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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Applied Mathematics and Nonlinear Sciences
Number of Followers: 2  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2444-8656
Published by Sciendo Homepage  [389 journals]
  • Research on Hazard Analysis and Control Strategy of Power System
           Dispatching Operation Based on Information Fusion Technology

    • Abstract: In this paper, a decision model for grid fault diagnosis based on multi-source information fusion is established, and the probability of component fault is obtained by using the change of switching quantity characteristic information and electrical quantity characteristic information during grid fault, and the static fault degree obtained by switching quantity information and the voltage and current energy distortion degree and fault degree obtained by electrical quantity information is used as independent evidence bodies, which are fused by improved D-S evidence fusion technique, and the fused The results are decided by improved fuzzy C-mean decision model to finally determine the fault components. Then, the optimal control of the grid is studied, and the corresponding self-healing control model is established according to the different operating conditions of the system so as to propose a self-healing control strategy of the microgrid based on the improved particle swarm algorithm. After analysis and verification, the accuracy of the grid fault diagnosis method proposed in this paper reaches 0.9681, and the diagnosis results are consistent with the pre-defined fault elements compared with FCM. The system network loss decreases from 0.146 kW to 0.031 kW, and the maximum power supply capacity increases from 1.574 to 2.468 after using the improved particle swarm algorithm-based microgrid self-healing control strategy. Therefore, the method in this paper can improve the reliability of grid operation and resist the risk of accidents.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Analysis of power system scheduling operation mode based on
           multi-objective optimization algorithm

    • Abstract: Exploring the majorization strategy of the power system (PS) dispatching operation is to achieve economic cost reduction and reduce environmental pollution. In this paper, starting from the PS dispatching model, the adaptive Corsi variance is introduced to get rid of the local optimum using particle swarm majorization procedure, and the adaptive Corsi variance multiple swarm coevolutionary procedure is constructed through coevolutionary strategy and information sharing strategy. The MCPSO-ACPM procedure is used to optimize the PS scheduling operation model, and experiments are conducted on both load and unit for the optimized scheduling model. From the load majorization results, the peak-to-valley variance is concentrated from 176.02KW to 110.51KW compared with the original load, and the peak-to-valley ratio is reduced by 0.718, which saves customers 98.63 yuan in electricity purchase cost. From the scheduling majorization prediction, the PS output power prediction value of 1 min during the day is closest to the actual measured value of output power, and its prediction deviation is about 2.67%. This shows that the use of a multi-objective majorization procedure can realize the optimal dispatch of PS and achieve the reduction of economic cost.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Research and reflection on college physical education classroom teaching
           based on SSGAN model

    • Abstract: Teaching behavior recognition has a wide range of applications in the smart classroom and is one of the important means to achieve educational intelligence. To improve the performance of indoor teaching behavior recognition using CSI in complex scenes, this paper proposes an indoor teaching behavior recognition algorithm based on multi-feature fusion MLSTM by eliminating background noise to circumvent the influence of the experimental environment on CSI. To address the problem, the model cannot generalize in recognizing new users, and the labeled samples of new users are difficult to obtain in large quantities in a short period of time. In this paper, a new user recognition algorithm based on the SSGAN model is constructed, and then the input and output of MLSTM are modified as the discriminator of SSGAN to improve the recognition performance of the model for new users by semi-supervised learning. The recognition accuracy of the M-LSTM model on the sports, daily, and dance datasets is 0.985, 0.966, and 0.944, respectively, and the recognition accuracy of the SSGAN model on the three datasets is also around 90%, as verified by different experiments. The p-value is less than 0.05, and the student’s interest in physical education in the experimental group is 2.6 times higher than that in the control group. Therefore, the model proposed in this paper has good practicality.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Building a “party building + political thinking” model for grassroots
           mass organizations in China in the era of big data

