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3rd World Conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Sun, Zhibin Chen, Yunsheng Li, Zhanying Jia, Lixia Gu, Baoquan Fan, Jingjing Wu, Xiaote Ultra High Voltage Transmission Company of China Southern Power Grid Co. Ltd. Guangzhou510000 China

ISBN: (纸本)9789819791231

Products, supplies, services, etc., of the power enterprise—an essential energy industry—are intrinsically linked to the production, operation, and long-term growth of every business, government agency, agricultural sector, service sector, and more. Clean energy, dependable energy, high-quality services, and energy conservation and emission reduction have all been elevated in recent years due to the ongoing power business reform and the development of industry regulatory rules. The internal control of power firms has been subject to increased pressures due to all of these factors. In this setting, it is crucial to analyze and mine huge data for vital indications of power firms’ internal control systems. This article presents the architecture of a machine learning-based neural network that can assess the quality of power firms’ internal controls. An enhancement to the DenseNet-based network is introduced in this article, which streamlines the network's structure and parameters while simultaneously enhancing the model's performance and the rate of convergence. Thirdly, to increase feature extraction's discriminative capabilities, the network implements an enhanced attention strategy. In the end, this essay uses the suggested network to assess the quality of power firms’ internal controls. This study presents experimental evidence that the suggested strategy for assessing the quality of power firms’ internal controls is effective. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： big data analysis Internal control system Machine learning Neural network

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Metagenomics studies in aquaculture systems: big data analysis, bioinformatics, machine learning and quantum computing

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2025年 118卷 108444页

作者： Coskuner-Weber, Orkid Alpsoy, Semih Yolcu, Ozgur Teber, Egehan de Marco, Ario Shumka, Spase Turkish German Univ Mol Biotechnol Sahinkaya Caddesi 106 TR-34820 Istanbul Turkiye Univ Nova Gorica Lab Environm & Life Sci Vipavska Cesta 13 Nova Gorica 5000 Slovenia Agr Univ Tirana Fac Biotechnol & Food Koder Kamza 1019 Tirana Albania

The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populations inherent in aquaculture systems exacerbate abiotic stressors and promote pathogenic spread, posing a risk to sustainability and yield. This study delves into the transformative potential of metagenomics, a method that directly retrieves genetic material from environmental samples, in elucidating microbial dynamics within aquaculture ecosystems. Our findings affirm that metagenomics, bolstered by tools in big data analytics, bioinformatics, and machine learning, can significantly enhance the precision of microbial assessment and pathogen detection. Furthermore, we explore quantum computing's emergent role, which promises unparalleled efficiency in data processing and model construction, poised to address the limitations of conventional computational techniques. Distinct from metabarcoding, metagenomics offers an expansive, unbiased profile of microbial biodiversity, revolutionizing our capacity to monitor, predict, and manage aquaculture systems with high accuracy and adaptability. Despite the challenges of computational demands and variability in data standardization, this study advocates for continued technological integration, thereby fostering resilient and sustainable aquaculture practices in a climate of escalating global food requirements.

关键词： Aquaculture big data analysis Bioinformatics Machine learning Metagenomics Quantum computing

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Exploration of personalized teaching paths in English education based on big data analysis

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 821-836页

作者： Guo, Yapei Li, Yunhua Hebei Normal Univ Inst Ethn Grp Shijiazhuang Peoples R China Hebei GEO Univ Sch Languages & Cultures Shijiazhuang Peoples R China

In the wave of data-driven educational innovation, personalized teaching has become a core issue in the field of English education. This study aims to explore the construction and implementation of personalized teaching paths in English education through big data analysis techniques. With the advancement of technology, traditional English education methods are gradually unable to meet the diverse and dynamic learning needs of students. Although personalized teaching has received widespread attention, existing research still faces challenges in integrating complex factors such as student preferences, learning habits, and contextual information to achieve teaching optimization. This study is divided into two main parts. Firstly, it applies probabilistic association analysis to conduct detailed research on student learning preferences, thus revealing individual learner differences. Secondly, it develops a new algorithm model that can integrate students' personalized preferences and real-time contextual information to recommend the most suitable personalized learning interests. This method not only helps to improve students' learning motivation and efficiency but also provides a new theoretical basis and practical scheme for the design of personalized teaching paths in English education.

