IEEE transactions on artificial intelligence最新文献_第2页

A Temporal–Spatial Graph Network With a Learnable Adjacency Matrix for Appliance-Level Electricity Consumption Prediction

IEEE transactions on artificial intelligence Pub Date : 2024-11-28 DOI: 10.1109/TAI.2024.3507734

Dandan Li;Jiaxing Xia;Jiangfeng Li;Changjiang Xiao;Vladimir Stankovic;Lina Stankovic;Qingjiang Shi

{"title":"A Temporal–Spatial Graph Network With a Learnable Adjacency Matrix for Appliance-Level Electricity Consumption Prediction","authors":"Dandan Li;Jiaxing Xia;Jiangfeng Li;Changjiang Xiao;Vladimir Stankovic;Lina Stankovic;Qingjiang Shi","doi":"10.1109/TAI.2024.3507734","DOIUrl":"https://doi.org/10.1109/TAI.2024.3507734","url":null,"abstract":"Predicting the electricity consumption of individual appliances, known as appliance-level energy consumption (ALEC) prediction, is essential for effective energy management and conservation. Despite its importance, research in this area is limited and faces several challenges: 1) the correlation between the usage of different appliances has rarely been considered for ALEC prediction; 2) a learnable strategy for obtaining the optimal correlation between different appliance behaviors is lacking; and 3) it is difficult to accurately quantify the usage relationship among different appliances. To address these issues, we propose a graph-based temporal–spatial network that employs a learnable adjacency matrix for appliance-level load prediction in this work. The network comprises a temporal graph convolutional network (TGCN) and a learnable adjacency matrix that enables us to utilize correlations between appliances and quantify their relationships. To validate our approach, we compared our model with six others: a TGCN model with a fixed adjacency matrix where all elements are set to 0; a TGCN model with a fixed adjacency matrix where all elements are set to 0.5, except for the diagonal; a TGCN model with a randomly generated adjacency matrix, except for the diagonal; an Aug-LSTM model; a model with ResNetPlus architecture; and a feed-forward deep neural network. Five houses in four datasets: AMPDs, REFIT, UK-DALE, and SC-EDNRR are utilized. The metrics used in this study include root mean square error, explained variance score, mean absolute error, F-norm and coefficient of determination. Our experiments have validated the accuracy and practicality of our proposed approach across different datasets.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"989-1002"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740203","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

Data-Driven Event-Triggered Control for Discrete-Time Neural Networks Subject to Actuator Saturation

IEEE transactions on artificial intelligence Pub Date : 2024-11-28 DOI: 10.1109/TAI.2024.3507736

Yanyan Ni;Zhen Wang;Xia Huang;Hao Shen

{"title":"Data-Driven Event-Triggered Control for Discrete-Time Neural Networks Subject to Actuator Saturation","authors":"Yanyan Ni;Zhen Wang;Xia Huang;Hao Shen","doi":"10.1109/TAI.2024.3507736","DOIUrl":"https://doi.org/10.1109/TAI.2024.3507736","url":null,"abstract":"In this article, the data-driven event-triggered control is addressed for unknown discrete-time neural networks (DTNNs) under actuator saturation and external perturbation. The research problem is raised due to the following two reasons: 1) a practical system is often affected by external perturbations and it is costly to acquire an accurate system model; 2) the network bandwidth and the control inputs are always constrained due to physical hardware. To handle the above issues, the methodology is to first establish a model-based stability condition under the designed saturated event-triggered controller and then to transform the model-based stability condition into a data-based stability condition relying only on the perturbation-corrupted data via the extended S-lemma. The key results are: 1) a data-based DTNNs system representation is presented by collecting perturbation-corrupted state-input data. Then, a data-based stability criterion is derived and the saturated event-triggered controller is designed without an explicit system model; 2) an optimization method is presented that can maximize the estimation of attractor (EoA) and minimize the estimated domain of attraction (DoA) simultaneously. Finally, the effectiveness of the proposed approach is illustrated and some quantitative analyses are offered by two numerical examples.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"1003-1013"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740219","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

Industrial Process Monitoring Based on Deep Gaussian and Non-Gaussian Information Fusion Framework

IEEE transactions on artificial intelligence Pub Date : 2024-11-28 DOI: 10.1109/TAI.2024.3507732