    • Abstract: Although research into big data technologies is advancing quickly, it has not yet been used for the “party building + political thinking” paradigm of grassroots mass organizations. The structure, purpose, and method of clustering analysis in data mining technology, as well as the various data types involved in clustering analysis technology, are studied in this paper after first analyzing the development status of data mining technology and the issues in the process of data mining. Secondly, we investigate the advantages of big data mining for “Party formation + Civic Affairs” at the local level, including four points, the management of grassroots party members, the education of party members, the ideological dynamics of party members and cadres, and the development of the relationship between party groups and teachers and students. Moreover, the “task assessment quantification table” from the “party building + thinking and politics” model was utilized as the data source, and cluster analysis was carried out using the k-mean technique. The percentages of the three categories of grades in the cluster assessment, which were 30%, 62%, and 8%, were found to be compatible with the percentages of the three categories of scores, which were 21%, 68%, and 11%. This study brings some reference significance to both party building and thinking and the government work of college counselors; this contributes to raising the bar for political thinking, party organization, and governance.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • A study on the measurement and standardized assessment model of student
           learning outcomes in vocational institutions

    • Abstract: As society requires a deeper understanding and demand for the actual abilities of students in higher education institutions, traditional assessment tests no longer meet the current needs. This paper first divides assessment techniques into two main categories from an application perspective: assessment of student learning performance and in-depth cognitive diagnosis. Students are automatically provided with appropriate learning content based on their ability level and learning style, providing them with accurate and timely feedback. Secondly, a new fuzzy inference model is proposed to determine students’ student outcomes by addressing the obvious shortcomings of the fuzzy sets usually used for student outcome assessment. Finally, the validity and usefulness of its assessment model are verified by the student learning performance on a real data set. The results show that the fuzzy inference assessment model designed in this paper can obtain an assessment accuracy of 85.8% for the learner’s learning outcomes, which has a good assessment effect. And the fuzzy inference assessment model also retains the greatest advantage of linear fitting regression, which reflects the correlation between the parameters of students’ learning behaviors and the final learning outcomes. The assessment method based on the fuzzy inference model predicts learners’ learning risks and provides learning interventions in advance for smart learning, and also provides new ideas for deepening education reform.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • The application of communication art in tea packaging design under the
           modern aesthetic perspective

    • Abstract: This paper first explores the color emotion analysis of visual communication art, using linear equations and correlations to analyze the correlation between color emotion and image color space and classifying the degree of warmth and coldness and emotional expression of various colors. Then a multi-model hybrid color emotion calculation method is proposed, using positive-negative attribute values to get color positivity-taking values, using heavy-light to get color weight-taking values, and using warm-cold to get color heat-taking values, respectively. And the global color emotion feature of the image is calculated by using the extraction algorithm of central area weighted features. Finally, the influence of visual communication art on tea packaging is analyzed in terms of graphic design, color and layout of the packaging. In terms of packaging focus, 64 people think that they should focus on the embodiment of humanistic customs and culture, 87 people think they should focus on aesthetic aesthetics, and 75 people think they should highlight the characteristics of the product. In terms of the cultural and emotional orientation of packaging, 70.9% of people think that packaging diversity is the main tendency, and 50.9% think that retro style can better reflect the cultural characteristics of tea. The art of visual communication makes tea packaging design more design and aesthetic.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Study and experimental analysis of the effect of static stretching
           combined with resistance training on neck and shoulder syndrome in college
           students in the context of big data

