- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Analysis of High-Temperature Carrier Transport Mechanisms for High Al-Content Al <sub/>0.6</sub> Ga <sub/>0.4</sub> N MSM Photodetectors
摘要： We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CANDECOMP/PARAFAC (CP)-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student-t distribution that associates an individual hyperparameter with each element independently. For model inference under a fully Bayesian treatment, which can effectively prevent the overfitting problem and scales linearly with data size. In contrast to existing related works, our method can perform model selection automatically and implicitly without the need of tuning parameters. More specifically, it can discover the groundtruth of CP rank and automatically adapt the sparsity inducing priors to various types of outliers. In addition, the tradeoff between the low-rank approximation and the sparse representation can be optimized in the sense of maximum model evidence. The extensive experiments and comparisons with many state-of-the-art algorithms on both synthetic and real-world data sets demonstrate the superiorities of our method from several perspectives.
关键词： tensor factorization,robust factorization,tensor completion,video background modeling,variational Bayesian (VB) inference,Rank determination
更新于2025-09-23 15:21:01
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[IEEE 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) - Mangalore, India (2018.9.6-2018.9.8)] 2018 4th International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) - A 50?? CPW-FED Rhombus Shaped Patch Antenna Using Rightangled Isosceles Triangle Fractal
摘要： Short-term traf?c prediction plays a critical role in many important applications of intelligent transportation systems such as traf?c congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in traf?c data. In this paper, we present a novel short-term traf?c ?ow prediction approach based on dynamic tensor completion (DTC), in which the traf?c data are represented as a dynamic tensor pattern, which is able capture more information of traf?c ?ow than traditional methods, namely, temporal variabilities, spatial characteristics, and multimode periodicity. A DTC algorithm is designed to use the multimode information to forecast traf?c ?ow with a low-rank constraint. The proposed method is evaluated on real-world data sets and compared with other state-of-the-art methods, and the ef?cacy of the proposed approach is validated on the experiments of traf?c ?ow prediction, particularly when dealing with incomplete traf?c data.
关键词： missing data,dynamic tensor completion,Short-term traf?c ?ow prediction,multi-mode information
更新于2025-09-23 15:19:57
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[IEEE 2019 International Conference on Communication and Electronics Systems (ICCES) - Coimbatore, India (2019.7.17-2019.7.19)] 2019 International Conference on Communication and Electronics Systems (ICCES) - Saturation Optimization and Extrinsic Timing Analysis for Optically Controlled GFET
摘要： We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CANDECOMP/PARAFAC (CP)-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student-t distribution that associates an individual hyperparameter with each element independently. For model inference under a fully Bayesian treatment, which can effectively prevent the overfitting problem and scales linearly with data size. In contrast to existing related works, our method can perform model selection automatically and implicitly without the need of tuning parameters. More specifically, it can discover the groundtruth of CP rank and automatically adapt the sparsity inducing priors to various types of outliers. In addition, the tradeoff between the low-rank approximation and the sparse representation can be optimized in the sense of maximum model evidence. The extensive experiments and comparisons with many state-of-the-art algorithms on both synthetic and real-world data sets demonstrate the superiorities of our method from several perspectives.
关键词： tensor factorization,video background modeling,variational Bayesian (VB) inference,tensor completion,Rank determination,robust factorization
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Sequential Application of Static and Dynamic Mechanical Stresses for Electrical Isolation of Cell Cracks
摘要： Short-term traf?c prediction plays a critical role in many important applications of intelligent transportation systems such as traf?c congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in traf?c data. In this paper, we present a novel short-term traf?c ?ow prediction approach based on dynamic tensor completion (DTC), in which the traf?c data are represented as a dynamic tensor pattern, which is able capture more information of traf?c ?ow than traditional methods, namely, temporal variabilities, spatial characteristics, and multimode periodicity. A DTC algorithm is designed to use the multimode information to forecast traf?c ?ow with a low-rank constraint. The proposed method is evaluated on real-world data sets and compared with other state-of-the-art methods, and the ef?cacy of the proposed approach is validated on the experiments of traf?c ?ow prediction, particularly when dealing with incomplete traf?c data.
关键词： missing data,dynamic tensor completion,Short-term traf?c ?ow prediction,multi-mode information
更新于2025-09-19 17:13:59
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[IEEE 2018 Asia Communications and Photonics Conference (ACP) - Hangzhou (2018.10.26-2018.10.29)] 2018 Asia Communications and Photonics Conference (ACP) - Significant OSNR Improvement in Intensity Modulation and Coherent Detection Metro Links Enabled by Advanced Nonlinear DSP
摘要： In recent years, low-rank based tensor completion, which is a higher order extension of matrix completion, has received considerable attention. However, the low-rank assumption is not sufficient for the recovery of visual data, such as color and 3D images, when the ratio of missing data is extremely high. In this paper, we consider “smoothness” constraints as well as low-rank approximations and propose an efficient algorithm for performing tensor completion that is particularly powerful regarding visual data. The proposed method admits significant advantages, owing to the integration of smooth PARAFAC decomposition for incomplete tensors and the efficient selection of models in order to minimize the tensor rank. Thus, our proposed method is termed as “smooth PARAFAC tensor completion (SPC).” In order to impose the smoothness constraints, we employ two strategies, total variation (SPC-TV) and quadratic variation (SPC-QV), and invoke the corresponding algorithms for model learning. Extensive experimental evaluations on both synthetic and real-world visual data illustrate the significant improvements of our method, in terms of both prediction performance and efficiency, compared with many state-of-the-art tensor completion methods.
关键词： low-rank tensor approximation,PARAFAC model,CP model,smoothness,quadratic variation,total variation (TV),Tensor completion for images
更新于2025-09-16 10:30:52
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[IEEE 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC) - Fukuoka, Japan (2019.7.7-2019.7.11)] 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC) - How to Establish a Sustainable Ecosystem for Photonic Integrated Circuits? What are Major Hurdles to Overcome?
摘要： Short-term traf?c prediction plays a critical role in many important applications of intelligent transportation systems such as traf?c congestion control and smart routing, and numerous methods have been proposed to address this issue in the literature. However, most, if not all, of them suffer from the inability to fully use the rich information in traf?c data. In this paper, we present a novel short-term traf?c ?ow prediction approach based on dynamic tensor completion (DTC), in which the traf?c data are represented as a dynamic tensor pattern, which is able capture more information of traf?c ?ow than traditional methods, namely, temporal variabilities, spatial characteristics, and multimode periodicity. A DTC algorithm is designed to use the multimode information to forecast traf?c ?ow with a low-rank constraint. The proposed method is evaluated on real-world data sets and compared with other state-of-the-art methods, and the ef?cacy of the proposed approach is validated on the experiments of traf?c ?ow prediction, particularly when dealing with incomplete traf?c data.
关键词： missing data,dynamic tensor completion,Short-term traf?c ?ow prediction,multi-mode information
更新于2025-09-16 10:30:52