Pub Date : 2024-04-05DOI: 10.1109/TAI.2024.3385743
Huilin Deng;Hongchen Luo;Wei Zhai;Yanming Guo;Yang Cao;Yu Kang
In response to the rapid evolution of products in industrial inspection, this article introduces the cross-category few-shot anomaly detection (C-FSAD) task, aimed at efficiently detecting anomalies in new object categories with minimal normal samples. However, the diversity of defects and significant visual distinctions among various objects hinder the identification of anomalous regions. To tackle this, we adopt a pairwise comparison between query and normal samples, establishing an intimate correlation through fine-grained correspondence. Specifically, we propose the prioritized local matching network (PLMNet), emphasizing local analysis of correlation, which includes three primary components: 1) Local perception network refines the initial matches through bidirectional local analysis; 2) step aggregation strategy employs multiple stages of local convolutional pooling to aggregate local insights; and 3) defect-sensitive Weight Learner adaptively enhances channels informative for defect structures, ensuring more discriminative representations of encoded context. Our PLMNet deepens the interpretation of correlations, from geometric cues to semantics, efficiently extracting discrepancies in feature space. Extensive experiments on two standard industrial anomaly detection benchmarks demonstrate our state-of-the-art performance in both detection and localization, with margins of 9.8% and 5.4%, respectively.
{"title":"Prioritized Local Matching Network for Cross-Category Few-Shot Anomaly Detection","authors":"Huilin Deng;Hongchen Luo;Wei Zhai;Yanming Guo;Yang Cao;Yu Kang","doi":"10.1109/TAI.2024.3385743","DOIUrl":"https://doi.org/10.1109/TAI.2024.3385743","url":null,"abstract":"In response to the rapid evolution of products in industrial inspection, this article introduces the cross-category few-shot anomaly detection (C-FSAD) task, aimed at efficiently detecting anomalies in new object categories with minimal normal samples. However, the diversity of defects and significant visual distinctions among various objects hinder the identification of anomalous regions. To tackle this, we adopt a pairwise comparison between query and normal samples, establishing an intimate correlation through fine-grained correspondence. Specifically, we propose the prioritized local matching network (PLMNet), emphasizing local analysis of correlation, which includes three primary components: 1) Local perception network refines the initial matches through bidirectional local analysis; 2) step aggregation strategy employs multiple stages of local convolutional pooling to aggregate local insights; and 3) defect-sensitive Weight Learner adaptively enhances channels informative for defect structures, ensuring more discriminative representations of encoded context. Our PLMNet deepens the interpretation of correlations, from geometric cues to semantics, efficiently extracting discrepancies in feature space. Extensive experiments on two standard industrial anomaly detection benchmarks demonstrate our state-of-the-art performance in both detection and localization, with margins of 9.8% and 5.4%, respectively.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165058","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}
Pub Date : 2024-04-04DOI: 10.1109/TAI.2024.3384938
Iram Arshad;Saeed Hamood Alsamhi;Yuansong Qiao;Brian Lee;Yuhang Ye
Deep neural networks are susceptible to various backdoor attacks, such as training time attacks, where the attacker can inject a trigger pattern into a small portion of the dataset to control the model's predictions at runtime. Backdoor attacks are dangerous because they do not degrade the model's performance. This article explores the feasibility of a new type of backdoor attack, a data-free backdoor. Unlike traditional backdoor attacks that require poisoning data and injection during training, our approach, the iterative optimization trigger method (IOTM), enables trigger generation without compromising the integrity of the models and datasets. We propose an attack based on an IOTM technique, guided by an adaptive trigger generator (ATG) and employing a custom objective function. ATG dynamically refines the trigger using feedback from the model's predictions. We empirically evaluated the effectiveness of IOTM with three deep learning models (CNN, VGG16, and ResNet18) using the CIFAR10 dataset. The achieved runtime-attack success rate (R-ASR) varies across different classes. For some classes, the R-ASR reached 100%; whereas, for others, it reached 62%. Furthermore, we conducted an ablation study to investigate critical factors in the runtime backdoor, including optimizer, weight, “REG,” and trigger visibility on R-ASR using the CIFAR100 dataset. We observed significant variations in the R-ASR by changing the optimizer, including Adam and SGD, with and without momentum. The R-ASR reached 81.25% with the Adam optimizer, whereas the SGD with momentum and without results reached 46.87% and 3.12%, respectively.
