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This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms. [ABSTRACT FROM AUTHOR]

The objective of this special issue is to address recent research trends and developments in stochastic control systems and their applications to control, filtering, communication, manufacturing, fault detection, and systems [...]

The teaching characteristic of colleges and universities is determined by the teaching quality of teachers, but it is difficult to calculate with a linear mathematical explanation how to judge the teaching quality of teachers. In most colleges and universities, expert evaluations, supervision groups, peers listening to lectures in the classroom, and students' after-class evaluations are still used to determine the teaching ability of professors. It is undeniable that these judgment methods have certain practicality. Especially for new teachers, experts and peers can find problems in time and help new teachers quickly correct them, and they can also know how to better practice the "student-centered" teaching concept from the feedback of students after class. However, these judgment standards also have their limitations. For example, the classroom quality judgment standards for experts and peers are mostly set by some administrative departments according to social requirements, leadership requirements, and subjective cognition. Various institutions should have different weights for the same criterion, and if they all employ the same criteria, it will easily lead to imprecise conclusions. In addition, the expert group and students are also subjective and incomplete in the stage of teacher evaluation. Based on different nonquantitative factors, the evaluation standards for teachers in colleges and universities also need the scientific theoretical basis. Therefore, many scientists are considering whether to use a more intelligent and reasonable way to judge the quality of teaching. Nowadays, with the popularization of artificial intelligence (AI), the use of neural networks (NNs) is becoming more and more comprehensive. According to the characteristics of NNs, this paper uses the GA NNs algorithm to signify that this algorithm can be effectively used in the evaluation of teachers' teaching quality. In this paper, a large number of experimental results show that the use of GA NNs can be used to evaluate the quality of teaching, and the evaluation results can be obtained quantitatively in this way. For the assessment of teaching quality, quantitative results are also more powerful in teaching evaluation. [ABSTRACT FROM AUTHOR]

The development of a country's language and literature is the orientation of the country's spiritual strength, and it is also the embodiment of the national literary quality. Chinese language and literature has a very long history of development, the content of which is broad and deep, and there are countless literary works. It is impossible for one person to fully understand these works after spending his entire life. As a result, these literary resources are optimized and constructed and classified into various disciplinary contents, so that students can systematically learn literary knowledge according to their own interests. Therefore, this paper conducts a disciplinary analysis of the construction of Chinese language and literature resources by integrating the method of fuzzy mathematics. Through the study of fuzzy mathematics, the method of fuzzy cluster analysis is proposed to classify Chinese language and literature resources, and the method of fuzzy comprehensive evaluation is used to evaluate the contents of various subjects. This paper selected the richness of subject content, faculty strength, talent demand, and subject difficulty as evaluation indicators. By comparing the scores of various discipline indicators before and after resource optimization, the results showed that using fuzzy cluster analysis, disciplines 1 and 2 were popular disciplines. Disciplines 4 and 5 were general disciplines, and disciplines 3 and 4 were unpopular disciplines. Using the fuzzy comprehensive average, the comprehensive scores of the six disciplines were 84, 79, 80, 82, 83, and 75, respectively. The quality of resource construction in these disciplines was high. In addition, the scores given by experts were all above 70 points, with an average score of about 83 points. It showed that the method of using fuzzy mathematics to construct Chinese language and literature resources has achieved good results, and the results obtained by using the method of disciplinary analysis were reliable and feasible., Author(s): Yanli Li (corresponding author) [1]; Yun Li [1] 1. Introduction China's education system is continuously optimized, and the construction of Chinese language and literature resources should also be accelerated. [...]

Sports culture industry (CI), as a new sunrise industry, has achieved unprecedented development in the world, and many developed countries regard it as a pillar industry. In our country, the sports CI is quickly growing and contributing significantly to national economic growth. The layout mode of the sports CI is crucial in identifying regional growth advantages and resource development, as well as maximizing industrial function allocation and creating sports CI development strategy. This paper takes Dalian's sports CI as the research object and expounds the concept of sports culture, sports CI, and spatial layout of sports CI through analyzing and sorting out previous research results. This study analyses the evolution and spatial distribution of the Dalian sports CI, as well as the current challenges, using theories and methodologies from sports science and economic geography and proposes countermeasures for Dalian to develop its sports CI. Regional development theories, such as central place theory, growth pole theory, point axis theory, and Maslow's hierarchy of needs theory, have important theoretical value and practical guiding significance for the rationalization of spatial distribution of Dalian sports CI. On the basis of analyzing the resources, traffic network, and economic development of Dalian sports CI, the paper constructs the spatial distribution mode of Dalian sports CI, namely, two core points, three development axes, and multiple nodes, and puts forward the measures to optimize the spatial distribution of Dalian sports CI. This paper examines the current condition, resource architecture, and major structure of supply of leisure sports service items in the Zhejiang coastline area using the literature and field inquiry methodologies. On this basis, the optimization countermeasures of combining Zhejiang coastal leisure sports service products with recreational fishery and yacht economy are put forward to perfect the system of coastal leisure sports service products and improve the supply capacity., Author(s): Lu Qi (corresponding author) [1] 1. Introduction In recent years, with the improvement of people's living standard and the increase of leisure time, people's demand for coastal leisure sports [...]

