IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第9页

Circuit Implementation of Memristive Fuzzy Logic for Blood Pressure Grading Quantification

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-06-04 DOI: 10.1109/TETCI.2024.3404004

Ya Li;Shaojun Ji;Qinghui Hong

{"title":"Circuit Implementation of Memristive Fuzzy Logic for Blood Pressure Grading Quantification","authors":"Ya Li;Shaojun Ji;Qinghui Hong","doi":"10.1109/TETCI.2024.3404004","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3404004","url":null,"abstract":"Fuzzy logic can effectively deal with many uncertain problems due to its unique fuzziness and insensitivity to data, so it is widely used in health scenarios with precision grading quantification. Therefore, an analog circuit of memristive fuzzy logic for blood pressure grading quantification is designed in this paper. The circuit includes 1) fuzzifier module, 2) rule base module, 3) inference engine module. The fuzzifier module uses a memristor array to build a membership function circuit that can be fully programmed in parallel, and converts the input systolic and diastolic blood pressure signals into corresponding membership degrees through the circuit. The rule base module mainly implements fuzzy rules based on blood pressure fuzzy semantic sets through analog circuits. The function of the inference engine module is to transform the blood pressure rules stored in the rule base into the mapping relationship between fuzzy semantic sets, and to infer the results of blood pressure grading quantification. The PSPICE simulation results show that the calculation precision of the memristive fuzzy logic circuit can reach about 99.7%, and the accuracy rate of the circuit to achieve the blood pressure grading quantification reaches 98.69%. Compared to traditional digital circuits, this circuit has significant advantages in terms of power consumption and computational speed.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"654-667"},"PeriodicalIF":5.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Least Information Spectral GAN With Time-Series Data Augmentation for Industrial IoT

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-06-04 DOI: 10.1109/TETCI.2024.3406719

Joonho Seon;Seongwoo Lee;Young Ghyu Sun;Soo Hyun Kim;Dong In Kim;Jin Young Kim

{"title":"Least Information Spectral GAN With Time-Series Data Augmentation for Industrial IoT","authors":"Joonho Seon;Seongwoo Lee;Young Ghyu Sun;Soo Hyun Kim;Dong In Kim;Jin Young Kim","doi":"10.1109/TETCI.2024.3406719","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3406719","url":null,"abstract":"In industrial Internet of Things (IIoT) systems, imbalanced datasets are prevalent because of the relative ease of acquiring normal operational data compared to abnormal or faulty data. An unbalanced distribution of data may lead to a biased learning problem, resulting in performance degradation of deep learning models. Data augmentation approaches based on generative adversarial networks (GAN) have been proposed to mitigate biased learning problems. However, GAN-based approaches constructed solely with convolutional neural networks may be incapable of extracting temporal properties from data. To utilize the temporal properties of data, a novel GAN structure consisting of an embedding network and recurrent neural networks is proposed in this paper. Additionally, in the novel GAN model based on mean-squared error, modified loss and mutual information terms are employed to improve training stability. From simulation results, it is confirmed that classification accuracy can be significantly improved by up to 54% based on the proposed method when compared with conventional fault diagnosis methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"757-769"},"PeriodicalIF":5.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TROPE: Triplet-Guided Feature Refinement for Person Re-Identification

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-06-04 DOI: 10.1109/TETCI.2024.3406411

