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In recent years, China is trying to develop various educational systems. The system of higher education is the main concern of China’s education policy. The second concern is vocational education. At present, the management situation of Vocational Education in China is not optimistic [1]. We can not think that all the mistakes are caused by the neglect of the society. In the highly developed computer software technology today, experts suggest that we can use software technology to study various problems of vocational education management mechanism. This paper describes the current situation of the management system of vocational education. The characteristics of the management system are also mentioned in the article. On this basis, this paper also puts forward the development method of management mechanism which can improve the quality of management.

In this paper, we measure the performance of supervised learning algorithms on imbalanced class datasets. Supervised learning is considered to be the most advanced and mature from other types of learning in machine learning. On the other hand, imbalanced class data sets refer to an unequal amount of data for each class in the data sets. Many real-world datasets exhibit an imbalanced class distribution. Hence, this paper aims to compare the supervised learning algorithms in classifying the output for imbalanced dataset. This paper also focused on finding the significance of balancing the dataset to the results. This study is conducted by using three different datasets and made a comparison with three supervised learning algorithms chosen which are Logistic Regression, Random Forest and k-Nearest Neighbours. In the experiments, all three datasets used are labelled data with imbalanced class. Due to the imbalanced class for all datasets chosen, a sampling technique with combination of under-sampling and oversampling is implemented to all datasets in data preparation steps. The performance of the algorithms is measured through Classification Accuracy (CA) value. From the experiments, it is proven that Random Forest shown the best results, for both imbalanced and balanced datasets.

In Vehicular Ad hoc Networks (VANET), topology varies very frequently because vehicles move in high speed. VANET is deployed on road. In GPSR Protocol, as the data packets are forwarded depending upon on the beacon information sent by neighboring nodes, which contain the location of neighbors. Hence nodes move in mobile nature as they change their positions. Mobile pattern of node is reflected by Mobility Models, as they characterize the movement of users who are mobile on the road, with respect to their direction, velocity and location over a period of time. Mobility models are practiced in implementation of protocols and the pattern by which the mobility models reflect real world practices of vehicles on the roads. Using simple pattern randomly, graph constrained mobility models are common practices while doing research. These models donot describe mobility of vehicles in realistic manner. For instance while accelerating and decelerating in the presence of nearby vehicles, these situations greatly affect the Network performance. Selecting the mobility model which is realistic one is the main focus of this paper. To address the challenges such as high mobility of vehicle nodes on the road and random topology, VANET needs a suitable mobility model to obtain improved Packet Delivery Ratio, Throughput, End to End Delay etc. This paper first implements the GPSR Protocol and then GPSR is analyzed by applying different mobility models such as Random Way Point, Gauss Markov, Manhattan Grid, Reference Point Group and Random direction. Results are analyzed by taking the following parameters: Routing overhead, Throughput, PDR and End to End Delivery. The implementation is carried out using NS—2.35 and Bonmotion is used to create mobility models.

Transformers in power system usually take current differential protection as the main protection, but the protection is easy to be affected by inrush currents and then maloperates. Sympathetic inrush is one kind of inrush currents, which is caused by energization of an adjacent transformer. There were many transformer differential protection maloperation cases caused by sympathetic inrush in the field, while the conventional second harmonic restraint method did not work effectively. In this paper, a method based on substation-area currents and curve fitting is proposed to identify sympathetic inrush. The method is presented using the characteristic that sympathetic inrush and initial inrush alternatively appear in reverse polarities. The characteristic exists continuously during sympathetic inrush. In order to verify effectiveness of the proposed method, PSCAD/EMTDC software is used in this paper to build sympathetic inrush model and obtain simulation data. Simulation results prove that the proposed method can effectively identify sympathetic inrush.

The level of garbage treatment represents the development level of the city, which requires each city to deal with the garbage problem. At present, China has been promoting waste classification and recycling, which will gradually improve the waste treatment supply chain system. However, with the continuous improvement of the supply chain, garbage treatment will become a benefit chain, which will lead to the risk of supply chain interruption. With the gradual pursuit of profit and lean, the probability of disruption risk in waste treatment supply chain is gradually increasing. Firstly, this paper analyzes the modes and types of reverse supply chain disruption. Then, this paper puts forward some link strategies. Finally, this paper puts forward the mode of computer simulation.

With the improvement of people’s living standards, there are more and more styles of house decoration design. People find that the accuracy of interior design of traditional houses is very poor. Small errors will affect the visual effect. People gradually focus on the computer software. With the development of computer technology, there are more and more types of interior design. People are also looking for the main way to optimize the design process of computer software. This paper mainly describes the basic principles of interior decoration. On this basis, this paper describes the main production process of computer effect drawing of interior design. Finally, the main optimization strategies are proposed.