In the field of condition-based maintenance, maintenance costs and system reliability criteria are the primary considerations for traditional maintenance management. These methods lack consideration of the environmental impact caused by equipment degradation, such as excessive emissions and energy consumption. In addition, because equipment degradation has various impacts on the ecological environment, companies with excessive emissions and energy consumption can receive huge fines, making it of great value to study ecoconscious maintenance strategies. In this paper, we propose a condition-based maintenance strategy considering energy consumption and carbon dioxide emissions. The major objective of the research is to extend a model which integrates ecological aspects with maintenance decision-making and optimization. The simulation and sensitivity analyses conducted verify that the model proposed can minimize total costs, as well as the environmental impact., 1. Introduction Maintenance refers to maintaining and repairing equipment and systems to ensure they are in good condition. It is an indispensable activity related to machines, ranging from grinding or [...]

Occupying the same crucial position as land resources, ecological resources originate from the marine system nourish various aspects of human welfare and the world economy. It is significant to apply efficient decision-making approaches, which assist with the achievements of sustainable development and environmental protection. This paper aims to review decision-making approaches in marine management and present a comprehensive bibliometric study. To achieve this goal, bibliometric analysis is used to analyze the current status and emerging trends in this field, including the cluster network, the collaboration network, and burst detection. The results show that the number of publications in this field has increased rapidly, and it has become an increasing interdisciplinary research subject. Besides, marine conservation is the largest clusterand the USA is the most productive country in marine management research and the marine policy and NOAA are the most productive journal and institution, respectively. Furthermore, 'Diversity,' 'Management,' and 'Population' are the most influential keywords, and 'People' and 'Public participation' are the emerging trends in decision-making and marine management. Our work clarifies where we are now in terms of marine management and decision-making and how do they serve each other to achieve sustainable development. More importantly, some deep insights could be given that motivate researchers to learn the connections and collaborations between natural science and social study, which is our effort to deal with the social-ecological obstacle as well., Author(s): Wei Zhou [1]; Yan Chen (corresponding author) [1]; Qiu Shu (corresponding author) [1] 1. Introduction Ecological resources that originate from the marine system nourish various aspects of human welfare [...]

The article discusses various reports published within the issue including one by Li on the fractal time series from the point of view of systems of fractional order, article by Liu on the chaotic time series and an article by Cattani on the fractal shapes and the symmetries of DNA from a view of fractal time series.

With the development of computer technology, computers have become a part of people's lives and the Internet has connected the world's networks as a whole. Computer technology is changing people's study, life, and work. People's traditional education mode, thinking, content, method, and talent training program have a significant impact. The development from traditional to computer technology-based teaching methods has brought new developments and leaps in educational technology. This paper analyzes the research background, significance, and research status of the advanced mathematics auxiliary teaching system, introduces the related technologies and development modes used in the development of the system, and especially discusses the access database technology by ADO and the mathematical expression based on MathML language. Secondly, starting from the actual teaching, we analyze the functional requirements and performance requirements of the system in detail and make detailed planning and design for the system architecture, database selection, functional modules, etc. The design and implementation process of this teaching system are summarized. The teaching strategy inference engine is the key to the personalization and intelligence of the ICAI system. According to the learning models provided by different students, the system designs a corresponding teaching sequence for the learners by controlling the meta-knowledge of the domain knowledge base. The teaching strategy inference engine cuts the domain knowledge tree, selects the knowledge points suitable for the student, and sorts the selected knowledge points reasonably to generate an optimal teaching sequence. According to the students' learning situation, combined with the teaching rules in the teaching rule library, the students' grades are dynamically adjusted, so as to select new learning content for students and provide teaching suggestions in time. The student model is the premise of the ICAI system to achieve individualization and intelligence. The system makes a comprehensive evaluation and diagnosis of students through fuzzy comprehensive evaluation and fuzzy reasoning. On this basis, a cognitive student model is established, which is the teaching strategy that provided the basis for the formulation. [ABSTRACT FROM AUTHOR]

Deep learning is an important concept introduced into modern learning science. It is different from the surface learning of mechanically and passively acquiring knowledge and storing individual information but emphasizes learners' active and critical learning. It wants them to understand the full meaning of what they have learned. By establishing a link between existing knowledge and new knowledge, it transfers existing knowledge to a new environment, makes decisions, and solves problems. Deep learning plays an important role in students' learning. Deep learning ability is the key factor affecting the quality of learning and the development of students' academic ability. The quality of in-depth teaching is difficult to guarantee, which requires a complete, comprehensive, and evaluation system to evaluate it. This paper introduces the analytic hierarchy process to weight the indexes in mathematics deep learning and puts forward some suggestions on creating an environment for deep learning. The experimental results show that teachers' teaching accounts for the highest proportion of primary indicators, reaching 67%. Multimedia resources account for the highest proportion of secondary indicators, reaching 73.01%. This paper then puts forward some suggestions for indicators with large weights. [ABSTRACT FROM AUTHOR]

There is a huge amount of mathematical information in the world, and mathematics is everywhere and nowhere. Bayesian theory is based on a process of statistical inference that requires the calculation of general and prior information to obtain a posteriori information. Its main features are the use of probabilities to represent all forms of uncertainty and the use of probabilistic rules to enable learning and inference, estimating the probability of future occurrences by calculating the probability of a past time. In order to bring mathematics closer to life, this paper explores the teaching of mathematical applications in terms of material selection, teaching arrangement, and professional integration. At the same time, in order to better realize mathematics application teaching, effectively improve the classroom effect of mathematics application teaching, and make students better accept mathematical knowledge and apply it to practical applications, the design of mathematics application teaching in this paper is also based on Naive Bayes. [ABSTRACT FROM AUTHOR]

In feedback control systems, delay as a generic part of many processes, including industrial [1], communication [2], economical [3], traffic [4], automotive technology [5], and biological [6] ones, is a [...]

