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A Flight Arrival Time Prediction Method Based on Cluster Clustering-Based Modular With Deep Neural Network

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第6期25卷 6238-6247页

作者： Deng, Wu Li, Kunpeng Zhao, Huimin Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

With the rapid development of the air transportation industry, air traffic is facing a severe test. The accurate prediction of the estimated arrival time (EAT) plays an important role in rational and efficient scheduling of flights. Aircraft EAT predictions often rely on aircraft performance parameters or physical trajectories. This gives poor consideration to the flight environment and take-off and landing conditions. This paper applies a data-driven methodology for ETA prediction. A Cluster clustering-based modular integrated DNN (CC-MIDNN) is proposed. According to the information of flight mission and flight environment, it decomposes complex modeling tasks into multiple distinct subtasks using cluster-based clustering. Then Bayesian optimization is used to design corresponding parallel sub-networks to learn different sub-tasks in order to reduce the complexity of the subsequent sub-networks. Finally, a new integration scheme is proposed to solve the overfitting problem due to the reliance on the training accuracy of sub-modules. To verify the effectiveness of the method, CC-MIDNN performs EAT prediction on flight data of European Airlines LIS airport in 2017. The experimental results show that the CC-MIDNN improves the time prediction accuracy by 5.92 minutes and keeps the error within 13 minutes. Compared with other deep learning algorithms and integration algorithms, the CC-MIDNN has high accuracy and stability.

关键词： Regression prediction estimated arrival time deep neural network cluster clustering-based modular integrated algorithm Bayesian optimization

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Improving generalization performance of landslide susceptibility model considering spatial heterogeneity by using the geomorphic label-based LightGBM

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2024年第9期83卷 361页

作者： Sun, Deliang Wu, Xiaoqing Wen, Haijia Shi, Shuxian Gu, Qingyu Chongqing Normal Univ Sch Geog & Tourism Key Lab GIS Applicat Res Chongqing 401331 Peoples R China Chongqing Univ Sch Civil Engn Key Lab New Technol Construct Cities Mt Area Natl Joint Engn Res Ctr Geohazards Prevent Reservo Chongqing 400045 Peoples R China East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China

Previous landslide susceptibility assessments often overlook the heterogeneity of landslides across different regions and their generalization ability. The objective of this study was to explore the landslide susceptibility model from geomorphic zones-based K-means constructed sub-regions to geomorphic label-based matching whole regions. To achieve this, we divided the study area into slope units based on curvature calculation and utilized a Light Gradient Boosting Machine (LightGBM) to construct the susceptibility model. For model training, we randomly selected fourteen townships in Fengjie County, Chongqing, China. By employing K-means clustering, we established geomorphic zones for the landslide susceptibility model, which were assigned a corresponding geomorphic label. Notably, the model divided the entire study area into four geomorphic zones, with zone 4 demonstrating the highest accuracy of 0.85. Additionally, we developed two comparative models to assess the effectiveness of our proposed approach. The first comparative model considered only ten conditioning factors, resulting in a model accuracy of 0.73. The second comparative model incorporated the classification results of geomorphic labels as explanatory variables for landslides and achieved a model accuracy of 0.75. In this study, we successfully established a model with an accuracy of 0.87 by automatically matching and generalizing the remaining townships with the existing geomorphic labels. Our findings suggest that a geomorphic label-based model, adopting a framework from sub-regions to the whole region, offers better quantification of the landslide occurrence mechanism in the region and demonstrates stronger generalization ability compared to the overall-based model.

关键词： Landslide susceptibility Geomorphic label Generalization ability Spatial heterogeneity LightGBM integrated algorithm

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integrated OPTIMIZATION OF HYBRID STEAM-SOLVENT INJECTION IN POST-CHOPS RESERVOIRS UNDER UNCERTAINTY 42

INTEGRATED OPTIMIZATION OF HYBRID STEAM-SOLVENT INJECTION IN...

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ASME 42nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE)

作者： Hou, Senhan Yang, Shikai Jiang, Liwu Yang, Daoyong Univ Regina Regina SK Canada

ISBN: (纸本)9780791886915

In this paper, an integrated algorithm is proposed to optimize the hybrid steam-solvent injection-production schemes for post-CHOPS reservoirs under uncertainty. First of all, the newly developed pressure-gradient-based (PGB) sand failure criterion is employed to generate dendritic (or fractal-like) and regional wormhole networks, based on which the net present value (NPV) is defined and used as the objective function. A hybrid optimization technique based on the genetic algorithm (GA) is then integrated with the orthogonal array (OA) and Tabu search to maximize the objective function by rationalizing the injection and production parameters and simultaneously retarding the displacement front to extend the reservoir life under uncertainty. In a given CHOPS well, not only can the proposed method be used to determine the overall morphology of the wormhole network, including intensity and coverage, but also design, evaluate, and optimize performance of any potential EOR processes in a post-CHOPS reservoir within a unified, efficient, and accurate framework.

