Application Of Network Technology Research Articles

Biomechanics mechanism of sports promoting psychological benefits based on blockchain and intelligent network security

The application of intelligent network technology has realized the transformation of people’s production and lifestyle, and also promoted the transformation of physical education teaching mode. At present, the application of intelligence in the field of sports is becoming more and more powerful. Using the advanced methods and technologies of intelligent network security, this paper aimed to realize the optimization of sports under the security of intelligent network for psychological benefits and study the sports factors that promote psychological benefits. This paper proposed a target tracking algorithm to guide the integration of intelligent network and sports that promote psychological benefits. Using the target tracking in intelligent network security for reference, the biological mechanism of movement promoting psychological benefits was studied to achieve the integration between the two. In the expert evaluation results of the research on sports and psychological benefits of adolescents, creativity and subjective well-being scored the highest and the lowest, with 97.2 and 93.6 points respectively. Therefore, it is very important to use intelligent network security to study the biological mechanism of sports promoting psychological benefits.

Application of 3D vision and networking technology for measuring the positional accuracy of industrial robots

Application of 3D vision and networking technology for measuring the positional accuracy of industrial robots

Application of Deep Neural Network Technology for Multi‐scale CFD Modeling in Porous Media

Application of Deep Neural Network Technology for Multi‐scale CFD Modeling in Porous Media

Research on the Interactive Technology of 6G Network Online Classroom for Ideological and Political Education Infiltration

Aiming at the application of 6G network technology in online classroom interaction of ideological and political education, this paper first analyzes the challenges faced by online classroom interaction of ideological and political education; and discusses the advantages of 6G network technology in education and its role in improving online classroom interaction. Through literature research and case analysis, this study studies the application and realization of 6G network technology in online classroom interaction of ideological and political education. Then, this paper puts forward a framework of 6G network classroom interaction technology for ideological and political education. This mode can provide more diverse interactive ways for online ideological and political classroom, thus enhancing students' interest and enhancing teaching effect. Finally, this study puts forward the future research direction of 6G network classroom interaction technology. This paper can provide useful reference for the development of interactive technology in online classroom of ideological and political education.

Application of 5G network technology in agricultural production and information management

Establishing a management information system that adapts to the continuous development of agricultural production management today, adopting advanced 5G technology and means, and comprehensively managing the entire agricultural production is an inevitable trend in the development of agricultural production management. In order to improve the effect of agricultural production informatization management, this paper combines 5G (fifth-generation) network technology to construct agricultural production and informatization management models. In view of the massive dynamic agricultural production management data, this paper studies the efficient multi-copy management strategy, which can consider the two aspects of load balancing and energy efficiency at the same time, so as to achieve efficient data access and energy efficiency. Moreover, this paper introduces a time series-based file access heat calculation model. In terms of determining the number of copies of files, this paper proposes a copy factor allocation algorithm centered on the ranking of file access popularity, and gives a characteristic function. Through cluster analysis, it can be seen that the agricultural production informatization management model based on 5G technology proposed in this paper can effectively improve the efficiency of agricultural information management.

INFRASTRUKTUR JARINGAN KOMPUTER BERBASIS CISCO PACKET TRACER DI LABKOM E-18 UNIVERSITAS MUHAMMADIYAH SUKABUMI

Computer network technology plays a crucial role in supporting teaching and learning activities. A Local Area Network (LAN) is a type of computer network that connects devices within a specific area. The application of network technology supports the learning process, including in the Computer Laboratory of the Information Technology Education Program. This research aims to design and configure the computer network infrastructure in the laboratory using wired media. The method used is the Network Development Life Cycle (NDLC), which relies on previous development processes. The research methodology involves simulation with Cisco Packet Tracer to design network topology and configure devices. The research results include the design of computer network infrastructure using wired media and optimal IP configuration. Findings indicate that the devices within the designed network topology are interconnected and can access the internet effectively.