关键词： personalized teaching English education big data analysis learning preferences contextual information probabilistic association analysis learning path recommendation

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Research on Fracturing Sweet Spot and Cluster Design Based on big data analysis and Intelligent Fusion Algorithms

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CHEMISTRY AND TECHNOLOGY OF FUELS AND OILS 2025年第1期61卷 91-98页

作者： Zhang, Dejun Zhang, Wenchen Sitiwalidi, Abidan Liu, Mingyan Liu, Yongjie Petrochina Data & Intelligence Co Xinjiang Oilfield Co Karamay Xinjiang Peoples R China HongYou Software Co Ltd Karamay Peoples R China

This study addresses numerous issues in traditional fracturing sweet spot identification and segment cluster design by proposing a method based on big data analysis and intelligent fusion algorithms. First, based on the key influencing factors for sweet spot identification, a combined big data analysis method using K-Means and KNN is employed to perform clustering analysis, aiming to achieve the goal of identifying fracturing sweet spots. Secondly, an algorithm integrating expert experience, Self-Organizing Map (SOM) clustering, K-Means, and KNN is introduced to carry out segment cluster design, thereby realizing the objective of intelligent segment cluster design based on the comprehensive sweet spot *** research demonstrates that in the sweet spot identification process, there are many influencing factors. The combination of K-Means and KNN for big data analysis can accurately identify geological sweet spots, engineering sweet spots, and the comprehensive sweet spot index. In the segment cluster design process, the expert experience model first integrates geological and engineering data from the block, forming design schemes through inductive analysis. Intelligent algorithms centered around SOM, K-Means, and KNN are capable of deeply analyzing geological and engineering data, achieving the goals of stratification and clustering.

关键词： big data analysis sweet spot identification expert experience intelligent fusion algorithms segment cluster design

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Enhancing the evaluation and improvement strategies of English education outcomes through big data analysis

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 455-471页

作者： Yu, Jianjun Zhejiang Tech Inst Econ Sch Culture & Arts 16 Xuezheng Rd Hangzhou 310018 Zhejiang Peoples R China

As big data technology continues to penetrate the field of education, the assessment of English education is at a pivotal moment of transformation. Traditional assessment methods, by overlooking the rich data of learning processes and individual differences, fail to meet the demands of modern educational evaluation. This study aims to explore the application of big data analysis in evaluating and enhancing the learning outcomes of English education. Initially, it introduces the revolutionary significance of big data analysis for transforming English education assessment methods, emphasizing the customization of personalized learning pathways and the data support for educational decisions. Subsequently, the paper analyzes the deficiencies in current research methodologies, such as the disconnect between analysis models and educational theories, and the lack of precision in data processing. Building on this, the paper proposes a novel assessment method based on the Graph Attention Network (GAT)-Transformer, which can more accurately analyze and evaluate English learning outcomes, particularly suitable for dealing with complex learner interaction data. Moreover, by integrating the inquiry community theory, this study constructs a framework for improving English education outcomes, based on the interaction of three core elements: teaching presence, social presence, and cognitive presence, and proposes specific enhancement strategies. This research not only theoretically enriches the application of big data in the assessment of English education but also provides practical guidance for educational practitioners.

关键词： big data analysis English education learning outcome evaluation graph attention network-transformer inquiry community theory improvement strategies

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Application and Outlook of Intelligent big data analysis in Internet of Things Security

Application and Outlook of Intelligent Big Data Analysis in ...