    • Abstract: In order to explore the effect of variable resistance training with edge computing and information physical system model combined with computer vision under static traction in neck and shoulder rehabilitation training of college students. In this paper, 90 cases of neck and shoulder patients admitted to XX Hospital from February 2022 to December 2022 with neck and shoulder syndrome in a university in Shanghai, were selected as study subjects and randomly divided into 45 cases of the observation group and 45 cases of the control group. Edge computing and physical information system were used for data processing, and patients in the observation group received variable resistance training combined with static traction, and patients in the control group received static traction. The results showed that after rehabilitation training, the effective rates of the observation group and the control group were 91.11% and 71.11%, respectively, and the differences between the groups were statistically significant (P<0.05). Comparing the visual analog scale (VAS) and neck disability index (NDI) of the two groups, the VAS score of the observation group was lower than that of the control group after treatment (P<0.05), and the NDI score of the observation group was lower than that of the control group (P<0.05). It indicates that the use of static stretching and resistance training for neck and shoulder syndrome in college students is more effective in relieving patients’ neck pain and is worthy of clinical promotion and application.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Research on standardization and quantitative extraction of information of
           ancient building components based on BIM

    • Abstract: The standardization and quantitative extraction of component information are accomplished by establishing an information model of ancient buildings. Based on BIM technology, i.e., model visualization, virtual simulation and BIM-related software, this paper combines the dimensional types of components to realize the parameterization settings of components and further designs the information structure of ancient building components. The standardization rate, repetition rate, repeatability coefficient, and standardization coefficient of the components were calculated by quantitative methods, followed by the determination of standardized components and the quantitative extraction of component attribute features based on the BIM model map. Based on the standardization of the information of the five-room, nine-purlin-hipped ancient building members based on BIM, the spatial location of the ancient building members was determined with the following parameters: bucket size 32.5 mm, eaves height 2180 mm, net height 1802.8 mm, and side legs 23.1 mm. BIM application provides a new way of thinking for archiving and protecting ancient architectural data.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • A Markov Chain Prediction Model Based on Rural Tourism Supply and Demand
           Matching Governance Model from the Perspective of Cultural Tourism

    • Abstract: Exploring rural tourism supply and demand matching to promote high-quality rural tourism development. In this paper, we start from the Markov chain model, use the gray GM(1,1) model to divide the state of the Markov chain model and correct the relative error for the weighted Markov chain prediction. The corrected errors are used to construct the gray-weighted Markov chain model, and the arithmetic tests and example data analysis are conducted for the model. In terms of the model accuracy, it was improved by 12.75%, 9.28%, and 7.98% compared with the ARIMA model, ES model, and W-Markov model, respectively. From the perception of supply-demand matching, four demands are in low perception, and three demands are in high perception. This indicates that the use of the gray-Markov chain model can effectively realize the analysis of rural tourism supply and demand matching and also provides theoretical support for rural tourism to realize the supply and demand matching with tourists.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Innovative exploration of the implementation path of teaching reform of
           law courses based on Internet technology

    • Abstract: The teaching reform of law courses is to further enhance the development speed of the law education business. In this paper, a practical teaching platform for law courses is constructed based on Internet technology, and context-aware class case matching is performed by using word embedding of the BERT network and contextual information extraction of the Bi-LSTM model. Then the local semantic feature extraction by CNN network and collaborative filtering algorithm based on user preferences are used to achieve intelligent recommendation analysis of law course cases, and experimental simulation analysis is conducted for the teaching platform and recommendation algorithm. In terms of platform performance, the average response time is 24.5ms, which is 12.67% and 26.99% less than that of the Mucuo platform and Tencent Classroom, respectively. From the recommendation algorithm, the accuracy of recommendation based on students’ preferences is 68%. This shows that the practical teaching platform of law courses can be the implementation path of law teaching reform in the Internet era.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Exploring the path of high-quality development of teacher education and
           teaching based on collaborative filtering algorithm