Divya Singh;Jimson Mathew;Mayank Agarwal;Mahesh Govind

{"title":"TROPE: Triplet-Guided Feature Refinement for Person Re-Identification","authors":"Divya Singh;Jimson Mathew;Mayank Agarwal;Mahesh Govind","doi":"10.1109/TETCI.2024.3406411","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3406411","url":null,"abstract":"Person Re-Identification (PRid) has garnered research attention with the rising popularity of intelligent video surveillance. Deep learning methods like Convolutional Neural Networks (CNN) are vital in PRid. The CNNs acquire image characteristics that are distinctive, referred to as image features, by analyzing the entire image. This process enables them to recognize and differentiate between various images. However, in the case of PRid, this search across the entire image may lead the model to emphasize distinctive image features of the background while neglecting subtle but essential distinguishing regions of the person. This tendency can be observed in the heat maps generated from trained models. Therefore, it is crucial to direct the model's attention towards the vital regions of a person. Relying solely on global features might be limited in effectiveness, as it does not sufficiently capture essential finer details. Therefore, it becomes necessary to pinpoint significant features at a local level and guide the model to prioritize these features for improved results. Inspired to identify and prioritize vital regions based on local features, we propose TROPE (Triplet-Guided Feature Refinement for Person Re-Identification) in this paper. The technique involves analyzing the intermediate features of hard positive and hard negative images and generating weight vectors. These vectors are utilized to shift the attention of the network to specific regions of interest. The proposed method achieves 87.5% mAP and 95.6% Rank1 accuracy on Market1501. 77.6% mAP and 88.8% Rank1 accuracy on DukeMTMC and 78.5% mAP, 80.1% Rank1 accuracy on CUHK03 dataset.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"706-716"},"PeriodicalIF":5.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FCPFS: Fuzzy Granular Ball Clustering-Based Partial Multilabel Feature Selection With Fuzzy Mutual Information

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-06-03 DOI: 10.1109/TETCI.2024.3399665

Lin Sun;Qifeng Zhang;Weiping Ding;Tianxiang Wang;Jiucheng Xu

{"title":"FCPFS: Fuzzy Granular Ball Clustering-Based Partial Multilabel Feature Selection With Fuzzy Mutual Information","authors":"Lin Sun;Qifeng Zhang;Weiping Ding;Tianxiang Wang;Jiucheng Xu","doi":"10.1109/TETCI.2024.3399665","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3399665","url":null,"abstract":"In the partial multilabel learning, incorrect labels are annotated because of their low quality and poor recognition. To decrease secondary errors in partial multilabel classification, this paper proposes a novel fuzzy granular ball clustering-based partial multilabel feature selection scheme with fuzzy mutual information. First, to overcome the defect that the traditional granular ball model cannot be applied to partial multilabel classification and its splitting rules are anomalous and stochastic, an objective function is designed by the fuzzy membership degree, the splitting rules and termination conditions are redesigned, and a new fuzzy granular ball clustering method using fuzzy <italic>k</i>-means can be developed to preprocess partial multilabel data. Second, to reduce the impact of noise labels, the instance set of each granular ball is generated according to fuzzy granular ball clustering instead of neighborhood class, and the fuzzy similarity relationship between instances is constructed. Subsequently, granular ball-based fuzzy entropy measures and fuzzy mutual information and their properties are proposed in granular ball-based partial multilabel systems. Finally, the dependence and relevance between features and label sets are studied, the significance of features based on fuzzy mutual information is presented, and then a heuristic partial multilabel feature selection method is constructed to enhance the effect of partial multilabel data classification. Experiments on 18 partial multilabel datasets illustrate the availability of our method compared to other multilabel classification algorithms in its classification effect.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"590-606"},"PeriodicalIF":5.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-06-03 DOI: 10.1109/TETCI.2024.3405370

Hongzong Li;Jun Wang

引用次数: 0

Clustering-Based Short-Term Wind Speed Interval Prediction With Multi-Objective Ensemble Learning

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-06-03 DOI: 10.1109/TETCI.2024.3400852