The large-scale development of renewable energy power is faced with challenges such as the reverse distribution of wind and photovoltaic resources and power loads. The main challenge currently facing is the randomness and volatility of renewable energy power generation. Based on the concept, this paper studied the bundled transmission capacity planning. First, this paper proposed new energy output characteristics based on probability theory. According to the historical data of wind power and photovoltaic power generation, this paper considered the timing output characteristics and output change characteristics of wind power and photovoltaic power generation under different time scales. Then, the optimization model is established based on the objective function of minimizing total cost. Finally, this paper solved optimization problems with reinforcement learning algorithms and determined the installed capacity of thermal power. The research results of this paper have a certain significance for the bundled delivery of wind power and photovoltaics in new energy-rich areas.

Architectural projects, carried out by the public sector in Iraq using traditional methods suffer from lots of problems such as the inaccurate estimation of time and cost, and the poor cooperation among project team members. To tackle these problems, it is important to benefit from the experiences of the pioneering countries in this field such as the UK and USA. These countries kept pace with digital development and established new systems in construction, namely Building Information Modelling (BIM). This paper investigates the similarities and differences between the BIM standards and guidelines in the UK and USA. The findings of this comparative analysis can build a knowledge base for establishing BIM standards in Iraq. The paper identified the similar aspects to be adopted in the Iraqi standard of BIM, such as the design techniques, the designers’ roles and responsibilities, and some aspects in the information management. In addition, their differences are distinguished, which indicate the specificity of BIM application in each country. For the different aspects, the Iraqi BIM standard can either adopt the descriptions of the UK or USA such as the method of structuring design data, or reflect the Iraqi specificity such as the national library of BIM.

The analysis of data useful for further process and find the usefulness for the futher. In education sector, Admission is hottest topic. For the counselling of the admission in reputed institution or foreign university, the past data of the person are taken into the consideration. In this Paper, Admission data taken from the reputed Website (Dataset Weblink) and conduct the study of classification methods like Naïve bayes and its accuracy using Matlab. The performance of different classification methods from MATLAB classifier learner toolbox for admission data. Finally, the paper choosen the suitable classification method for the admission data set.

Garden landscape is the soft decoration of living environment. By creating a comfortable and pleasant environment, landscape design can not only improve the microclimate, but also improve people’s spiritual life. With the rapid development of rural areas, rural landscape planning and design has become an important direction of rural sustainable development in the future, which has also become a new starting point of building a beautiful China. However, how to plan and construct the relevant landscape design has become an important research topic. With the increasing status of BIM in the construction industry, BIM has been applied to landscape planning. This paper first analyzes the difference between BIM and traditional landscape design software. Then, this paper puts forward a detailed rural landscape planning and design scheme, which can better improve the living standards of rural people.

In recent years, the convolutional neural network (CNN) has had a wide application in hyperspectral image (HSI) classification. HSI has many spectral and spatial features, which is well known that different spectral bands and spatial positions in the cubes have different discriminative abilities. Therefore, this paper proposes a classification method with CNN, which uses attention-enhanced spectral and spatial features (CNN-ASS). First, we use spectral and spatial subnetworks to extract spectral and spatial features. At the same time, spectral attention and spatial attention are added to the two subnetworks, respectively. Then, we sum the weights of the classification results of the two subnetworks to get the final classification result. This paper conducts experiments on three typical hyperspectral image data sets, and the experiment results show the CNN-ASS has a competitive advantage compared with some advanced methods.

Space based information network system is a network system composed of spacecraft and spacecraft, which plays an immeasurable role in commercial and military, so it is very important to carry out simulation research on it. In this paper, the idea of Multi-Agent modeling is applied to study the simulation modeling of space-based network information system. This paper first introduces the space-based network information architecture, Multi-Agent system and RepastHPC, a Multi-Agent high-performance computing simulation toolbox. The simulation example is Starlink’s information support capability under AIM-260 “A aims and directs B to strike” fire attack mode, and the process of message Starlink transmission is mainly studied. In this paper, the space-based network information system is abstracted into route agent and sat agent, the decision-making method is given for each behavior, and a feasible Multi-Agent system is established. The simulation experiment of information transmission is carried out on RepastHPC platform, and the feasibility of applying Multi-Agent system to space-based network information system is verified.

Skin is an extraordinary human structure. It frequently suffered from many known and unknown disease. Therefore, diagnosis of human skin diseases is the most uncertain and complicated branch of science. It has been observed that most of the cases remain unnoticed because of the lack of better medical infrastructure and facilities. This paper is devoted to solve this challenge. Therefore, this paper effectively proposed (CNN-SVM-MAA) system which combines Convolutional Neural Network with Support Vector Machine classifier to develop a Mobile Android Application. Thus, to evaluate the performance of the proposed system several experiments are conducted on our dataset. This dataset consists around 3000 images which collected from a lot of sources like Beni-Suef University Hospital, Cairo University Hospital and various websites as well to be more accurate and realistic. A comparative study of applying different Feature extraction algorithms with different classifiers was accomplished. The results obtained showed the adequacy of the proposed (CNN-SVM-MAA) system how many skin diseases images have been detected from skin disease dataset. Which lead to detect skin disease and provide the user with the disease name and treatment related prescription with high accuracy.

