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学者姓名:辛景民

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Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers EI Scopus CSSCI-E SCIE
期刊论文 | 2021 , 6 (3) , 412-426 | IEEE Transactions on Sustainable Computing
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Abstract :

The massive and irregular load surges challenge datacenter power infrastructures. As a result, power mismatching between supply and demand has emerged as a crucial availability issue in modern datacenters which are either under-provisioned or powered by intermittent power sources. Recent proposals have employed energy storage devices such as the uninterruptible power supply(UPS) to address this issue. However, current approaches lack the capacity of efficiently handling the irregular and unpredictable power mismatches. In this paper, we propose Hybrid and Hierarchical Energy Buffering (HHEB), a novel heterogeneous and adaptive scheme that could enable various energy storage devices(ESDs) to be efficiently integrated into existing datacenters for dynamically dealing with power mismatches. Our techniques exploit the diverse characteristics of different ESDs and intelligent load assignment algorithms to improve the dependability and efficiency of datacenter power systems. We evaluate the HHEB design with a prototype. Compared with a homogenous battery energy buffering system, HHEB could improve energy efficiency by 39.7%, extend UPS lifetime by 4.7X, promote energy availability by 3.2X, reduce system downtime by 41% and effectively improve the energy availability of various energy buffers in different hierarchies. It allows datacenters to adapt to various power supply anomalies, thereby improving operational efficiency, dependability and availability.

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GB/T 7714 Longjun Liu , Hongbin Sun , Chao Li et al. Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers [J]. | IEEE Transactions on Sustainable Computing , 2021 , 6 (3) : 412-426 .
MLA Longjun Liu et al. "Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers" . | IEEE Transactions on Sustainable Computing 6 . 3 (2021) : 412-426 .
APA Longjun Liu , Hongbin Sun , Chao Li , Tao Li , Jingmin Xin , Nanning Zheng . Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers . | IEEE Transactions on Sustainable Computing , 2021 , 6 (3) , 412-426 .
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China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program EI SCIE
期刊论文 | 2021 , 13 (2) , 6-19 | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
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Abstract :

In this article, we introduce the Intelligent Vehicles Future Challenge of China (IVFC), which has lasted 12 years. Some key features of the tests and a few interesting findings of IVFC are selected and presented. Through the IVFCs held between 2009 and 2020, we gradually established a set of theories, methods, and tools to collect tests' data and efficiently evaluate the performance of autonomous vehicles so that we could learn how to improve both the autonomous vehicles and the testing system itself.

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GB/T 7714 Wang, Fei-Yue , Zheng, Nan-Ning , Li, Li et al. China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program [J]. | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2021 , 13 (2) : 6-19 .
MLA Wang, Fei-Yue et al. "China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program" . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 13 . 2 (2021) : 6-19 .
APA Wang, Fei-Yue , Zheng, Nan-Ning , Li, Li , Xin, Jingmin , Wang, Xiao , Xu, Linhai et al. China's 12-Year Quest of Autonomous Vehicular Intelligence: The Intelligent Vehicles Future Challenge Program . | IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE , 2021 , 13 (2) , 6-19 .
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Efficient Repair Analysis Algorithm Exploration for Memory With Redundancy and In-Memory ECC EI SCIE
期刊论文 | 2021 , 70 (5) , 775-788 | IEEE TRANSACTIONS ON COMPUTERS
WoS CC Cited Count: 1
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Abstract :

In-memory error correction code (ECC) is a promising technique to improve the yield and reliability of high density memory design. However, the use of in-memory ECC poses a new problem to memory repair analysis algorithm, which has not been explored before. This article first makes a quantitative evaluation and demonstrates that the straightforward algorithms for memory with redundancy and in-memory ECC have serious deficiency on either repair rate or repair analysis speed. Accordingly, an optimal repair analysis algorithm that leverages preprocessing/filter algorithms, hybrid search tree, and depth-first search strategy is proposed to achieve low computational complexity and optimal repair rate in the meantime. In addition, a heuristic repair analysis algorithm that uses a greedy strategy is proposed to efficiently find repair solutions. Experimental results demonstrate that the proposed optimal repair analysis algorithm can achieve optimal repair rate and increase the repair analysis speed by up to 10(5) x compared with the straightforward exhaustive search algorithm. The proposed heuristic repair analysis algorithm is approximately 28 percent faster than the proposed optimal algorithm, at the expense of 5.8 percent repair rate loss.