Application of neural network technologies to assess the competence of personnel in the tasks of controlling the operational risk of a credit institution

The article is devoted to issues of controlling the operational risks of a credit institution associated with the actions of personnel. Operational risk control is an important aspect of a credit institution’s business. Despite the fact that the Bank of Russia in regulatory documents described in detail the set of actions that banks should take to control operational risks, in practice credit institutions experience serious difficulties in dealing with operational risk associated with the actions of personnel. This may be explained, first, by the difficulty of identifying and formalizing the specified risk. One of the main sources of operational risks associated with personnel actions is employees’ lack of qualifications. This can lead to reduced availability and quality of services provided by credit institutions, as well as possible financial and reputational losses. The purpose of the research conducted by the authors is to improve the system of control of operational risks in a credit institution using artificial intelligence technologies, including the development of tools for assessing in an automated mode the level of criticality of the influence of personnel competence on the occurrence of operational risk events. To achieve this goal, an artificial neural network (ANN) was developed using the high-level Keras library in Python. This paper defines a set of key indicators that have the most significant impact on the possibility of operational risk associated with the actions of the personnel in a credit institution. The article presents the results of checking the generated sets of training and test data using application software packages that implement mathematical methods to assess the consistency of the generated data sets. The paper presents graphs showing the results of training and testing of the artificial neural network that has been constructed. The results obtained are new and may allow credit institutions to significantly increase the efficiency of their work by digitalizing the solution of tasks to control the level of operational risk associated with the actions of personnel.

Individual Intervention and Assessment of Students' Physical Fitness Based on the "Three Precision" Applet and Mixed Strategy Optimised CNN Networks

With the development of network technology and intelligent application platforms, the "Three Precision" applet as a method of individual intervention for students' physical fitness can not only enable students to obtain the improvement of physical fitness and lifelong sports habits, but also establish a new bridge of cooperation between home and school. The analysis method of student physical fitness individual intervention assessment is affected by a variety of factors such as the framework design of the WeChat applet platform and the subjectivity of the intervention, which leads to the inefficiency of the student physical fitness individual intervention assessment method. To address this problem, we analyse the mode and content of students' physical fitness individual intervention based on the "Three Precision" applet, extract the feature vectors of students' physical fitness individual intervention, construct a system of students' physical fitness individual intervention assessment indexes, and establish a method of students' physical fitness individual intervention assessment based on big data technology and WeChat applet by combining the mushroom propagation optimization algorithm and convolutional neural network. Individual intervention assessment method based on big data technology and WeChat applet. The effectiveness and robustness of the proposed method are verified by using the data recorded in the "Three Precision" applet as the input data of the model. The results show that the proposed method meets the real-time requirements and improves the prediction accuracy of the individual intervention assessment method, which significantly improves the efficiency of the individual intervention assessment of students' physical fitness.

The Application of Computer Network Technology in Civil Aviation Electronic Information Engineering

In this information age, electronic information engineering plays a crucial role in the field of civil aviation, providing indispensable support for efficient operation and security management. This article introduces the close relationship between civil aviation electronic information engineering and computer network technology, analyzes the advantages of network technology in civil aviation electronic communication engineering, and explores in detail the specific application scenarios and roles of computer network technology in civil aviation electronic information engineering. With the continuous innovation and development of technology, network technology will continue to play a more important role in civil aviation electronic information engineering, promoting the civil aviation industry to move towards a higher level.

Rapid investigating of phase I metabolites of SR9009 in vitro horse liver microsomes via feature-based molecular networking approach: Potential applications in doping control

SR9009, a peroxisome proliferator-activated receptor δ (PPARδ) agonist, is known for its potential benefits in energy homeostasis. It failed to receive the United States Food and Drug Administration (USFDA) approval and its illegal distribution has raised concerns. As a result, it has been classified as a prohibited substance by the World Anti-Doping Agency and the International Federation of Horseracing Authorities (IFHA). This study emphasizes the application of the in-silico molecular networking technology to analyze phase I drug metabolites in horses, distinguishing it from conventional methodologies in forensic science. Feature-based molecular networking (FBMN) analysis identified 15 metabolites, with novel major N-dealkylated metabolite (-C8H7NO4S), indicative of diverse metabolic modifications in horse liver microsomes incubation assay. Additionally, a proposed metabolic pathway of SR9009 in the in vitro assay was outlined, including the previously known dehydroxylated metabolite. Finally, the metabolic pathways included in this study were as follows: hydroxylation, dehydrogenation, N-dealkylation dihydroxylation, and combinations. Molecular networking provided insights into MS spectra connectivity, facilitating rapid interpretation and accurate detection of previously undiscovered metabolites. In conclusion, this study contributes to the understanding of SR9009 metabolism in horses and underscores the importance of advanced analytical techniques, such as molecular networking, in enhancing the accuracy and efficiency of metabolite analysis for forensic and doping control purposes.