With the rapid development of artificial intelligence (AI) concept technology, it promotes the innovation of educational concept. Mostly for the education information analysis in the class of mathematics in the university, it should be based on a big data-driven system to promote the quality of teaching in the classroom. In the method of teaching math in university, teachers should take full advantage of the benefits of a big data-driven system powered by AI, grow a good teaching model for students, promote education through big data, progressively teach students according to their aptitude, develop in a tailored direction, increase teaching quality and effectiveness, and finally create more great talents for our country. For the sake of improving the resource sharing and the management level of the curriculum which teaches advanced knowledge about mathematics teaching, based on a particle swarm optimization algorithm, an advanced math teaching system is proposed in this paper. The fusion model that can be used in the teaching process of math in university is constructed, the adaptive scheduling of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resources is realized by an optimization algorithm used for fusion particle swarm, the autocorrelation feature of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resources is extracted, and the adaptive optimization of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resource fusion is realized by fuzzy correlation feature matching and statistical analysis. In the process of particle swarm optimization, the combination of statistical features is studied and managed, the resource scheduling and information fusion are realized, and the management capability of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching is promoted, and the experimental results demonstrate that the designed system has a high integration of teaching information resources and strong information scheduling ability and improved the management level of the curriculum which teaches advanced knowledge about mathematics complementary teaching. [ABSTRACT FROM AUTHOR]

The times are advancing, the Internet is constantly advancing, and there are many examples of the combination of technology and life. The way people buy agricultural products is no longer limited to the physical market, but is being transformed into an agricultural product e-commerce website to achieve digital transformation. So far, a large number of agricultural products have been collected on the agricultural product e-commerce website, they come from all over the country, and different types and different regional agricultural products have appeared. On the other hand, users are faced with a large number of commodities on the Internet. If they simply use search engines, consumers need to spend a lot of time to find the commodities they are interested in by browsing. Searching from the recommended ones greatly reduces the product set and optimizes the performance of the e-commerce platform. Therefore, the generation of the personalized recommendation system meets the needs of the market. This research will improve the related recommendation algorithm by improving the user similarity calculation method based on artificial intelligence technology, improve the execution efficiency and accuracy of the improved algorithm, and use related technologies to develop an agricultural product e-commerce recommendation system. It has important theoretical and practical significance to save users' browsing time, improve users' shopping experience, and speed up the construction of agricultural product e-commerce., Author(s): FengQin ZhuanSun (corresponding author) [1]; JiaoJiao Chen [1]; Wenlong Chen [1]; Yan Sun [1] 1. Introduction With the continuous advancement of the Internet, cloud computing, and big data technologies, [...]

The instability of single-phase grounding faults makes it difficult to track the fault development process accurately. In this paper, a transition conductance tracking method for single-phase grounding fault identification is established to perceive the health state of the distribution network. To solve the situation that the insulation parameters of the neutral ungrounded system are difficult to obtain, a phase-shift method is proposed to measure the insulation parameters accurately. Based on the measurement results of insulation parameters, a transition conductance tracking model is constructed to monitor the changes of transition conductance in real time. Since the transition conductance varies steadily, it can be used as an important parameter to evaluate the development of a single-phase grounding fault. The innovative tracking method is verified by the simulation model, and the error analysis shows that the tracking error of transition conductance is less than 9%. The simulation results demonstrate that the proposed method can track the development of grounding faults effectively and give a quick warning to the operator. The fault identification theory adopted in this paper is not limited by the neutral grounding mode and especially can be applied to situations where the neutral point parameters are not adjusted., Author(s): Zhuochao Wu (corresponding author) [1,2]; Mingyu Fang [1,2]; Jiexuan Cai [1,2]; Weixing Qian [1,2]; Zhenya Ji [1] 1. Introduction The single-phase grounding faults (SPGFs) in the medium voltage distribution [...]

With the rapid development of information technology and the Internet, it is difficult for university readers to find books of real interest or value from a large number of books by relying only on traditional retrieval-based services. This paper applies data mining technology and personalized recommendation algorithm based on semantic classification for new book recommendation service in university libraries. The personalized recommendation algorithm based on semantic classification establishes a book feature model and a reader preference model based on title keywords. The different recommendation strategies in the system framework are detailed. For the borrowing data of different colleges and departments, the improved association rule algorithm is used to mine the book association rules, and the reader's borrowing history is matched with the association rules to generate a book recommendation list; according to the reader's borrowing preference characteristics, the reader preference model is used as the basis. Class subdivision and then combined with the book feature model and reader preference model, the collaborative filtering recommendation algorithm and the content-based recommendation algorithm are applied to generate a book recommendation list. The active service method not only improves the service level of the university library, makes the development of the university library more comprehensive and humanized but also explores the potential information needs of readers, improves the borrowing rate of books in the collection, and maximizes the utilization rate of book resources. In the experiment of this paper, the personalized recommendation algorithm division of semantic classification is adopted. According to the division of its algorithm, the corpus is divided into 9603 training documents and 3299 test documents, with certain accuracy., Author(s): Nan Pang (corresponding author) [1] 1. Introduction The rapid development of the Internet and information technology has gradually freed human beings from the state of information scarcity and stepped [...]