Keyword :

reliability Memory repair yield repair analysis algorithm in-memory ECC

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GB/T 7714 Lv, Minjie , Sun, Hongbin , Xin, Jingmin et al. Efficient Repair Analysis Algorithm Exploration for Memory With Redundancy and In-Memory ECC [J]. | IEEE TRANSACTIONS ON COMPUTERS , 2021 , 70 (5) : 775-788 .
MLA Lv, Minjie et al. "Efficient Repair Analysis Algorithm Exploration for Memory With Redundancy and In-Memory ECC" . | IEEE TRANSACTIONS ON COMPUTERS 70 . 5 (2021) : 775-788 .
APA Lv, Minjie , Sun, Hongbin , Xin, Jingmin , Zheng, Nanning . Efficient Repair Analysis Algorithm Exploration for Memory With Redundancy and In-Memory ECC . | IEEE TRANSACTIONS ON COMPUTERS , 2021 , 70 (5) , 775-788 .
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DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud EI
会议论文 | 2021 , 2021-July | 2021 International Joint Conference on Neural Networks, IJCNN 2021
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Abstract :

Object proposals generated based on sparse points from the raw point cloud have been widely used in 3D object detection. However, following the above scheme, most existing proposal generators have two problems, one is that the features for proposal generation constrain the detection performance by containing insufficient information; the other is that the sparse points obtained from the raw point cloud are misaligned with their corresponding objects in location and feature aspects. In this paper, we propose a dense-to-sparse proposal generation approach for 3D object detection, which can deal with the two problems simultaneously. Our approach utilizes the 3D CNN backbone to output dense features as a supplement to the original sparse point features for proposal generation. Besides, an object-aware feature pooling module is designed to address the misalignment between sparse points and corresponding objects. Experiments on the KITTI dataset show that our method outperforms the existing sparse-style methods and other published state-of-the-art methods. © 2021 IEEE.

Keyword :

Object detection Digital signal processing Object recognition Computer vision

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GB/T 7714 Yan, Xinrui , Huang, Yuhao , Chen, Shitao et al. DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud [C] . 2021 .
MLA Yan, Xinrui et al. "DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud" . (2021) .
APA Yan, Xinrui , Huang, Yuhao , Chen, Shitao , Nan, Zhixiong , Xin, Jingmin , Zheng, Nanning . DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud . (2021) .
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A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms EI SCIE
期刊论文 | 2021 , 463 , 212-225 | NEUROCOMPUTING
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Abstract :

Object detection and semantic segmentation are two fundamental techniques for Intelligent Vehicles (IV) and Advanced Driving Assistance System (ADAS). Motivated by recent studies demonstrating that object detection and semantic segmentation are two highly-correlated tasks, this paper handles the problem of joint object detection and semantic segmentation in traffic scenes. Existing methods perform the joint object detection and semantic segmentation by sharing the same backbone network, but always ignore the interactive connection between the subdividing detection branch and segmentation branch, leading to the insufficient interaction between the two branches. Considering this situation, this paper proposes a joint object detection and semantic segmentation model with the cross-attention and inner-attention mechanisms. The cross-attention mechanism enables to build up the essential interaction between the subdividing detection branch and segmentation branch to fully make use of their correlation. In addition, the inner-attention contributes to strengthening the representations of feature maps in the model. Given an image, an encoder-decoder network is firstly used to extract initial feature maps. Then, the inner-attention mechanism is applied to strengthen the initial feature maps to obtain segmentation feature maps. Subsequently, the cross-attention mechanism utilizes the segmentation feature maps to guide the generation of object detection feature maps. Finally, the semantic segmentation is performed on the segmentation feature maps and object detection is performed on the detection feature maps. In the experiments, two well-known public traffic datasets are used to evaluate our model. Our model achieves the highest performance in comparison with several recently-proposed methods. In addition, some ablation studies are conducted to evaluate the proposed inner-attention and cross-attention mech-anisms, and experiment results validate their effectiveness. (c) 2021 Elsevier B.V. All rights reserved.

Keyword :

Joint detection and segmentation Attention Inner-attention Cross-attention

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GB/T 7714 Nan, Zhixiong , Peng, Jizhi , Jiang, Jingjing et al. A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms [J]. | NEUROCOMPUTING , 2021 , 463 : 212-225 .
MLA Nan, Zhixiong et al. "A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms" . | NEUROCOMPUTING 463 (2021) : 212-225 .
APA Nan, Zhixiong , Peng, Jizhi , Jiang, Jingjing , Chen, Hui , Yang, Ben , Xin, Jingmin et al. A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms . | NEUROCOMPUTING , 2021 , 463 , 212-225 .
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Subspace-based algorithms for localization and tracking of multiple near-field sources EI SCIE Scopus
期刊论文 | 2021 , 6 (3) , 412-426 | IEEE Transactions on Sustainable Computing
WoS CC Cited Count: 16
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Abstract :