Research on the Application of Network Technology in Computer Aided Education

As a new technical means, computer network technology should be reasonably applied in modern teaching. Educators should constantly sum up their experience in teaching practice, explore various new forms of computer network-assisted education so that it can be perfectly combined with computer teaching and achieve a flexible and innovative education mode, so as to cultivate more computer professionals for society.

Research on Data Fusion and Analysis Based on Artificial Intelligence Algorithm in Sensor Networks

This paper discusses the data fusion and analysis technology based on artificial intelligence algorithm in sensor networks. Firstly, the basic concept, composition and structure of sensor network are summarized, and its characteristics and challenges are analyzed. Then, the basic principle of data fusion technology and its application in sensor networks are introduced, and the basic concept, classification and application of artificial intelligence algorithm in data fusion are described in detail. On this basis, this paper constructs a data fusion framework based on artificial intelligence, and proposes a new data fusion method combining deep learning and machine learning algorithm. This method can effectively improve the accuracy and reliability of sensor data and reduce data redundancy and noise interference. In addition, the thesis also conducts in-depth research on the application scenarios of data fusion and analysis in sensor networks, including Environmental monitoring, intelligent transportation, medical health and other fields. The effectiveness and practicability of the proposed method are verified by experiments and case studies. Experimental results show that the data fusion technology based on artificial intelligence algorithm can significantly improve the data processing ability and analysis accuracy of sensor networks, and provide strong support for related applications. Finally, this paper summarizes the main conclusions of the study, and points out the shortcomings of current research and future prospects. In the future, the data fusion method based on artificial intelligence will be further optimized and improved, and its application in more fields will be expanded, which will provide more powerful support for the development and application of sensor network technology.

Closing the Gap on Addiction Recovery Engagement with an AI-infused Convolutional Neural Network Technology Application—A Design Vision

Currently, real-time detection networks elaborate the technical details of the Faster Regional Convolution Neural Network (R-CNN) recognition pipeline. Within existing R-CNN literature, the evolution exhibited by R-CNN is most profound in terms of computational efficiency integrating each training stage to reduce test time and improvement in mean average precision (mAP), which can be infused into an artificially intelligent (AI), machine learning (ML), real-time, interactive, recovery capital application (app). This article introduces a Region Proposal Network (RPN) that shares full-image convolutional features with a real-time detection AI-ML infused network in an interactive, continuously self-learning wrist-wearable real-time recovery capital app for enabling cost-free region proposals (e.g., instantaneous body physiological responses, mapped connections to emergency services, sponsor, counselor, peer support, links to local and specific recovery capital assets, etc.). A fully merged RPN and Faster R-CNN deep convolutional unified network in the app can simultaneously train to aggregate and predict object bounds and objectness scores for implementing recovery capital real-time solutions (e.g., baseball card scoring dashboards, token-based incentive programs, etc.) A continuous training scheme alternates between fine-tuning RPN tasks (e.g., logging and updating personal client information, gamification orientation) and fine-tuning the detection (e.g., real-time biometric monitoring client’s behavior for self-awareness of when to connect with an addiction specialist or family member, quick response (QR) code registration for a 12-step program, advanced security encryption, etc.) in the interactive app. The very deep VGG-16 model detection system has a frame rate of 5fps within a graphic processing unit (GPU) while accomplishing sophisticated object detection accuracy on PASCAL Visual Object Classification Challenge (PASCAL VOC) and Microsoft Common Objects in Context (MS COCO) datasets. This is achieved with only 300 proposals per real-time retrieved data capture point, information bit or image. The app has real-time, infused cartographic and statistical tracking tools to generate Python Codes, which can enable a gamified addiction recovery-oriented digital conscience. Faster R-CNN and RPN can be the foundations of an interactive real-time recovery capital app that can be adaptable to multiple recovery pathways based on participant recovery plans and actions. This paper discusses some of the critical attributes and features to include in the design of a future app to support and close current gaps in needed recovery capital to help those who are dealing with many different forms of addiction recovery.