Zhiqiang Ge

{"title":"Industrial Process Monitoring Based on Deep Gaussian and Non-Gaussian Information Fusion Framework","authors":"Zhiqiang Ge","doi":"10.1109/TAI.2024.3507732","DOIUrl":"https://doi.org/10.1109/TAI.2024.3507732","url":null,"abstract":"For industrial process monitoring, Gaussian and non-Gaussian data-driven models are two important representatives that have been developed separately in the past years. Although several attempts have been made to combine Gaussian and non-Gaussian data information for integrated process monitoring, this information fusion strategy can be further enhanced under the idea and framework of deep learning. Particularly, through collaborative learning and layer-by-layer information transformation, more patterns of both Gaussian and non-Gaussian components can be effectively extracted in different hidden layers of the deep model. Then, a further Bayesian model fusion strategy is formulated to ensemble monitoring results from both Gaussian and non-Gaussian data-driven models. Therefore, the main contribution of this article is to propose a deep Gaussian and non-Gaussian information fusion framework for data-driven industrial process monitoring. Both feasibility and superiority of the developed model are confirmed through a detailed industrial benchmark case study. Compared to both Gaussian and non-Gaussian deep models, the new deep information fusion model has obtained more satisfactory monitoring results.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"979-988"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740310","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

Aperiodically Intermittent Control Approach to Finite-Time Synchronization of Delayed Inertial Memristive Neural Networks

IEEE transactions on artificial intelligence Pub Date : 2024-11-28 DOI: 10.1109/TAI.2024.3507740

Yuxin Jiang;Song Zhu;Mouquan Shen;Shiping Wen;Chaoxu Mu

引用次数: 0

CheckSelect: Online Checkpoint Selection for Flexible, Accurate, Robust, and Efficient Data Valuation

IEEE transactions on artificial intelligence Pub Date : 2024-11-27 DOI: 10.1109/TAI.2024.3506494

Soumi Das;Manasvi Sagarkar;Suparna Bhattacharya;Sourangshu Bhattacharya

{"title":"CheckSelect: Online Checkpoint Selection for Flexible, Accurate, Robust, and Efficient Data Valuation","authors":"Soumi Das;Manasvi Sagarkar;Suparna Bhattacharya;Sourangshu Bhattacharya","doi":"10.1109/TAI.2024.3506494","DOIUrl":"https://doi.org/10.1109/TAI.2024.3506494","url":null,"abstract":"In this article, we argue that data valuation techniques should be <italic>flexible, accurate, robust, and efficient</i> (FARE). Here, accuracy and efficiency refer to the notion of identification of most important data points in less time compared to full training. Flexibility refers to the ability of the method to be used with various value functions, while robustness refers to the ability to be used with different data distributions from a related domain. We propose a two-phase approach toward achieving these objectives, where the first phase, checkpoint selection, extracts important model checkpoints while training on a related dataset, and the second data valuation and subset selection (DVSS) phase extracts the high-value subsets. A key challenge in this process is to efficiently determine the most important checkpoints during the training, since the total value function is unknown. We pose this as an online sparse approximation problem and propose a novel online orthogonal matching pursuit algorithm for solving it. Extensive experiments on standard datasets show that CheckSelect provides the best accuracy among the baselines while maintaining efficiency comparable to state of the art. We also demonstrate the flexibility and robustness of CheckSelect on a standard domain adaptation task, where it outperforms existing methods in data selection accuracy without the need to retrain on the full target-domain dataset.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"968-978"},"PeriodicalIF":0.0,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740218","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

A Scalable Unsupervised and Back Propagation Free Learning With SACSOM: A Novel Approach to SOM-Based Architectures

IEEE transactions on artificial intelligence Pub Date : 2024-11-26 DOI: 10.1109/TAI.2024.3504479

