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MULTI-SOURCE AND HETEROGENEOUS ONLINE MUSIC EDUCATION MECHANISM: AN ARTIFICIAL INTELLIGENCE-DRIVEN APPROACH

FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2023年第6期31卷

作者： Yang, Yuanyuan Dolly, Raveena Judie Alassafi, Madini O. Slowik, Adam Alsaadi, Fawaz E. Xinxiang Univ Xinxiang 453003 Peoples R China Karunya Inst Technol & Sci Coimbatore 641114 Tamil Nadu India King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah 21589 Saudi Arabia Koszalin Univ Technol Koszalin Poland

In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user's preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects.

关键词： Artificial Intelligence Heterogeneous Online Music Education Personalized Recommendation Multimodal Network Model clustering algorithm

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Construction of intelligent supervision platform for college students' physical health for intelligent medical service decision-making

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FRONTIERS IN PHYSICS 2023年 11卷

作者： Guo, Fei Harbin Univ Sch Phys Educ Harbin Heilongjiang Peoples R China

Due to the rapid changes in current technology, machine learning and high-performance computing in medical applications also usher in new development opportunities. They are widely used in medical data analysis, diagnostic decision-making, disease prediction, disease assisted diagnosis, disease prognosis evaluation, new drug research and development, health management, and other fields. The impact of medical application on daily life is also increasing, which makes the use of intelligent medical service decision-making more extensive. However, with the continuous improvement and development of the population's physical fitness, the physical fitness of university students is deteriorating. Physical decline has become a common concern. Therefore, it is of great significance to investigate the physical condition of college students and find a more suitable method to promote the physical health of college students. It helps college students better engage in learning and life, enabling them to adapt to work faster and better meet the current social development needs for college students' physical fitness. For this reason, this paper proposes the idea of building a smart supervision platform for college students' physical health through smart medical service decision-making. Through empirical research on this platform, it is found that the method of building the platform proposed in this paper is more conducive to the improvement of college students' physical health. The excellent grade of freshmen in this platform is 5.4% higher than that of the traditional platform, and the excellent grade of sophomores in the test is 6.31% higher than that of the traditional platform, the excellent grade of college students' physical health test on this platform accounts for a higher proportion. The platform provides corresponding personalized sports programs through real-time monitoring of students' physical health, so as to realize teaching students in accordance with their aptitude,

关键词： college students physical health intelligent medical service decision-making clustering algorithm sports culture smart security service system

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Pre-selection of monitoring stations for marine water quality using affinity propagation: A case study of Xincun Lagoon, hainan, China

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年第PT B期325卷 116666页

作者： Fang, Xin Luo, Chengshu Zhang, Dongrong Zhang, Haifeng Qian, Jian Zhao, Canghai Hou, Zonghao Zhang, Yifei Minist Nat Resources Inst Oceanog 2 Hangzhou 310012 Peoples R China Nanjing Univ Sch Geog & Ocean Sci Nanjing 210093 Peoples R China Key Lab Nearshore Engn Environm & Ecol Secur Zhej Hangzhou 310000 Peoples R China Zhejiang Dev & Planning Inst Hangzhou 310030 Peoples R China

The development, protection, and restoration of bays require works in scientific research and applications, and the success of which depends on a well deployment of monitoring stations for marine water quality. However, for bays without historical data, it is difficult to carry out related research on deployment of the monitoring stations, resulting in very few research works. This paper has introduced the affinity propagation (AP) clustering algorithm and achieved good results by correcting the preferences. The results show that under the given preference, that is, when the value of M is -6800, the number of monitoring stations in the Xincun lagoon area is 24. Simultaneous the sensitivity analysis of preferences shows that the number of exemplars decreases with lower preferences, that is, when M decreased from -4000 to -12000, the number also decreased from 70 to 36. However, some exemplars remain unchanged or being changed to adjacent positioning. This shows the stability of computation results and the rationality of AP. The research results can be well applied to other bays, even open waters.

关键词： Environmental investigation station clustering algorithm Layout optimization Bias parameter selection Coastal waters

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On influence of clustering population on accuracy of population total estimation

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2020年第2期90卷 234-251页

作者： Wywial, Janusz L. Sitek, Grzegorz A. Univ Econ Katowice Dept Stat Econometr & Math Katowice Poland

The paper compares the accuracy of using cluster samples and using stratified samples to estimate a population total. Several clustering algorithms are used to partition a finite population into strata or clusters. Several variants of stratified sampling designs and one-stage cluster sampling designs, including those dependent on various inclusion probabilities, are taken into account. The accuracies of the estimators are compared using simulation experiments. The results of this paper let us conclude that partitioning a population into clusters could significantly improve the accuracy of estimating the total using sampling dependent on inclusion probabilities proportional to the aggregated auxiliary variable. Moreover, the considered estimators based on cluster sampling designs could be easily used in practice.