In this paper, we investigate the problems of estimating and tracking the location parameters (i.e., directions-of-arrival (DOAs) and ranges] of multiple near-field (NF) narrow-hand sources impinging on a symmetric uniform linear array, and a simple subspace-based algorithm for localization of NF sources (SALONS) is presented, where the computationally burdensome eigen-decomposition and spectrum peak searching are avoided. In the SALONS, the DOAs and ranges are estimated separately with a one-dimensional subspace-based estimation technique, where the null spaces are obtained through the linear operation of the correlation matrices formed from the antidiagonal elements of the noiseless array covariance matrix, and the estimated DOAs and ranges are automatically paired without any additional procedure. Then the statistical analysis of the presented batch SALONS is studied, and the asymptotic mean-squared-error expressions of the estimated DOAs and ranges are derived. Furthermore, an online algorithm is developed for tracking the multiple moving NF sources with crossover points on their trajectories. The effectiveness and the theoretical analysis of the presented algorithms are verified through numerical examples, and the simulation results show that the proposed algorithms provide good estimation and tracking performance for DOAs and show satisfactory estimation and tracking performance for ranges.

Keyword :

Linear operation near-field source localization uniform linear array

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GB/T 7714 Longjun Liu , Hongbin Sun , Chao Li et al. Subspace-based algorithms for localization and tracking of multiple near-field sources [J]. | IEEE Transactions on Sustainable Computing , 2021 , 6 (3) : 412-426 .
MLA Longjun Liu et al. "Subspace-based algorithms for localization and tracking of multiple near-field sources" . | IEEE Transactions on Sustainable Computing 6 . 3 (2021) : 412-426 .
APA Longjun Liu , Hongbin Sun , Chao Li , Tao Li , Jingmin Xin , Nanning Zheng . Subspace-based algorithms for localization and tracking of multiple near-field sources . | IEEE Transactions on Sustainable Computing , 2021 , 6 (3) , 412-426 .
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A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes CPCI-S
会议论文 | 2020 | International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)
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Abstract :

Object detection and semantic segmentation are two fundamental techniques of various applications in the fields of Intelligent Vehicles (IV) and Advanced Driving Assistance System (ADAS). Early studies separately handle these two problems. In this paper, inspired by some recent works, we propose a deep neural network model for joint object detection and semantic segmentation. Given an image, an encoder-decoder convolution network extracts a set of feature maps, these feature maps are shared by the detection branch and the segmentation branch to jointly carry out the object detection and semantic segmentation. In the detection branch, we design a PriorBox initialization mechanism to propose more object candidates. In the segmentation branch, we use the multi-scale atrous convolution to explore the global and local semantic information in traffic scenes. Benefiting from the PriorBox Initialization Mechanism (PBIM) and Multi-Scale Atrous Convolution (MSAC), our model presents the competitive performance. In the experiments, we widely compare with several recently-proposed methods on the public Cityscapes dataset, achieving the highest accuracy. In addition, to verify the robustness and generalization of our model, the extension experiments are also conducted on the well-known VOC2012 dataset.

Keyword :

semantic segmentation traffic scenes object detection

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GB/T 7714 Peng, Jizhi , Nan, Zhixiong , Xu, Linhai et al. A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes [C] . 2020 .
MLA Peng, Jizhi et al. "A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes" . (2020) .
APA Peng, Jizhi , Nan, Zhixiong , Xu, Linhai , Xin, Jingmin , Zheng, Nanning . A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes . (2020) .
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A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes EI Scopus
会议论文 | 2020 | 2020 International Joint Conference on Neural Networks, IJCNN 2020
SCOPUS Cited Count: 1
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Abstract :

Object detection and semantic segmentation are two fundamental techniques of various applications in the fields of Intelligent Vehicles (IV) and Advanced Driving Assistance System (ADAS). Early studies separately handle these two problems. In this paper, inspired by some recent works, we propose a deep neural network model for joint object detection and semantic segmentation. Given an image, an encoder-decoder convolution network extracts a set of feature maps, these feature maps are shared by the detection branch and the segmentation branch to jointly carry out the object detection and semantic segmentation. In the detection branch, we design a PriorBox initialization mechanism to propose more object candidates. In the segmentation branch, we use the multi-scale atrous convolution to explore the global and local semantic information in traffic scenes. Benefiting from the PriorBox Initialization Mechanism (PBIM) and Multi-Scale Atrous Convolution (MSAC), our model presents the competitive performance. In the experiments, we widely compare with several recently-proposed methods on the public Cityscapes dataset, achieving the highest accuracy. In addition, to verify the robustness and generalization of our model, the extension experiments are also conducted on the well-known VOC2012 dataset. © 2020 IEEE.