    • Abstract: In this paper, firstly, a collaborative filtering algorithm based on users is used to mine the past behaviors of the target users. Items are glanced and composed to gain insight into the user’s preferred items and to mine preference information, followed by searching for users with similar preferences to the user in the system and calculating the similarity between users and users using cosine similarity and pearson similarity, and predicting the target user’s rating of an element based on the rating value of an item given by a nearby user. Then the personalized teaching system is designed by the actual situation of the online teacher teaching courses on the platform and the course demands of the platform students, and the path of high-quality improvement of teacher training and teaching is studied, mainly focusing on three aspects: clear value orientation, good personalized system teaching planning, and implementation of multiple evaluations. When analyzing the student-teacher ratio of teacher education institutions, the student-teacher ratio of Central China Normal University has the largest value, 24.79%, while the values of East China Normal University and Nanjing Normal University are relatively small, 15.97% and 15.39%, respectively. Either based on theory or based on practice, any institution of higher education with an excessive student-teacher ratio. This study helps teachers grow professionally and provides a good foundation for eventual individualized student development.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Analysis of factors influencing social responsibility in private education
           industry based on multimodal discourse analysis model of big data

    • Abstract: The analysis of factors influencing social responsibility in the private education industry is to promote better implementation of social responsibility in the private education industry. In this paper, a multimodal depth quantization algorithm is constructed by using a convolutional neural network and deep denoising self-coding network, and a multimodal discourse analysis model is jointly constructed based on the MDQS algorithm and multimodal semantic space. For the model constructed in this paper, quantitative analysis of the data is carried out by examples and performance evaluation experiments. From the performance evaluation, the average accuracy of the model is improved by 22.52%, 27.19% and 7.87% compared with the CCQ algorithm, SEPH algorithm and CDQ algorithm, respectively. In terms of the influencing factors, the highest frequency of the word “interest” is 20.43% and the lowest frequency of the word “culture” is 13.72%. This shows that the multimodal discourse analysis model can effectively analyze the factors influencing the fulfillment of social responsibility in the private education industry and help the private education industry to make targeted improvements to implement social responsibility.
      PubDate: Sat, 30 Sep 2023 00:00:00 GMT
  • Optimization study of intelligent decision-making system for coal
           processing plant based on big data analysis

    • Abstract: Optimizing the intelligent decision-making system of coal processing plants is better to improve the economic efficiency of coal processing plants and realize the high-quality development of coal processing plants. In this paper, an integrated intelligent decision-making platform for a coal processing plant is constructed based on big data technology, and the intelligent data analysis techniques of the platform are optimized by using an improved whale optimization algorithm and BP neural network. Examples analyze the optimized crude coal slurry and flotation systems’ processes, and the economic benefits are analyzed. From the optimization of the crude coal slurry sorting system, the ash content in the 0.25mm particle size region was reduced from 55.37% to 13.12%, and the ash content in the −0.125mm particle size region was reduced from 42.68% to 15.96%. From the flotation system optimization, when the flotation time increases from 120s to 180s, the ash content increases from 16.27% to 17.19%, and then to 240s, the ash content increases to 19.44%. Using the integrated intelligent decision-making platform can achieve a net increase in revenue of 4,276,800 yuan for the crude coal slurry sorting system and a net increase of 11,274,200 yuan for the flotation system. This shows that the integrated intelligent decision-making platform can improve the coal processing plant’s quality and efficiency and promote intelligent production.
      PubDate: Wed, 27 Sep 2023 00:00:00 GMT
  • Energy-saving and noise-reducing integrated task allocation model for
           machining systems and its application

    • Abstract: In this paper, firstly, based on the application model of optimal scheduling of machining system for green manufacturing, two application models of the energy-saving scheduling model and energy-saving and noise-reducing scheduling model in a multi-model framework are combined, and the resource environment coefficient matrix of the two application models is established as well as the solution process is studied with the parallel machine problem. Then the system's architecture is constructed, and its basic operation flow, functional modules, etc., are designed and conceived. The application of the system is studied in conjunction with a gear machining workshop of a machine tool factory, and the machining system's energy and noise reduction performance is verified based on experiments. The results show that the energy consumption of the machining system is reduced by 0.514 kW-h by machining only the above six gear parts with a small difference in the maximum machining completion time and that the spindle speed has the most significant effect on the machine tool machining noise at a significance level α of 0.05. The analysis of this study verifies that the energy-saving and noise-reducing scheduling arrangement method can reduce the system machining energy consumption and noise, which is important for green manufacturing.
      PubDate: Wed, 27 Sep 2023 00:00:00 GMT
  • Cluster control technology of transmission line intelligent inspection
           drones based on 5G communication