The enterprise financial management cloud platform can conveniently obtain computing resources such as network, storage, and services according to actual needs based on the network. The maintenance of resources can be realized in a general way, and various computers and their storage resources can be effectively integrated and effectively utilized. But internal conflicts within the company, such as asset management issues, can seriously hinder the company's long-term growth. In order to solve the above problems, this paper analyzes the actual needs of the current financial information management personnel of enterprises and the business process of financial management. Combined with the characteristics and requirements of the current enterprise financial management, the integrated fuzzy mathematics cluster analysis was introduced to complete the design and implementation of the financial IMS, and to build a modern, systematic, and scientific enterprise management system. It greatly improves the financial level of the enterprise and the intelligent level of enterprise management, provides objective and strong support for enterprise decision-making through the enterprise's financial management ability, and finally realizes the long-term development of the enterprise. The prediction effect of the samples under the FCM is shown in Table 3. The prediction effect of the training samples of the normal company is 83.33%, and the prediction effect of the training samples of the ST company is 80%. The test sample prediction effect of normal company is 83.33%, the test sample prediction effect of ST company is 80%, and the comprehensive prediction effect under the FCM is 85.28%., Author(s): Xianli Zhang (corresponding author) [1] 1. Introduction Although Chinese enterprises have achieved many achievements in the construction of financial informatization, there are still many deficiencies, especially in the aspect [...]

Buffer operators can effectively weaken the impact disturbance of the system and restore the original appearance of the system. However, the buffer strength of the existing buffer operator is fixed, and the buffer strength cannot be adjusted. So the buffer effect for the system impact disturbance is not flexible enough, and the accuracy of the prediction model established is not high. In order to solve this problem, this paper proposes weakened buffer operators with time-varying parameters, analyzes the properties of the newly constructed buffer operator, and proves the superiority of the new buffer operators theoretically. Finally, the new buffer operators proposed in this paper are applied to the prediction of the number of employees in China's air transport industry and the unsafe events in China's civil aviation. The results show that the proposed buffer operators are better than the existing buffer operators, which further verify the validity and practicability of the proposed buffer operators., Author(s): Pumei Gao [1]; Jiefang Liu (corresponding author) [2] 1. Introduction In the quantitative prediction of the system, the model often loses its effectiveness due to the disturbance of external [...]

The research presents an analysis and comparison of the Taguchi Design and Response Surface Methodology (RSM) in optimizing the laser cutting machine parameters on dimension accuracy for stainless steel products. The paper studies effects of input factors such as cutting speed, nitrogen pressure, power, and frequency on the quality cutting of stainless steel (304) specimens. In this paper, two objectives are examined: targeting laser-cut edge to perpendicular 90 degrees and maximizing the cutting accuracy. The paper proposes a simple formula to optimize both targets by minimizing one new function definition, i.e., dimensional error. An L9 orthogonal array of Taguchi Methodology is adopted to minimize the number of experiments and shorten analysis time to achieve the optimal parameters. These results are compared with RSM. Box-Behnken Design (BBD) type of RSM requires more experiments than the Taguchi approach. RSM regression models as the quadratic functions of the control factors are developed to minimize the dimensional error of cutting products. Then, Analysis of Variance (ANOVA) and graphs will be analyzed to determine the influences of variables on the responses. Both Taguchi's method and RSM found that the most influential factor on dimension accuracy is cutting speed, followed by laser power. While Taguchi provides good graphic visualization for quickly predicting the optimum condition, it cannot examine the interaction effects as RSM due to the lack of data. Besides, RSM reveals the percentage contribution of factors on dimensional error. Cutting speed has a maximum contribution, i.e., 39% of the total. The interaction of cutting speed and power contributes 16% of the total. In this study, RSM can predict optimum conditions more accurately than Taguchi. There are misleading results from the Taguchi method compared with RSM. However, the difference between these objective values is insignificant. The validation experiments show that the Taguchi method can be a practical approach for optimization problems. It can help reduce cost and time and achieve the desired optimal outputs. With cutting problems requiring high precision, the RSM method is highly recommended for identifying optimal parameter settings and interaction effects. With problems that their experimental runs consume high cost and time, Taguchi can be a suitable method for screening the significant variables. Although Taguchi and RSM are used widely for optimization problems in many fields, choosing the right methodology for various objectives is still a concern with different arguments and needs further research. Therefore, this study could be an adequate reference for parameter optimization problems in various fields., Author(s): Vi Nguyen [1]; Faisal Altarazi [2]; Thanh Tran (corresponding author) [1] 1. Introduction Laser cutting is one of the most widespread methods to shape and separate a metal workpiece [...]

The purpose of this note is (i) to point to a reference missing in the paper (Mayo-Maldonado et al., 2018) and (ii) to show that some of its content is covered in a more general setting by the results in that reference (Goknar, 1972)., 1. Introduction As explained in the sequel, some of the results developed for specific circuits in Sections 5 and 7 of the paper given in [1] can simply be obtained [...]

Earth and Environmental Systems (EES) models play a crucial role in facilitating the research and development in science and engineering disciplines. To better conceptualize the EES by using mathematical equations, [...]

The tremendous development of digital computers in the last decades has provided continuous improvement of various sophisticated methods in computational electromagnetics (CEM). CEM analysis methods provide results within an appreciably [...]

This paper presents a backpropagation neural network algorithm for data compression and data storage. Data compression or establishing model ten coding is the most basic idea of traditional data compression. The traditionally designed ideas are mainly based on reducing the redundancy in the information and focus on the coding design, and its compression ratio has been hovering around dozens of percent. After the traditional coding compression of information, it is difficult to further compress by similar methods. In order to solve the above problems, the information that takes up less signal space can be used to represent the information that takes up more signal space to realize data compression. This new design idea of data compression breaks through the traditional limitation of relying only on coding to reduce data redundancy and achieves a higher compression ratio. At the same time, the information after such compression can be repeatedly compressed, and it has a very good performance. This is the basic idea of the combination of neural network and data compression introduced in this paper. According to the theory of multiobjective function optimization, this paper puts forward the theoretical model of multiobjective optimization neural network and studies a multiobjective data compression method based on neural network. According to the change of data characteristics, this method automatically adjusts the structural parameters (connection weight and bias value) of neural network to obtain the largest amount of data compression at the cost of small information loss. This method has the characteristics of strong adaptability, parallel processing, knowledge distributed storage, and anti-interference. Experimental results show that, compared with other methods, the proposed method has significant advantages in performance index, compression time and compression effect, high efficiency. and high-quality robustness., Author(s): Dahuan Wei [1]; Gang Feng (corresponding author) [1] 1. Introduction In large-scale database, the storage and analysis of its internal information and complex data usually take a lot of [...]

