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Predicting Eddy current losses in large generator rotor using data-driven short connection deep Gaussian process

engineering Applications of artificial intelligence 2025年 158卷

作者： Guo, Hai Cai, Jinlin Wang, Likun Zhang, Junxing Marignetti, Fabrizio Zhang, Bixian School of Data Science and Artificial Intelligence Wenzhou University of Technology Wen Zhou 325035 China College of Computer Science and Engineering Dalian Minzu University Dalian 116600 China College of Electrical and Electronic Engineering Harbin University of Science and Technology Harbin 150080 China Department of Electrical and Information Engineering University of Cassino and South Lazio Cassino 03043 Italy

In response to the increased prediction errors observed when applying deep Gaussian process (DGP) models to the eddy current loss data of large generator rotors, an explainable artificial intelligence(AI)-based DGP prediction method incorporating short connections design is proposed. This method reparameterizes multivariate normal random variables between layers and concatenates processed input-layer data to construct new inter-layer inputs for the subsequent layer, thereby establishing a short-connected DGP model. This approach effectively mitigates the accumulation of prediction errors due to increased model depth, significantly enhancing the model's adaptability and predictive accuracy in complex data structures. Experimental results demonstrate that the short-connected DGP model achieved exceptional predictive performance for eddy current loss estimation, as evidenced by a coefficient of determination of 0.9886 and a mean squared error of 0.0026. Furthermore, as the number of layers increases, the short-connected DGP model outperforms both existing DGP models and alternative approaches in prediction accuracy. The proposed AI method not only robustly supports high-precision prediction of eddy current losses in the rotor slot filling components of large generators, but also serves as an efficient and reliable modeling tool for their design and optimization, thereby possessing significant theoretical and practical value. © 2025

关键词： Current loss prediction Deep gaussian process Explainable artificial intelligence Large generator

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Breast Cancer Immunohistochemical Image Generation: a Benchmark dataset and Challenge Review

arXiv

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arXiv 2023年

作者： Zhu, Chuang Liu, Shengjie Yu, Zekuan Xu, Feng Aggarwal, Arpit Corredor, Germán Madabhushi, Anant Qu, Qixun Fan, Hongwei Li, Fangda Li, Yueheng Guan, Xianchao Zhang, Yongbing Singh, Vivek Kumar Akram, Farhan Sarker, Md Mostafa Kamal Shi, Zhongyue Jin, Mulan The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China Academy for Engineering and Technology Fudan University Shanghai China Beijing Chaoyang Hospital Capital Medical University Beijing China Biomedical Engineering Department Georgia Tech and Emory University United States The Atlanta Veterans Affairs Medical Center United States Biomedical Engineering Department Emory University United States Data Science Institute Imperial College London United Kingdom The School of Computer and Electrical Engineering Purdue University United States The School of Computer Science and Technology Harbin Institute of Technology Shenzhen China The Department of Computer Engineering and Mathematics Rovira I Virgili University Spain The Department of Pathology and Clinical Bioinformatics Erasmus Medical Center Netherlands The Institute of Biomedical Engineering University of Oxford Oxford United Kingdom

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan. From the perspective of saving manpower, material and time costs, directly generating IHC-stained images from Hematoxylin and Eosin (H&E) stained images is a valuable research direction. Therefore, we held the breast cancer immunohistochemical image generation challenge, aiming to explore novel ideas of deep learning technology in pathological image generation and promote research in this field. The challenge provided registered H&E and IHC-stained image pairs, and participants were required to use these images to train a model that can directly generate IHC-stained images from corresponding H&E-stained images. We selected and reviewed the five highest-ranking methods based on their PSNR and SSIM metrics, while also providing overviews of the corresponding pipelines and implementations. In this paper, we further analyze the current limitations in the field of breast cancer immunohistochemical image generation and forecast the future development of this field. We hope that the released dataset and the challenge will inspire more scholars to jointly study higher-quality IHC-stained image generation. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Cooperative Sensing and Heterogeneous Information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach

arXiv

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arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Fractal resistive–capacitive–inductive shunted Josephson junction: Theoretical investigation and microcontroller implementation

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Physica A: Statistical Mechanics and its Applications 2023年 611卷

