IEEE transactions on artificial intelligence最新文献_第3页

IEEE Transactions on Artificial Intelligence Publication Information IEEE人工智能学报

IEEE transactions on artificial intelligence Pub Date : 2025-03-03 DOI: 10.1109/TAI.2025.3544009

引用次数: 0

Guest Editorial: Operationalizing Responsible AI 嘉宾评论：实施负责任的人工智能

IEEE transactions on artificial intelligence Pub Date : 2025-03-03 DOI: 10.1109/TAI.2025.3527806

Qinghua Lu;Apostol Vassilev;Jun Zhu;Foutse Khomh

引用次数: 0

Large-Scale Heliostat Field Optimization for Solar Power Tower System Using Matrix-Based Differential Evolution 基于矩阵差分进化的太阳能塔式系统定日镜场优化

IEEE transactions on artificial intelligence Pub Date : 2025-03-03 DOI: 10.1109/TAI.2025.3545813

Dan-Ting Duan;Jian-Yu Li;Bing Sun;Xiao-Fang Liu;Qiang Yang;Qi-Jia Jiang;Zhi-Hui Zhan;Sam Kwong;Jun Zhang

{"title":"Large-Scale Heliostat Field Optimization for Solar Power Tower System Using Matrix-Based Differential Evolution","authors":"Dan-Ting Duan;Jian-Yu Li;Bing Sun;Xiao-Fang Liu;Qiang Yang;Qi-Jia Jiang;Zhi-Hui Zhan;Sam Kwong;Jun Zhang","doi":"10.1109/TAI.2025.3545813","DOIUrl":"https://doi.org/10.1109/TAI.2025.3545813","url":null,"abstract":"Intelligent optimization of a solar power tower heliostat field (SPTHF) is critical for harnessing solar energy in various scenarios. However, existing SPTHF optimization methods are typically based on specific geometric layout constraints and assume that each heliostat has the same size and height. As a result, these methods are not flexible or practical in many real-world SPTHF application scenarios. Therefore, this article proposes a novel flexible SPTHF (FSPTHF) model that is more practical and involves fewer assumptions. This model enables the use of different layouts and simultaneous optimization of the parameters of each heliostat. As an FSPTHF can involve hundreds or even thousands of heliostats, optimizing the parameters of all heliostats results in a challenging large-scale optimization problem. To efficiently solve this problem, this article proposes a matrix-based differential evolution algorithm, called HMDE, for large-scale heliostat design. The HMDE uses a matrix-based encoding and representation method to improve optimization accuracy and convergence speed, incorporating two novel designs. First, a dual elite-based mutation method is proposed to enhance the convergence speed of HMDE by learning from multiple elite individuals. Second, a multi-level crossover method is proposed to improve the optimization accuracy and convergence speed by integrating element-level and vector-level crossover based on matrix representation. Extensive experiments were conducted on 30 problem instances based on real-world data with three different layouts and problem dimensions up to 12 000, where state-of-the-art algorithms were used for comparison. The experimental results show that the proposed HMDE can effectively solve large-scale FSPTHF optimization problems.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2422-2436"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GeoDCL: Weak Geometrical Distortion-Based Contrastive Learning for Fine-Grained Fashion Image Retrieval 基于弱几何扭曲的细粒度时尚图像检索对比学习

IEEE transactions on artificial intelligence Pub Date : 2025-02-28 DOI: 10.1109/TAI.2025.3545791

Ling Xiao;Toshihiko Yamasaki

{"title":"GeoDCL: Weak Geometrical Distortion-Based Contrastive Learning for Fine-Grained Fashion Image Retrieval","authors":"Ling Xiao;Toshihiko Yamasaki","doi":"10.1109/TAI.2025.3545791","DOIUrl":"https://doi.org/10.1109/TAI.2025.3545791","url":null,"abstract":"This article addresses fine-grained fashion image retrieval (FIR), which aims at the detailed and precise retrieval of fashion items from extensive databases. Conventional fine-grained FIR methods design complex attention modules to enhance attribute-aware feature discrimination. However, they often ignore the multiview characteristics of real-world fashion data, leading to diminished model accuracy. Furthermore, our empirical analysis revealed that the straightforward application of standard contrastive learning methods to fine-grained FIR often yields suboptimal results. To alleviate this issue, we propose a novel weak geometrical distortion-based contrastive learning (GeoDCL) strategy. Specifically, GeoDCL incorporates both a novel positive pair design and a novel contrastive loss. GeoDCL can be seamlessly integrated into state-of-the-art (SOTA) fine-grained FIR methods during the training stage to enhance performance during inference. When GeoDCL is applied, the model structures of SOTA methods require no modifications. Additionally, GeoDCL is not utilized during inference, ensuring no increase in inference time. Experiments on the FashionAI, DeepFashion, and Zappos50K datasets verified GeoDCL's effectiveness in consistently improving SOTA models. In particular, GeoDCL drastically improved ASENet_V2 from 60.76% to 66.48% in mAP on the FashionAI dataset.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2409-2421"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancement of Robot Dynamics Learning by Integrating Analytical Models into Deep Neural Networks: A Data Fusion Perspective 通过将分析模型集成到深度神经网络中来增强机器人动力学学习：一个数据融合的视角