关键词： integrated algorithm post-CHOPS uncertainty wormhole networks framework

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Prediction of Road Rutting Depth Based on PSO-CatBoost 9

Prediction of Road Rutting Depth Based on PSO-CatBoost

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9th International Conference on Computer and Communication Systems (ICCCS)

作者： Xing, Zhen-zhen Hao, Jun Pei, Li-li Weng, Yu-han Li, Hang-ying Changan Univ Sch Informat Engn Xian Peoples R China Changan Univ Sch Data Sci & Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9798350350227;9798350350210

The rutting depth is one of the important indicators for evaluating the performance of road use. Accurately predicting the Rutting Depth Index (RDI) can timely detect and solve the problem of road damage, ensuring driving safety. This article constructs a multi influencing factor dataset for RDI prediction by integrating road detection, climate, and traffic flow data from the AB expressway in Shaanxi Province, China. By comparing the prediction results of four models, namely Random Forest(RF), XGBoost, CatBoost, and LightGBM, it was found that XGBoost and CatBoost has the highest accuracy on the test dataset. To further improve the predictive precision of above model, we utilized the Particle Swarm Optimization (PSO) technique to meticulously adjust the hyperparameters of both XGBoost and CatBoost. The experimental results show that using the PSO-XGBoost and PSO-CatBoost models to predict RDI, the R2 scores reached 0.8029 and 0.8135, respectively. Compared with the XGBoost and CatBoost models, the accuracy improved by 0.0655 and 0.022, respectively. This indicates that the application of PSO significantly enhances the predictive accuracy of both XGBoost and CatBoost models for road rutting prediction, with the PSO-CatBoost model demonstrating superior performance. Consequently, it provides valuable data for road maintenance strategies.

关键词： road rutting depth index integrated algorithm CatBoost PSO optimization algorithm

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Modelling and analysis of initial icing roughness with fixed-grid enthalpy method based on DPM-VOF algorithm

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Chinese Journal of Aeronautics 2022年第7期35卷 168-178页

作者： Jie LIU Peng KE Department of Civil Helicopter Research and Development China Helicopter Research and Development InstituteTianjin 300000China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China

Ice particles could form under the continuous impingement of incoming supercooled droplets in icing conditions,which will change the surface roughness to enhance the further heat and mass transfer during icing process.A fixed-grid porous enthalpy method based on the improved Discrete Phase Model(DPM)and Volume of Fluid(VOF)integrated algorithm is developed to solve the multiphase heat transfer problem to give more detailed demonstration of the formation of initial ice *** algorithms to determine the criterion of transformation from DPM to VOF and the allocation of source items during transformation are improved to the general DPM-VOF *** verification cases,namely two glycerine-solution droplets impact and single droplet freeze,are conducted to verify the accuracy and reliability of the enthalpy-DPMVOF method,where the simulation results match well with experiment *** roughness on a NACA0012 airfoil is precisely captured and the effects on convective heat transfer characteristics are preliminarily *** results illustrate that the enthalpy-DPM-VOF method could successfully capture the characteristics of motion and the phase change process of droplet,as well as balance the calculation accuracy and efficiency.

关键词： Discrete phase model Fixed-grid porous enthalpy method Ice roughness Icing modelling integrated algorithm Multiphase heat transfer Volume of fluid

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An integrated Medical Recommendation Mechanism Combining Promote Product Singular Value Decomposition and Knowledge Graph 18th

An Integrated Medical Recommendation Mechanism Combining Pro...

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18th International Conference on Advanced Data Mining and Applications (ADMA)

作者： Sun, Yibo Liu, Chenlei Tong, Xue Hu, Bing Univ Queensland St Lucia Qld 4072 Australia Nanjing Univ Posts & Telecommun Post Big Data Technol & Applicat Engn Res Ctr Jia Post Ind Technol Res & Dev Ctr State Posts BurInternet Things Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Minist Educ Broadband Wireless Commun Technol Engn Res Ctr Nanjing Peoples R China

ISBN: (纸本)9783031220630;9783031220647

Due to the increasingly serious problem of information overload, it is difficult for medical staff to accurately screen out data conducive to patient consultation when dealing with massive amounts of diagnostic information. In this paper, we propose a hybrid recommendation mechanism integrating the improved Singular Value Decomposition and Knowledge Graph for medical information. It can effectively analyze the characteristic relationship between patients and medical item information and give appropriate medical recommendation scheme information based on patients' interests and preferences. We design the Promote Product Singular Value Decomposition (PPSVD) algorithm and integrate knowledge graph technology to construct the medical system's recommendation model. Finally, the experimental results show that the integrated medical recommendation model proposed has higher accuracy and usability than any other algorithms.