{"title":"IOTM: Iterative Optimization Trigger Method—A Runtime Data-Free Backdoor Attacks on Deep Neural Networks","authors":"Iram Arshad;Saeed Hamood Alsamhi;Yuansong Qiao;Brian Lee;Yuhang Ye","doi":"10.1109/TAI.2024.3384938","DOIUrl":"https://doi.org/10.1109/TAI.2024.3384938","url":null,"abstract":"Deep neural networks are susceptible to various backdoor attacks, such as training time attacks, where the attacker can inject a trigger pattern into a small portion of the dataset to control the model's predictions at runtime. Backdoor attacks are dangerous because they do not degrade the model's performance. This article explores the feasibility of a new type of backdoor attack, a \u0000<italic>data-free</i>\u0000 backdoor. Unlike traditional backdoor attacks that require poisoning data and injection during training, our approach, the iterative optimization trigger method (IOTM), enables trigger generation without compromising the integrity of the models and datasets. We propose an attack based on an IOTM technique, guided by an adaptive trigger generator (ATG) and employing a custom objective function. ATG dynamically refines the trigger using feedback from the model's predictions. We empirically evaluated the effectiveness of IOTM with three deep learning models (CNN, VGG16, and ResNet18) using the CIFAR10 dataset. The achieved runtime-attack success rate (R-ASR) varies across different classes. For some classes, the R-ASR reached 100%; whereas, for others, it reached 62%. Furthermore, we conducted an ablation study to investigate critical factors in the runtime backdoor, including optimizer, weight, “REG,” and trigger visibility on R-ASR using the CIFAR100 dataset. We observed significant variations in the R-ASR by changing the optimizer, including Adam and SGD, with and without momentum. The R-ASR reached 81.25% with the Adam optimizer, whereas the SGD with momentum and without results reached 46.87% and 3.12%, respectively.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165002","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}
Pub Date : 2024-04-02DOI: 10.1109/TAI.2024.3384337
Rachel Selva Dhanaraj;M. Sridevi
Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets—CelebA, CIFAR-10, and MNIST—were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss.
{"title":"Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack","authors":"Rachel Selva Dhanaraj;M. Sridevi","doi":"10.1109/TAI.2024.3384337","DOIUrl":"https://doi.org/10.1109/TAI.2024.3384337","url":null,"abstract":"Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets—CelebA, CIFAR-10, and MNIST—were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165060","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}
Deep neural networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples—input samples that have been perturbed to force DNN-based models to make errors. As a result, adversarial machine learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in antisocial AI applications. The emergence of new AI technologies that leverage large language models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing antisocial applications at scale. AdvML for social good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent prosocial applications. Regulators, practitioners, and researchers should collaborate to encourage the development of prosocial applications and hinder the development of antisocial ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating prosocial applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community.
{"title":"Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally","authors":"Shawqi Al-Maliki;Adnan Qayyum;Hassan Ali;Mohamed Abdallah;Junaid Qadir;Dinh Thai Hoang;Dusit Niyato;Ala Al-Fuqaha","doi":"10.1109/TAI.2024.3383407","DOIUrl":"https://doi.org/10.1109/TAI.2024.3383407","url":null,"abstract":"Deep neural networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples—input samples that have been perturbed to force DNN-based models to make errors. As a result, adversarial machine learning (AdvML) has gained a lot of attention, and researchers have investigated these vulnerabilities in various settings and modalities. In addition, DNNs have also been found to incorporate embedded bias and often produce unexplainable predictions, which can result in antisocial AI applications. The emergence of new AI technologies that leverage large language models (LLMs), such as ChatGPT and GPT-4, increases the risk of producing antisocial applications at scale. AdvML for social good (AdvML4G) is an emerging field that repurposes the AdvML bug to invent prosocial applications. Regulators, practitioners, and researchers should collaborate to encourage the development of prosocial applications and hinder the development of antisocial ones. In this work, we provide the first comprehensive review of the emerging field of AdvML4G. This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating prosocial applications. Finally, we elaborate upon various challenges and open research issues that require significant attention from the research community.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165062","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}
Pub Date : 2024-03-31DOI: 10.1109/TAI.2024.3408130
Huan Li;Jun Fu;Tianyou Chai
To address the challenge of globally optimal control of path-constrained switched systems, a hybrid intelligent dynamic optimization method is proposed by combining the biobjective particle swarm optimization (PSO) method and a gradient descent method, which simultaneously obtains globally optimal switching instants and input and guarantees rigorous satisfaction of the path constraints over the continuous time horizon. First, the path constraint of switched systems is discretized into multiple point constraints, and then the right-hand side of the path constraint ( $leq 0$ ) is substituted with a negative value ( $leq-varepsilon$ ). Second, the single-objective constrained dynamic program of switched systems is transformed into a biobjective unconstrained dynamic program where each particle intelligently adjusts its objectives to detect the global optimum area satisfying the constraints, depending on its current position in the search space by the search mechanism of PSO. Third, the deterministic optimization method is deployed in the detected global optimum area to locate a feasible solution satisfying the Karush–Kuhn–Tucker (KKT) conditions to a specified tolerance of dynamic optimization of switched systems. Moreover, it is proved that the hybrid intelligent dynamic optimization method can obtain the optimal solution satisfying the first-order approximation KKT conditions within a finite number of iterations. Finally, the results of numerical simulations show the effectiveness of the presented method in terms of improving the solution accuracy and guaranteeing rigorous satisfaction of the path constraint.