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作者： Tan, Fei Guo, Huiyong Wuhan Donghu University Hubei Wuhan China

As the Internet of Things (IoT) technology develops rapidly, the interconnection of various devices and sensors has brought great convenience to people's lives and work. However, at the same time, IoT systems also face security threats and intrusion risks. Therefore, designing an efficient and reliable IoT intrusion detection model has become an important issue that urgently needs to be addressed today. The main function of the perception layer of the IoT is to achieve intelligent perception of information acquisition, capture, and target recognition, and generate massive amounts of new data. In the IoT, the transmission and transmission of information are mainly completed through the network layer. At the application level, it can achieve fields such as smart grids, monitoring services, smart homes, environmental monitoring, public safety, green agriculture, and industrial monitoring. Intelligent processing and analysis of the large amount of data generated by the perception layer can effectively manage the risk of IoT intrusion. This article explores an IoT intrusion detection model based on convolutional neural network (CNN) algorithm and random forest (RF) algorithm, aiming to improve the security and stability of IoT systems by combining the advantages of deep learning and traditional machine learning algorithms. How to promote big data analysis methods to intelligent management and operational optimization is the core of the development of the IoT industry. This article ultimately proves through experiments that the AUC (area under the curve) of the CNN-RF model is closer to 1 than the CNN model, and has high classification ability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： big data analysis Information safety Internet of things security Neural network algorithms

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Numerical simulation and big data analysis for assessing the geothermal utilization potential of deep-buried pipe systems

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JOURNAL OF BUILDING ENGINEERING 2025年 106卷

作者： Li, Chao Jiang, Chao Chen, Hao Chen, Kai Yang, Chuang Lv, Jiaojiao Lin, Kunhong Changan Univ Sch Energy & Elect Engn Xian Key Lab Adv Transport Power Machinery Xian 710064 Peoples R China Changan Univ Sch Energy & Elect Engn Shaanxi Key Lab New Transportat Energy & Automot E Xian 710064 Peoples R China PetroChina Changqing Oilfield Co Explorat & Dev Res Inst Xian 710018 Peoples R China PowerChina Northwest Engn Corp Ltd Xian 710065 Peoples R China

As a clean and sustainable source, geothermal energy is a key component in the global energy transition. One of the main approaches to geothermal energy utilization involves mid-to-deep layer buried pipe heating technology, emphasizing the efficient and accurate assessment of heat transfer performance. This study presents a three-dimensional, full-scale numerical modeling of heat transfer in deeply buried pipes. Based on the simulation results, a database for evaluating the heat transfer rate of buried pipes is developed. This database serves as the basis for neural network predictions of heat transfer rate. The proposed predictive model is evaluated, and the results indicate that its computational efficiency is over 1000 times higher than that of conventional numerical models. Aside from the initial 1 h of heat transfer, the maximum relative error of the predictions compared to the numerical results is within 0.5 %. This study provides innovative approaches for the theoretical evaluation and technical optimization of deeply buried pipe geothermal systems. The findings contribute to the improvement of energy supply systems, accelerating energy conservation, reducing emissions, and enhancing ecological restoration.

关键词： Geothermal energy Deep buried pipe heat transfer systems Numerical simulations big data analysis Neural network models

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big data analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

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IEEE ACCESS 2020年 8卷 80049-80062页

作者： Gao, Honghu Liu, Shifeng Cao, Guangmei Zhao, Pengfei Zhang, Jianhai Zhang, Peng Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Beijing Logist Informat Res Base Beijing 100044 Peoples R China Beijing Jingtou Urban Util Tunnel Investment Co L Beijing 100027 Peoples R China

This paper proposed improved measures for the shortest path fare scheme of urban rail transit. Firstly, this paper simulated Beijing rail transit by using Anylogic simulation technology and shortest path algorithm. Then, in order to find the travel time between any originations and destinations, this research measured the inbound time, waiting time, interval time, section running time, transfer time and outbound time. In addition, this paper used big data analysis technology to obtain the actual travel time distribution between any originations and destinations by processing the basic data of passengers entering and leaving the station. Finally, by comparing the valid path travel time calculated by any originations and destinations with the actual travel time distribution of passengers, the path taken by majority of passengers was pushed back to determine the ticket price based on the mileage of the path taken by the majority of passengers. The results reduced the dependence on government subsidies by rail transit operation and made up for the operation and maintenance costs.