This paper proposed a Bayesian optimized extreme gradient boosting (XGBoost) model to recognize small-scale faults across coalbeds using reduced seismic attributes. Firstly, the seismic attributes of the mining area were preprocessed to remove abnormal samples and high-noise samples. Secondly, chi-square bins were performed for each feature of the processed attributes. The weight of evidence (WOE) was calculated in each container, and each element's information value (IV) was obtained to characterize the importance of each feature. Features with low information values are reduced to remove high-noise attributes. Thirdly, the reduced attributes are decomposed by variational modal decomposition (VMD) to obtain new features. Finally, the optimized XGBoost model and traditional methods were constructed to identify and locate faults across coalbeds. Here, the Bayesian-optimized XGBoost objective function was used to balance the training weights of asymmetric examples. As the Bayesian optimization algorithm quickly falls into local optimums, it does not easily balance the ?exploit? and ?explore? approaches. Therefore, this paper proposed an adaptive balance factor change algorithm to overcome this shortcoming. Comparing the identification outcomes, the optimized XGBoost model has a higher prediction accuracy than the BP neural network, support vector machines (SVMs), K-means clustering, extreme learning machines (ELMs), and random forests (RFs). In summary, the proposed method can improve the identification accuracy of small-scale faults in coal mining areas., Author(s): Xin Wang [1]; Changwei Ding (corresponding author) [1]; Tongjun Chen [2]; Ting Yu [1] 1. Introduction Small-scale faults are prone to geological accidents during coal mining, including roof falls, [...]

This paper presents an efficient energy management scheme for an islanded DC microgrid included with vanadium redox flow battery (VRFB) with supercapacitor based hybrid energy storage system for a 3-phase variable load profile. The analysed islanded DC microgrid system is composed with sources such as lithium-ion batteries, fuel cell, supercapacitors, and vanadium redox flow battery (VRFB), along with involved DC/DC (buck, boost, and bidirectional) and DC/DC converters. In this paper, an adaptive proportional plus integral (PI) control based energy management scheme is implemented. The main theme on which the performance of this system is analysed is the state of charge of energy storage devices (VRFB, supercapacitor, and Li-ion battery) and the overall efficiency of system. The voltage spikes due to sudden change in load have been regulated by using supercapacitor and Li-ion battery with VRFB. To minimize the fuel consumption, an adaptive proportional plus integral control strategy is being used. In this paper the modelling and simulations are carried out by using MATLAB/SIMULINK software to demonstrate minimizing the effect of load fluctuations with the help of supercapacitor and batteries by synchronizing it to the DC microgrid., Author(s): S. Hameed [1]; I. Prabhakar Reddy [2]; V Ganesh [1]; Aruna rai Vadde (corresponding author) [3] 1. Introduction In the modern world, when the conventional sources of energy are [...]

This paper takes the physical fitness test data and the physical health self-assessment data as the research objects. The decision tree algorithm is used to construct a decision tree model for students who fail to meet the physical test. Thus, the classification of students with different physical qualities is realized. The association rule Apriori algorithm is used to mine the association of physical fitness test indexes so as to judge the hidden law between students' physical fitness and behavior habits and get the correlation information of various physical health indexes. The back propagation (BP) neural network algorithm is used to establish the physical fitness test prediction model. By using these data mining models, this paper explores the hidden association information in college students' physical test data, which can provide more scientific and effective guidance for students' physical tests., Author(s): Junwu Suo [1]; Cuixiang Guo (corresponding author) [2]; Guifang Wang [1] 1. Introduction There are many reasons for the decline of young people's physical health, one of which is [...]

In this paper, a kind of BAM neural networks with leakage delays in the negative feedback terms and time-varying delays in activation functions was considered. By constructing a suitable Lyapunov function and using inequality techniques, some sufficient conditions to ensure the existence and exponential stability of antiperiodic solutions of these neural networks were derived. These conditions extend some results recently appearing in recent papers. Lastly, an example is given to show the feasibility of these conditions., 1. Introduction It is known that neural networks have been applied in numerous fields, such as pattern recognition, classification, associative memory, optimization, signal and image processing, parallel computation, and nonlinear [...]

This paper is concerned with consensus of heterogeneous nonlinear multiagent systems via distributed control. Both the cases of leaderless and leader-following are systematically investigated. Different from some existing results, completed consensus can be reached in this paper among heterogeneous multiagent network instead of bounded-consensus. First, a novel distributed control protocol is proposed, and some general consensus criterions are derived for multiagent systems without leader. Second, a leader with unknown but bounded input for the heterogeneous multiagent network is considered; aperiodically intermittent communications among followers are considered to avoid channel blocking in this case. Finally, two simulation examples are presented to verify the effectiveness of the main results., 1. Introduction In recent years, distributed coordinated control of multiagent systems has been widely studied due to its easy implementation, strong robustness, and high self-organizability. There were many applications of [...]

1. Introduction Singular systems, also referred to as descriptor systems, semistate systems, differential-algebraic systems, or generalized state-space systems, have been one of the main research topics in control theory for [...]

