检索结果-内蒙古大学图书馆

Frequency multiplexed photothermal correlation tomography for non-destructive evaluation of manufactured materials

International Journal of Extreme Manufacturing 2025年第3期7卷 533-546页

作者： Pengfei Zhu Rongbang Wang Koneswaran Sivagurunathan Stefano Sfarra Fabrizio Sarasini Clemente Ibarra-Castanedo Xavier Maldague Hai Zhang Andreas Mandelis Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory(CVSL)Laval UniversityQuebec G1V 0A6Canada Centre for Composite Materials and Structures(CCMS) Harbin Institute of TechnologyHarbin 150001People’s Republic of China Center for Advanced Diffusion-Wave and Photoacoustic Technologies(CADIPT)and Institute for Advanced Non-Destructive and Non-Invasive Diagnostic Technologies(IANDIT) Department of Mechanical and Industrial EngineeringUniversity of TorontoTorontoCanada Department of Industrial and Information Engineering and Economics(DIIIE) University of L’AquilaI-67100 L’Aquila(AQ)Italy Department of Chemical Engineering Materials Environment&UDR INSTM Sapienza University of RomeRome I-00184Italy

Infrared thermography has been widely applied in real industrial inspection of aerospace,energy management systems,engines,and electric ***,two-dimensional imaging modality limits its ***,a technique named frequency multiplexed photothermal correlation tomography(FM-PCT)was developed to enable non-destructive and contactless cross-sectional imaging for manufactured material evaluation and *** combining advantages of photothermal tomography and pulsed thermography,FM-PCT facilitates the generation of three-dimensional thermal images through temporal superposition(stacking)of two-dimensional images from sequential subsurface ***-PCT image processing involves pulsed excitation signals to which frequency delay and matched filtering techniques are *** features of FM-PCT are high-resolution three-dimensional tomographic imaging under low camera frame-rate conditions with self-correcting capability for diffusion(blurring)correction of subsurface images due to cross-correlation processing of individual frequencies in the Fourier decomposition spectrum of the excitation ***,FM-PCT extends truncated-correlation photothermal coherence tomography from chirp and pulsed signals to more general linear heating ***-in thermography and x-ray computed tomography validation demonstrate that 3D FM-PCT imaging accurately reveals subsurface discontinuities/defects in solids despite the diffusive nature of thermal-wave imaging.

关键词： photothermal coherence tomography nondestructive evaluation infrared thermography impact damage

来源：评论

学校读者我要写书评

暂无评论

Towards Interpretable, Attention-Based Crime Forecasting

Towards Interpretable, Attention-Based Crime Forecasting

引用

2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Ma, Yujunrong Qi, Xiaowen Nakamura, Kiminori Bhattacharyya, Shuvra S. University of Maryland Department of Electrical and Computer Engineering College Park United States University of Maryland Department of Criminology and Criminal Justice College Park United States Institute for Advanced Computer Studies University of Maryland Department of Electrical and Computer Engineering College Park United States

ISBN: (纸本)9798350337020

While the use of machine learning techniques in high stake fields, such as medical diagnosis and criminal justice, has been increasing in recent years, concerns have been raised regarding the lack of transparency and interpretability of the algorithms used. In this paper, we propose the use of interpretable attention-based ConvLSTM models for crime forecasting application. This approach combines the power of ConvLSTM models in capturing spatio-temporal patterns with the interpretability of attention mechanisms, allowing for the identification of key geographic areas in the input data that contribute to the prediction. We demonstrate the effectiveness of this approach through experiments on real-world crime data, showing that our model demonstrates high accuracy in crime predictions while providing insightful visualization that enhances the interpretability of prediction results. © 2023 IEEE.