IEEE transactions on artificial intelligence Pub Date : 2025-02-26 DOI: 10.1109/TAI.2025.3544591

Erfaan Rezvanfar;Jing Wang;Clarence W. de Silva

{"title":"Enhancement of Robot Dynamics Learning by Integrating Analytical Models into Deep Neural Networks: A Data Fusion Perspective","authors":"Erfaan Rezvanfar;Jing Wang;Clarence W. de Silva","doi":"10.1109/TAI.2025.3544591","DOIUrl":"https://doi.org/10.1109/TAI.2025.3544591","url":null,"abstract":"Precise modeling of dynamical systems can be crucial for engineering applications. Traditional analytical models often struggle when capturing real-world complexities due to challenges in system nonlinearity representation and model parameter determination. Data-driven models, such as deep neural networks (DNNs), offer better accuracy and generalization but require large quantities of high-quality data. The present article introduces a novel method called the synthesized-data neural network (SDNN), which integrates analytical models, which represent physics, with DNNs to enhance the dynamic model. The main steps of the present method are as follows. The first three degrees of freedom (DOF) of a Kinova Gen3 Lite manipulator are formulated using the Euler–Lagrange equations of motion. The experimental data are recorded from the manipulator. Simulated data from the analytical model are combined with experimental data to train the neural network. The model’s performance is evaluated using the mean squared error (MSE) in real-time experiments with the Kinova Gen3 Lite manipulator. Training datasets represent 14 trajectories, with the MSE calculated for four testing trajectories. The obtained results have led to the following conclusions. The SDNN model has shown improved performance in predicting joint torques when compared to the purely analytical model or the purely data-driven model. The SDNN, when trained with synthesized data from 14 trajectories (SDNN-14), achieved the lowest MSE of 2.14, outperforming the analytical model (MSE of 2.81) and the neural network trained solely on experimental data (MSE of 3.05).","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2384-2394"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedECE: Federated Estimation of Causal Effect Based on Causal Graphical Modeling 基于因果图模型的因果效应的联邦估计

IEEE transactions on artificial intelligence Pub Date : 2025-02-25 DOI: 10.1109/TAI.2025.3545794

Yongsheng Zhao;Kui Yu;Guodu Xiang;Xianjie Guo;Fuyuan Cao

{"title":"FedECE: Federated Estimation of Causal Effect Based on Causal Graphical Modeling","authors":"Yongsheng Zhao;Kui Yu;Guodu Xiang;Xianjie Guo;Fuyuan Cao","doi":"10.1109/TAI.2025.3545794","DOIUrl":"https://doi.org/10.1109/TAI.2025.3545794","url":null,"abstract":"Causal effect estimation as a basic task in causal inference has been widely studied in past decades. In recent years, preserving data privacy has gained significant attention due to increasing incidents of data abuse and data leakage, however, most existing methods do not consider the problem of protecting data privacy when calculating causal effects. Thus in this article, we propose a FedECE (federated estimation of causal effect) framework for causal effect estimation in a federated setting using causal graphical modeling, which comprises two modules: a federated causal structure learning (FedCSL) module and a federated causal effect (FedCE) module. We first instantiate the FedECE framework with a basic FedECE algorithm, called FedECE-B. FedECE-B presents a layer-wise cooperative optimization strategy to learn a global skeleton by the consideration of preserving data privacy. In addition, a distributed optimal consensus strategy for V-structure identification is proposed to orient edges in the learned global skeleton. To tackle the CPDAG problem in the learned causal structure, FedECE-B presents a progressively integrated multiset strategy for federated causal effect computation. To further improve the computational efficiency and accuracy of FedECE-B, we also propose the FedECE-L and FedECE-O algorithms. The extensive experiments validate the effectiveness of the proposed methods.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2327-2341"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Analysis and Design of a Weighted Personalized Quantum Federated Learning 加权个性化量子联邦学习的性能分析与设计

IEEE transactions on artificial intelligence Pub Date : 2025-02-25 DOI: 10.1109/TAI.2025.3545393

Dev Gurung;Shiva Raj Pokhrel

引用次数: 0

ITF-VAE: Variational Auto-Encoder Using Interpretable Continuous Time Series Features 使用可解释的连续时间序列特征的变分自编码器

IEEE transactions on artificial intelligence Pub Date : 2025-02-25 DOI: 10.1109/TAI.2025.3545396

