Reservoir water level prediction using combined CEEMDAN-FE and RUN-SVM-RBFNN machine learning algorithms by Lan-ting Zhou, Guan-lin Long, Can-can Hu, Kai Zhang

“…This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms. By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and fuzzy entropy (FE) with the new and highly efficient Runge–Kuta optimizer (RUN), adaptive parameter optimization for the support vector machine (SVM) and radial basis function neural network (RBFNN) algorithms was achieved. …”
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Single-Frame Infrared Target Detection Based on Fast Content-Related Modeling by Zipeng Zhang, Xidong Zhao, Wenzheng Wang, Yuqi Han, Chenwei Deng, Zhuokai Li, Linbo Tang

“…Finally, we propose an efficient solving algorithm with stepwise reconstruction from seed images sampled from infrared images, enabling linear-time extraction of infrared small targets. …”
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Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing by Li WANG, Qing WEI, Lianming XU, Yuan SHEN, Ping ZHANG, Aiguo FEI

“…In public emergencies such as accident relief, rescue workers are faced with challenges, such as poor communication, unstable navigating, and inaccurate disaster sensing.It is necessary to deploy an emergency unmanned aerial vehicle (UAV) network to guarantee the services of communication-navigating-sensing.Aiming at alleviating the problem of limited energy of UAV, a low-energy-consumption deployment of an emergency UAV network was first proposed for integrated communication-navigating-sensing (ICNS).The proposed scheme was able to realize network topology reconstruction and role cognition on demand.Then, a particle swarm algorithm based hierarchical matching decision-making algorithm was presented to jointly optimize three sub-problems, including the associations between UAVs and users, the resource allocation for multi-role UAV communications, and the UAV position.Simulation results show that the proposed ICNS scheme can achieve flexible adaptation of the multi-objective requirements and limited network resources, and dramatically reduce the demand for the number of UAVs and the deployment energy consumption.…”
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Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing by Li WANG, Qing WEI, Lianming XU, Yuan SHEN, Ping ZHANG, Aiguo FEI

“…In public emergencies such as accident relief, rescue workers are faced with challenges, such as poor communication, unstable navigating, and inaccurate disaster sensing.It is necessary to deploy an emergency unmanned aerial vehicle (UAV) network to guarantee the services of communication-navigating-sensing.Aiming at alleviating the problem of limited energy of UAV, a low-energy-consumption deployment of an emergency UAV network was first proposed for integrated communication-navigating-sensing (ICNS).The proposed scheme was able to realize network topology reconstruction and role cognition on demand.Then, a particle swarm algorithm based hierarchical matching decision-making algorithm was presented to jointly optimize three sub-problems, including the associations between UAVs and users, the resource allocation for multi-role UAV communications, and the UAV position.Simulation results show that the proposed ICNS scheme can achieve flexible adaptation of the multi-objective requirements and limited network resources, and dramatically reduce the demand for the number of UAVs and the deployment energy consumption.…”
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The Point Cloud Reduction Algorithm Based on the Feature Extraction of a Neighborhood Normal Vector and Fuzzy-c Means Clustering by Hongxiao Xu, Donglai Jiao, Wenmei Li

“…Non-feature points are then sampled using an enhanced farthest point sampling technique. Finally, the algorithm integrates edge points, feature points, and non-feature points to generate simplified point cloud data. …”
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An integrated optimization model of network behavior victimization identification based on association rule feature extraction by Shengli ZHOU, Linqi RUAN, Rui XU, Xikang ZHANG, Quanzhe ZHAO, Yuanbo LIAN

“…The identification of the risk of network behavior victimization was of great significance for the prevention and warning of telecom network fraud.Insufficient mining of network behavior features and difficulty in determining relationships, an integrated optimization model for network behavior victimization identification based on association rule feature extraction was proposed.The interactive traffic data packets generated when users accessed websites were captured by the model, and the implicit and explicit behavior features in network traffic were extracted.Then, the association rules between features were mined, and the feature sequences were reconstructed using the FP-Growth algorithm.Finally, an analysis model of telecom network fraud victimization based on network traffic analysis was established, combined with the stochastic forest algorithm of particle swarm optimization.The experiments show that compared with general binary classification models, the proposed model has better precision and recall rates and can effectively improve the accuracy of network fraud victimization identification.…”
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Optimizing Strategies in Patients Affected by Tumors Infiltrating the Skull: A Single Center Experience by Giuseppe Emmanuele Umana, Sruthi Ranganathan, Salvatore Marrone, Jessica Naimo, Matteo Giunta, Angelo Spitaleri, Marco Fricia, Gianluca Ferini, Gianluca Scalia