作者： Ramadoss, Janarthanan Ngongiah, Isidore Komofor Chamgoué, André Chéagé Kingni, Sifeu Takougang Rajagopal, Karthikeyan Center for Artificial Intelligence Chennai Institute of Technology Chennai600069 India Department of Physics Faculty of Science University of Bamenda P.O. Box 39 Bamenda Cameroon School of Geology and Mining Engineering University of Ngaoundere P.O. Box: 115 Meiganga Cameroon Department of Mechanical Petroleum and Gas Engineering National Advanced School of Mines and Petroleum Industries University of Maroua P.O. Box 46 Maroua Cameroon Center for Nonlinear Systems Chennai Institute of Technology Tamilnadu Chennai600069 India Department of Electronics and Communications Engineering University Centre for Research & Development Chandigarh University Punjab Mohali140413 India

The paper captures the analytical and numerical investigations of the fractal resistive–capacitive–inductive shunted Josephson junction (FRCLSJJ) and its microcontroller implementation (MCI). The rate equations of FRCLSJJ are established and based on the Routh–Hurwitz criterion, two equilibrium points are reported with one unconditionally stable and the other unstable for the direct current used for junction excitation less than or equal to one. When this current is greater than one, the FRCLSJJ exhibits no equilibrium point. The contribution of fractal parameters to the dynamics of FRCLSJJ is investigated on fast bursting, regular spiking, relaxation behaviors, and periodic bursting. The FRCLSJJ is characterized by fine dynamics such as different presentations of complex behaviors, the coexistence of periodic and chaotic hidden attractors, chaotic hidden attractors, antimonotonicity, periodic attractors, and periodic and chaotic bubble hidden attractors for varying system parameters. And in the last section of the investigation, the MCI of FRCSJJ is realized with the results establishing a qualitative agreement with numerically simulated results. © 2023 Elsevier B.V.

关键词： Fractals

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PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding

arXiv

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arXiv 2024年

作者： Zhang, Ruochi Wu, Haoran Liu, Chang Li, Huaping Wu, Yuqian Li, Kewei Wang, Yifan Deng, Yifan Chen, Jiahui Zhou, Fengfeng Gao, Xin Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Syneron Technology Guangzhou510700 China Beijing Life Science Academy Beijing102209 China School of Biomedical Sciences LKS Faculty of Medicine The University of Hong Kong Hong Kong Thuwal23955 Saudi Arabia Thuwal23955 Saudi Arabia College of Software Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task. PepHarmony innovatively combines both sequence- and structure-level information into a sequence-level encoding module through contrastive learning. We carefully select datasets from the Protein data Bank (PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences and structures. The experimental data highlights PepHarmony's exceptional capability in capturing the intricate relationship between peptide sequences and structures compared with the baseline and fine-tuned models. The robustness of our model is confirmed through extensive ablation studies, which emphasize the crucial roles of contrastive loss and strategic data sorting in enhancing predictive performance. The proposed PepHarmony framework serves as a notable contribution to peptide representations, and offers valuable insights for future applications in peptide drug discovery and peptide engineering. We have made all the source code utilized in this study publicly accessible via GitHub at https://***/zhangruochi/PepHarmony or http://***/supp/. © 2024, CC BY-NC-ND.

关键词： Peptides

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PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation

arXiv

引用

arXiv 2022年

作者： Zhai, Shuwei Wang, Guotai Luo, Xiangde Yue, Qiang Li, Kang Zhang, Shaoting The School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China Shanghai Artificial Intelligence Laboratory Shanghai200030 China Department of Radiology West China Hospital Sichuan University Chengdu610041 China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China

The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire. In this paper, we propose to annotate a segmentation target with only seven points in 3D medical images, and design a two-stage weakly supervised learning framework PA-Seg. In the first stage, we employ geodesic distance transform to expand the seed points to provide more supervision signal. To further deal with unannotated image regions during training, we propose two contextual regularization strategies, i.e., multi-view Conditional Random Field (mCRF) loss and Variance Minimization (VM) loss, where the first one encourages pixels with similar features to have consistent labels, and the second one minimizes the intensity variance for the segmented foreground and background, respectively. In the second stage, we use predictions obtained by the model pre-trained in the first stage as pseudo labels. To overcome noises in the pseudo labels, we introduce a Self and Cross Monitoring (SCM) strategy, which combines self-training with Cross Knowledge Distillation (CKD) between a primary model and an auxiliary model that learn from soft labels generated by each other. Experiments on public datasets for Vestibular Schwannoma (VS) segmentation and Brain Tumor Segmentation (BraTS) demonstrated that our model trained in the first stage outperformed existing state-of-the-art weakly supervised approaches by a large margin, and after using SCM for additional training, the model’s performance was close to its fully supervised counterpart on the BraTS dataset. © 2022, CC BY.