Qingling Zhu;Yaojian Xu;Qiuzhen Lin;Zhong Ming;Kay Chen Tan

{"title":"Clustering-Based Short-Term Wind Speed Interval Prediction With Multi-Objective Ensemble Learning","authors":"Qingling Zhu;Yaojian Xu;Qiuzhen Lin;Zhong Ming;Kay Chen Tan","doi":"10.1109/TETCI.2024.3400852","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3400852","url":null,"abstract":"As a renewable and green energy source, wind energy has attracted great attention from academia and industry in recent decades. However, it is challenging to integrate wind energy into smart grids due to the instability and randomness of wind speed. To solve this problem, this paper proposes a clustering-based short-term wind speed interval prediction with multi-objective ensemble learning, which can provide an accurate and reliable wind speed interval prediction to support energy dispatch planning. First, a clustering-based uncertainties estimation method segments the initial wind sequence into several groups and determines the estimated width for each group. Second, a variational mode decomposition is employed to acquire the sub-sequence matrix of wind speed, and then a Hurst exponent-based model selection method is used to choose and train an optimal model for each sub-sequence based on its long-term correlation. Finally, an improved multi-objective optimizer is utilized to determine the optimal superposition weights of the prediction results for each model. The proposed approach is evaluated using eight cases from two wind farms, which are published by the National Renewable Energy Laboratory. Experimental results indicate that the proposed approach outperforms several state-of-the-art studies, demonstrating a higher prediction interval coverage probability and a narrower prediction interval width.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"304-317"},"PeriodicalIF":5.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bi-Search Evolutionary Algorithm for High-Dimensional Bi-Objective Feature Selection 用于高维双目标特征选择的双搜索进化算法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-30 DOI: 10.1109/TETCI.2024.3393388

Hang Xu;Bing Xue;Mengjie Zhang

{"title":"A Bi-Search Evolutionary Algorithm for High-Dimensional Bi-Objective Feature Selection","authors":"Hang Xu;Bing Xue;Mengjie Zhang","doi":"10.1109/TETCI.2024.3393388","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3393388","url":null,"abstract":"High dimensionality often challenges the efficiency and accuracy of a classifier, while evolutionary feature selection is an effective method for data preprocessing and dimensionality reduction. However, with the exponential expansion of search space along with the increase of features, traditional evolutionary feature selection methods could still find it difficult to search for optimal or near optimal solutions in the large-scale search space. To overcome the above issue, in this paper, we propose a bi-search evolutionary algorithm (termed BSEA) for tackling high-dimensional feature selection in classification, with two contradictory optimizing objectives (i.e., minimizing both selected features and classification errors). In BSEA, a bi-search evolutionary mode combining the forward and backward searching tasks is adopted to enhance the search ability in the large-scale search space; in addition, an adaptive feature analysis mechanism is also designed to the explore promising features for efficiently reproducing more diverse offspring. In the experiments, BSEA is comprehensively compared with 9 most recent or classic state-of-the-art MOEAs on a series of 11 high-dimensional datasets with no less than 2000 features. The empirical results suggest that BSEA generally performs the best on most of the datasets in terms of all performance metrics, along with high computational efficiency, while each of its essential components can take positive effect on boosting the search ability and together make the best contribution.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3489-3502"},"PeriodicalIF":5.3,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-modal Authentication Model for Occluded Faces in a Challenging Environment 挑战性环境中的隐蔽人脸多模式身份验证模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-30 DOI: 10.1109/TETCI.2024.3390058

Dahye Jeong;Eunbeen Choi;Hyeongjin Ahn;Ester Martinez-Martin;Eunil Park;Angel P. del Pobil

{"title":"Multi-modal Authentication Model for Occluded Faces in a Challenging Environment","authors":"Dahye Jeong;Eunbeen Choi;Hyeongjin Ahn;Ester Martinez-Martin;Eunil Park;Angel P. del Pobil","doi":"10.1109/TETCI.2024.3390058","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3390058","url":null,"abstract":"Authentication systems are crucial in the digital era, providing reliable protection of personal information. Most authentication systems rely on a single modality, such as the face, fingerprints, or password sensors. In the case of an authentication system based on a single modality, there is a problem in that the performance of the authentication is degraded when the information of the corresponding modality is covered. Especially, face identification does not work well due to the mask in a COVID-19 situation. In this paper, we focus on the multi-modality approach to improve the performance of occluded face identification. Multi-modal authentication systems are crucial in building a robust authentication system because they can compensate for the lack of modality in the uni-modal authentication system. In this light, we propose DemoID, a multi-modal authentication system based on face and voice for human identification in a challenging environment. Moreover, we build a demographic module to efficiently handle the demographic information of individual faces. The experimental results showed an accuracy of 99% when using all modalities and an overall improvement of 5.41%–10.77% relative to uni-modal face models. Furthermore, our model demonstrated the highest performance compared to existing multi-modal models and also showed promising results on the real-world dataset constructed for this study.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3463-3473"},"PeriodicalIF":5.3,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PV-SSD: A Multi-Modal Point Cloud 3D Object Detector Based on Projection Features and Voxel Features PV-SSD：基于投影特征和体素特征的多模态点云三维物体检测器

