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Acta Mathematica Sinica,English Series 2025年第2期41卷 498-521页

作者： Xiangyu Zheng Songxi Chen JD Technology Beijing 100176P.R.China Department of Statistics and Data Science Tsinghua UniversityBeijing 100084P.R.China

Tree-based models have been widely applied in both academic and industrial settings due to the natural interpretability, good predictive accuracy, and high scalability. In this paper, we focus on improving the single-tree method and propose the segmented linear regression trees(SLRT) model that replaces the traditional constant leaf model with linear ones. From the parametric view, SLRT can be employed as a recursive change point detect procedure for segmented linear regression(SLR) models,which is much more efficient and flexible than the traditional grid search method. Along this way,we propose to use the conditional Kendall's τ correlation coefficient to select the underlying change points. From the non-parametric view, we propose an efficient greedy splitting method that selects the splits by analyzing the association between residuals and each candidate split variable. Further, with the SLRT as a single-tree predictor, we propose a linear random forest approach that aggregates the SLRTs by a weighted average. Both simulation and empirical studies showed significant improvements than the CART trees and even the random forest.

关键词： Regression trees segmented linear regression split selection tree pruning

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A parallel solver framework for fully implicit monolithic fluid-structure interaction

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Acta Mechanica Sinica 2025年第2期41卷 88-115页

作者： Yujie Sun Qingshuang Lu Ju Liu Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and TechnologyShenzhen 518055China

We propose a suite of strategies for the parallel solution of fully implicit monolithic fluid-structure interaction(FSI).The solver is based on a modeling approach that uses the velocity and pressure as the primitive variables,which offers a bridge between computational fluid dynamics(CFD)and computational structural *** spatiotemporal discretization leverages the variational multiscale formulation and the generalized-αmethod as a means of providing a robust discrete *** particular,the time integration scheme does not suffer from the overshoot phenomenon and optimally dissipates high-frequency spurious modes in both subproblems of *** on the chosen fully implicit scheme,we systematically develop a combined suite of nonlinear and linear solver *** a block factorization of the Jacobian matrix,the Newton-Raphson procedure is reduced to solving two smaller linear systems in the multi-corrector *** first is of the elliptic type,indicating that the algebraic multigrid method serves as a well-suited *** second exhibits a two-by-two block structure that is analogous to the system arising in *** by prior studies,the additive Schwarz domain decomposition method and the block-factorization-based preconditioners are invoked to address the linear *** the number of unknowns matches in both subdomains,it is straightforward to balance loads when parallelizing the algorithm for distributed-memory *** use two representative FSI benchmarks to demonstrate the robustness,efficiency,and scalability of the overall FSI solver *** particular,it is found that the developed FSI solver is comparable to the CFD solver in several aspects,including fixed-size and isogranular scalability as well as robustness.

关键词： Fluid-structure interaction Monolithic coupling Implicit time integration Iterative methods Vortex-induced vibration

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Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

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International Journal of Intelligent Computing and Cybernetics 2023年第3期16卷 629-646页

作者： Haoning Pu Zhan Wen Xiulan Sun Lemei Han Yanhe Na Hantao Liu Wenzao Li Chengdu University of Information Technology ChengduChina Education Department of Sichuan Province Education Informatization and Big Data CenterChengduChina Network and Data Security Key Laboratory of Sichuan Province UESTCChengduChina

Purpose–The purpose of this paper is to provide a shorter time cost,high-accuracy fault diagnosis method for water *** pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing *** the time-consuming empirical mode decomposition(EMD)method and the more efficient classification provided by the convolutional neural network(CNN)method,a novel classification method based on incomplete empirical mode decomposition(IEMD)and dual-input dual-channel convolutional neural network(DDCNN)composite data is proposed and applied to the fault diagnosis of water ***/methodology/approach–This paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient(MFCC)and a neural network model of ***,the sound signal is decomposed by IEMD to get numerous intrinsic mode functions(IMFs)and a residual(RES).Several IMFs and one RES are then extracted by MFCC ***,the obtained features are split into two channels(IMFs one channel;RES one channel)and input into ***–The Sound dataset for Malfunctioning Industrial Machine Investigation and Inspection(MIMII dataset)is used to verify the practicability of the *** results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the *** with EMD,51.52% of data preprocessing time,67.25% of network training time and 63.7%of test time are saved and also improve *** limitations/implications–This method can achieve higher accuracy in fault diagnosis with a shorter time ***,the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research ***/value–This method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

关键词： Fault identification Neural nets DDCNN IEMD MFCC

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Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight

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Computer Modeling in Engineering & sciences 2025年第4期143卷 875-909页

作者： Iman S.Al-Mahdi Saad M.Darwish Magda M.Madbouly Department of Information Technology Institute ofGraduate Studies and ResearchAlexandriaUniversityAlexandria21526Egypt Faculty of Computers and Data Science Alexandria UniversityAlexandria5432042Egypt

Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare *** problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical *** challenges often lead to inefficient and less *** predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational *** work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel *** features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction *** hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the *** enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional *** proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over ***,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medi

关键词： Heart disease prediction feature selection ensemble deep learning optimization genetic algorithm(GA) ensemble deep learning tunicate swarm algorithm(TSA) feature selection

