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A multiscale differential-algebraic neural network-based method for learning dynamical systems

International Journal of Mechanical System Dynamics 2024年第1期4卷 77-87页

作者： Yin Huang Jieyu Ding College of Computer Science&Technology Qingdao UniversityQingdaoChina School of Mathematics and Statistics Qingdao UniversityQingdaoChina Center for Computational Mechanics and Engineering Simulation Qingdao UniversityQingdaoChina

The objective of dynamical system learning tasks is to forecast the future behavior of a system by leveraging observed ***,such systems can sometimes exhibit rigidity due to significant variations in component parameters or the presence of slow and fast variables,leading to challenges in *** overcome this limitation,we propose a multiscale differential-algebraic neural network(MDANN)method that utilizes Lagrangian mechanics and incorporates multiscale information for dynamical system *** MDANN method consists of two main components:the Lagrangian mechanics module and the multiscale *** Lagrangian mechanics module embeds the system in Cartesian coordinates,adopts a differential-algebraic equation format,and uses Lagrange multipliers to impose constraints explicitly,simplifying the learning *** multiscale module converts high-frequency components into low-frequency components using radial scaling to learn subprocesses with large differences in *** results demonstrate that the proposed MDANN method effectively improves the learning of dynamical systems under rigid conditions.

关键词： dynamical systems learning multibody system dynamics differential-algebraic equation neural networks multiscale structures

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Hybrid CNN & Random Forest Model for Effective Clove Leaf Disease Dignosis 4

Hybrid CNN & Random Forest Model for Effective Clove Leaf Di...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Banerjee, Deepak Sharma, Neha Upadhyay, Deepak Singh, Vijay Kumar, Gotte Ranjith Singh Gill, Kanwarpartap Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350307757

Clove (Syzygium aromaticum) farming is at the crossroads of history and innovation, producing a versatile spice prized for its culinary or medicinal uses. This study investigates the use of sophisticated methods of machine learning for automated categorization of illnesses affecting clove leaves. Our model, which uses neural network convolution (CNN) or random forest methods, has remarkable precision, recall, or F1-Score for various disease classifications. Clove Rust, Clove Leaf Spot, Clove Anthrax, Clove Powdery Mildew, Clove Leaves Blight, Clove Bacterial Leaf Spot, Clove Fusarium Wilt, or Clove Septoria Leaf Spot are all rigorously analyzed in the categorization model. Precision ratings range from 88.12% to 92.23%, indicating that the model is accurate in recognizing cases of each condition. The model's ability to minimize false negatives and capture all relevant instances is shown in its high recall values, which range from 87.38% to 90.83%. The F1-Score varies from 88.58% to 91.41%, indicating the model's overall efficacy and a harmonic equilibrium between precision and recall. The number of cases for each illness class is represented by support metrics, which reveals how it varies across the dataset. The model obtains an amazing general precision of 97%, demonstrating its ability to correctly classify occurrences across a wide range of conditions. The model's solid performance is further consolidated by macro, weighted, or micro averages, emphasizing its flexibility to varied datasets. The weighted average, which takes into account class imbalances, displays values of 89.73%, confirming the algorithm's accuracy in diverse dataset configurations. The micro-average strongly matches with individual class measures, offering an in-depth evaluation of the model's general efficacy. Finally, the combination of CNN with Random Forest is useful in increasing automated illness detection in clove leaves. © 2024 IEEE.

关键词： Diseases

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Joint Prediction of SOH and RUL for Lithium Batteries Considering Capacity Self Recovery and Model Drift

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IEEE Internet of Things Journal 2025年第12期12卷 22187-22196页

作者： Jia, Zhen Li, Zhifei Wang, Zhengdong Liu, Zhenbao Vong, Chi-Man Xi'an University of Architecture and Technology School of Mechanical and Electrical Engineering Xi'an710055 China Northwestern Polytechnical University School of Civil Aviation Xi'an710072 China University of Macau Department of Computer and Informa tion Science Macau999078 China

Accurate monitoring of the state of health (SOH) and remaining useful life (RUL) of lithium batteries is a key technology to realize their stable and economic operation. The current data-driven methods have problems such as local prediction errors caused by capacity self-recovery effect, model drift phenomenon, and inaccurate trend prediction caused by complex degradation process. In this paper, we propose a joint SOH and RUL forecasting based on the fusion of Kolmogorov-Arnold Network (KAN), Deep Bidirectional Long Short-Term Memory Network (DBLSTM) and adaptive mechanism method (KAN-HDBLSTM-AM). The method innovatively introduces an adaptive mechanism that considers the capacity degradation trend for different prediction steps, suppresses the accumulation of errors in the model prediction process, and further improves the prediction accuracy by combining the nonlinear approximation capability of KAN and the mastery of data temporal characteristics of DBLSTM. Experimental validation shows that the proposed method can effectively overcome the prediction errors caused by capacity self-recovery and model drift, and can retain sufficient accuracy under long prediction steps, with the prediction trend being more in line with the actual change trend and the prediction error being smaller compared with other methods. © 2014 IEEE.