Hendrik Klopries;Andreas Schwung

{"title":"ITF-VAE: Variational Auto-Encoder Using Interpretable Continuous Time Series Features","authors":"Hendrik Klopries;Andreas Schwung","doi":"10.1109/TAI.2025.3545396","DOIUrl":"https://doi.org/10.1109/TAI.2025.3545396","url":null,"abstract":"Machine learning algorithms are driven by data. However, the quantity and quality of data in industries are limited due to multiple process constraints. Generating artificial data and performing a transfer learning task is a common solution to overcome these limitations. Recently, deep generative models have become one of the leading solutions for modeling a given source domain. The main hindrance to using those machine learning approaches is the lack of interpretability. Therefore, we present a novel variational autoencoder approach to generate time series data on a probabilistic latent feature representation and enhance interpretability within the generative model and the output trajectory. We sample selective and parameter values for certain continuous function candidates to assemble the synthetic time series. The sparse design of the generative model enables direct interpretability and matches an estimated posterior distribution of the detected components in the source domain. Through residual stacking, conditionality, and a mixture of prior distributions, we derive a stacked version of the evidence lower bound to learn our network. Tests on synthetic and real industrial datasets underline the performance and interpretability of our generative model. Depending on the model and function candidates, the user can define a trade-off between flexibility and interpretability. Overall, this work presents an innovative interpretable representation of the latent space and further developed evidence lower bound criterion driven by the designed architecture.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"2314-2326"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling Efficient and Interpretable Cybersecurity Reasoning Through Hyperdimensional Computing 通过超维计算实现高效和可解释的网络安全推理

IEEE transactions on artificial intelligence Pub Date : 2025-02-25 DOI: 10.1109/TAI.2025.3545394

Ali Zakeri;Hanning Chen;Narayan Srinivasa;Hugo Latapie;Mohsen Imani

{"title":"Enabling Efficient and Interpretable Cybersecurity Reasoning Through Hyperdimensional Computing","authors":"Ali Zakeri;Hanning Chen;Narayan Srinivasa;Hugo Latapie;Mohsen Imani","doi":"10.1109/TAI.2025.3545394","DOIUrl":"https://doi.org/10.1109/TAI.2025.3545394","url":null,"abstract":"Knowledge graphs play a crucial role in addressing the complexities of cybersecurity, as the increasing frequency and sophistication of cyber threats pose significant challenges to traditional defense technologies. In this article, we propose a novel reasoning model, called INCYSER, that is tailored for cybersecurity. By leveraging hyperdimensional computing (HDC) as a symbolic and transparent computational model, INCYSER offers efficient and interpretable reasoning capabilities, ensuring reliable and trustworthy outcomes. Our model combines embedding-based unsupervised learning and HDC-based graph representation learning to construct a general representation for cybersecurity knowledge graphs, enabling diverse tasks including reasoning and general graph operations. Experimental evaluations demonstrate the effectiveness and efficiency of INCYSER, surpassing state-of-the-art models in link prediction and triple classification tasks. Additionally, a comprehensive ablation study examines the impact of various hyperparameters, showcasing the versatility of INCYSER. This work contributes to advancing the field of cybersecurity by introducing an interpretable and representation-based reasoning model for cybersecurity knowledge graphs.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 9","pages":"2395-2408"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144926891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Operator Selection for Meta-Heuristics: A Survey 元启发式自适应算子选择研究综述

IEEE transactions on artificial intelligence Pub Date : 2025-02-25 DOI: 10.1109/TAI.2025.3545792

Jiyuan Pei;Yi Mei;Jialin Liu;Mengjie Zhang;Xin Yao

{"title":"Adaptive Operator Selection for Meta-Heuristics: A Survey","authors":"Jiyuan Pei;Yi Mei;Jialin Liu;Mengjie Zhang;Xin Yao","doi":"10.1109/TAI.2025.3545792","DOIUrl":"https://doi.org/10.1109/TAI.2025.3545792","url":null,"abstract":"Appropriate selection of search operators plays a critical role in meta-heuristic algorithm design. Adaptive selection of suitable operators to the characteristics of different optimization stages is an important task that owns promising potential to improve the performance of a meta-heuristic algorithm. A variety of adaptive operator selection methods have been proposed in last decades, from the machine learning and optimization communities. However, the existing studies have not been systematically reviewed so far. To fill the gap, this article provides a comprehensive survey of adaptive operator selection for meta-heuristics. According to the information required for selection, adaptive operator selection methods are classified into two categories: 1) stateless methods; and 2) state-based methods. Each category is further summarized into several key components. The strategies of each component belonging to the two categories are reviewed respectively. The motivation, strengths and weaknesses of the proposed strategies are also discussed. Furthermore, studied meta-heuristics and optimization problems in the literature are summarized. The effects from the difference of meta-heuristics and problems to the specific design of methods are discussed, together with the guidance of selecting the suitable method in different application scenarios. At the end, emerging challenges that could guide further research are discussed.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 8","pages":"1991-2012"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10904096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0