Gaurav R. Hirani;Kevin I-Kai Wang;Waleed H. Abdulla

{"title":"A Scalable Unsupervised and Back Propagation Free Learning With SACSOM: A Novel Approach to SOM-Based Architectures","authors":"Gaurav R. Hirani;Kevin I-Kai Wang;Waleed H. Abdulla","doi":"10.1109/TAI.2024.3504479","DOIUrl":"https://doi.org/10.1109/TAI.2024.3504479","url":null,"abstract":"The field of computer vision is predominantly driven by supervised models, which, despite their efficacy, are computationally expensive and often intractable for many applications. Recently, research has expedited alternative avenues such as self-organizing maps (SOM)-based architectures, which offer significant advantages such as tractability, the absence of back-propagation, and feed-forward unsupervised learning. However, these SOM-based approaches frequently suffer from lower accuracy and limited generalization capabilities. To address these shortcomings, we propose a novel model called split and concur SOM (SACSOM). SACSOM overcomes the limitations of closely related SOM-based algorithms by utilizing multiple parallel branches, each equipped with its own SOM modules that process data independently with varying patch sizes. Furthermore, by creating groups of classes and using respective training samples to train independent subbranches in each branch, our approach accommodates datasets with a large number of classes. SACSOM employs a simple yet effective labeling technique requiring minimal labeled samples. The outputs from each branch, filtered by a threshold, contribute to the final prediction. Experimental validation on MNIST-digit, Fashion-MNIST, CIFAR-10, and CIFAR-100 demonstrates that SACSOM achieves competitive accuracy with significantly reduced computation time. Furthermore, it exhibits superior performance and generalization capabilities, even in high-noise scenarios. The weights of the single-layered SACSOM provide meaningful insights into the patch-based learning pattern, enhancing its tractability and making it ideal from the perspective of explainable AI. This study addresses the limitations of current clustering techniques, such as K-means and traditional SOMs, by proposing a lightweight, manageable, and fast architecture that does not require a GPU, making it suitable for low-powered devices.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"955-967"},"PeriodicalIF":0.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740386","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

APR-Net: Defense Against Adversarial Examples Based on Universal Adversarial Perturbation Removal Network

IEEE transactions on artificial intelligence Pub Date : 2024-11-22 DOI: 10.1109/TAI.2024.3504478

Wenxing Liao;Zhuxian Liu;Minghuang Shen;Riqing Chen;Xiaolong Liu

{"title":"APR-Net: Defense Against Adversarial Examples Based on Universal Adversarial Perturbation Removal Network","authors":"Wenxing Liao;Zhuxian Liu;Minghuang Shen;Riqing Chen;Xiaolong Liu","doi":"10.1109/TAI.2024.3504478","DOIUrl":"https://doi.org/10.1109/TAI.2024.3504478","url":null,"abstract":"Adversarial attack, a bleeding-edge technique that attempts to fool deep learning classification model by generating adversarial examples with imperceptible perturbations, is becoming a growing threat in artificial intelligence fields. Preprocessing models that remove perturbations are an effective approach for enhancing the robustness of classification models. However, most existing methods overlook a critical issue: although powerful preprocessing operations can remove adversarial perturbations, they may also weaken the representation of key features in the image, leading to decreased defense performance. To address this, we propose a novel universal defense model, APR-Net, which aims to remove adversarial perturbations while effectively preserving high-quality images. The key innovation of APR-Net lies in its dual-module design, which consists of a denoising module and an image restoration module. This design not only effectively eliminates imperceptible adversarial perturbations but also ensures the restoration of high-quality images. Unlike existing methods, APR-Net does not require modifications to the classifier architecture or specialized adversarial training, making it highly versatile. Extensive experiments on the ImageNet dataset demonstrate that APR-Net provides strong defense against various adversarial attack algorithms, significantly improves image quality, and outperforms other state-of-the-art defense methods in terms of overall performance.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"945-954"},"PeriodicalIF":0.0,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740273","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

CH-Net: A Cross Hybrid Network for Medical Image Segmentation

IEEE transactions on artificial intelligence Pub Date : 2024-11-20 DOI: 10.1109/TAI.2024.3503541

Jiale Li;Aiping Liu;Wei Wei;Ruobing Qian;Xun Chen

{"title":"CH-Net: A Cross Hybrid Network for Medical Image Segmentation","authors":"Jiale Li;Aiping Liu;Wei Wei;Ruobing Qian;Xun Chen","doi":"10.1109/TAI.2024.3503541","DOIUrl":"https://doi.org/10.1109/TAI.2024.3503541","url":null,"abstract":"Accurate and automated segmentation of medical images plays a crucial role in diagnostic evaluation and treatment planning. In recent years, hybrid models have gained considerable popularity in diverse medical image segmentation tasks, as they leverage the benefits of both convolution and self-attention to capture local and global dependencies simultaneously. However, most existing hybrid models treat convolution and self-attention as independent components and integrate them using simple fusion methods, neglecting the potential complementary information between their weight allocation mechanisms. To address this issue, we propose a cross hybrid network (CH-Net) for medical image segmentation, in which convolution and self-attention are hybridized in a cross-collaborative manner. Specifically, we introduce a cross hybrid module (CHM) between the parallel convolution layer and self-attention layer in each building block of CH-Net. This module extracts attention with distinct dimensional information from convolution and self-attention, respectively, and uses this complementary information to enhance the feature representation of both components. In contrast to the traditional approach where each module learned independently, the CHM facilitates the interactive learning of complementary information between convolutional layer and self-attention layer, which significantly enhances the segmentation capabilities of the model. The superiority of our approach over various hybrid models is demonstrated through experimental evaluations conducted on three publicly available benchmarks: ACDC, synapse, and EM.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"934-944"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740311","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