This paper proposed a panel data clustering model based on D-vine and C-vine and supported a semiparametric estimation for parameters. These models include a two-step inference function for margins, two-step semiparameter estimation, and stepwise semiparametric estimation. In similarity measurement, similarity coefficients are constructed by a multivariate Hierarchical Nested Archimedean Copula (HNAC) model and compound PCC models, which are HNAC and D-vine compound model and HNAC and C-vine compound model. Estimation solutions and models evaluation are given for these models. In the case study, the clustering results of HNAC and D-vine compound model and HNAC and C-vine compound model are given, and the effect of different copula families on clustering results is also discussed. The result shows the models are effective and useful., 1. Introduction When we use panel data to cluster, it is difficult to use the spatial correlation and cross-correlation of temporal dimension simultaneously. There is very little literature in the [...]

The existing results on consensus of multiagent systems are mainly based on homogeneous systems; that is, all agents have the same dynamic model. This paper focuses on consensus of heterogeneous multiagent systems, which consist of first-order and secondorder integrator agents. Based on integral sliding mode control, the consensus protocols of heterogeneous multiagent systems with disturbances are investigated under the directed networks. Some sufficient conditions for finite-time consensus are obtained by utilizing Lyapunov stability theory. Finally, some examples verify the effectiveness of the proposed control schemes., 1. Introduction Distributed cooperative control of multiagent systems (MAS) has attracted much attention due to its engineering applications in spacecraft, multiple mobile robots, transportation planning, and so forth [1-3]. An [...]

Complex optimization problems can be tackled by means of mathematical programming methods as well as by means of (meta)heuristic methods. On the one hand, mathematical programming methods give us a [...]

In this paper, the theory of viscoelastic material relaxation modulus testing, testing method, and material relaxation modulus parameter solution method is studied. Based on the IoT sensor test devices and the Hertzian mathematics theory, the relationship between the compression amount, pressure, and relaxation modulus of a rigid spherical indenter pressed into a flat viscoelastic material is studied. In combination with the Boltzmann superposition principle, the relaxation modulus test method and theory for viscoelastic materials are proposed. A good fitting curve was obtained to solve the relaxation modulus parameters of various materials under the three-element Maxwell model. The experimental setup and the results of the parameter fitting were analyzed to verify the feasibility and accuracy of the results. [ABSTRACT FROM AUTHOR]

The stock market is usually regarded as the bellwether of the economy, which can reflect the economic operation of a country or region. As a significant part of the financial market, the equity market plays a critical role in the financial sector. Whether in academia or investment field, stock market forecasts always excite great interest. Financial news is an important source of information in the financial market, which reflects the mood swings of investors and often goes hand in hand with the market trend. However, due to the unstructured and professional characteristics of financial news, there are challenges in accurately quantifying their emotional tendencies. This research is based on Hidden Markov Model (HMM) to segment financial news text. The recognition and classification of news emotion is carried out by bidirectional long short-term memory (BI-LSTM) algorithm, and long short-term memory(LSTM) model is trained with text emotion index and stock market transaction data to realize the prediction of stock market. The results show that BI-LSTM algorithm performs better than the emotional dictionary algorithm in emotional recognition. And the emotional index of financial news text can enhance the accuracy of stock market prediction to a certain extent. Compared with using stock market technical index and news text vector only, the prediction accuracy can be improved by about 2%., Author(s): Jianxin Bi (corresponding author) [1] 1. Introduction The stock market is often regarded as a barometer reflecting the national economic situation. The fluctuation of stock price can reflect the [...]

In the farming era, the ancestors of Miao moved to a mountainous area in Qiandongnan to avoid wars. When they started their settlement construction, people gave priority to how to deal appropriately with the great survival pressure they were facing. This paper uses the methods GIS spatial analysis, morphological index, and spatial syntax to explain the spatial characteristics of Miao traditional settlements from the perspective of both regional scale and individual settlement and explores the adaptation mechanism. The results show that (1) spatial distribution of settlements shows a tendency of agglomeration and significant spatial heterogeneity; the maximum kernel density is in the Leikaitai area, which is featured by an inclined ?T? shape; (2) settlements are concentrated in areas mainly around Qingshui River and Duliu River, with an elevation of 500-1000m, terrain relief of 10-20m, and the slope of 5-15°; (3) the external boundary of settlement is mainly finger-shaped and buildings showed a large concentration of small distribution; and (4) settlements have generally formed an overall landscape pattern of ?mountain-water-field-forest-building,? with the space center appearing inside the settlements and the road connecting the outside of the settlement. This paper summarizes the intrinsic relationship among settlements' spatial characteristics, the natural environment, and the social and economic environment and concludes the internal morphological evolution of the settlement which has shifted from survival adaptability to active search for development. The results of this research can provide a valuable reference for traditional settlement protection, utilization, and sustainable development., Author(s): Yalun Lei [1]; Hongtao Zhou (corresponding author) [1]; Meng Wang [2]; Chuan Wang [3] 1. Introduction In the 1950s, French geographer Mark Sone proposed the theory that geographical conditions [...]

Because most of the production and transmission components of the power system are exposed to the natural environment, they are vulnerable to the threat of natural disasters for a long time and the resulting circuit damage is also large. With the improvement of people's living standards and the improvement of electricity demand, more and more attention has been paid to the normal operation of the power system. Improving the disaster resistance ability of the power system is an important prerequisite to ensure the national economy and life. In view of the above problems, in order to solve and improve the power supply recovery ability of the distribution network in the face of various disasters, this paper focuses on the analysis of the disaster response stage of the intelligent distribution network under natural disasters and the comprehensive data and related indicators of the power grid system, so as to establish the evaluation index system of the power grid's predisaster tolerance and postdisaster resilience and, through the introduction of the fuzzy comprehensive evaluation theory, quantifying the disaster response capability of the distribution network. Finally, through the introduction of case analysis, the case results show that the intelligent distribution network disaster response ability evaluation algorithm based on fuzzy comprehensive evaluation constructed in this paper can accurately calculate the disaster response ability of the distribution network and has an important guiding role in the disaster prevention and reduction of the distribution network., Author(s): Jianna Ouyang [1]; Shan Li (corresponding author) [1]; Yangjun Zhou [1]; Yubo Zhang [1]; Rongrong Wu [1] 1. Introduction Power grid is an important foundation for the sustainable development [...]