{"title":"Hybrid Intelligent Optimization of Nonlinear Switched Systems With Guaranteed Feasibility","authors":"Huan Li;Jun Fu;Tianyou Chai","doi":"10.1109/TAI.2024.3408130","DOIUrl":"https://doi.org/10.1109/TAI.2024.3408130","url":null,"abstract":"To address the challenge of \u0000<italic>globally</i>\u0000 optimal control of path-constrained switched systems, a hybrid intelligent dynamic optimization method is proposed by combining the biobjective particle swarm optimization (PSO) method and a gradient descent method, which simultaneously obtains globally optimal switching instants and input and guarantees rigorous satisfaction of the path constraints over the continuous time horizon. First, the path constraint of switched systems is discretized into multiple point constraints, and then the right-hand side of the path constraint (\u0000<inline-formula><tex-math>$leq 0$</tex-math></inline-formula>\u0000) is substituted with a negative value (\u0000<inline-formula><tex-math>$leq-varepsilon$</tex-math></inline-formula>\u0000). Second, the single-objective constrained dynamic program of switched systems is transformed into a biobjective unconstrained dynamic program where each particle intelligently adjusts its objectives to detect the global optimum area satisfying the constraints, depending on its current position in the search space by the search mechanism of PSO. Third, the deterministic optimization method is deployed in the detected global optimum area to locate a feasible solution satisfying the Karush–Kuhn–Tucker (KKT) conditions to a specified tolerance of dynamic optimization of switched systems. Moreover, it is proved that the hybrid intelligent dynamic optimization method can obtain the optimal solution satisfying the first-order approximation KKT conditions within a finite number of iterations. Finally, the results of numerical simulations show the effectiveness of the presented method in terms of improving the solution accuracy and guaranteeing rigorous satisfaction of the path constraint.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443089","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}
Pub Date : 2024-03-30DOI: 10.1109/TAI.2024.3404913
G. S. Asha Rani;P. S. Lal Priya
An increased demand in electric vehicle (EV) charging facilities has necessitated intelligent energy management systems (EMSs), to control and monitor the available energy sources in these charging stations. The goal is to create a charging schedule for EVs that minimizes the operating cost of the charging station while ensuring all connected EV's charging demands. Model predictive control (MPC) has been widely used for EMS. The challenge with MPC is that a precise representation of the underlying physical system's dynamics is essential. In this study, machine learning methods are combined with conventional MPC to build a data-driven MPC (DMPC) which can adapt to the changes in the system's behavior over time. As new data become available, the data-driven model can be updated and the MPC algorithm can be reoptimized to reflect the current behavior of the system. Ensemble learning is an effective machine learning technique that increases the effectiveness and accuracy of decision making by utilizing the combined knowledge of several models. Out of the several methods available for implementing ensemble learning, adaptive random forest (ARF) algorithm with affine functions and convex optimization is selected. The results show comparable performance of DMPC with respect to MPC implemented on a well-established mathematical model of the system.