关键词： ConvLSTM Interpretable machine learning Self attention

来源：评论

学校读者我要写书评

暂无评论

Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

引用

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4290-4302页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Temporally consistent video colorization with deep feature propagation and self-regularization learning

引用

Computational Visual Media 2024年第2期10卷 375-395页

作者： Yihao Liu Hengyuan Zhao Kelvin CKChan Xintao Wang Chen Change Loy Yu Qiao Chao Dong Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China Department of Electrical&Computer Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798 Applied Research Center Tencent PCGShenzhenChina

Video colorization is a challenging and highly ill-posed *** recent years have witnessed remarkable progress in single image colorization,there is relatively less research effort on video colorization,and existing methods always suffer from severe flickering artifacts(temporal inconsistency)or unsatisfactory *** address this problem from a new perspective,by jointly considering colorization and temporal consistency in a unified ***,we propose a novel temporally consistent video colorization(TCVC)*** effectively propagates frame-level deep features in a bidirectional way to enhance the temporal consistency of ***,TCVC introduces a self-regularization learning(SRL)scheme to minimize the differences in predictions obtained using different time *** does not require any ground-truth color videos for training and can further improve temporal *** demonstrate that our method can not only provide visually pleasing colorized video,but also with clearly better temporal consistency than state-of-the-art methods.A video demo is provided at https://***/watch?v=c7dczMs-olE,while code is available at https://***/lyh-18/TCVC-Tem porally-Consistent-Video-Colorization.

关键词： video colorization temporal consistency feature propagation self-regularization

来源：评论

学校读者我要写书评

暂无评论

Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition 7

Self-Paced Neutral Expression-Disentangled Learning for Faci...

引用

7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Wu, Zhenqian Li, Xiaoyuan Ren, Yazhou Pu, Xiaorong Zhu, Xiaofeng He, Lifang University of Electronic Science And Technology of China School of Computer Science And Engineering China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Lehigh University Department of Computer Science And Engineering United States

ISBN: (纸本)9798350359145

The accuracy of facial expression recognition is typically influenced by the following factors: high similarities across different expressions, disturbing factors, and the subtle and rapid micro-movements of the face. One potential solution to overcome these obstacles is to leverage the hidden neutral information present in neutral expression images. In this paper, we propose a Self-Paced Neutral Expression-Disentangled Learning (SPNDL) model, which aims to disentangle the neutral information from facial expressions, facilitating the extraction of crucial features and variations. This approach enables the capture of discriminative details within similar expressions and the detection of micro-facial movements. To enhance the learning of these neutral expression-disentangled features (NDFs) and mitigate the challenges posed by non-convex optimization, we propose a self-paced learning (SPL) strategy based on NDFs during the training phase. SPL learns samples from easy to complex by increasing the number of samples selected into the training process, which enables to effectively suppress the negative impacts caused by low-quality samples and inconsistently distributed NDFs. Experiments on three popular databases (i.e., CK+, Oulu-CASIA, and RAF-DB) show the effectiveness of our proposed method. © 2023 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Balancing Fairness and Accuracy for Predictive Models in Criminal Justice Applications Using Multi-Objective Optimization Methods

Balancing Fairness and Accuracy for Predictive Models in Cri...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Qi, Xiaowen Ma, Yujungrong Nakamura, Kiminori Bhattacharyya, Shuvra S. University of Maryland Department of Electrical and Computer Engineering Institute for Advanced Computer Studies College ParkMD20740 United States University of Mary-land Department of Criminology and Criminal Justice College ParkMD20740 United States

ISBN: (纸本)9781665410205

In the field of predictive modeling for criminal justice applications, the dual challenges of ensuring fairness and maintaining interpretability are crucial. This paper addresses these challenges by introducing a new approach to optimizing decision trees using evolutionary algorithms (EAs). Our approach focuses on refining decision trees to achieve a balance between accuracy and algorithmic fairness, a task complicated by potential bias present in historical data. By leveraging the principles of multi-objective optimization, our model systematically trades off prediction accuracy and fair-ness. The evolutionary process characterized by selection, crossover, and mutation is tailored to fit the decision tree structure, ensuring that model development is not only accurate but also promoting measurable fairness. Experimental results demonstrate the effectiveness of our approach in providing interpretable and fair predictive models that can be considered for high-stakes applications in criminal justice. More broadly, this research makes a significant contribution to the field of explainable machine learning, providing a powerful framework for engineering systems that are transparent, fair, and adaptable to different data environments. © 2024 IEEE.

关键词： Forestry

来源：评论

学校读者我要写书评

暂无评论

Deep Multi-View Clustering via View-Specific Representation and Global Graph 7

Deep Multi-View Clustering via View-Specific Representation ...

