Design and analysis of intelligent service chain system for network security resource pool by Zenan WANG, Jiahao LI, Chaohong TAN, Dechang PI

“…The traditional network security architecture ensures network security by directing traffic through hardware based network security function devices.Since the architecture consists of fixed hardware devices, it leads to a single form of network security area deployment and poor scalability.Besides, the architecture cannot be flexibly adjusted when facing network security events, making it difficult to meet the security needs of future networks.The intelligent service chain system for network security resource pool was based on software-defined network and network function virtualization technologies, which can effectively solve the above problems.Network security functions of virtual form were added based on network function virtualization technology, combined with the existing hardware network elements to build a network security resource pool.In addition, the switching equipment connected to the network security elements can be flexibly controlled based on software-defined network technology.Then a dynamically adjustable network security service chain was built.Network security events were detected based on security log detection and a expert library consisting of security rules.This enabled dynamic and intelligent regulation of the service chain by means of centralized control in the face of network security events.The deployment process of the service chain was mathematically modeled and a heuristic algorithm was designed to realize the optimal deployment of the service chain.By building a prototype system and conducting experiments, the results show that the designed system can detect security events in seconds and automatically adjust the security service chain in minutes when facing security events, and the designed heuristic algorithm can reduce the occupation of virtual resources by 65%.The proposed system is expected to be applied to the network security area at the exit of the campus and data center network, simplifying the operation and maintenance of this area and improving the deployment flexibility of this area.…”
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Ensemble Methods for Parameter Estimation of WRF‐Hydro by Arezoo RafieeiNasab, Michael N. Fienen, Nina Omani, Ishita Srivastava, Aubrey L. Dugger

“…Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation.…”
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Enhancing Crowd Safety at Hajj: Real-Time Detection of Abnormal Behavior Using YOLOv9 by Amani A. Alsabei, Tahani M. Alsubait, Hosam H. Alhakami

“…Leveraging deep learning, this research accurately identifies features of abnormal behavior from the HAJJv2 dataset, specifically curated and annotated for the Hajj context. Optimization of the YOLOv9 algorithm for this scenario demonstrated superior performance metrics (mean Average Precision (mAP@0.5), Recall, and Precision) when compared with its predecessors (YOLOv4, YOLOv5, YOLOv7, and YOLOv8), highlighting significant improvements in detection accuracy and real-time applicability. …”
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Hybrid feedback control of PMSG-based WECS with multilevel flying-capacitor inverter enhanced by fractional order extremum seeking by Younes El Khlif, Abdelmounime El Magri, Rachid Lajouad, Omar Bouattane

“…Additionally, a fractional-order extremum seeking control (FOESC) algorithm for Maximum Power Point Tracking (MPPT) is introduced, which optimizes power capture by adjusting turbine speed from the DC side without requiring a detailed system model (model-free approach). …”
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Traffic light detection and recognition based on deep learning for autonomous-rail rapid tram by XIONG Qunfang, LIN Jun, YUAN Xiwen, XU Yanghan, YUE Wei, LI Yuanzhengyu

“…In order to further enhance the model's generalization performance, an image diagnosis algorithm was introduced before signal recognition, which generates warnings for complex conditions, such as overexposure and backlighting. …”
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Secure Latency-Aware Task Offloading Using Federated Learning and Zero Trust in Edge Computing for IoMT by Waleed Almuseelem

“…Moreover, this algorithm determines the optimal offloading strategy based on the global model while considering trustworthiness, the heterogeneous nature of tasks, resource availability, and latency. …”
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Predicting Trip Duration and Distance in Bike-Sharing Systems Using Dynamic Time Warping by Ahmed Ali, Ahmad Salah, Mahmoud Bekhit, Ahmed Fathalla

“…These two contributions of the proposed approach complement existing prediction models for rentals and returns, providing a comprehensive solution for BSS optimization. …”
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Research on Electric Spot Trading of Electric Vehicles Based on Block Chain by ZHANG Zhening, ZHANG Xinyu, GONG Gangjun

“…Finally,by designing intelligent contract, the optimization process of trading scheme was validated by a genetic algorithm.The simulation results show that through the efficient operation of the smart contract of block chain, the formulation of the optimal trading scheme to meet the objectives of multiple participants is achieved, which can provide a reference for improving the safety, reliability and efficient coordination of the electric vehicle charging market.…”
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Distributed Photovoltaic Distribution Voltage Prediction Based on eXtreme Gradient Boosting and Time Convolutional Networks by Fang Yuan, Yong Lu, Zhi Xie, Shenxiang Dai

“…Then, the model is improved and optimized using residual module with bottle sea sheath algorithm. …”
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An Efficient and Cost-Effective Vehicle Detection and Tracking System for Collision Avoidance in Foggy Weather by Naeem Raza, Muhammad Asif Habib, H. M. Sajid Imran, Abdul Qayum, Sajida Perveen, Sohail Jabbar, Shahzad Sarwar, Mudassar Ahmad