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A Powerful Approach in Visualization: Creating Photorealistic Landscapes with AI by Gusztáv Jakab, Enikő Magyari, Benedek Jakab, Gábor Timár

“…To test the method, we applied a procedure: we ran the algorithm on a current topographic map of a sample area and compared the resulting image with the view model provided by Google Earth.…”
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Integrating direct observation and environmental DNA data to enhance species distribution models in riverine environments by Luca Carraro

“…These results will contribute to designing efficient strategies for integrated biomonitoring in river networks.…”
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Super‐Resolution Ultrasound Radiomics Can Predict the Upstaging of Ductal Carcinoma In Situ by Liang Yang, Xiaoxian Li, Zhiyuan Wang, Qian Li, Juan Fu, Xuebin Zou, Xia Liang, Xu Liu, Ruirui Zhang, Junjun Chen, Hui Xie, Yini Huang, Jianhua Zhou

“…A super‐resolution reconstruction algorithm was used to enhance the resolution of original high resolution (HR) ultrasound images and obtain SR images. …”
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Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning by Zhe Wang, Jiali Duan, Fengzhang Luo, Xiaoyu Qiu

“…First, the complete ensemble empirical mode decomposition with adaptive noise algorithm decomposes load data, and a dynamic time warping-based k-medoids clustering algorithm reconstructs subsequences aligned with system load components. …”
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Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms by Songsheng Lin, Huanting Chen, Yin Zheng, Quanji Xie, Xuehua Shen, Huichuan Lin, Shuo Lin, Yan Li

“…Four deep learning algorithms were evaluated. Each model was trained to reconstruct the SPD curves and predict the corresponding optical and chromatic parameters. …”
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Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics by Guanglin Liang, Linchong Huang, Chengyong Cao

“…Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. …”
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Mode-Voltage Constrained Control for Adaptive Optics System by Ruitao Wang, Youming Guo, Nanfei Yan, Ying Yang, Lanqiang Zhang, Qiong Tu, Lingxiao Li, Changhui Rao

“…The direct slope algorithm is used to obtain the mode coefficient of the control voltage, which is then followed by obtaining the constrained control voltage through the proportional-integral controller and transition matrix. …”
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Automatic Scan-to-BIM—The Impact of Semantic Segmentation Accuracy by Jidnyasa Patil, Mohsen Kalantari

“…Given the rapid advancement of deep learning algorithms in recent years, it is crucial to analyze how their accuracy impacts reconstruction quality. …”
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On the Convergence of Normal and Curvature Calculations with the Height Function Method for Two-Phase Flow by Antonio Cervone, Sandro Manservisi, Jieyun Pan, Ruben Scardovelli, Stéphane Zaleski

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Adaptive Support Weight-Based Stereo Matching with Iterative Disparity Refinement by Alexander Richter, Till Steinmann, Andreas Reichenbach, Stefan J. Rupitsch

“…Real-time 3D reconstruction in minimally invasive surgery improves depth perception and supports intraoperative decision-making and navigation. …”
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HarSoNet: a two-stage point cloud registration method integrating soft and hard matching by Qiongdan Huang, Jiapeng Wang, Jiejing Han, Shilin Kang

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Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey by Wenqi Lyu, Hao Sheng, Wei Ke, Xiao Ma

“…We present a systematic review of 17 mainstream light field spatial super-resolution techniques, evaluating their performance across seven public datasets. Integrating experimental results, we specifically analyze the performance of deep learning-based super-resolution algorithms at various magnification levels. …”
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A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation by Zhen Yang, Mingxuan Wang, Shikun Xie

“…Two lightweight surface reconstruction techniques are implemented: (1) algorithmic reconstruction, which delivers a 6.3 mm elevation error at 95% confidence in complex intersections, and (2) template matching, which replaces road markings, poles, and vegetation using bounding boxes. …”
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