    • Abstract: This paper firstly analyzes the network topology model of the UAV cluster network and wireless 5G communication channel model by modeling and briefly analyzes the idea of topology movement control for flying self-organized networks. Then, a cluster-based structure and reinforcement learning clustered routing protocol is proposed for the problem of easy breakage of routing forwarding paths caused by smart inspection of transmission lines based on UAV clusters for 5G communication. Finally, a cluster structure-based precedence routing protocol is designed, an adaptive routing protocol based on location and link quality Q-learning is used between clusters, and fast and reliable routing is achieved by combining the routing table maintained by itself. The simulation results show that ARP-L-Q (average end-to-end delay 4.22, average packet loss rate 88.09%, average packet rate 2.37, average control overhead 2.52) protocol performs better than GPSR and GACB protocols, and the experiment verifies that ARP-L-Q protocol can better achieve the high dynamic reconfiguration, high stability and reliability, and low communication delay of UAV cluster-based 5G communication network. Characteristics and requirements. This study has application prospects in both civil emergency and military mobile communication and has certain military significance, theoretical value and application value for thus promoting UAV innovation.
      PubDate: Wed, 27 Sep 2023 00:00:00 GMT
  • Analysis of the Path to Improve the Effectiveness of Ideological and
           Political Education in Universities Based on Information Fusion Technology

    • Abstract: This paper firstly constructs a reasonable education resource model according to the features of Civic Education Resources (CERs) and proposes an integration scheme of CER Library in universities based on information fusion technology. Secondly, the storage structure of Lucene’s inverted index is optimized for the management features of the CER Model, and a full-text index library of educational resources for resource retrieval is constructed. Then the advantages and features of information fusion techniques are used to provide college students with exclusive, practical, personalized and customized Civic Education measures to innovate the concept of ideological and political education (IPE) in colleges and universities. Finally, through the subject index of ideological education resources constructed based on the LDA model, the semantic processing of user queries, the design of effective experimentations to confirm the accuracy of the retrieval of ideological education resources, and its evaluation indexes are considered comprehensively from several aspects such as retrieval speed and accuracy rate. The results show that the maximum P @ N value of improved Lucene index retrieval is 1, which is 0.4 larger than that of traditional Lucene-based index retrieval, and the average performance of improved Lucene index retrieval is improved than that of traditional Lucene-based index retrieval in P @ N indexes. This study helps universities to innovate the concept of IPE to retain the ideas up to date and retain pace with the times.
      PubDate: Wed, 27 Sep 2023 00:00:00 GMT
  • A study on teaching English in higher education based on an improved deep
           belief network

    • Abstract: This paper combines machine learning with acoustic features to design an automatic pronunciation error correction system. The article first adopts Meier’s inverse spectral coefficients and random forest algorithm to classify and detect learners’ pronunciation errors and clarify learners’ pronunciation problems, from which the MFCC-RF model is proposed. Then, using the feature self-learning capability of deep belief networks and the OneClass idea of SVM, we proposed a DBN-SVM model to overcome the shortcomings of the MFCC-RF model in pronunciation classification and error detection due to unbalanced samples and missing data, which resulted in low error detection rate and poor coverage of error types. By comparing the model’s performance for pronunciation error detection, the DBN-SVM model was more accurate than the other two algorithms in detecting the three error types with a stable accuracy of around 80%. Finally, when the experimental class was taught with the automatic pronunciation error correction system, the experimental class improved by 19.5 points after one semester of study, while the control class only improved by 6.8 points. Hence, the DBN-SVM model-based pronunciation mistake correction system has significantly impacted the speed of change and advancement in English teaching techniques while substantially enhancing the quality of oral pronunciation and learning efficiency of English learners.
      PubDate: Wed, 27 Sep 2023 00:00:00 GMT
  • Application of Information Fusion Technology in Innovative Teaching of
           Music Theory Courses in Universities