Thermo-Hydric Study of Wood-Based Materials under Thermal Comfort Conditions.

This paper tackles the issue of moisture variation in wood-based materials, explicitly focusing on melamine-coated particleboard (hereafter referred to as melamine) and medium-density fiberboard (MDF) used in the third phase of wood industry transformation. The approach involves a comprehensive strategy for predicting moisture content variation, incorporating numerical simulation, experimental testing, and the application of artificial neural network (ANN) technology to enhance accuracy in furniture manufacturing. The developed ANN models are tailored to predict moisture content changes under specific thermal comfort conditions. Remarkably, these models demonstrate high precision, with an average error margin of only 1.40% for 8% moisture content (MC) and 2.85% for 12% MC in melamine, as well as 1.42% for 8% MC and 2.25% for 12% MC in MDF. These levels of precision surpass traditional models, emphasizing this study's novelty and practical relevance to the industrial context. The findings indicate that ANN models adapt to diverse environmental conditions, presenting a robust tool for optimizing moisture management in wood-based materials. This research contributes valuable insights for improving the reliability and efficiency of moisture content predictions in the wood industry.

Design and implementation of computer network security detection and control system

With the widespread application of computer network technology, computer network security defense capability has become a focus of attention in various industries. The extensive use of computer information systems in various sectors has significantly improved work efficiency but has also introduced security risks and management issues. The deployment of network security detection and control systems allows for effective security monitoring and management of computer operations and real-time network information. This paper presents a computer network security detection and control system based on human-computer interaction, which enables users to handle daily key business processes. The work principles and overall architecture of the network security detection and control system are analyzed and demonstrated. It offers functions such as filtering options, address rules, network security detection, network unreachability, overall traffic analysis, subnet definition, fault diagnosis, and security analysis. Testing and analysis indicate that the systems design achieves the intended goals. In the application of network security features, it can effectively combine various forces to enhance the quality and efficiency of network security, making it widely applicable in various industry units.

Analysis on Modern Application and Development Demand of Electronic Information Engineering

In recent years, there has not been a large-scale promotion of high-precision technology, resulting in the core technology can not be effectively applied in enterprises and related industries still need to further develop electronic information engineering. In this regard, combined with the market economic development situation, intelligence and digitalization has become the main direction of the development of electronic information engineering, through the construction of a perfect information management platform, can make efficient use of resources, a wide range of popularization of science and technology. In the context of the Internet era, people's daily life has changed dramatically, especially the information transmission will become more rapid, efficient and convenient. In the expanding scope of application of computer network technology, can realize the efficient transmission and sharing of information, in the field of electronic information engineering, through the application of computer network technology can effectively ensure the quality of information, and lay a good foundation for the smooth progress of information engineering reform work. In the process of promoting the efficient integration of the two, can make the electronic information engineering needs to be met, which should also increase the integration of resources, computer network technology research and development and application work, so that people's daily lives to enjoy more convenient conditions. The application of digital information processing technology can break through the traditional mode of communication in the application of the process of various shortcomings, prompting the development of digital information engineering more and more rapid. The application of this technology can make all walks of life for information activities to fully meet the needs of all sectors of society, to ensure the smooth and stable information construction activities, so we need to combine the actual situation to further optimize the digital signal construction system, so that it plays a greater role in electronic information engineering. Based on the above situation, this paper focuses on the innovation and development of modern electronic information engineering.

Analysis and Development Trend of Traditional Media and New Media Communication

In the context of the new era, people's access to information has gradually been reformed with the development of science and technology. The wide application of network technology in new media communication not only improves the limitations of traditional media communication, but also effectively promotes the development of the news media communication industry to a higher level, efficiently realizes the instantaneous, large and comprehensive nature of information dissemination, and plays a huge role in the healthy and sustainable development of society. Since the 21st century, the rapid development of Internet information and digital technology, new media communication has also emerged. Cultural communication and news communication have ushered in new development opportunities, but also inevitably some challenges. Advances in digital technology are driving the digital transformation of traditional publications. The dissemination of culture and news is also increasingly the mainstream form of Internet culture and news new media communication. Many developed countries are actively using Internet technology to develop new media communication. This paper will analyse in detail the basic status quo of new media communication under the background of traditional media and the Internet era and look forward to its future development trend.