引用

7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Pu, Jingyu Cui, Chenhang Ren, Yazhou Wen, Zichen Wu, Tianyi Pu, Xiaorong He, Lifang University of Electronic Science And Technology of China School of Computer Science And Engineering China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Lehigh University Department of Computer Science And Engineering Bethlehem United States

ISBN: (纸本)9798350359145

Multi-view clustering can improve clustering performance by leveraging the complementary information from multiview data and has garnered growing interest. Despite significant advancements has been established in this field, most existing methods primarily concentrate on learning the latent representation, neglecting the latent graph that typically reveals relationships between different instances. To address this limitation, this paper presents a new framework of Deep Multi-View Clustering via View-specific Representation and Global Graph (DVGMVC) to address the aforementioned issue. Our method leverages the latent graph of each view to enhance representation quality and incorporates a global graph that combines discriminative information to enhance the performance of deep embedded clustering. Specifically, we employ autoencoders to learn embedded features and construct latent graphs from each view. Subsequently, a weighted two-layer plain residual network refines the latent feature using topological information derived from the latent graphs. As various views adhere to the same foundational cluster structure across multiple views, DVGMVC merges the embedded features of all views to create a unified feature. By leveraging the Graph Convolutional Network (GCN) module, which exploits information from both the latent graphs and the global graph generated from the global feature, a unified embedded feature is generated to capture comprehensive topological information among samples. This unified embedded feature is then employed to generate pseudo labels in a self-supervised manner, guiding unified embedding learning and clustering. In a self-supervised manner, such pseudo-label information guides unified embedding learning and clustering. In-depth experiments on public datasets has proven the state-of-the-art clustering effectiveness of our proposed method. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Sampling from the Mean-Field Stationary Distribution 37

Sampling from the Mean-Field Stationary Distribution

引用

37th Annual Conference on Learning Theory, COLT 2024

作者： Kook, Yunbum Zhang, Matthew S. Chewi, Sinho Erdogdu, Murat A. Li, Mufan Georgia Institute of Technology United States Department of Computer Science University of Toronto Vector Institute Canada School of Mathematics Institute for Advanced Study United States Department of Operations Research and Financial Engineering Princeton University United States

We study the complexity of sampling from the stationary distribution of a mean-field SDE, or equivalently, the complexity of minimizing a functional over the space of probability measures which includes an interaction term. Our main insight is to decouple the two key aspects of this problem: (1) approximation of the mean-field SDE via a finite-particle system, via uniform-in-time propagation of chaos, and (2) sampling from the finite-particle stationary distribution, via standard log-concave samplers. Our approach is conceptually simpler and its flexibility allows for incorporating the state-of-the-art for both algorithms and theory. This leads to improved guarantees in numerous settings, including better guarantees for optimizing certain two-layer neural networks in the mean-field regime. A key technical contribution is to establish a new uniform-in-N log-Sobolev inequality for the stationary distribution of the mean-field Langevin dynamics. © 2024 Y. Kook, M. S. Zhang, S. Chewi, M. A. Erdogdu & M.(. Li.

关键词： Probability distributions

来源：评论

学校读者我要写书评

暂无评论

Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

来源：评论

学校读者我要写书评

暂无评论

Rate Outage Constrained Energy Efficiency Under MISO Interference Channels

引用

IEEE Transactions on Wireless Communications 2025年第6期24卷 4677-4689页

作者： Lu, Yang Chi, Chong-Yung Ai, Bo Zhong, Zhangdui Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operationand also with the School of Computer Science and Technology Beijing100044 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China

This paper investigates the rate outage constrained (ROC) energy efficiency (EE) under multiple-input single-output (MISO) interference channels, where only channel distribution information (CDI) is available at base stations (BSs). An EE maximization problem is formulated under the constraints of tolerable rate outage probability of each user and the power budget of each BS. Due to the computationally intractable form of the considered problem, two equivalent formulations are derived and four algorithms are proposed, where two are based on the block successive upper bound minimization (BSUM) method and the other two are based on the successive convex approximation (SCA) method. Further, the philosophies behind the BSUM-based algorithms and the SCA-based algorithms are analyzed. Numerical results demonstrate the efficacy of the proposed algorithms, while the BSUM-based algorithms are much more computationally efficient than all the state-of-the-art algorithms, as long as all the coupling requirements on quality of service (like ROC transmission) can be fused into EE. Besides, the impacts of power budget, circuit power and tolerable outage probability on the ROC EE are revealed and discussed. © 2025 IEEE. All rights reserved.

关键词： Budget control

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：