{"title":"Data-Driven Model Predictive Control for Hybrid Charging Stations Using Ensemble Learning","authors":"G. S. Asha Rani;P. S. Lal Priya","doi":"10.1109/TAI.2024.3404913","DOIUrl":"https://doi.org/10.1109/TAI.2024.3404913","url":null,"abstract":"An increased demand in electric vehicle (EV) charging facilities has necessitated intelligent energy management systems (EMSs), to control and monitor the available energy sources in these charging stations. The goal is to create a charging schedule for EVs that minimizes the operating cost of the charging station while ensuring all connected EV's charging demands. Model predictive control (MPC) has been widely used for EMS. The challenge with MPC is that a precise representation of the underlying physical system's dynamics is essential. In this study, machine learning methods are combined with conventional MPC to build a data-driven MPC (DMPC) which can adapt to the changes in the system's behavior over time. As new data become available, the data-driven model can be updated and the MPC algorithm can be reoptimized to reflect the current behavior of the system. Ensemble learning is an effective machine learning technique that increases the effectiveness and accuracy of decision making by utilizing the combined knowledge of several models. Out of the several methods available for implementing ensemble learning, adaptive random forest (ARF) algorithm with affine functions and convex optimization is selected. The results show comparable performance of DMPC with respect to MPC implemented on a well-established mathematical model of the system.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443110","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}
Pub Date : 2024-03-27DOI: 10.1109/TAI.2024.3405405
Zixiang Wei;Yiting Wang;Lichao Sun;Athanasios V. Vasilakos;Lin Wang
Low-light images often suffer from limited visibility and multiple types of degradation, rendering low-light image enhancement (LIE) a nontrivial task. Some endeavors have been made to enhance low-light images using convolutional neural networks (CNNs). However, they have low efficiency in learning the structural information and diverse illumination levels at the local regions of an image. Consequently, the enhanced results are affected by unexpected artifacts, such as unbalanced exposure, blur, and color bias. This article proposes a novel framework, called ClassLIE, that combines the potential of CNNs and transformers. It classifies and adaptively learns the structural and illumination information from the low-light images in a holistic and regional manner, thus showing better enhancement performance. Our framework first employs a structure and illumination classification (SIC) module to learn the degradation information adaptively. In SIC, we decompose an input image into an illumination map and a reflectance map. A class prediction block is then designed to classify the degradation information by calculating the structure similarity scores on the reflectance map and mean square error (MSE) on the illumination map. As such, each input image can be divided into patches with three enhancement difficulty levels. Then, a feature learning and fusion (FLF) module is proposed to adaptively learn the feature information with CNNs for different enhancement difficulty levels while learning the long-range dependencies for the patches in a holistic manner. Experiments on five benchmark datasets consistently show our ClassLIE achieves new state-of-the-art performance, with 25.74 peak signal-to-noise ratio (PSNR) and 0.92 structural similarity (SSIM) on the LOw-Light (LOL) dataset.
{"title":"ClassLIE: Structure- and Illumination-Adaptive Classification for Low-Light Image Enhancement","authors":"Zixiang Wei;Yiting Wang;Lichao Sun;Athanasios V. Vasilakos;Lin Wang","doi":"10.1109/TAI.2024.3405405","DOIUrl":"https://doi.org/10.1109/TAI.2024.3405405","url":null,"abstract":"Low-light images often suffer from limited visibility and multiple types of degradation, rendering low-light image enhancement (LIE) a nontrivial task. Some endeavors have been made to enhance low-light images using convolutional neural networks (CNNs). However, they have low efficiency in learning the structural information and diverse illumination levels at the local regions of an image. Consequently, the enhanced results are affected by unexpected artifacts, such as unbalanced exposure, blur, and color bias. This article proposes a novel framework, called ClassLIE, that combines the potential of CNNs and transformers. It classifies and adaptively learns the structural and illumination information from the low-light images in a holistic and regional manner, thus showing better enhancement performance. Our framework first employs a structure and illumination classification (SIC) module to learn the degradation information adaptively. In SIC, we decompose an input image into an illumination map and a reflectance map. A class prediction block is then designed to classify the degradation information by calculating the structure similarity scores on the reflectance map and mean square error (MSE) on the illumination map. As such, each input image can be divided into patches with three enhancement difficulty levels. Then, a feature learning and fusion (FLF) module is proposed to adaptively learn the feature information with CNNs for different enhancement difficulty levels while learning the long-range dependencies for the patches in a holistic manner. Experiments on five benchmark datasets consistently show our ClassLIE achieves new state-of-the-art performance, with 25.74 peak signal-to-noise ratio (PSNR) and 0.92 structural similarity (SSIM) on the LOw-Light (LOL) dataset.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169750","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}
Pub Date : 2024-03-27DOI: 10.1109/TAI.2024.3382248
Khurram Khan;Atiq ur Rehman;Adnan Khan;Syed Rameez Naqvi;Samir Brahim Belhaouari;Amine Bermak
This work proposes a novel split and kernel-merge clustering (S-KMC), a nonparametric clustering algorithm that combines the strengths of hierarchical clustering, partitional clustering, and density-based clustering. It consists of two main phases: splitting and merging. In the splitting phase, a ranking-based operator is used to divide the data into optimal subclusters. In the merging phase, a kernel function estimates the density of these subclusters after projecting them onto a straight line passing through their centers, facilitating the merging operation. S-KMC is fully nonparametric, eliminating the need for prior information about the data. It effectively handles 1) shape diversity, 2) density variability, 3) high dimensionality, 4) outliers, and 5) missing values. The algorithm offers easily tunable hyperparameters, enhancing its applicability to complex problems and robustness against data anomalies. Experimental analysis on 21 benchmark datasets demonstrates the improved performance of S-KMC in terms of cluster accuracy, handling high-dimensional data, and managing data anomalies and outliers. Comprehensive comparisons with state-of-the-art techniques further validate the superior or comparable performance of the proposed S-KMC algorithm.