The construction industry has a long history in China. Some advanced technology about architecture has been gradually excavated out. As an important part of the building, the quality of building materials should be paid attention to. In recent years, various safety problems caused by the quality of building materials are worth pondering [1]. On this basis, the task of building material detection will become more arduous. Experts believe that the application of computer technology can better assist the work of material detection. This paper briefly analyzes the advantages of computer technology in material testing. Finally, this paper puts forward the concrete application of computer technology in building materials.

In recent years, the problem of excessive obesity and poor physique of teenagers has become increasingly prominent. Teenagers lack of life experience and labor training. Their psychological quality is also very poor. The limitation of exam oriented education can never be broken through. In order to solve these problems, the Ministry of education put forward the rectification measures of sports professional education. Let physical education become a compulsory course for children. Put students’ physical and mental health first. Nowadays, the learning method of network teaching is more and more popular. Based on the Internet, the sharing of physical education curriculum teaching resources has become an innovative way for students to learn sports curriculum content. This paper puts forward the current situation of the sharing of physical education teaching resources. This paper also puts forward the design principle of shared resource platform. On this basis, this paper puts forward specific strategies for the construction of shared resources.

The development speed of digital media technology is increasing year by year. However, the lack of artistic talents in digital media is a serious problem. In many schools, the upper limit of the level of digital art graduates is very low. The integration of production and education of art has become the main research direction of scholars. In order to effectively implement the green city rectification policy, the State advocates the construction of urban ecosystem. However, the traditional theory of ecosystem construction has been ignored. Many scholars put forward the construction of urban ecological circle with the integration of digital art illustration and teaching. This paper analyzes the characteristics of the integration of art production and education in digital media. On this basis, this paper puts forward the construction path of urbanization ecosystem.

With the reform of education system, China has changed from the traditional concept of achievement education to the direction of comprehensive quality education. At present, according to the big data, quality education has become the mainstream theory of education. At present, college students in China have many objections to physical education. With the general decline of College Students’ physical quality, the reform of college education and teaching has become an urgent problem. This paper first analyzes the important role of quality education in college physical education. Then, this paper puts forward some practical problems of physical education reform. Finally, some suggestions are put forward [1].

Wireless Infrastructure less networks consist of wireless mobile nodes with heterogeneous and constrained resources, to establish internet connectivity regardless of the location. The applications the networks are disaster relief, health care and military. These applications are sensitive, and demands the reliable, and best quality of communication. Routing is one of the resources to enable the communication. Routing is acceptable in an infrastructure less networks if it satisfies the quality of service requirements. Thus the paper aims to investigate the existing QoS routing protocol performance in various conditions. The aim is to achieve a broad understanding of QoS routing protocol designed for Wireless Infrastructure less networks, and further pave the pathway for future research. The paper analyses the QoS routing protocol performance in presence of bottleneck intermediate node, and it is the novel aspect of the work.

With the development of Internet of things (hereinafter referred to as IOT) technology, IBM smart earth strategic plan has gradually become the goal of human development, which has gradually realized the global resource sharing and co construction. Therefore, the IOT system based on computer has become the future development direction, which also promotes the realization of digitization, intelligence and networking in many fields, including university library. In order to improve the service of the library, colleges need to constantly transform and upgrade the service pattern of the library, which provides important intelligent services for readers. Through the global positioning system, radio frequency identification, sensor equipment, etc., the IOT system realizes the interconnection between readers and books, which provides users with a full range of reader services. The IOT can provide intelligent management, reader behavior analysis, intelligent self-borrowing and returning books and materials long-term preservation and other conveniences for the smart library, which provides important help for the intelligent management of the library. Through the IOT technology, the library can improve the service efficiency and collection resource utilization rate, which will improve the state of intelligent management and service. First of all, this paper puts forward the main problems of long-term preservation of digital book resources in library. Then, this paper constructs the hierarchical structure of Smart Library. Finally, some suggestions are put forward.

According to the analysis of big data, With the rise of online education and teaching in domestic universities, students’ learning behavior data has grown exponentially. How to deal with the massive data, explore the deficiencies in the teaching environment, help the teachers optimize the teaching content and teaching process, and gradually become the focus of education researchers. Based on the analysis of the data characteristics of the teaching platform, this paper proposes a series of visual big data methods for teaching scenarios. Through the programming realization of visual big data method, teachers can intuitively present knowledge for students, timely understand the students’ learning status, and carry out targeted classroom teaching and teaching evaluation. Students can form knowledge framework and interconnection system to promote knowledge expression and Internalization, and complete self-assessment and discover their own learning problems; teaching managers can master the teaching effect of teachers and students’ learning, and adjust the goals of teaching management and decision-making. This paper combines the self-developed online education and teaching - Shidayun classroom, to apply the visual big data method to the actual teaching, which has produced good practical effects and effectively promoted the development of wisdom education.