关键词： Shortest path Anylogic simulation travel time time distribution big data analysis pricing scheme of urban rail transit

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big data analysis of the Internet of Things in the digital twins of smart city based on deep learning

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 128卷 167-177页

作者： Li, Xiaoming Liu, Hao Wang, Weixi Zheng, Ye Lv, Haibin Lv, Zhihan Shenzhen Univ Sch Architecture & Urban Planning Res Inst Smart Cities Shenzhen 518060 Peoples R China Shenzhen Key Lab Spatial Smart Sensing & Serv Shenzhen 518060 Peoples R China MNR Technol Innovat Ctr Terr & Spatial Big Data Shenzhen 518060 Peoples R China Minist Nat Resources North Sea Bur North China Sea Offshore Engn Survey Inst Qingdao Peoples R China Uppsala Univ Fac Arts Dept Game Design Uppsala Sweden

The study aims to conduct big data analysis (BDA) on the massive data generated in the smart city Internet of things (IoT), make the smart city change to the direction of fine governance and efficient and safe data processing. Aiming at the multi-source data collected in the smart city, the study introduces the deep learning (DL) algorithm while using BDA, and puts forward the distributed parallelism strategy of convolutional neural network (CNN). Meantime, the digital twins (DTs) and multi-hop transmission technology are introduced to construct the smart city DTs multi-hop transmission IoTBDA system based on DL, and further simulate and analyze the performance of the system. The results reveal that in the energy efficiency analysis of model data transmission, the energy efficiency first increases and then decrease as the minimum energy collected alpha(0) increases. But a more suitable power diversion factor rho is crucial to the signal transmission energy efficiency of the IoT-BDA system. The prediction accuracy of the model is analyzed and it suggests that the accuracy of the constructed system reaches 97.80%, which is at least 2.24% higher than the DL algorithm adopted by other scholars. Regarding the data transmission performance of the constructed system, it is found that when the successful transmission probability is 100% and the exponential distribution parameters lambda is valued 0.01 similar to 0.05, it is the closest to the actual result, and the data delay is the smallest, which is maintained at the ms level. To sum up, improving the smart city's IoT-BDA system using the DL approach can reduce data transmission delay, improve data forecasting accuracy, and offer actual efficacy, providing experimental references for the digital development of smart cities in the future. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Deep learning Smart city Digital twins Internet of Things big data analysis

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big data analysis of a mini three-axis CNC machine tool based on the tuning operation of controller parameters

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期99卷 1077-1083页

作者： Chang, Wen-Yang Wu, Sheng-Jhih Natl Formosa Univ Dept Mech & Comp Aided Engn 64 Wunhua Rd Huwei Township 632 Yunlin Taiwan

The characteristic responses of a mini three-axis computer numerical control (CNC) machine tool based on the controller tuning operation were investigated for big data estimation. The major tuning parameters included the position control gains K-p, the position feed-forward control gains K-f, the speed control gains K-v, and the gain ratios K-g of the position and speed control values in manufacturing industries. K-p gains of 10, 30, 50, 80, 100, 200, 300, and 400rad/s, K-f gains of 0, 30, 50, 60, 80, and 100%, K-v gains of 30, 50, 70, 100, 300, 900, 2000, and 3000rad/s, and K-g ratios of (1:1), (3:1), (5:1), and (7:1) were analyzed for smart productivity. The results show that the settling times at different K-p values were almost constant when the K-p gain was over 200rad/s. The maximum overshoots, when the feed-forward gain is over 60%, almost increased with increasing feed-forward gains. However, the overshoot of the three-axis CNC machine tool decreased as the K-v gain increased until the K-v gain reached 70 rad/s. The settling times at a constant K-g ratio decreased with an increase in the K-p and K-v gains. The characteristic responses of the tuning operations were enabled with connectivity to a cloud network to share the big data, to support decision making, and to adjust operations in real time.

关键词： big data analysis CNC machine tool Curve fitting Position control mode Smart productivity

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