关键词： Sampling design auxiliary variable aggregated data inclusion probabilities strata clustering algorithm estimation efficiency

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Research on Production Profiling Interpretation Technology Based on Microbial DNA Sequencing Diagnostics of Unconventional Reservoirs

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ENERGIES 2023年第1期16卷

作者： Yang, Haitong Wang, Lei Qiang, Xiaolong Ren, Zhengcheng Wang, Hongbo Wang, Yongbo Wang, Shuoliang China Univ Geosci Beijing Sch Energy Resources Beijing 100190 Peoples R China China Univ Petr Sch Petr Engn Beijing 102249 Peoples R China Greatwall Drilling Co China Natl Petr Corp CNPC Geol Res Inst Beijing 100101 Peoples R China PetroChina Changqing Oilfield Co Gas Prod Plant 2 Yulin 719000 Peoples R China

Production profiling technology is an important method for monitoring the dynamics of oil and gas reservoirs which can effectively improve the efficiency of oil recovery. Production profiling is a technique in which a test instrument is lowered from the tubing to the bottom of the well to measure flow, temperature, pressure, and density in a multi-layer section of a producing well. Normal production profiling process needs to stop production, operate complex, consume long time and high cost. Furthermore, the profile cannot be continuously monitored for a long time. To address these limitations, this paper proposes a production profiling interpretation method based on reservoir primitive microbial DNA sequencing. The microbial stratigraphic baseline with high-resolution features is obtained by sampling and DNA sequencing of produced fluid and cuttings from different wells. Specifically, the random forest algorithm is preferred and improved by comparing the accuracy, precision, recall, F1-score, and running time of three clustering methods: Naive-Bayes classifier, random forest classifier, and back-propagation classifier. Constructing PSO-random forest model is based on stratigraphic records and produced fluid bacteria features. The computational accuracy and efficiency of this method allows it to describe the production profile for each formation. Moreover, this test process does not need to stop production with simple operation and does not pollute the formation. Meanwhile, by sampling fluid production at different stages, it can achieve the purpose of long-term effective dynamic monitoring of the reservoir.

关键词： DNA extraction DNA sequencing production profiling dynamic monitoring clustering algorithm

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Application of big data in construction of a book management model 24

Application of big data in construction of a book management...

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4th International Conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Zhang, Zhenyu School of Marxism HoHai University Jiangsu Changzhou China

ISBN: (纸本)9798400709753

Big data mining techniques are the primary technological means for extracting the inherent patterns from massive information, and they have currently gained widespread application. This paper analyzes the system architecture of existing library management systems and explores how to add a new data mining module on the basis of existing library management systems, with the primary focus being the interface issue between the new data mining module and the original system. A data mining module structure based on Web Services is designed, where the invocation of the new data mining sub-module is realized through residing a stub in the original system. The original data of the library management system is analyzed, and the data is re-extracted, integrated, and standardized to meet the requirements of data mining. A data model compatible with mining is also designed to facilitate the improvement of mining efficiency. An analysis was conducted on personalized services and management decisions for readers, and a book recommendation service based on readers' reading interests was proposed to improve service quality. Through association rule mining and cluster analysis, intelligent decision-making in the book management process was achieved, improving management quality. © 2024 Copyright held by the owner/author(s).

关键词： Association rules Big data clustering algorithm Data mining Library management

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TransFNN: A Novel Overtemperature Prediction Method for HVDC Converter Valves Based on an Improved Transformer and the F-NN algorithm

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SENSORS 2023年第8期23卷 4110页

作者： Zhou, Sihan Qin, Liang Sun, Hui Peng, Bo Ruan, Jiangjun Wang, Jing Tang, Xu Wang, Xiaole Liu, Kaipei Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Elect Power Res Inst State Grid Anhui Elect Power Hefei 230601 Peoples R China

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve's future overtemperature state, which is characterized by the valve's cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer-FCM-NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters;(ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared;with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically.