关键词： Prediction models

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Fortifying Cloud Data Security with the Maritime Search and Rescue Algorithm 2

Fortifying Cloud Data Security with the Maritime Search and ...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Monica, K.M. Parameswaran, T. Swapna, J. Prabu, S. Kaliappan, S. Vit University School Of Computer Science And Engineering Chennai India Cmr University Lakeside Campus School Of Engineering And Technology Department Of Computer Science And Engineering Bangalore India Veltech Rangarajan Dr.Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Tamilnadu Chennai India Mahendra Institute Of Technology Department Of Ece Namakkal India Lovely Professional University Division Of Research And Development Punjab Phagwara India

ISBN: (纸本)9798350381665

Cloud computing has fundamentally transformed the accessibility and payment models of services within distributed system environments. In response to heightened security concerns, cloud service providers offer diverse resources spanning Infrastructure, Software, Platform, and Database services, supported by security measures such as Service Level Agreements (SLAs), access control rules, and cryptographic methods. This paper introduces a novel data encryption technique, termed Data Security in Cloud Computing using Maritime Search and Rescue Algorithm (DSCC-MSRA). By integrating an enhanced cypher block chaining algorithm with logical substitution and the Maritime Search and Rescue Algorithm (MSRA) for key generation, this method provides a unique approach to encryption. Through rigorous optimization and analysis, it has undergone significant refinement, with comparisons against industry-standard algorithms showcasing its efficacy. The primary objective is to elevate encryption procedures and introduce complexity to cryptanalysis, ultimately enhancing data security in cloud computing. © 2024 IEEE.

关键词： Cryptography

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Transfer Learning Empowered Multi-Class Classification of Kidney Diseases: A Deep Learning Approach 2

Transfer Learning Empowered Multi-Class Classification of Ki...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Sharma, Gunjan Anand, Vatsala Chauhan, Rahul Pokhariya, Hemant Singh Gupta, Sheifali Sunil, G. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350371314

The growing rate of chronic kidney disease and a shortage of renal specialists make renal dysfunction a major worldwide health concern. This study, which makes use of technological breakthroughs, suggests an AI-driven system for the autonomous diagnosis of kidney abnormalities, with an emphasis on chronic kidney disease, which is characterized by a steady reduction in function. Many conditions, such as cysts, stones, and tumors, can cause renal failure, these conditions frequently demonstrate no symptoms at first. In an attempt to overcome the lack of medical professionals, this research uses deep learning and a modified fine-tuned InceptionV3 pre-trained model to examine 12,446 Computed Tomography (CT) full abdomen and urogram images that were obtained from several hospital databases. The dataset includes both normal and common kidney disease images such as cysts, tumors, and stones. The proposed model exhibits improved accuracy through careful annotation and model training, it achieves remarkable performance metrics such as 96.52% accuracy rate, along with average precision of 97.76%, 98.28% recall and 0.95 F1 score. With its adaptable clinical diagnosis solution, this cutting-edge technology makes kidney disorders easily identifiable, allowing for immediate action in the treatment of renal disorders. © 2024 IEEE.

关键词： Tumors

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Advanced Machine Learning Techniques for Fake News Detection: A Comprehensive Analysis Using the LIAR Dataset 3rd

Advanced Machine Learning Techniques for Fake News Detectio...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Paul, Subrata Ghosh, Shivnath Mitra, Anirban Bhattacharya, Pronaya Department of CSE-AI Brainware University West Bengal Barasat India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University West Bengal Kolkata India Department of Computer Science and Engineering Amity School of Engineering and Technology and Research and Innovation Cell Amity University West Bengal Kolkata India

ISBN: (纸本)9789819624676

In today’s digital world, fake news presents a serious challenge to social cohesiveness, trust among individuals, and the functioning of democracy. Overcoming this issue demands novel solutions that make efficient use of machine learning (ML) for identifying and battle disinformation. This paper examines the important problem of fake news, including its meaning, its consequences, and the significance of social media in its rapid spread. The study emphasises the complexities of identifying fake news, focusing on sophisticated techniques like AI-generated content and the widespread dissemination of misinformation. In order to tackle such obstacles, the paper uses ML approaches, particularly the LIAR dataset and the XGBoost model, to create a reliable fake news detection system. The outcomes show that these methods have been successful at successfully recognising false information, highlighting ML’s prospective in reducing misinformation. This research contributes to the broader discourse on media literacy and the need for reliable information in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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On the chronological understanding of the homogeneous dielectric barrier discharge

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High Voltage 2023年第6期8卷 1132-1150页

作者： Xinpei Lu Zhi Fang Dong Dai Tao Shao Feng Liu Cheng Zhang Dawei Liu Lanlan Nie Chunqi Jiang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina College of Electrical Engineering and Control Science Nanjing Tech TechnologyNanjingChina School of Electric Power South China University of TechnologyGuangzhouChina Institute of Electrical Engineering Chinese Academy of SciencesBeijingChina Frank Reidy Research Center for Bioelectrics Old Dominion UniversityNorfolkVirginiaUSA Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginiaUSA

Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.