In most cases, the blasting object is the rock mass. Because the rock mass has the characteristics of anisotropy and inhomogeneity, there are often structural surfaces such as joints, fissures, faults, and weak interlayers, but details are basically impossible. Compared with the rock, these structural surfaces are weak parts, and the explosive energy required for breaking is smaller. It is difficult to take into account the existence of each weak side when the explosives are arranged in the blasthole. Therefore, after the explosive explodes in the rock mass, the explosive gas will first rush out from these weak parts, entraining individual fragments to form flying rocks. Aiming at the problem that it is difficult to accurately obtain the rock motion information in the blasting process of open-pit mines, this paper selects the kernel function to establish the support vector machine model and optimizes the parameters of the support vector machine model to obtain the optimal blasting rock trajectory prediction model. Under the protection of special protection devices, the information of the rock movement during the blasting process is collected at a higher frequency, and the analysis algorithm of the rock movement characteristics is researched on the basis of inertial navigation technology. The algorithm is used to analyze and output the rock movement. The curve of velocity, position, and kinetic energy provides a theoretical and technical basis for the study of rock movement law in the blasting process of open-pit mines. Through the experimental analysis, based on the cross-validation method, through the support vector machine model parameter optimization and comparison evaluation parameters, the optimal prediction model of the blasting rock trajectory is obtained as the support vector machine model based on the radial basis kernel function. The mean value of root mean square error and mean absolute error of this model are 0.102 and 0.0674, respectively, and the performance is the most robust among the three types of models., Author(s): Jianyang Yu (corresponding author) [1,2]; Xiaobo Liu [2,3]; Liancheng Wang [2]; Hao Wu [2] 1. Introduction Blasting is an important means of open-pit mine production. The blasting process releases [...]

At present, the field of construction engineering is limited by various situations, such as complex construction environments and many uncertain factors. Therefore, on the basis of the engineering network diagram, this paper proposes a construction project schedule management method based on the fuzzy logic neural network algorithm. By building a neural network, a large amount of historical data is input, and computers are allowed to calculate the key routes, thus predicting the construction period, and a construction project in a city is taken as an example for simulation experiments. The traditional construction period management scheme expects a construction period of 55 days. The planned construction period optimized by the project management technology integrating fuzzy logic neural network algorithm is 55 days, which is 2 days less than the traditional construction project schedule management technology and will not cause construction period delay. The simulation results show that this algorithm is more accurate and more efficient in calculating the key lines when dealing with large-scale projects, which can help the construction unit to quickly find the optimal strategy and effectively reduce the construction delay and capital loss caused by uncertainty factors., Author(s): Xiaobing Yu [1]; Hengzhong Zuo (corresponding author) [2] 1. Introduction Construction project schedule control refers to the preparation of the project schedule according to the overall schedule objectives and [...]

Polygroups are an extended form of groups and a subclass of hypergroups that follow group-type axioms. In this paper, we define a triplet single-valued neutrosophic set, which is a generalization of fuzzy sets, intuitionistic fuzzy sets, and neutrosophic sets, and we combine this novel concept with hypergroups and polygroups. Firstly, the main goal of this paper is to introduce hypergroups, polygroups, and anti-polygroups under a triplet single-valued neutrosophic structure and then present various profound results. We also examine the interaction and properties of level sets of triplet single-valued neutrosophic polygroups and (normal) subpolygroups. Secondly, we rank the alternatives and select the best ones in a single-valued neutrosophic environment using the weighted cosine similarity measure between each alternative and the ideal alternative. Finally, we provide an example that clearly shows how the proposed decision-making method is applied., Author(s): M. Shazib Hameed [1]; Zaheer Ahmad [1]; Shahbaz Ali [2]; Augustine Larweh Mahu (corresponding author) [3]; Walid F. A Mosa [4] 1. Introduction The classical methods of mathematical analysis [...]

Multivariate statistical analysis is a new statistical method emerging in recent years. Due to the continuous update and continuous development of Internet technology, statistical analysis of data from all walks of life is required. The method of multivariate statistics was soon applied to all walks of life, bringing great convenience to people's work and industry. The method of multivariate statistics is used to process and evaluate the data, guide the school's physical education work, and make suggestions for various functional units. This paper focuses on the multivariate statistical analysis of principal components and factors, uses factor analysis to comprehensively evaluate college students, and uses the variance method to make statistics on the four-year physical education performance of 141 undergraduates in grades 13 and four subjects. And based on this, the effect of different majors on their academic achievement is discussed. This paper believes that in college physical education, this method should be applied to daily physical education, and students should be rationally analyzed and scientifically evaluated. This model can make a comprehensive investigation of the education work so that their education work can be improved more effectively, and sports talents in colleges and universities can get better development., Author(s): Bin Ding (corresponding author) [1]; Yanchun Jian [1]; Xingliang Yuan [1] 1. Introduction The study of multivariate statistics has been applied in many industrial fields. At present, universities in [...]