Over the past year, the COVID-19 outbreak deeply and thoroughly changed the way the world is, and plenty of people died because of this virus. To control the epidemic, all the state governments in the United States took the mask mandates to ask people to wear masks in public. However, many people doubt if the mask can help them prevent the spread of the virus, and there are few researches about the effectiveness of the mask mandates in each state. To tackle this issue, this paper proposed an evaluating system for the effectiveness of mask mandate in each state in the U.S.A, which can be utilized to analyze whether mask mandates can slow down the spread of the virus. And if the effectiveness of mask mandates is different in different states, this paper will continue to find out which factor can most affect the effectiveness. The experimental results demonstrated that even though the new cases in some states still increased after the mask mandates, it can be seemed that the mask mandates do control the spread of the virus in general. According to the research, the paper can find that the most influential factor to the effectiveness is the infection rate. There are also some secondary factors to the effectiveness of mask mandates. Through these researches, they can analyze the result to develop more effective mandates to control the epidemic and help governments and companies develop new products to fit the needs during the epidemic.

The contamination of water bodies becomes a very big threat to the world. This happens due to pollution, domestic and industry waste and over population. This threat will lead to low rainfall, famines etc. In recent years, the research on monitoring and analyzing water quality is increasing. The water quality has to be examined in people living area, ponds, lakes, rivers and also the water bodies near the industries. People not having safe drinking water will lead to many diseases. The older methods of officials collecting sample from the water bodies, analyzing in labs, providing the results and taking control actions will be costly and takes more time. The main disadvantage is the lack of real time data analysis is not possible in the traditional systems. In this paper, a real time water quality method is analyzed and the results are verified with the traditional methods. This method uses high level processor, sensors, networking and measurement devices to give the real time data. The important parameters of water like temperature, turbidity and pH levels are monitored continuously. The user can verify the data anywhere anytime using the web interface provided in the method. The data analysis is also provided in tabulation and graphical representations. This paper provides an optimal method for monitoring the water quality in an efficient way. Traditional methods that rely on collecting water samples, testing and analyses in water laboratories are not only costly but also lack the capability for real-time data capture, analyses and fast dissemination of information to relevant stakeholders for making timely and informed decisions.

Under the rapid development of globalization, the prevention and treatment of aquatic epidemics has caused widespread concern due to international trade exchanges frequently. Although the epidemic disease information is gathered and published by World Organization for Animal Health (OIE) in regular, some vital characteristics of such disease are not able to obtain directly. Therefore, this technical paper designs a real-time monitoring system jointed Geographic Information System (GIS) and view tables, which aims to help managers or scholars to analyse the aquatic epidemics tendency between the different species and districts. There are mainly three contributions in this paper: (i) the real-time monitoring system of aquatic epidemics researches is first developed from the worldwide perspective; (ii) system provides the superior visual results of statistical analysis on user interface based on the technical processing on data association and light or colour intensities presentations; (iii) Furthermore, the web service is constructed and extended on the separation framework contained web frontend and backend the purpose of which is to reach efficiency of exploitation and failure process.

In consideration of imaging principle and sampling process of the X-ray imager, the collected images often appear blurry and noisy. We propose to add image processing in the reconstruction process to optimize the 3D reconstruction surface results of X-ray images in this paper. This paper proposes an automatic image data acquisition method to acquire images. According to the characteristics of X-ray images, an image processing method is drawn up. In this paper, the original image and the processed X-ray image are divided into two groups, and three-dimensional reconstruction is performed respectively, and the visualization effect and reconstruction time of the reconstruction result is compared. Experiments show that the running speed of the program is improved, and the visualization results show that the surface texture refinement and the reconstruction structure of the reconstruction effect after image processing have also been well optimized.

The progress of medicine can lead the development of human civilization to a great extent. There is no doubt that the progress of medicine will provide a more happy life for human beings. Nowadays, the number of doctors and nurses in our country is in short supply [1]. As our country is making every effort to develop medical cause, the task of training medical students is very important. The reform of traditional medical education has become the main topic in the medical field. The emergence of computer technology provides the dawn for the future of medical education. With the progress of computer technology, great changes have taken place in the field of medical teaching. This paper briefly introduces the theoretical knowledge of computer teaching. On this basis, this paper introduces the application and influence of computer technology in the field of medical teaching.

This paper is aimed at traditional word embedding models and Bidirectional Encoder Representations from Transformers (BERT) that cannot learn text semantic knowledge, as well as convolutional neural network (CNN) and Bidirectional long short-term memory (BiLSTM) unable to distinguish the importance of words, proposing an improved Chinese short text classification method based on ERNIE_BiGRU model. Firstly, learning text knowledge and information through the Enhanced Representation through Knowledge Integration (ERNIE) enhances the model’s semantic representation capabilities. Secondly, considering that CNN can only extract local features of the text while ignoring the semantic relevance between contextual information, and the Bidirectional Gating Recurrent Unit (BiGRU) is simpler, has fewer network parameters and faster calculation speed than the BiLSTM, the combination of CNN and BiGRU enables the model to capture both local phrase-level features and contextual structure information. Finally, according to the importance of features, the attention mechanism is used to assign different weights to improve the classification effect of the model. The experimental results show that the ERNIE_CNN_BiGRU_Attention (ECBA) model used in this paper has achieved good results in the task of Chinese short text classification.