{"title":"A Nonparametric Split and Kernel-Merge Clustering Algorithm","authors":"Khurram Khan;Atiq ur Rehman;Adnan Khan;Syed Rameez Naqvi;Samir Brahim Belhaouari;Amine Bermak","doi":"10.1109/TAI.2024.3382248","DOIUrl":"https://doi.org/10.1109/TAI.2024.3382248","url":null,"abstract":"This work proposes a novel split and kernel-merge clustering (S-KMC), a nonparametric clustering algorithm that combines the strengths of hierarchical clustering, partitional clustering, and density-based clustering. It consists of two main phases: splitting and merging. In the splitting phase, a ranking-based operator is used to divide the data into optimal subclusters. In the merging phase, a kernel function estimates the density of these subclusters after projecting them onto a straight line passing through their centers, facilitating the merging operation. S-KMC is fully nonparametric, eliminating the need for prior information about the data. It effectively handles 1) shape diversity, 2) density variability, 3) high dimensionality, 4) outliers, and 5) missing values. The algorithm offers easily tunable hyperparameters, enhancing its applicability to complex problems and robustness against data anomalies. Experimental analysis on 21 benchmark datasets demonstrates the improved performance of S-KMC in terms of cluster accuracy, handling high-dimensional data, and managing data anomalies and outliers. Comprehensive comparisons with state-of-the-art techniques further validate the superior or comparable performance of the proposed S-KMC algorithm.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164997","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}
Pub Date : 2024-03-27DOI: 10.1109/TAI.2024.3405468
Akash C. Babu;Jeevanand Seshadrinath
This work introduces a novel online signal processing and machine learning (ML) framework designed for the incipient diagnosis of stator interturn faults (SITF) in three-phase squirrel cage induction motors. Addressing the critical need for incipient fault detection to prevent severe motor damage, the framework focuses on motor speed estimation, incipient fault detection, fault severity estimation, and faulty phase identification using only stator currents. A distinctive contribution lies in the proposed interacting multiple model (IMM) framework that leverages carefully selected motor current signatures as features, offering a comprehensive strategy for stator fault diagnosis not explored previously. The article pioneers the use of the selected harmonics with ML models to estimate a fault severity indicator, which is developed based on insights from the motor's physics of failure. Experimental validation showcases the fault indicator's effectiveness under diverse operating conditions, demonstrating its utility in fault severity assessment. Suitable standalone ML model is selected, or an ensemble is constructed from a pool of ML models at each stage of the IMM framework. Further, a feature relevance analysis is also performed to garner insights into the contributions of each handpicked feature in predicting the fault indicator.