关键词： Distillation

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Full-pose Trajectory Tracking of Overactuated Multi-Rotor Aerial Vehicles with Limited Actuation Abilities

TechRxiv

引用

TechRxiv 2023年

作者： Hamandi, Mahmoud Al-Ali, Ismail Seneviratne, Lakmal Franchi, Antonio Zweiri, Yahya The Department of Aerospace Engineering Khalifa University of Science and Technology Abu Dhabi United Arab Emirates The Department of Mechanical Engineering Khalifa University Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Abu Dhabi United Arab Emirates The Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands The Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy LAAS CNRS Université de Toulouse Toulouse31000 France Khalifa University Abu Dhabi United Arab Emirates Center for Artificial Intelligence and Robotics New York University Abu Dhabi Saadiyat Island Abu Dhabi129188 United Arab Emirates

This paper presents a novel optimization-based full-pose trajectory tracking method to control overactuated multirotor aerial vehicles with limited actuation abilities. The proposed method allocates feasible control inputs to track a reference trajectory, while ensuring the tracking of the reference position, and while tracking the closest feasible attitude. The optimization simultaneously searches for a feasible trajectory and corresponding feasible control inputs from the infinite possible solutions, while ensuring smooth control inputs. The proposed real-time algorithm is tested in extensive simulation on multiple platforms with fixed and actuated propellers. The simulation experiments show the ability of the proposed approach to exploit the complex set of feasible forces and moments of overactuated platforms while allocating smooth feasible control inputs. © 2023, CC BY.

关键词： Trajectories

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Stellar classification using supervised machine learning

引用

AIP Conference Proceedings 2024年第1期3122卷

作者： S. Swathi S. Saranya K. Vijayalakshmi V. Aswini Department of Artificial Intelligence & Data Science Panimalar Engineering College Chennai India Department of Computer Science Sree Saraswathi Thyagaraja College Pollachi Tamil Nadu India Department of Computer Science & Engineering Sri Sairam Institute of Technology Chennai India Department of Computer Science & Engineering Panimalar Engineering College (Chennai City Campus) Chennai India

In this work, objects from Sloan Digital Sky Survey data Release 17 (SDSS DR17) [1] were classified as dwarfs or giants using learning methods. The classification was created by supervised learning. Machine learning algorithms like K Nearest Neighbors, Naive Bayes Classifier, Support Vector Classification and Random Forest were developed in addition to Decision Trees. The algorithm that performed the worst was naive Bayes. Random Forest, which performed the best, was able to successfully classify the cases in the dataset that were marked as stars.

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A 50THz Ultra-Wideband Nano-Photonics Perfect Absorber Biosensor for Label-Free Detection of Circulating Cancer Exosomes: Advancing Early Cancer Diagnostics