With the development of the times, more and more kinds of energy, and now, the country is vigorously advocating the use of new energy and clean energy, so electric vehicles came into being. Its main purpose is to replace the traditional car fuel consumption and use the new car power consumption to achieve the role of protecting the environment. But for electric cars, it needs to solve a problem with real-time charging compared to conventional cars. Because with the development of the industrial age, gas stations have been basically spread around the main road and various large residential areas, to facilitate the traditional car refueling. But electric vehicles have only been proposed and manufactured for a few years, and the charging piles have not yet been fully distributed everywhere to meet the charging needs of electric vehicles. Therefore, the charging of electric vehicles is one of the core issues of whether electric vehicles can be popularized. Therefore, the purpose of this paper is to use deep learning algorithm to optimize the scheduling of real-time charging of electric vehicles. This paper refers to the planning ideas of gas station establishment at home and abroad, and after collecting the data on the purchase and growth rate of electric vehicles in recent years, the statistical research is carried out, and then the deep learning algorithm is used to unify and study all the data, so as to get a roughly needed charging pile size and distribution map. Then the sandbox simulation is carried out to get the experimental results. Experimental results show that the strategy of charging optimization scheduling based on deep learning algorithm can help electric vehicles to realize real-time charging better, more convenient and more user-friendly.

With the development of artificial intelligence technology, the intelligent requirement of robot path planning (hereinafter referred to as PP) is higher and higher. Therefore, people gradually study the optimization of PP algorithm more and more mature. At present, the traditional robot has been unable to meet the requirements of task efficiency, which requires the robot to have the function of autonomous and safe path finding. Therefore, we must optimize the traditional PP algorithm, which can find a time-consuming and safe path in many feasible paths. Therefore, PP is the basis of the exploration task. Traditional ant colony algorithm (hereinafter referred to as ACA) has many problems, such as slow convergence speed, low efficiency, easy local optimization and other defects, which have many restrictions on robot PP. Therefore, this paper proposes an Improved ACA (hereinafter referred to as IACA), which can optimize the path selection by improving pheromone. Firstly, this paper analyzes the TMR (hereinafter referred to as TMR) PP and the principle of IACA. Then, the PP of TMR is simulated and tested, which reduces the cycle times. Therefore, the IACA can effectively improve the search efficiency of the optimal path, which improves the overall performance of PP.

With the popularity of the Internet, the server terminal will receive massive data every day. Therefore, China has entered the era of big data. Mobile terminal applications have subverted people’s traditional user experience, which has brought significant changes in business model. Therefore, big data has become an important strategic resource, which has become an important means of technological innovation used by enterprises. Through big data mining and other technologies, enterprises can analyze the global price changes in international trade (hereinafter referred to as ITT). By analyzing user behavior, enterprises can push appropriate products to users. Therefore, big data technology has been applied to ITT, especially Cross Border E-Commerce (hereinafter referred to as CBEC). Therefore, this paper first analyzes the application process of big data technology to CBEC. Then, this paper puts forward the future business model. Finally, some suggestions are put forward.

Customer relationship management based on data mining helps to strengthen the cultivation of high-quality customers and improve customer loyalty and loyalty. China’s e-commerce industry market is becoming more and more prosperous, with the data volume increasing exponentially. Mining the purchasing behavior data of e-commerce industry customers has become a major way for major e-commerce platforms to increase sales volume. Traditional mining algorithms cannot meet the needs of big data analysis. According to different purposes, we will combine mining algorithm and cloud computing platform to mine the potential information of these data and provide beneficial guidance for enterprise operation. This paper starts with the impact of big data on rural e-commerce customers. At the same time, this paper analyzes the current situation of rural e-commerce and proposes the design of rural e-commerce system based on data mining [1].

– Data Communication in customer oriented sector has significant information which is considered as a vital part in enhancing and improving customer base. Loyal customers in business sector are based on buying and selling of products or making use of services provided. Generally, the customers may switch on to variant enterpriser who affords best services with best price hence retaining them is a tedious task unless their communiaction data regarding service is analysed in periodic and to keep track of customer, the enterpriser should know the purchasing pattern and needs of loyal customersThe discovered pattern is also helps to afford proper discounts to the customers at the right time. This research paper experiments customer data in Telecommunication sector to discover useful and interesting patterns. There are so many data mining techniques put forward in revealing the pattern and this paper discusses all the possible mining techniques and evaluates an association rule mining with clustered data to create best rules. For clustering, Hierarchical agglomerative is used and FP-Growth algorithm is used for association rule mining. Both clustered data and association rules results regarding data communication are presented. The research work is implemented in weka tool and assessed with suitable evaluation metrics.

Since the Internet was proposed many years ago, the application of the Internet has become diverse. Now, with the importance of education, the Internet is gradually being used in teaching. Therefore, the purpose of this paper is to explore the application of online and offline teaching mode in embedded system courses under the background of the Internet. In this paper, after consulting the literature on embedded systems and online and offline education models, the data model is constructed by using BP neural network algorithm to process and analyze the relevant data, and the experimental results are drawn. Experimental results show that the Internet-based online and offline teaching model is of some help to the embedded system curriculum.