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Enhancing Network Intrusion Detection using Artificial Neural Networks: An Analysis of the UNSW-NB15 dataset 2

Enhancing Network Intrusion Detection using Artificial Neura...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Arun, Chalana B. Anusha, M. Kodipalli, Ashwini Rao, Trupthi Rohini, B.R. Gargi, N. Department of Artificial Intelligence and Data Science Global Academy of Technology Bangalore India Department of Artificial Intelligence and Data Global Academy of Technology Bangalore India

ISBN: (纸本)9798331504960

Securing networks from illicit activities has remained the top priority in the constantly evolving world on information technology. Intrusion detection systems are struggling to keep up with the increasing complexity of network attacks which increased the demand for advanced methods such as Machine Learning and Deep Learning. This work emphasizes the usage of Artificial Neural Networks (ANN) for enhancing the skill to detect network incursion in IDS. Leveraging the data compilation UNSW-NB15, which evokes various internet traffic and threat instances, we created ANN-based IDS that takes benefit from neural network to detect specific patterns in data. To train and evaluate the model, multiple optimizers such as SGD, RMSprop, and Adam, in two configurations i.e. with and without dropout were compared. The reliability of newly built IDS is ensured by substantial data preparation, feature selection, along with the model tuning. The UNSW-NB15 dataset, well-known for its high data volume and accurate network inclusion, provides a significant standard to assess the impact of the suggested framework. The study shows that the ANN model using Adam optimizer and dropout outperforms with an accuracy of 94.81%, demonstrating the potential to enhance the safety of networks greatly. This paper highlights the productivity of ANNs in improving the practice of detection of network attacks, giving a solid method to combat the increasing cyber risk through extensive evaluation and testing. © 2024 IEEE.

关键词： Network intrusion

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Web Application Development using Flask - Faculty Directory Management 14

Web Application Development using Flask - Faculty Directory ...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Tarunika, R. Rohit Ram, S.S. Sujithra, M. Sudha Devi, D. Department of Computing-Data Science Coimbatore Institute of Technology India Department of Computing-Data Science Coimbatore Institute of Technology India

The main goal of web development is to create, build and maintain websites. It is what allows the user to experience seamless performance when accessing a website. The web applications landscape has evolved tremendously over the years using various web development frameworks. A web development framework is a set of resources and tools for software developers to build and manage web applications, web services and websites. One such framework is Flask written in Python. The objective is to create a faculty directory using Flask with a goal to provide a centralized resource that can be used by students, staff, and administrators to easily find and connect with faculty members. By improving access to information about faculty members, the directory project aims to promote collaboration and facilitate the sharing of knowledge within the academic community. © Grenze Scientific Society, 2023.

关键词： Python

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Non-Intrusive Load Event Identification Algorithm Based on Color Coding

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1019-1031页

作者： Hu, Wen-Yu Li, Guo-Nong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China

Non-invasive load monitoring (NILM) can help residents monitor the operation of household appliances and achieve the purpose of energy conservation and emission reduction. Load event identification is a key task of non-intrusive load monitoring. In order to enrich the characteristics of load events and improve the identification accuracy of load events, a load event identification algorithm based on color coding (CCA) is proposed. On the basis of retaining the basic waveform of active power, the three characteristics of active power (R), reactive power (G) and reactive power change trend (B) are fused to construct the color image of load events, and the image is trained and rec-ognized based on the AlexNet convolutional neural network with parameter adjustment. The experimental results show that this load event identification algorithm can stably and effectively distinguish the load events of different devices. Compared with three commonly used classifiers, the results show that this algorithm is superior to the traditional event classification algorithm based on power sequence. © 2016, J. Network Intell. All rights reserved.

关键词： Reactive power

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Research on Mining Association Rules for Classroom Concentration Based on Apriori Algorithm 7

Research on Mining Association Rules for Classroom Concentra...

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7th IEEE Information technology and Mechatronics Engineering Conference, ITOEC 2023

作者： Bai, Jingqiu Kong, Huafeng Hu, Wenhan Wuhan Business University Department of Information Engineering Hubei China Major in Data Science and Big Data Technology China The Main Research Direction Is the Computer Application China

ISBN: (纸本)9798350334197

This paper studies the correlation between students' concentration in class and learning interest, emotional state and other influencing factors. By collecting students' classroom status data, a data set suitable for data mining is constructed. Based on the idea of Apriori algorithm, the Anaconda Navigator programming tool is used to mine the association rules of students' classroom concentration. Through data mining based on Apriori algorithm, and the association rules between students' emotional state, learning interest and other factors and in-class test scores are output. The results of strong association rules show that the better students' emotional state, learning interest and other states are, the better their classroom concentration performance is. Mining the potential influencing factors of students' classroom concentration provides valuable reference theory for students to improve classroom concentration and teachers to improve classroom teaching effect, which is helpful for teachers and students to build a more efficient classroom together in the future. © 2023 IEEE.

关键词： Association rules

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An Image Semantic Communication System Based on GAN and Relative Position-Encoding

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IEEE Communications Letters 2025年第6期29卷 1280-1284页

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptography Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

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SN Computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

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