“…The optimized deep-SORT outperforms pre-processing, inference, NMS post-processing, and update time, achieving higher FPS on foggy and the BDD100K dataset video sequences compared to the baseline deep-SORT and strong-SORT vehicle tracking algorithms. …”
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Research on ionospheric parameters prediction based on deep learning by Yuntian FENG, Xia WU, Xiong XU, Rongqing ZHANG

“…For ionospheric parameter prediction, the short-term and daily mean value prediction of ionospheric parameters was established by long short-term memory (LSTM) predictive neural network modeling.Two methods of point-by-point prediction and sequence prediction were utilized.Furthermore, in order to predict the hourly and daily changes of ionospheric parameters, the proposed scheme was optimized by multidimensional prediction and empirical mode decomposition (EMD) algorithm.Finally, the feasibility of the proposed optimization algorithm in improving the prediction accuracy of ionospheric parameters is verified.…”
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P-Band PolInSAR Sub-Canopy Terrain Retrieval in Tropical Forests Using Forest Height-to-Unpenetrated Depth Mapping by Chuanjun Wu, Jiali Hou, Peng Shen, Sai Wang, Gang Chen, Lu Zhang

“…A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. …”
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Research on RF Intensity Temperature Sensing based on 1D-CNN by DING Meiqi, GUI Lin, WANG Ziyi, SHANG Disen, QIAN Min, LI Qiankun

“…The model is trained with the training set data and validated with the test set data to optimize the model parameters for optimal performance. …”
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Effect of tool angle in nanocutting of single crystal GaN using diamond cutter by Yongqiang WANG, Hao XIA, Zhihang HU, Shuaiyang ZHANG, Shaohui YIN

“…The influence of tool angles is systematically explored by simulating cutting processes with a wide range of rake angles (−18°, −12°, −6°, 6°, 12°, 18°) and flank angles (−18°, −12°, −6°, 6°, 12°, 18°). Post-simulation analysis utilizes sophisticated algorithms to dissect the deformation mechanisms: employed to identify, characterize, and quantify the evolution of dislocations, including their types (e.g., perfect dislocations, partial dislocations), Burgers vectors, and densities within the workpiece. …”
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Air-ground collaborative multi-source orbital integrated detection system: Combining 3D imaging and intrusion recognition. by Mengyuan Yan, Xingyu Yang, Wei Gao, Lifan Rong, Shengbo Li, Yuan Xiong

“…The system employs a lightweight rail inspection vehicle equipped with dual LiDARs and an Astro camera, synchronized with an unmanned aerial vehicle (UAV) carrying industrial-grade LiDAR. We propose an improved LiDAR odometry and mapping with sliding window (LOAM-SLAM) algorithm enables real-time dynamic mapping, while an optimized iterative closest point (ICP) algorithm achieves high-precision point cloud registration and colorization. …”
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TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance by Xuanbai Yu, Olivier Caspary

“…These findings offer new insights to support reliable predictive maintenance in industrial settings and provide a new perspective for future research into active vibration control, where vibration signal analysis, feature extraction, and mathematical modeling play key roles in optimizing control algorithms and enhancing the efficiency of adaptive control systems.…”
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Research and application of deep learning object detection methods for forest fire smoke recognition by Luhao He, Yongzhang Zhou, Lei Liu, Yuqing Zhang, Jianhua Ma

“…This study investigates the application effectiveness of deep learning-based object detection technology in forest fire smoke recognition by using the YOLOv11x algorithm to develop an efficient fire detection model. …”
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Estimating Soil Cd Contamination in Wheat Farmland Using Hyperspectral Data and Interpretable Stacking Ensemble Learning by Liang Zhong, Meng Ding, Shengjie Yang, Xindan Xu, Jianlong Li, Zhengguo Sun

“…Ultimately, the SHAP method was used to shed light on the model’s decision-making process. The results show that (1) FOD can further highlight the spectral features, thereby strengthening the correlation between soil Cd content and wavelength; (2) the CARS algorithm extracted 3.4–6.8% of the feature wavelengths from the full spectrum, and most of them were the wavelengths with high correlation with soil Cd; (3) the optimal estimation precision was achieved using the FOD1.5-SNV spectral pre-processing combined with the Stacking model (<i>R</i>² = 0.77, RMSE = 0.05 mg/kg, RPD = 2.07), and the model effectively quantitatively estimated soil Cd contamination; and (4) SHAP further revealed the contribution of each base model and characteristic wavelengths in the Stacking modeling process. …”
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Proximal Neural Networks based reconstruction for few-view CT applications by Hoang Trieu Vy Le, Caroline Bossuyt, Julie Escoda, Marius Costin, Jan De Beenhouwer, Jan Sijbers

“…Recent advancements in Plug-and-Play (PnP) algorithms have shown promise for solving imaging inverse problems by utilizing the capabilities of Gaussian denoising algorithms to handle complex optimization tasks. …”
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Rapid calculation scheme for electricity quantity and green certificate allocation considering demand response within the municipal scope by FAN Qiannan, WANG Zhongrong, YU Liya, LIANG Xiaohang, BAO Jie, LIU Shuyong

“…This paper focuses on the day-ahead allocation scenario of electricity quantity and certificates considering demand response within the municipal scope, constructs a leader-follower game model, and solves it through the Kriging model surrogate optimization method. …”
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