    • Abstract: In this paper, we first investigate the steps to implement a fusion algorithm that determines the quality function through fuzzy theory. Then, we utilize D-S evidence theory for decision-level fusion to identify the target data based on the collected data. The two algorithms, fuzzy theory and D-S evidence theory, are then combined. The affiliation function in fuzzy theory calculates the information collected from the sensors to find out the confidence level and patterns. Finally, the information fusion technology was analyzed in terms of its usage rate in music theory teaching, its impact on piano playing, and its impact on music theory teaching. In terms of utilization rate, 21 student teachers had information fusion technology utilization rate between 50% and 60%, and 21 student teachers had information fusion technology utilization rate between 50% and 60%. In terms of the impact of information fusion technology on the teaching of music theory, a comparative analysis of the pre and post test data T=−6.55 (P<0.0001) showed that the difference in the post test data was significantly higher than that of the pre test. This indicates that the facilitating effect of information fusion technology on music theory teaching is obvious.
      PubDate: Sat, 23 Sep 2023 00:00:00 GMT
  • A Study on the Impact of Cloud Computing Performance Efficiency on Task
           Resource Scheduling

    • Abstract: In this paper, the inertia weighting strategy of the particle swarm is improved by using the properties of periodicity and fixed upper and lower bounds of sinusoidal function to model the task scheduling problem in cloud computing as a mathematical problem, and the improved particle swarm algorithm is discretized, and the improved discrete particle swarm algorithm is applied to task scheduling by corresponding encoding method. The task scheduling algorithm (PSOACO) that fuses the fast convergence and small computational power of the particle swarm algorithm with the global exploration capability of the ant colony algorithm for scheduling tasks is proposed. Two test cases, PageRank and wordcount, are selected to measure the performance of the PSO-ACO algorithm. In the performance comparison running the PageRank test case, the PSO-ACO algorithm obtains a performance speedup ratio of 3.8 times that of the native Domino when 50,000 pages are added. In the execution time comparison for the wordcount test case with an additional data set, the PSO-ACO algorithm is nearly 2.8 times faster than the native Domino when adding 1GB of data. Thus, the fusion algorithm reduces the task completion time and achieves a balance between the algorithm’s computational effort and the scheduling’s convergence performance.
      PubDate: Sat, 23 Sep 2023 00:00:00 GMT
  • Research on the current situation and countermeasures of student
           employment management in higher education institutions based on multiple
           regression analysis

    • Abstract: Employment management in higher education institutions has an important influence on the employment situation of graduates, and this study aims to give corresponding countermeasures by analyzing the current situation of employment management. This paper investigates and studies the employment situation and the perceptions of employment management of graduates from higher education institutions and obtains relevant data on employment and employment management. The correlation between career guidance courses, career guidance methods and career guidance websites and graduates’ employment rate is analyzed using multiple regression methods to develop countermeasures for employment management optimization. The regression coefficients of professional construction, ideological construction and psychological counseling of career guidance courses on their comprehensive evaluation were 0.1654, 0.0872 and −0.0475, respectively. The regression coefficients of the three influencing factors on the comprehensive evaluation of the career guidance website were 0.7485, −0.0213 and 0.1457. the regression coefficients of the three influencing factors on the comprehensive evaluation of career guidance methods were 0.7485, −0.0213 and 0.1457. The multiple regression models achieved good significance. Based on the multiple regression analysis, the key factors of employment management in higher education institutions were clarified by data modeling methods, which helped to better propose countermeasures for employment management.
      PubDate: Sat, 23 Sep 2023 00:00:00 GMT
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