This paper makes a deep comparison and research on the cost of prefabricated concrete structure which is popularized vigorously by our country. By comparing the cost of three high-rise concrete residential projects with different prefabrication rates, this paper makes a detailed comparison of the total above-ground cost, civil construction and installation, and the cost of prefabricated components. On this basis analyzes the influence of adjusting prefabricated components on the prefabrication rate and the influence of prefabrication rate on the change of cost. Finally, it is discussed that the cost of prefabricated housing project is higher than that of traditional cast-in-place concrete housing mainly because of the increase of steel and concrete content of prefabricated components, transportation cost, main material consumption and comprehensive unit price. In this paper, the cost comparison and difference of prefabricated housing provides reference and reference for the future cost analysis of prefabricated housing.

The rapid development of electric vehicles and the popularization of related engineering applications require us to accurately predict the load of large-scale electric vehicles. In view of the current development status of electric vehicles, this paper first analyses the data basis and method selection of electric vehicle load forecasting. Then, a load forecasting method for large-scale electric vehicles based on FWA-BP is proposed, which fully considers the fluctuation characteristics of electric vehicles. Finally, the method proposed in this paper is verified by a simulation case, and the prediction results prove the effectiveness of the proposed method.

Electrical impedance spectroscopy combined with Neural Networks can be a powerful combination to identify biological materials. This paper utilizes a data set containing two biological samples taken from different species and applies the most popular methods of dimensionality reduction. This is done in order to find out which method is able to minimize computational demand and maximize accuracy in the classification test. This paper proposes that the classic PCA method is the fastest and the most accurate under the configurations used.

With the progress of the times, we find that with the development of high technology and the dawn of a new era of high technology, the modern society has become more and more dependent on computers, if suddenly lose computers, the world will be in a panic. Therefore, the development of computer software technology in the future is the trend, will get more attention and development. Therefore, the purpose of this paper is to explore the modern application of computer software technology and future development trends. After searching for the industry about the penetration of computer software technology and the annual growth of high-tech enterprises and network data on computer technology, this paper uses K NN algorithm to conduct comprehensive analysis and evaluation, and discusses the modernization of computer software technology and the future development trend of computer software technology. The experimental results show that the modern application of computer software technology in the future is the trend, which will get the key help and full development of the country.

The COVID-19 pandemic and various infection control measures have made a great impact on the regular operation of global integrated circuit companies,which caused the“Chips shortage” Phenomenon in the automotive industry. Based on data collected from official channels, industry associations, SIA and enterprises, this paper performed an empirical analysis on the structure of global integrated circuit market, and studied supply and demand in the market. The results of the analyses showed that:1) the pandemic control measures and the re-allocation of market resources are the short-term factors unbalancing the supply and demand for automotive chips. 2) the global shortage of Chips of the automotive industry may continue and the risk of breaking the industrial chain is increasing. Because it is a long industry chain which relies on efficient cross-border collaboration, but it is lack of international prevention and control cooperation mechanism altogether. The current situation presents both challenges and opportunities to China. This paper proposes that China should take efforts in strengthening international cooperation; and improve the core competitiveness of China’s chip industry by the top-level designing and encouraging domestic enterprises to invest in research and development,and to improve their technical performance.

The establishment of online learning platform through the Internet is a more popular way of learning. However, the existing Internet learning platform is still not intelligent enough, especially in data integration and analysis. Therefore, this paper proposes the application and research of computer BD(Big Data) based on structure in Internet learning. In this paper, the structure of computer BD and Internet learning methods for in-depth research and analysis, the existing problems in the Internet learning, combined with structural computer BDAT(Big Data Analysis Technology), established a new Internet learning BD analysis method. In view of the complex source of Internet learning data and large amount of invalid information, this paper optimizes and improves the traditional BD analysis data preprocessing method according to the characteristics of Internet students’ learning. Through the optimization and improvement of the method in this paper, the data analysis ability of the system is improved. In order to further verify the practical application effect of structural computer BD in Internet learning, this paper takes the freshmen of computer major of grade 2018 in a university as the research object of this experiment and carries out relevant investigation and research. According to the survey, 90.3% of students think that BD analysis can improve their mastery of knowledge in online learning. The analysis shows that the structure-based computer BD plays a good auxiliary role in students’ Internet learning.