DGeC: Dynamically and Globally Enhanced Convolution

IEEE transactions on artificial intelligence Pub Date : 2024-11-20 DOI: 10.1109/TAI.2024.3502577

Zihang Zhang;Yuling Liu;Zhili Zhou;Gaobo Yang;Xin Liao;Q. M. Jonathan Wu

{"title":"DGeC: Dynamically and Globally Enhanced Convolution","authors":"Zihang Zhang;Yuling Liu;Zhili Zhou;Gaobo Yang;Xin Liao;Q. M. Jonathan Wu","doi":"10.1109/TAI.2024.3502577","DOIUrl":"https://doi.org/10.1109/TAI.2024.3502577","url":null,"abstract":"We explore the reasons for the poorer feature extraction ability of vanilla convolution and discover that there mainly exist three key factors that restrict its representation capability, i.e., regular sampling, static aggregation, and limited receptive field. With the cost of extra parameters and computations, existing approaches merely alleviate part of the limitations. It drives us to seek a more lightweight operator to further improve the extracted image features. Through a closer examination of the convolution process, we discover that it is composed of two distinct interactions: spatial-wise interaction and channel-wise interaction. Based on this discovery, we decouple the convolutional blocks into these two interactions which not only reduces the parameters and computations but also enables a richer ensemble of interactions. Then, we propose the dynamically and globally enhanced convolution (DGeC), which includes several components as follows: a dynamic area perceptor block (DAP) that dynamically samples spatial cues, an adaptive global context block (AGC) that introduces the location-aware global image information, and a channel attention perceptor block (CAP) that merges different channel-wise features. The experiments on ImageNet for image classification and on COCO-2017 for object detection validate the effectiveness of DGeC. As a result, our proposed method consistently improves the performance with fewer parameters and computations. In particular, DGeC achieves a 3.1% improvement in top-1 accuracy on ImageNet dataset compared to ResNet50. Moreover, with Faster RCNN and RetinaNet, our DGeC-ResNet50 also consistently outperforms ResNet and ResNeXt.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"921-933"},"PeriodicalIF":0.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740315","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

Consistent Counterfactual Explanations via Anomaly Control and Data Coherence

IEEE transactions on artificial intelligence Pub Date : 2024-11-19 DOI: 10.1109/TAI.2024.3496616

Maria Movin;Federico Siciliano;Rui Ferreira;Fabrizio Silvestri;Gabriele Tolomei

{"title":"Consistent Counterfactual Explanations via Anomaly Control and Data Coherence","authors":"Maria Movin;Federico Siciliano;Rui Ferreira;Fabrizio Silvestri;Gabriele Tolomei","doi":"10.1109/TAI.2024.3496616","DOIUrl":"https://doi.org/10.1109/TAI.2024.3496616","url":null,"abstract":"Algorithmic recourses are popular methods to provide individuals impacted by machine learning models with recommendations on feasible actions for a more favorable prediction. Most of the previous algorithmic recourse methods work under the assumption that the predictive model does not change over time. However, in reality, models in deployment may both be periodically retrained and have their architecture changed. Therefore, it is desirable that the recourse should remain valid when such a model update occurs, unless new evidence arises. We call this feature <italic>consistency</i>. This article presents anomaly control and data coherence (ACDC), a novel model-agnostic recourse method that generates counterfactual explanations, i.e., instance-level recourses. ACDC is inspired by anomaly detection methods and uses a one-class classifier to aid the search for valid, consistent, and feasible counterfactual explanations. The one-class classifier asserts that the generated counterfactual explanations lie on the data manifold and are not outliers of the target class. We compare ACDC against several state-of-the-art recourse methods across four datasets. Our experiments show that ACDC outperforms baselines both in generating consistent counterfactual explanations, and in generating feasible and plausible counterfactual explanations, while still having proximity measures similar to the baseline methods targeting the data manifold.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 4","pages":"794-804"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740316","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