IEEE Photonics Journal

引用

IEEE Photonics Journal 2025年第4期17卷

作者： Hamza, Musa N. Islam, Mohammad Tariqul Lavadiya, Sunil Din, Iftikhar ud Sanches, Bruno Koziel, Slawomir Naqvi, Syeda Iffat Panda, Abinash Alibakhshikenari, Mohammad Virdee, Bal Mariyanayagam, D. Jayanthi, R. Fernando, S. Islam, Md. Shabiul University of Raparin College of Science Department of Physics Sulaymaniyah 46012 Iraq Universiti Kebangsaan Malaysia Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Selangor Bangi 43600 Malaysia Marwadi University Department of Information and Communication Technology Gujarat Rajkot 360003 India University of Engineering and Technology Telecommunication Engineering Department Mardan 23200 Pakistan Escola Politécnica da Universidade de São Paulo Department of Electronic Systems Engineering São Paulo Brazil Reykjavik University Engineering Optimization & Modeling Center Reykjavik 102 Iceland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk 80-233 Poland University of Engineering & Technology Taxila Department of Telecommunication Engineering Pakistan CMR Institute of Technology Department of Electronics and Communication Engineering Bengaluru 560037 India University of Rome “Tor Vergata” Electronics Engineering Department Rome 00133 Italy Dogus University Department of Electrical and Electronics Engineering Umraniye Istanbul 34775 Turkey London Metropolitan University Department of Center for Communications Technology London N7 8DB United Kingdom Multimedia University Centre for Advanced Devices and Systems Faculty of Artificial Intelligence and Engineering Selangor Cyberjaya 63100 Malaysia

This paper addresses the challenge of early-stage cancer diagnosis using microwave imaging (MWI) techniques by targeting circulating exosomes, recently identified as promising cancer biomarkers. We introduce an innovative nano-photonic perfect absorber (NPA) operating in the terahertz (THz) range, offering a significant improvement over existing MWI-based approaches in terms of simplicity, sensitivity, and specificity. Unlike previous THz absorbers, the proposed NPA achieves an exceptionally wide operating bandwidth from 100 GHz to 50 THz with an absorption efficiency exceeding 97.5%, while featuring an ultra-compact nanoscale footprint (100 × 100 nm², thickness 30 nm). The design integrates a silver (Ag) resonator and a nickel (Ni) ground plane on a silicon dioxide (SiO₂) substrate, with meticulously tuned geometries to create multiple resonance modes, enabling continuous broadband absorption. Full-wave electromagnetic simulations validate the structure's performance, including electric and magnetic field distributions, surface currents, and scattering parameters. Comparative analysis with state-of-the-art absorbers demonstrates the superior bandwidth, absorption stability, and angular robustness of our device. Furthermore, we demonstrate the NPA's unique ability to act as a label-free biosensor for exosome detection, where cancerous exosomes consistently induce stronger electric field responses than normal exosomes due to their distinct molecular compositions. These results confirm the proposed NPA as a novel, highly effective platform for non-invasive, early-stage cancer diagnostics via MWI. © 2009-2012 IEEE.

关键词： Early Cancer Diagnostics Label-Free Exosome Detection Nano-Photonic Perfect Absorber (NPA) Terahertz (THz) Imaging UltraWideband Absorber

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EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid

EnsembleNTLDetect: An Intelligent Framework for Electricity ...

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Yogesh Kulkarni Sayf Hussain Z Krithi Ramamritham Nivethitha Somu Pune Institute of Computer Technology Pune India College of Engineering Guindy Anna University Chennai India Robert Bosch Centre for Data Science & Artificial Intelligence IIT Madras Chennai India IIT Bombay Mumbai India

ISBN: (纸本)9781665424288

artificial intelligence-based techniques applied to the electricity consumption data generated from the smart grid prove to be an effective solution in reducing Non Technical Loses (NTLs), thereby ensures safety, reliability, and security of the smart energy systems. However, imbalanced data, consecutive missing values, large training times, and complex architectures hinder the real time application of electricity theft detection models. In this paper, we present EnsembleNTLDetect, a robust and scalable electricity theft detection framework that employs a set of efficient data pre-processing techniques and machine learning models to accurately detect electricity theft by analysing consumers’ electricity consumption patterns. This framework utilises an enhanced Dynamic Time Warping Based Imputation (eDTWBI) algorithm to impute missing values in the time series data and leverages the Near-miss undersampling technique to generate balanced ***, stacked autoencoder is introduced for dimensionality reduction and to improve training efficiency. A Conditional Generative Adversarial Network (CTGAN) is used to augment the dataset to ensure robust training and a soft voting ensemble classifier is designed to detect the consumers with aberrant consumption patterns. Furthermore, experiments were conducted on the real-time electricity consumption data provided by the State Grid Corporation of China (SGCC) to validate the reliability and efficiency of EnsembleNTLDetect over the state-of-the-art electricity theft detection models in terms of various quality metrics.

关键词： Training Measurement Heuristic algorithms Time series analysis data models Real-time systems Smart grids

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