关键词： dielectric barrier pollution

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Learning-based Dynamic 3D Placement of Multiple Aerial Base Stations with Heterogeneous Traffic

Learning-based Dynamic 3D Placement of Multiple Aerial Base ...

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2023 VTS Asia Pacific Wireless Communications Symposium, APWCS 2023

作者： Hoang, Linh T. Nguyen, Chuyen T. Le, Hoang D. Pham, Anh T. University of Aizu Graduate School of Computer Science and Engineering Aizuwakamatsu Japan Hanoi University of Science and Technology School of Electrical and Electronic Engineering Viet Nam

ISBN: (纸本)9798350316803

Deployment of Unmanned Aerial Vehicles (UAVs) as aerial base stations (ABSs) has emerged as a promising solution to enable high-quality services in next-generation mobile networks. One key challenge is how to place the UAV-mounted Base Stations (UAV-BSs) dynamically over time in response to changes in network situations. Conventional methods usually utilize location information to place the UAV-BSs at the centroid of all users to maximize the average downlink rate. This approach might become ineffective when the traffic demand is highly heterogeneous among users, causing the spatial distribution of traffic demand not to correlate with the distribution of users. This paper considers a time-evolving multi-user multi-server system with heterogeneous traffic, where a group of UAV-BSs provides different services to ground mobile users. We formulate a long-term maximization problem of the user's time-average download speed with the queue stability constraint under random movements of ground users. An actor-critic framework of deep reinforcement learning is proposed to position multiple UAV-BSs efficiently based on a comprehensive view of the network situation, including spatial statistics of the users, traffic demand, and the network's downlink rate. The framework's core module is a deep neural network (DNN) that obtains the UAV's location and three encoded heatmaps of network statistics to predict the optimal movement for UAV-BSs. Extensive simulations demonstrate the guaranteed convergence and great potential of the proposed method regarding the backlog queue length and the average download speed of users. © 2023 IEEE.

关键词： Base stations

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Road Crack Segmentation Based on Dual Branch Attention Mechanism and Multi-Scale Features 2

Road Crack Segmentation Based on Dual Branch Attention Mecha...

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2nd IEEE International Conference on Control, Electronics and computer technology, ICCECT 2024

作者： Chen, Zhaoxuan Jia, Haixin Peng, Jianbo Liu, Huilin School of Computer Science and Engineering Northeastern University Shenyang China School of Mechanical and Electrical Engineering China University of Mining and Technology-Beijing Beijing China

ISBN: (纸本)9798350380958

In road surface crack segmentation algorithms, obtaining comprehensive contextual information is crucial. While many solutions use the Transformer architecture for global information, its computational complexity requires combining it with CNN for feature extraction, making multi-scale contextual information extraction challenging. Although UNet-based networks are popular for fine-grained features, their decoder introduces redundant contents. To address above-mentioned issues, The paper proposes DBAUNet, incorporating SVD-ASPP (Same Value Dense ASPP) for global information extraction and DBAM (Dual Branch Attention Mechanism) to enhance crack information while suppressing background information. SVD-ASPP prevents crack disconnections, ensuring more accurate segmentation results. © 2024 IEEE.

关键词： Information retrieval

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Application of Willow Catkin Optimization Algorithm for Wireless Sensor Network 16th

Application of Willow Catkin Optimization Algorithm for Wi...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Chu, Shu-Chuan Guo, Buyue Sun, Bing Yang, Hong-Mei Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Artificial Intellifence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819615346

Metaheuristic algorithms have shown strong advantages in solving real application problems, and excellent metaheuristic algorithms have emerged in recent years. The Willow Catkin Optimization (WCO) algorithm is a novel metaheuristic algorithm for modeling willow reproductive behavior, which has a simple structure, is easy to implement, and has excellent search capability. In this paper, the WCO algorithm is employed to address the coverage problem of wireless sensor networks (WSNs) in 2D and 3D environments and the experimental results are compared with other classical algorithms and lates proposed algorithms, and the experimental results show that the WCO algorithm has excellent performance in solving the node coverage problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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