The late 2019 outbreak of Coronavirus Disease (COVID-19) had an indelible imprint on the humanity. The world is recovering from the outbreak but there is danger of a second wave of the outbreak. To get rid of the outbreak it is necessary to prevent the viral transmission and it is need of the hour to maintain social distancing and wear masks in public areas. The governments are providing strict guidelines to wear masks in public places. It is not manually feasible to check if people are wearing masks or not. In this paper, process of detecting face masks in public places is automated using Convolutional Neural Networks by performing comparative analysis on Sequential bi-layered CNN, VGG-16 CNN and MobileNetV2 CNN architectures. Among these three architectures MobileNetV2 outperformed with a performance accuracy of 99.2%. The efficient Deep Learning architecture of detecting face masks can be achieved with the help of IoT (Internet of Things) devices and cameras, of those who are not following guidelines in public places. Such a system is very useful in post outbreak period and can be installed in public places such as Railway Stations, Airports, Parks, Schools, colleges, offices etc. to track and ensure wearing of masks by people. The contribution of this paper is not to reel-off the finding from the original paper on Face Mask detection with various architectures rather to provide results on the efficiency of using the MobileNetV2 architecture in comparison with Sequential CNN and VGG-16 architectures for crowd analysis mask detection.

3D reconstruction is a key technology for robots to explore unknown environments. The 3D model of the scene can be used to navigate the robot or used for scene segmentation. But when the scenes become larger or the features turns to rare, the frequently-used multi-scales matching and optimization algorithm of images and point cloud cannot work well. In this paper, we make the first attempt to split the 3D-reconstruction structure into 2 parts: pose estimation with the help of RGB images and reconstruction using poses from the aforementioned part and depth images from RGB-D cameras. All of these efforts are for accelerating the speed of pose estimation and promoting the precision of the reconstructed model, the results shows the proposed algorithm in the paper can do better than other reconstruction algorithms.

This paper is primarily focused on understanding the basics of cloud computing economics – specific pricing models and general cost structure and compares between basic cost structures. It also examines 15 pricing models dependent on various bases and attempts to consolidate the different pricing methods used to guarantee maximum profitability to both the service provider and consumer. To avail the services of cloud computing from the cloud service provider, the organization has to incur individual costs. There are three types of costs: fixed, variable, and semi-variable costs. Each type is computed and analyzed. There are more than 30 pricing models though the pay-as-you-go model, subscription model, and pay-for-resources models are in demand. To consider and discuss the basics of cloud computing, emphasizing its economics and pricing models considering various aspects and costing components, using and estimating plans from various cloud Registering suppliers. The paper also considers identifying gaps, if any, present in the current pricing models that are highly preferred with other pricing models that are designed but not implemented due to practical conditions and fear of higher percentage of risk and error and to compare the basic prices provided by the cloud service providers.

This paper presents a portable heart rate and SpO2 blood-oxygen-level monitoring system with robust performance. The system is designed to be user interactive and kept its architecture as minimalistic as possible. The system is small in size, and user compatibility is high compared with other conventional medical devices. This system is based on a MAX30100 sensor which can produce results with high accuracy. Anyone using this device can move during the monitoring of vital signs of the heart. This paper describes the practical application cases in which this type of system is mainly used where the end-user has size and mobility requirements in the first place. A custom-designed GUI is implemented on an OLED to display the measured sensor data in real-time.

The processing requirements of 3D model data in modern manufacturing industry and the data type characteristics of 3D model data source. With the popularity of modern cloud computing platform and the technical advantages of cloud computing platform, the 3D model information is stored in the information system, and the cloud computing technology is used to manage the information system. The purpose of this article is to combine cloud computing technology with lightweight 3D visualization technology such as acrobat 3D to realize lightweight visualization of 3D model in information system. With the advantage of cloud computing platform which does not occupy local resources and has large data processing capacity combined with lightweight 3D visualization technology such as Acrobat 3D,3D model data can be visualized in cloud fast traffic. This paper investigates and studies, compares the speed of reading information between the information system using cloud computing technology and the ordinary local storage information system, and tries to find out the advantages of cloud computing compared with the traditional information system. In addition, this paper also optimizes lightweight on the basis of 3D model data set. Reduce the collective product of its data without destroying the visualization of its basic information.

Industrial competition has been extended from the traditional price and quality competition to warranty competition, from the single manufacturer competition to the whole supply chain system competition. Existing research mainly focuses on the decision-making problems of warranty period and price. However, with the increasing complexity and safety requirements of CNC machine tools and other engineering equipment, customers have put forward higher requirements for product quality and equipment reliability. In this paper, the equipment product deteriorating with aging is taken as the research object, and a two-level service supply chain profit model under Stackelberg game with strong manufacturer bundled sales of “product + warranty” is constructed. Moreover, the impact of warranty period and preventive maintenance frequency on the equilibrium price of product-service package is discussed. This paper provides a new and scientific approach for the optimization of product service strategy.

As the cornerstone for urbanization, public transportation needs to raise attraction. Hence, the key motivation of this research is about balancing the needs of customized-buses and commuters. Regarding satisfaction and fairness of matching schemes, this paper explores the characteristics of customized-bus services and combines actual needs to establish a multi-objective bilateral matching optimization model including maximum matching correlation degree, minimum correlation deviation and maximum matching logarithm. To show the indicators information, the methodologies introduces the grey correlation analysis method adopting four data types. The satisfaction level is obtained by solving the maximum matching scheme that matches the appropriate customized-bus for commuters. The proposed model meets the requirements of operators and commuters, may further improve the attraction of public transportation and its service quality, which can be the key contributions of this paper.