关键词： HVDC converter valve cooling system time series prediction Transformer clustering algorithm

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Risk Control of Energy Performance Fluctuation in Multi-Unit Housing for Weather Uncertainty

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BUILDINGS 2023年第7期13卷

作者： Wang, Jiahe Mae, Masayuki Taniguchi, Keiichiro Univ Tokyo Grad Sch Engn Dept Architecture 7-3-1 Hongo Bunkyo Ku Tokyo 1138656 Japan

With the acceleration of urban development, the population density of urban cities has increased. As the spatial characteristics of multi-unit housing (MUH) perfectly fit this developmental trend and, simultaneously, have high energy efficiency, the number of MUHs has increased rapidly in recent decades. Although many studies have proposed high energy efficiency strategies, weather uncertainty leads to errors between the operational performance of building energy and simulated values. This study introduces a robust optimization framework that incorporates uncertainty considerations into the optimization process to suppress energy consumption fluctuations and improve the average building energy consumption performance. Neural networks are used to model the uncertainty of multiple weather elements as normal distributions for each hour, and the accuracy of the uncertainty model is validated by calculating the mean absolute percentage error (MAPE) between the mean values of the distribution and the measurement values, which ranges from 3% to 13%. The clustering algorithm is proposed to replace the sampling method to complete the sampling work from the normal distribution space of the weather elements to serve the subsequent optimization process. Compared with the traditional method, the sampling results of the clustering algorithm show better representativeness in the sample space. The robust optimization results show that the average energy consumption of the optimal scheme decreases by 13.4%, and the standard deviation decreases by approximately 17.2%, which means that the optimal scheme, generated by the robust optimization framework proposed in this study, has lower average energy consumption results and a more stable energy consumption performance in the face of weather uncertainty.

关键词： building performance gap robust optimization uncertain weather conditions deep learning clustering algorithm multi-unit housing

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E-commerce personalized recommendation analysis by deeply-learned clustering

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2020年 71卷

作者： Wang, Kai Zhang, Tiantian Xue, Tianqiao Lu, Yu Na, Sang-Gyun Wonkwang Univ Dept Businesss Adm 460 Iksandae Ro Iksan Jeonbuk South Korea Pingdingshan Univ Coll Foreign Languages Southern Sect Weilai RD Xincheng Dist Pingdingshan Henan Peoples R China

With the development of Internet, personalized recommendation has played an important role in human modern lives. Since the number of users' data is always large-scale, traditional algorithms cannot effectively cope with e-commerce personalized recommendation tasks. This paper proposes an e-commerce product personalized recommendation system based on learning clustering representation. Traditional kNN method has limitation in selecting adjacent object set. Thus, we introduce neighbor factor and time function and leverage dynamic selection model to select the adjacent object set. We combine RNN as well as attention mechanism to design the e-commerce product recommendation system. Comprehensive experimental results have shown the effectiveness of our proposed method. (c) 2020 Elsevier Inc. All rights reserved.

关键词： clustering algorithm Deep learning Recommendation system

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Impacts of climate change on the wetlands in the arid region of Northwestern China over the past 2 decades

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ECOLOGICAL INDICATORS 2023年 149卷

作者： Wang, Ruimei Ding, Jianli Ge, Xiangyu Wang, Jinjie Qin, Shaofeng Tan, Jiao Han, Lijing Zhang, Zhe Xinjiang Univ Coll Geog & Remote Sensing Sci Urumqi 800017 Peoples R China Xinjiang Univ Xinjiang Key Lab Oasis Ecol Urumqi 830017 Peoples R China Xinjiang Univ Higher Educ Inst Key Lab Smart City & Environm Modelling Urumqi 830017 Peoples R China

Climate change has caused inland wetlands shrinkage and exacerbated problems, such as sustainable development and ecological security, for years. These issues are mainly pronounced in the inland arid area. The ecological environment's deterioration is especially severe in the drylands of the interior. However, dryland wetland changes and their response to climate are poorly understood. This study uses the K-means algorithm in Google Earth Engine (GEE) to classify two typical dryland wetlands (Ebinur and Bosten Lakes) for rapidly and accurately detecting dryland wetland changes. Moreover, it explores the long-term spatial-temporal variation in wetland distribution. In addition, it investigates the response of various lakes to climate change in northern and southern Xinjiang using wavelet analysis. The study's results showed that K-means clustering in the GEE platform has a high classification accuracy (Kappa > 0.8) in wetland classification, making it a feasible approach. The terminal lake wetland types, represented by the Ebinur Lake, changed significantly between 2001 and 2021. In contrast, the inflow-outflow lake wetland types, represented by the Bosten Lake, perform more consistently. Significant spatial-temporal variation is observed at Ebinur Lake, with the lake gradually shrinking and trans-forming into a marsh, where the largest marsh proportion degrades into non-wetland during the year. Bosten Lake experienced frequent conversions between marsh and non-wetland throughout the year. Furthermore, the responses of various dryland lakes to climate change are consistent, and a low precedes precipitation and follows evapotranspiration. However, their sensitivity to climate response varies, with the terminal lake being most affected by climate change. Mastering the dynamic changes and climate response of dryland wetlands achieves the sustainable development goals of drylands, including carbon neutrality and peak carbon dioxide emissions.

关键词： Dryland wetland clustering algorithm Wetland condition Delayed response

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