关键词： Medical recommendation system integrated algorithm PPSVD Knowledge graph

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Obstacle Avoidance Control for Static and Dynamic Obstacles on Intelligent Mobile Platforms in Greenhouses 22

Obstacle Avoidance Control for Static and Dynamic Obstacles ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Kang Meng, Fei Department of System Science University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798331527471

This work focuses on the utilization of Mecanum wheel mobile platforms in greenhouse. Its aim is to improve the search efficiency and optimize path planning of Mecanum wheel mobile platforms in intricate environments through the improved A* algorithm. Through the integration of the Dynamic Window Approach (DWA) with the improved A* algorithm and accounting for the kinematic constraints specific to Mecanum wheel mobile platforms, the objective is to achieve swift and precise path planning in dynamic environments. Experimental results demonstrate that the utilization of this integrated algorithm enables Mecanum wheel mobile platforms to surpass traditional algorithms in path planning time, path length, and adaptability to dynamic environments. Furthermore, combining the improved A* algorithm with DWA effectively avoids randomly occurring obstacles, ensuring smooth operation of the platform along the globally optimal path. This intelligent integration into smart agriculture boosts efficiency and intelligence in production operations. © 2024 IEEE.

关键词： integrated algorithm Mecanum Wheel Mobile Platform Path Planning algorithms Smart Greenhouses

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Effect of combined strategy on mitigating air pollution in China

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2021年第3期23卷 1027-1043页

作者： Jia, Shuwei Henan Agr Univ Coll Informat & Management Sci 15 Longzi Lake Zhengzhou 450046 Henan Peoples R China

Air pollution has worsened as a result of increased traffic congestion in cities. Using air pollution caused by motor vehicle emissions (mainly by carbon monoxide, hydrocarbon, nitrogen oxide, and particulate matter) as an example, in this study, we applied an integrated algorithm comprising system dynamics, entropy weight method, and gray system theory to establish a weighted logic function. A vehicle pollutant control model (including the transport, health, and environmental subsystems) was established by VENSIM software. The medium- and long-term key variable analysis showed that the integrated algorithm, fully reflecting the advantages of system dynamics and gray system theory, more accurately described air pollution caused by vehicle emissions. Our research results also revealed that the combined strategy of penalties and air pollution charging fee had a threefold effect: reduced congestion and emissions, improved health impact index, and reduced number of illegal trips. Specifically, the degree of traffic congestion, number of illegal trips, and the degree of air pollution decreased by approximately 82.40%, 69.35%, and 68.91%, respectively, whereas the health impact index increased by about 279.03%. This finding provides guidance for improving policy and optimizing management and control modes. [GRAPHICS] .

关键词： System dynamics Air pollution integrated algorithm Emission reduction Threefold effect

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An Accurate Recommendation Method of University Library Digital Resources Based on User Interest

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International Journal of High Speed Electronics and Systems 2025年

作者： Wu, Xiuhua Sang, Guoqiang Zhejiang College of Security Technology Zhejiang Province Wenzhou City325000 China Wenzhou University Zhejiang Province Wenzhou City325000 China

With the advent of the digital age, university libraries are faced with the challenge of how to efficiently manage and recommend huge digital resources to meet the diversified needs of users. Traditional recommendation system (RS) often ignores the evolution of user interests over time and the importance of personalized recommendation. This research aims to solve the above problems through the accurate recommendation algorithm of university library digital resources that integrates the evolution of users’ interests. This paper first discusses the background of the dynamic change in user interest and the urgent need for an efficient and accurate RS. The motivation is to better understand and predict user interests through a new algorithmic framework to improve the accuracy and user satisfaction of the RS. We propose a RS framework that combines the user interest evolution model and attention mechanism based on Transformer, and design a hybrid RS by combining content recommendation and collaborative filtering technologies. The system is designed to capture subtle changes in user interests and solve cold start problems, thereby providing more personalized and accurate recommendations. In the experimental part, we use the real university library digital resource dataset to verify. Experimental results show that compared with traditional RSs, our approach has significantly improved on several evaluation indicators such as accuracy, recall rate and F1 score, while also performing well in terms of diversity and novelty. This proves that our approach can effectively improve user satisfaction and interactive experience. © The Author(s)

关键词： digital resources integrated algorithm precise recommendation university libraries User interest

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Diabetes prediction model based on deep belief network

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2021年第4期21卷 817-828页

作者： Lang, Li-Ying Gao, Zheng Wang, Xue-Guang Zhao, Hui Zhang, Yan-Ping Sun, Sheng-Juan Zhang, Yong-Jian Austria, Ramir S. Hebei Univ Engn Handan 056038 Hebei Peoples R China Hebei Univ Technol Tianjin 300401 Peoples R China Hebei Univ Engn Sch Informat & Elect Engn Handan 056038 Hebei Peoples R China Hebei Univ Engn Sch Math & Phys Handan 056038 Hebei Peoples R China Univ Cordilleras Coll Teacher Educ Baguio 2600 Philippines

Diabetes is a disease that seriously endangers human health. Early detection and early treatment can reduce the likelihood of complications and mortality. The predictive model can effectively solve the above problems and provide helpful information for the clinic. Based on this, it is proposed to apply the idea of integrated algorithm in DBN algorithm, collect the hospital data by investigating its related factors, clean and process the collected data, and sample and model the processed data multiple times. It is shown that a single DBN classifier is better than support vector machine and logistic regression algorithm. The model established by the integrated deep confidence network has a significant improvement in classification accuracy compared to a single DBN classifier, and solves the unstable classification effect of a single DBN classifier.

关键词： Diabetes mellitus deep learning integrated algorithm deep believe neural network prediction model

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