As we know already MANET means Mobile ad-hoc network that works made by two words Mobile and Ad-hoc Network collection of moving nodes shows mobile and kind of temporarily network which do not have any fixed point or centralised administration. Here assortment of nodes forms a network.it can be adjust mobility to bandwidth in high or low level. In this paper, we evaluate performance of DSR protocol. In this unique design shows the results in terms of how protocol changes quickly according to nodes movement along with compare by correlation method. The result of this paper is coming after analysing of different behaviour of DSR can be applied in other new protocols that are using DSR framework. In this paper I showed results in term of break time, PDF, delay, throughput etc by simulation. These simulation results are applied on correlation method and finding out in which point DSR protocol parameters are performing good performance. In this result shows when pause time is increases along with no of nodes then correlation will be high.

The assessment of the anatomical radiography course is to measure the student’s ability to identify anatomy on radiographic images. Conventional paper tests have used radiographic images. The limitations of this method are the number of radiographic films and the time-consuming assessment process. This study aimed to develop a computer-based test (CBT) to multiply the instruments, store pictures, and speed up the assessment time. The CBT software can reproduce radiographic images using digital files and a feature to add overlay markers. The placement of markers is done intuitively via a user interface with drag and drop motion recognition. The CBT software also provides features for instrument bank, instrument selection and management for exams, a relational database for examinee identities and exam entries, and automatic grading. CBT development uses the Waterfall Software Life Cycle (SDLC), with black-box testing using the normal value equivalent partition method. The CBT usability test was carried out by measuring user perceptions (5 administrators and 60 students) and comparing them with conventional methods at the 95% significance level. Research time decreased significantly during instrument preparation and the assessment process. User perceptions show that 76% of students and 85% of managers give positive assessments. There is no difference between conventional and CBT assessments with a p-value = 0.067 (p-value > 0.05) so that CBT is very potential to be used as a substitute for conventional paper-based tests in examining anatomical radiography courses.

To order and provide services, IoT devices need to communicate and talk to many other devices before these services are provided. However, in complex and heterogeneous systems, certain devices need to trust each other. In this paper, we present an approach that is combined of two lines of defense to help build trust in IoT scenarios and to provide more secure interaction. The paper presents a precisely engineered ontology that serves as the main knowledge base for the definition and semantic registration for the entities within the IoT perimeter to facilitate their automated deployment. We show, in this paper, how trust can be used as a pre-defense step for authorization in flexible and an adaptive manner using semantic web technologies. We develop a proof of concept implementation and give the complexity analysis for our approach.

With the continuous progress of my country’s social economy and the continuous improvement of people’s material living standards, people’s demand for teaching standards is increasing. In recent years, many higher vocational colleges in our country have hired many foreign teachers when opening English classes. Higher vocational colleges are different from traditional schools. They pay particular attention to cultivating students’ skills. Using foreign teachers in English classrooms can enable students to understand English knowledge and improve their later use ability. However, in English classroom teaching, too many foreign teachers have brought a lot of problems to the teaching ability evaluation system of English foreign teachers and caused difficulties to the teaching management of higher vocational colleges. Based on the fuzzy K-means clustering algorithm, this paper has launched a series of research and discussion on the evaluation of English teaching ability. In the context of the modern big data era, in order to improve the teaching ability of English teachers, improve the quality of college teachers, and provide students with better teaching services, teachers can start with the main factors that influence and influence the improvement of college English teaching ability. In this paper, by designing a college English teaching ability evaluation system, based on the establishment of the constraint parameter index analysis model, the ability evaluation of the big data information model is carried out, and the ability constraint function information is extracted. A combination of K-means clustering algorithms, clustering and integrating proficiency index parameters, completing the preparation of an educational resource allocation plan, and achieving scientific evaluation of the English language ability. The experimental results show that the use of the K-means fuzzy clustering algorithm to evaluate the English language proficiency has an excellent information fusion analysis capability. This improves the accuracy of educational ability evaluation and the efficiency of educational resources.

The reasonable structure and operation of a hearth can reduce the probability of serious depression and erosion of the lining of a blast furnace (BF). The temperature of the hearth lining is high inside and low outside. Theoretically, there is a heat balance state in the hearth. A three-dimensional computational fluid dynamics (CFD) calculation model that can simulate the theoretical heat balance state of the lining for different hearth design structures and operating parameters was established in this paper. Based on the calculation model, the response surface method was used to study the regression relationship between different factors and the thickness of the lining. In addition, the sensitivity of each factor to the thickness in a state of heat balance was evaluated. The analysis results showed that the blast furnace volume utilization factor and tapping temperature had a greater impact on the theoretical heat balance thickness of the hearth lining. These two factors had an obvious interaction. With the increase in the blast furnace volume utilization factor, the sensitivity of the heat balance thickness to the tapping temperature gradually decreased. The cooling water flow rate had a small effect on the heat balance thickness. The theoretical basis for the optimization of the hearth structure and the improvement of the operation method was provided in this paper.