration is faster and faster, which requires higher quality of English Teaching (hereinafter referred to as ET). Therefore, multimedia technology (hereinafter referred to as MT) has become an important tool in College ET, which can not only promote the cultivation of language expression ability and thinking logic ability, but also enhance students’ interest in learning. Through MT, colleges can change the traditional teaching methods and means, which will improve students’ interest and enthusiasm. Through the introduction of computer MT, colleges effectively improve the students’ learning quality. First, this paper analyzes the important role of computer MT in ET. Then, this paper constructs the important teaching platform function of MT in Colleges. Finally, some suggestions are put forward.

The development of computer technology promotes the reform of engineering industry. People gradually realize the convenience of computer technology. Civil engineering is the main specialty of construction engineering. Civil engineering includes many kinds of engineering. With the emergence of computer software development technology, many problems in civil engineering have been solved. Experts have developed a lot of computer software [1]. These softwares are widely used in civil engineering technology. This paper mainly lists the application of four kinds of computer software in civil engineering. In this paper, the main application skills of computer aided design, finite element software, GIS software and virtual reality software are mentioned.

With the development of information technology, the traditional paper knowledge information has been toward the trend of informatization. Therefore, the library materials information also gradually information zed. This paper first explains the development trend of library in China, and discusses the existing problems and optimization of library information, so as to provide readers with reference.

The traditional sculptures are all from the artist’s careful carving, which is also the artistic presentation. With the rapid development of computer technology, digital technology has been applied to art landscape sculpture. Through RPM technology and 3D printing technology, we can rapidly shape and mass produce art landscape sculpture. Through computer aided technology, RPM technology and 3D printing technology, we can save the labor cost and mold opening cost of traditional sculpture, which shortens the production cycle and improves the precision and structure of sculpture. At the same time, we can also develop personalized sculpture, which has seriously impacted the traditional sculpture industry. Computer aided design is in line with the digital age of new technical concepts, which has become the main means of landscape sculpture creation. Through computer software, designers can complete the virtual sculpture design intention and intention. Through the generation of VR, manufacturers can better communicate with customers, which makes up for the shortcomings of traditional sculpture in construction. Firstly, this paper analyzes the related concepts. Then, this paper puts forward the necessity of applying computer aided technology to modern landscape sculpture. Finally, some applications are proposed.

Now a Days Microwave MINI-LINK are widely used for point to point communication. Microwave link has become an essential form of communication network because of frequent interruption of Optical Fiber Cable network due to other sector development work, non-feasible area for Optical Fiber Cable laying (digging is difficult for OFC) or any unseen reason, on the other hand mini link network become so adaptable because their small wavelength allows conveniently sized antenna to direct them in narrow beam which can be pointed directly at receiving end. This allows nearby mini link equipment to use same frequencies without interfering any other link. The aim of this paper is to create a mini link network in such a way so that operation and support maintenance work can be done from a central location without moving at site and without purchasing any software. Another objective of this paper is to describe how to use installed equipment completely (i.e. 100% utilization of equipment) which are being used by network..

This century is described as Cloud Computing Technology over the Information Technology Business. Cloud computing advancement mostly relies on the Internet possesses the most advanced planning of computation. It records as piles of correlated and schmoozed hardware, software, and internet infrastructure. It blends grid computing and other computing as a community. A short overview of cloud computing is obtained from this research paper by consulting several papers on cloud technologies. The substance conveyed by this represents the perspective of shaping IT businesses and the idea of cloud computing.

In the era of big data, traditional data compression methods cannot complete data compression with features of large volume, rapid growth and complex structure, especially in the processing of real-time data compression. The compression algorithm proposed in this paper is improved on the basis of a lossless bit-swap compression coding algorithm. The coding algorithm is based on VAE model and uses classical ANS compression technology to code multiple potential variables, so as to realize the rapid compression and decompression of data. The experimental results show that the algorithm proposed in this paper can compress data with a high compression rate(47.65%), which is higher than LZAM algorithm and Bit-Swap algorithm.

Deep Learning (DL) models have tested to be very powerful in solving many hard problems. Especially, those are related to computer vision, text, speech, and classification. However, the blueprint of such models requires large space and elaboration that needs to be examined. Convolutional Neural Network (CNN) is the most popular neural network that can extract the features automatically as compared to conventional machine learning algorithms (CMLA). Our aim in this paper is to lessen the human attempt required to layout architectures by the use of a gadget architecture development process that allows the exploration of huge design space by automating sure version construction, alternative generation, and assessment. The main operations in CNN are Convolution, Pooling, Flattening, Full Connection between the input and output layer. The dataset taken as CIFAR 10 having 60,000 color images of 10 different classes is considered for the study where, various classes represent the images of cars, trucks, frogs, horses, trucks, cats, cars, airplanes, ships, and deer. It is expected that the performance of the CNN model can be further improved by using the deeper network architecture, or by an increasing number of epochs or data augmentation. In this paper, an attempt has been made to explain simple and deeper CNN models on the CIFAR 10 dataset and the comparison has been carried out to check the accuracy achieved from both the models.

Production safety is the most important problem in coal mine production, which requires us to monitor and monitor the coal mine production process in real time. Therefore, under the computer technology, we can carry out multi-sensor acquisition, including gas concentration, carbon monoxide content, environmental pressure, humidity, etc., which can collect and monitor the underground operation in real time. Through computer technology, coal mining enterprises can monitor and control safety production in real time. Through the sensor, we can monitor the working state of equipment, such as circulating fan, high-pressure water pump, winch and motor, which can deal with a variety of abnormal production problems, such as over-voltage, power loss of current, lack of phase. With the application of various sensors, the information system will collect more and more data, which will lead to great challenges for coal mine safety monitoring system. Therefore, we must enhance the data processing system, which will better coordinate the data interaction of various subsystems. Through the data processing system, we can improve the efficiency of coal mine safety monitoring, which will ensure the smooth operation of coal mining enterprises. Firstly, this paper analyzes the coal mine safety monitoring system. Then, this paper analyzes the source of the data. Finally, this paper analyzes the processing method.

Computer network technology promotes the update and progress of new media. It has brought great changes and influences to the whole news and entertainment media. The continuous improvement of computer science and technology level has led to the long-term progress of electronic information technology. In this way, the status and value of computer information technology in the media industry continue to rise [1]. In today’s era of new media prosperity, the most far-reaching impact of computer technology industry is the media industry we are familiar with. This paper briefly analyzes the status quo of media and its development. Through the cognition of media industry and new media computer, this paper describes the research on the role of computer technology in the media field in the new media era, and finally draws the corresponding conclusions.

With the rapid development of computer technology, BIM has become the most common tool in the construction industry, which occupies an important position in the virtual construction of Prefabricated building (hereinafter referred to as PB). Through the establishment of virtual construction engineering model, we can provide a complete construction engineering information base combined with digital technology. In the virtual construction of construction engineering, we can check the collision through BIM. At the same time, through BIM, we can carry out 3D model drilling of virtual construction. Through collaborative management, we can improve the production efficiency, which will save the cost and shorten the construction period. At present, the application of BIM in China is still in the exploratory stage, which requires us to give full play to BIM Technology. Through the Revit software, this paper simulates the construction site, buildings, tower crane, secondary masonry, etc., which can make the application of PB more clear. Firstly, this paper analyzes the related concepts. Then, this paper analyzes the application of BIM in the virtual construction of PB. Finally, some suggestions are put forward.