FUNDAMENTAL ASPECTS OF METROLOGICAL SUPPORT IN IoT

The application of intelligent sensors, network technologies, and machine learning in IoT and industry is increas- ingly widespread as a part of the development and implementation of Industry 4.0, Industry 5.0, and Smart City. It is necessary to review the fundamental principles of metrological support for production. This includes calibration, estimation of measurement uncertainty, traceability, and processing of large data sets to reproduce and compare the results of measurements of physical quan- tities remotely. Modern smart sensors are cost-effective, which makes traditional sensor calibration methods increasingly uneco- nomical. The utilization of advanced networking technologies, along with machine learning, complicates the pre-processing of measured values. Therefore, new solutions are required when it comes to implementing digital metrology. In this article, a metrological framework for the full life cycle of measured data in IoT is presented. It ensures transparency, comparability, consistent quality and reliability of measured data, processing methods and results. The OPC-UA digital data com- munication standard is considered, which provides a single interface for exchanging digital data with devices from different manu- facturers or via different protocols. The syntax of a machine-readable representation of SI units and derived quantities as well as the structure of the sensor network metadata model are also described. Special emphasis is placed on dynamic calibration of sen- sors, determining measurement uncertainty in sensor networks, and implementing digital calibration certificates in IoT and industry.

Analysis of the application of neural network technologies in predicting equipment failure in production

The relevance. The article considers the solution to the problem of analyzing and predicting changes in the dynamics of measuring instrument readings and identifying patterns describing the course and possible periods of failures of technological process equipment by constructing a predictive time series model. A critically important infrastructure facility of a thermal power plant was selected as the studied one – a water pump for pumping process water into neutralizer tanks (the first stage of chemical water purification) located in the chemical workshop of the Krasnoyarsk CHP-3. The general methodology of model construction, requirements for input data arrays, preprocessing algorithms for the formation of samples used for training and testing models are described. To build a predictive model, the work used elements such as recurrent layers of long-term short- term memory LSTM, MinMaxScaler regularization, Dropout layer to reduce the effect of retraining, the ‘Adam’ optimizer using error back propagation methods and optimization using the steepest descent method using conjugate gradients, the learning error function is the average root of the error (Root Mean Squared Error, RMSE), as well as plot_predictions visualization. The algorithms are implemented in Python, in the Anaconda Jupyter Nootebook core. Based on the results of testing the training, the model showed sufficient performance of this method, since the time spent on creating the model and obtaining a forecast plays an important role when launching the model in real production conditions. In conclusion, recommendations are given to improve the accuracy of the forecast, the developed interface of the production system is presented and the directions of future research in this area are outlined.

Організаційно-педагогічні умови оптимізації використання дистанційних технологій у процесі самоосвітньої діяльності студентів

The article deals with the necessity to optimize self-education activity of pedagogical university students’ in the context of innovative technologies introduction, time deficit, and the need for continuous professional development of the specialists. It presents the approaches of major Ukrainian researchers to the definitions and features of higher education students’ independent work. The attention is drawn to the project technology, in particular to the web-quest, which is considered to be an effective method of organizing students’ independent work at a pedagogical university. The organizational and pedagogical prerequisites for optimizing the effectiveness of pedagogical university students’ independent work are defined as follows: involvement of students in project activities based on an interdisciplinary approach; application of cloud network technologies in the organization of project activities of future teachers based on an interdisciplinary approach; the use of universal cognitive and metacognitive strategies of self-education in the process of project activities organized by means of cloud technologies; combination of both collective and individual forms of cognition in the project activities, which provides opportunities for self-actualization of the future teacher’s personality. The article summarizes the features that prove the feasibility of using web-quests in the independent study activity of prospective teachers: problem-based tasks; the role guidance; combination of individual and group forms of independent work; accumulation of different types of activity (cognitive, data search, analytical and exploratory). The study discusses a modern effective form of organizing students' independent work – an interdisciplinary web-quest, aimed at finding solutions to specific problems, the scope of which goes beyond the limits of a particular field of knowledge. The peculiar features of students’ and teachers’ activities are analyzed in accordance with the structure of the interdisciplinary web-quest (objectives, content, information, presentation components). Keywords: organizational and pedagogical prerequisites, independent work, students, optimization, project technology, web-quest, interdisciplinarity, cloud technologies.