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-29 DOI: 10.1109/TETCI.2024.3389710

Yongxin Shao;Aihong Tan;Zhetao Sun;Enhui Zheng;Tianhong Yan;Peng Liao

{"title":"PV-SSD: A Multi-Modal Point Cloud 3D Object Detector Based on Projection Features and Voxel Features","authors":"Yongxin Shao;Aihong Tan;Zhetao Sun;Enhui Zheng;Tianhong Yan;Peng Liao","doi":"10.1109/TETCI.2024.3389710","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3389710","url":null,"abstract":"3D object detection using LiDAR is critical for autonomous driving. However, the point cloud data in autonomous driving scenarios is sparse. Converting the sparse point cloud into regular data representations (voxels or projection) often leads to information loss due to downsampling or excessive compression of feature information. This kind of information loss will adversely affect detection accuracy, especially for objects with fewer reflective points like cyclists. This paper proposes a multi-modal point cloud 3D object detector based on projection features and voxel features, which consists of two branches. One, called the voxel branch, is used to extract fine-grained local features. Another, called the projection branch, is used to extract projection features from a bird's-eye view and focus on the correlation of local features in the voxel branch. By feeding voxel features into the projection branch, we can compensate for the information loss in the projection branch while focusing on the correlation between neighboring local features in the voxel features. To achieve comprehensive feature fusion of voxel features and projection features, we propose a multi-modal feature fusion module (MSSFA). To further mitigate the loss of crucial features caused by downsampling, we propose a voxel feature extraction method (VR-VFE), which samples feature points based on their importance for the detection task. To validate the effectiveness of our method, we tested it on the KITTI dataset and ONCE dataset. The experimental results show that our method has achieved significant improvement in the detection accuracy of objects with fewer reflection points like cyclists.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3436-3449"},"PeriodicalIF":5.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Addressing Machine Learning Problems in the Non-Negative Orthant 解决非负正交的机器学习问题

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-04-29 DOI: 10.1109/TETCI.2024.3379239

Ioannis Tsingalis;Constantine Kotropoulos

{"title":"Addressing Machine Learning Problems in the Non-Negative Orthant","authors":"Ioannis Tsingalis;Constantine Kotropoulos","doi":"10.1109/TETCI.2024.3379239","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3379239","url":null,"abstract":"Frequently, equality constraints are imposed on the objective function of machine learning algorithms aiming at increasing their robustness and generalization. In addition, non-negativity constraints imposed on the objective function aim to improve interpretability. This paper proposes a framework that solves problems in the non-negative orthant with additional equality constraints. This framework is characterized by an iteration complexity \u0000<inline-formula><tex-math>${mathcal{O}} {({ln}, {epsilon} ^{{ -varrho }})}$</tex-math></inline-formula>\u0000 with \u0000<inline-formula><tex-math>${epsilon}$</tex-math></inline-formula>\u0000 denoting the accuracy and \u0000<inline-formula><tex-math>${varrho}$</tex-math></inline-formula>\u0000 being the condition number. To avoid “zig-zagging”, a diminishing learning rate is adopted without harming the convergence of the learning procedure. Simple and well-established tools of the theory of Lagrange multipliers for constrained optimization are employed to derive the updating rules and study their convergence properties. To the best of our knowledge, this is the first time these tools are combined in a unified way to derive the proposed optimizer. Its efficiency is demonstrated by conducting classification experiments on well-known datasets, yielding promising results.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 6","pages":"3951-3965"},"PeriodicalIF":5.3,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0