本研究介绍了一种新型在线信号处理和机器学习（ML）框架，该框架专为三相鼠笼式感应电机定子匝间故障（SITF）的初期诊断而设计。为了满足初期故障检测的关键需求，防止严重的电机损坏，该框架重点关注电机速度估计、初期故障检测、故障严重性估计以及仅使用定子电流的故障相位识别。其独特之处在于提出了交互式多模型 (IMM) 框架，该框架利用精心选择的电机电流特征，为定子故障诊断提供了一种前所未有的综合策略。文章开创性地将选定的谐波与多模型（ML）模型结合使用，以估算故障严重性指标，该指标是基于对电机故障物理原理的深入了解而开发的。实验验证展示了故障指标在不同运行条件下的有效性，证明了其在故障严重性评估中的实用性。在 IMM 框架的每个阶段，都会选择合适的独立 ML 模型，或从 ML 模型池中构建一个集合。此外，还进行了特征相关性分析，以深入了解每个精选特征在预测故障指标方面的贡献。
{"title":"Interacting Multiple Model Framework for Incipient Diagnosis of Interturn Faults in Induction Motors","authors":"Akash C. Babu;Jeevanand Seshadrinath","doi":"10.1109/TAI.2024.3405468","DOIUrl":"https://doi.org/10.1109/TAI.2024.3405468","url":null,"abstract":"This work introduces a novel online signal processing and machine learning (ML) framework designed for the incipient diagnosis of stator interturn faults (SITF) in three-phase squirrel cage induction motors. Addressing the critical need for incipient fault detection to prevent severe motor damage, the framework focuses on motor speed estimation, incipient fault detection, fault severity estimation, and faulty phase identification using only stator currents. A distinctive contribution lies in the proposed interacting multiple model (IMM) framework that leverages carefully selected motor current signatures as features, offering a comprehensive strategy for stator fault diagnosis not explored previously. The article pioneers the use of the selected harmonics with ML models to estimate a fault severity indicator, which is developed based on insights from the motor's physics of failure. Experimental validation showcases the fault indicator's effectiveness under diverse operating conditions, demonstrating its utility in fault severity assessment. Suitable standalone ML model is selected, or an ensemble is constructed from a pool of ML models at each stage of the IMM framework. Further, a feature relevance analysis is also performed to garner insights into the contributions of each handpicked feature in predicting the fault indicator.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443086","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}
The accurate and rapid transfer of complex urban spatiotemporal data is crucial for urban computing tasks such as urban planning and public transportation deployment for smart-city applications. Existing works consider auxiliary data or propose end-to-end models to process complex spatiotemporal information into more complex deep features. However, the latter is incapable of decoupling spatiotemporal knowledge, which means these end-to-end models lack modularity and substitutability. A general modular framework that can automatically capture simple representations of complex spatiotemporal information is required. In this article, we thus propose a universal framework for the transfer of spatiotemporal knowledge based on a radial basis function (RBF). We termed this approach spatial–temporal RBF transfer framework (STRBF-TF). The proposed STRBF-TF generates simple RBF representations of spatiotemporal flow distribution with an RBF transfer block and also leverages a channel attention mechanism. Moreover, we propose two RBF kernel initializers suitable for the source and the target domains, respectively. The framework retains important spatiotemporal knowledge in simple representations for the reconfiguration of spatiotemporal feature distribution for fast and accurate transfer. We conducted cross-domain learning experiments on a large real-world telecom dataset. The results demonstrate the efficiency and accuracy of the proposed approach, as well as its suitability for real-world applications.
{"title":"Universal Transfer Framework for Urban Spatiotemporal Knowledge Based on Radial Basis Function","authors":"Sheng-Min Chiu;Yow-Shin Liou;Yi-Chung Chen;Chiang Lee;Rong-Kang Shang;Tzu-Yin Chang;Roger Zimmermann","doi":"10.1109/TAI.2024.3382267","DOIUrl":"https://doi.org/10.1109/TAI.2024.3382267","url":null,"abstract":"The accurate and rapid transfer of complex urban spatiotemporal data is crucial for urban computing tasks such as urban planning and public transportation deployment for smart-city applications. Existing works consider auxiliary data or propose end-to-end models to process complex spatiotemporal information into more complex deep features. However, the latter is incapable of decoupling spatiotemporal knowledge, which means these end-to-end models lack modularity and substitutability. A general modular framework that can automatically capture simple representations of complex spatiotemporal information is required. In this article, we thus propose a universal framework for the transfer of spatiotemporal knowledge based on a radial basis function (RBF). We termed this approach spatial–temporal RBF transfer framework (STRBF-TF). The proposed STRBF-TF generates simple RBF representations of spatiotemporal flow distribution with an RBF transfer block and also leverages a channel attention mechanism. Moreover, we propose two RBF kernel initializers suitable for the source and the target domains, respectively. The framework retains important spatiotemporal knowledge in simple representations for the reconfiguration of spatiotemporal feature distribution for fast and accurate transfer. We conducted cross-domain learning experiments on a large real-world telecom dataset. The results demonstrate the efficiency and accuracy of the proposed approach, as well as its suitability for real-world applications.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164999","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}
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