Keyword :

Image segmentation Object recognition Neural networks Convolution Advanced driver assistance systems Semantics Object detection Deep neural networks

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GB/T 7714 Peng, Jizhi , Nan, Zhixiong , Xu, Linhai et al. A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes [C] . 2020 .
MLA Peng, Jizhi et al. "A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes" . (2020) .
APA Peng, Jizhi , Nan, Zhixiong , Xu, Linhai , Xin, Jingmin , Zheng, Nanning . A Deep Model for Joint Object Detection and Semantic Segmentation in Traffic Scenes . (2020) .
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Gridless sparsity-based localization for near-field sources with symmetric linear array EI Scopus
会议论文 | 2020 , 2020-June | 11th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2020
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Abstract :

In this paper, we investigate the problem of estimating the directions-of-arrival (DOAs) and ranges of multiple near-field narrowband sources impinging on a symmetric uniform linear array (ULA). By forming a Toeplitz-like correlation matrix from the anti-diagonal elements of the array covariance matrix, a convex optimization problem for the resultant Toeplitz-like matrix reconstruction is established and further a gridless sparsity-based localization for near-field sources is proposed. The DOAs can then be retrieved by using the recovered correlation matrix according to root-MUSIC or Vandermonde decomposition theorem. Additionally, the ranges are obtained through a subspace-based estimator with the corresponding estimated DOAs, while the association of the estimated DOAs and ranges are completed at the same time. Finally, the numerical examples are provided to substantiate the performance of our proposed method, and the simulation results demonstrate that the proposed method provides remarkable and satisfactory estimation performance. © 2020 IEEE.

Keyword :

Covariance matrix Domain decomposition methods Array processing Convex optimization Numerical methods

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GB/T 7714 Zuo, Weiliang , Xin, Jingmin , Xiao, Tong et al. Gridless sparsity-based localization for near-field sources with symmetric linear array [C] . 2020 .
MLA Zuo, Weiliang et al. "Gridless sparsity-based localization for near-field sources with symmetric linear array" . (2020) .
APA Zuo, Weiliang , Xin, Jingmin , Xiao, Tong , Zheng, Nanning , Sano, Akira . Gridless sparsity-based localization for near-field sources with symmetric linear array . (2020) .
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Lymph Node Metastasis Classification Based on Semi-Supervised Multi-View Network EI Scopus
会议论文 | 2020 , 675-680 | 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
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Lymphatic metastasis is one of the most common proliferation pathways of thyroid carcinoma. Accurate diagnosis of lymph nodes is of great significance to surgical planning and prognosis. Due to the continuous development of deep learning recently, computer-aided diagnosis (CAD) systems for thyroid cancer have made considerable progress, but the research on the effective diagnosis of lymphatic metastasis remains insufficient. Focusing on this issue, we propose a semi-supervised multi-view network to diagnose lymph node metastasis, which combines coarse-view and fine-view to obtain a more comprehensive description. This method consists of three parts as follows: 1) joint probabilistic labels of the nodule partition information are generated by fuzzy clustering and perform semi-supervised learning on coarse-view with real labels; 2) an attention mechanism based network for fine-view is designed to capture various differentiated local features in a pyramid manner; 3) the two parts are then combined to extract global and local features more effectively to derive more accurate diagnostic reasoning. Especially, the introduction of fuzzy logic greatly reduces the impact of the uncertainty of the generated labels, thereby ensuring the effectiveness of the pseudo-labels. Extensive experiments on our collected dataset demonstrate that the proposed method is more efficient than other state-of-the-art methods. © 2020 IEEE.

Keyword :

Pathology Deep learning Semi-supervised learning Computer aided instruction Computer aided diagnosis Bioinformatics Learning systems Fuzzy logic

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GB/T 7714 Luo, Yiwen , Xin, Jingmin , Liu, Sijie et al. Lymph Node Metastasis Classification Based on Semi-Supervised Multi-View Network [C] . 2020 : 675-680 .
MLA Luo, Yiwen et al. "Lymph Node Metastasis Classification Based on Semi-Supervised Multi-View Network" . (2020) : 675-680 .
APA Luo, Yiwen , Xin, Jingmin , Liu, Sijie , Feng, Junqin , Ruan, Litao , Cui, Wei et al. Lymph Node Metastasis Classification Based on Semi-Supervised Multi-View Network . (2020) : 675-680 .
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