With the rapid development of civil aviation transportation, an increasing number of airport groups are formed. However, the existing literature on fairness mostly focuses on the fairness among airlines. There is no research on the realization of scheduling fairness among airports with overlapping resources in the airport group. The goal of this paper is to comprehensively consider efficiency and fairness in slot scheduling, where fairness should include both interairline and interairport fairness. Subsequently, we developed a collaborative optimization model for airport group that takes into account the above three objectives and then uses the ? -constraint method to solve it. In addition to considering the basic operational constraints of the airport, the model also sets different adjustment boundaries to achieve the scheduling priorities specified by IATA. Applying the model to the Yangtze River Delta airport group, the results show that improved flight schedules can significantly reduce flight congestion, and a relatively fair scheduling result can be obtained by weighing airlines' fairness and airports' fairness. The model can improve the transparency of slot scheduling decisions and can be used as an auxiliary tool to help make slot scheduling decisions., Author(s): Xianghua Tan (corresponding author) [1,2]; Shasha Wang [2,3]; Weili Zeng [2,3]; Zhibin Quan [4] 1. Introduction In recent years, with the rapid development of the civil aviation transportation industry, [...]

This paper examines the relationship between ownership concentration and bank performance. In order to verify the linear or nonlinear relationship between bank performance and ownership concentration, we select the data of the top 100 commercial banks in the BVD-Bankfocus database for regression analysis. The study found that there is no significant correlation between ownership concentration and bank performance, whether it is linear or nonlinear. Besides it found that the better the economic environment, the better the performance of commercial banks; the higher the leverage ratio, the higher the profitability, and the better the bank performance. It also found there is no correlation between bank performance and bank size., Author(s): Yongsheng Fang [1]; Zepeng Shangguan (corresponding author) [2] 1. Introduction As discussed by Iannotta et al. [1], the ownership structure of commercial banks can be determined by two dimensions: [...]

To address the problem of diagnostic accuracy and stability degradation caused by random selection of the initial parameters for the wavelet neural network (WNN) fault diagnosis model, this paper proposes a network troubleshooting model based on the improved gray wolf algorithm (IGWO) and the wavelet neural network. First, the convergence factor and policy for the weight update are redesigned in the IGWO algorithm. This study uses a nonlinear convergence factor to balance the global and local search capabilities of the algorithm and dynamically adjusts the weights according to the adaptability of the head wolf ? to strengthen its leadership position. Thereafter, the initial weights and biases of the WNN are optimized using the IGWO algorithm. During the backpropagation of the WNN error, momentum factors are introduced to prevent the model from falling into local optimization. Experimental results show that the IGWO algorithm is far better than GWO in terms of convergence speed and convergence accuracy. Furthermore, the average diagnostic accuracy of the IGWO-WNN model on the KDD-CUP99 dataset reaches 99.22%, which is 1.15% higher than that of the WNN model, and the stability of the diagnostic results is significantly improved., Author(s): Chengsheng Pan [1]; Aixin Jin (corresponding author) [1]; Wensheng Yang [1]; Yanyan Zhang [1] 1. Introduction With the development of big data and the Internet of Things (IOT) [1], [...]

With the rapid development of the world economy, the infrastructure of the nations is improving day by day. The transportation system is a backbone of infrastructure of any country. Transportation plays a vital role in environmental issues as well. For a country like China, municipal road plays an important role in the transportation system but the construction should be environmental friendly. The municipal road construction should be free from carbon emissions and the road should be of good quality to increase the efficiency of road transportation. The construction quality of the road is affected by the temperature, type of material, eco-friendly material, and material quality which makes the construction quality uncertain. The environmental issues are alarming. The natural resources are exploited in order to strengthen the infrastructure of any country. There is a need to find the ways to use eco-friendly material for infrastructure in all the developing nations. The traditional quality evaluation methods do not preprocess the construction quality data to keep track of the composition of environmental friendly material in the overall material. The collected dynamic monitoring data of municipal road construction quality are sparse and the key data is not accurate to evaluate the material and its consequences on the environment. Now is the time to pay attention to the environmental issues before any economic and social progress. The evaluation method of municipal road construction quality based on an improved genetic algorithm (GA) is proposed in this paper by considering carbon emissions to keep the environment free from pollution. In the process of quality evaluation of load, a spanning tree constraint method is also introduced to form a related constraint relationship. The improved GA is used to calculate municipal road construction flow parameters to ensure the optimal evaluation results. Experimental results show that the evaluation method is accurate and reliable and meets the requirement of the current era where environment issues are at priority., Author(s): Bing Hou (corresponding author) [1]; Chunyang Mao [1]; Liang Xu [1]; Botao Sun [1]; Jie Zhang [1] 1. Introduction Municipal roads refer to the roads that connect with roads [...]

Surface roughness is often considered the main purpose in contemporary computer numerical controlled (CNC) machining industry. Most existing optimization researches for CNC finish turning were either accomplished within certain manufacturing circumstances or achieved through numerous equipment operations. Therefore, a general deduction optimization scheme is deemed to be necessary for the industry. In this paper, the cutting depth, feed rate, speed, and tool nose runoff with low, medium, and high level are considered to optimize the surface roughness for finish turning based on L9(34) orthogonal array. Additionally, nine fuzzy control rules using triangle membership function with respective to five linguistic grades for surface roughness are constructed. Considering four input and twenty output intervals, the defuzzification using center of gravity is then completed. Thus, the optimum general fuzzy linguistic parameters can then be received. The confirmation experiment result showed that the surface roughness from the fuzzy linguistic optimization parameters is significantly advanced compared to that from the benchmark. This paper certainly proposes a general optimization scheme using orthogonal array fuzzy linguistic approach to the surface roughness for CNC turning with profound insight. [ABSTRACT FROM AUTHOR]