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第37届中国控制会议

作者： Haichuan Tang Kunting Zhang Dingfei Guo Lihao Jia Hong Qiao Yin Tian Cloud Computing Center Chinese Academy of Sciences Beijing Key Laboratory of Research and Application for Robotic Intelligence of “Hand-Eye-Brain” Interaction Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

Fault diagnosis is vital for normal operation of the rotating *** effective and reliable deep learning method known as stacked denoising autoencoder(SDAE) is investigated in this paper,which can extract the features from the pending signals with *** adaptive networks are designed to extract features automatically from time domain data and frequency domain data of motor vibration signal,***,the network parameters of the SDAE are trained to reconstruct the signal features,and clustering results are ***,a classification layer is added to the top layer of the SDAE network for the fault *** is shown that,the diagnosis accuracy with input of vibratory frequency signal is higher than that of time domain *** features extracted by SDAE can represent complex mapping relationships between signal and various running status,and the accuracy is improved comparing with traditional fault diagnosis methods.

关键词： Rotating motor Fault diagnosis SDAE Deep learning

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A Review of Fault Diagnosis for Traction Induction Motor

A Review of Fault Diagnosis for Traction Induction Motor

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第37届中国控制会议

作者： Yin Tian Dingfei Guo Kunting Zhang Lihao Jia Hong Qiao Haichuan Tang Cloud Computing Center Chinese Academy of Sciences Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences University of Chinese Academy of Sciences

With the rapid development of traction motor, the mechanical health monitoring and fault diagnosis field have entered the era of big data. Definite harmonic signals of the line current are located by a popular method known as motor current signature analysis. Different faults of an induction motor such as rotor, stator, bearing, vibration, air gap eccentricity and their different diagnosis techniques are also explored. In fact, the actual fault detection also has a deep development in the artificial *** is truly evident that the scope of this area is vast. Hence, acknowledging the need for future research, this review paper presents a birds eye view on different types of traction induction faults and their diagnostics schemes.

关键词： Traction induction motors fault diagnosis model-based approach current analysis artificial intelligence

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BASIC regenerating code: Binary addition and shift for exact repair

BASIC regenerating code: Binary addition and shift for exact...

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IEEE International Symposium on Information Theory

作者： Hanxu Hou Kenneth W. Shum Minghua Chen Hui Li Peking University Beijing Beijing CN Institute of Network Coding Chinese University of Hong Kong Hong Kong China Department of Information Engineering Chinese University of Hong Kong Hong Kong China Shenzhen Engineering Laboratory of Converged Networks Technology Shenzhen Key Laboratory of Cloud Computing Technology and App. Shenzhen Graduate School Peking University Shenzhen China

ISBN: (纸本)9781479904440

Regenerating code is a class of storage codes that achieve the optimal trade-off between storage capacity and repair bandwidth, which are two important performance metrics in data storage systems. However, existing constructions of regenerating codes rely on expensive computational operations such as finite field multiplication. The high coding and repair complexity limit their applications in large-scale practical storage systems. In this paper, we show that it is possible to achieve the full potential of regenerating codes with low computational complexity. In particular, we propose a new class of regenerating codes, called BASIC codes, that can achieve two specific points (i.e., minimum-bandwidth and minimum-storage regenerating points) on the storage and repair bandwidth trade-off curve, using only binary addition and shift operations in the coding and repair processes. Although in this paper we focus on constructing and analyzing BASIC codes for two specific exact-repair settings, our framework can be generalized to develop BASIC codes for more general exact- and functional-repair regenerating codes.

关键词： Maintenance engineering Encoding Polynomials Vectors Symmetric matrices Bandwidth Computational complexity

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General Greedy De-bias Learning

arXiv

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arXiv 2021年

作者： Han, Xinzhe Wang, Shuhui Su, Chi Huang, Qingming Tian, Qi The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China SmartMore Beijing100085 China Huawei Cloud & AI Shenzhen518129 China

Neural networks often make predictions relying on the spurious correlations from the datasets rather than the intrinsic properties of the task of interest, facing with sharp degradation on out-of-distribution (OOD) test data. Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios. Others implicitly identify the dataset bias by special design low capability biased models or losses, but they degrade when the training and testing data are from the same distribution. In this paper, we propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and base model. The base model is encouraged to focus on examples that are hard to solve with biased models, thus remaining robust against spurious correlations in the test stage. GGD largely improves models' OOD generalization ability on various tasks, but sometimes over-estimates the bias level and degrades on the in-distribution test. We further re-analyze the ensemble process of GGD and introduce the Curriculum Regularization inspired by curriculum learning, which achieves a good trade-off between in-distribution (ID) and out-of-distribution performance. Extensive experiments on image classification, adversarial question answering, and visual question answering demonstrate the effectiveness of our method. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge. Codes are available at https://***/GeraldHan/GGD. Copyright © 2021, The Authors. All rights reserved.

关键词： Curricula

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Stream Attention Based U-Net for L3DAS23 Challenge

Stream Attention Based U-Net for L3DAS23 Challenge

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Honglong Wang Yanjie Fu Junjie Li Meng Ge Longbiao Wang Xinyuan Qian Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China The Chinese University of Hong Kong Shenzhen China Huiyan Technology (Tianjin) Co. Ltd Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

Machine learning applications of 3D audio are gaining increasing interest in recent years. In this paper, we propose a stream attention based U-Net to remove background noise and reverberation based on ICASSP Signal Processing Grand Challenge 2023: L3DAS23 Challenge 1 Audio-only track task1 3D Speech Enhancement. Results show that proposed method achieves superior performance than the official baseline model.

关键词： Adaptation models Solid modeling Three-dimensional displays Machine learning Speech enhancement Signal processing 3D audio

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An end-to-end foreground-aware network for person re-identification

arXiv

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arXiv 2019年

作者： Liu, Yiheng Zhou, Wengang Liu, Jianzhuang Qi, Guojun Tian, Qi Li, Houqiang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Noah’s Ark Lab Huawei Technologies Company Limited Shenzhen518129 China Huawei Cloud EI Product Department Cloud & AI Huawei Technologies

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region that inevitably contains the scene background, which incurs ambiguity to distinguish different pedestrians and degrades the accuracy. Thus, we propose an end-to-end foreground-aware network to discriminate against the foreground from the background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for the foreground, we introduce the camera ID of each pedestrian image for background modeling. The foreground branch and the background branch are optimized collaboratively. By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to backgrounds, which greatly reduces the negative impact of changing backgrounds on pedestrian matching across different camera views. Notably, in contrast to existing methods, our approach does not require an additional dataset to train a human landmark detector or a segmentation model for locating the background regions. The experimental results conducted on three challenging datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of our approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

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Noncollinearity-modulated Electronic Properties of Monolayer CrI3

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Physical Review Applied 2019年第5期11卷 054042-054042页

作者： Lingling Ren Qian Liu Pengxiang Xu Zhicheng Zhong Li Yang Zhe Yuan Ke Xia Center for Advanced Quantum Studies and Department of Physics Beijing Normal University Beijing 100875 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518005 China Key Laboratory of Magnetic Materials and Devices & Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo 315201 China Department of Physics and Institute of Materials Science and Engineering Washington University in St. Louis St. Louis Missouri 63130 USA Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology Shenzhen 518055 China

Introducing noncollinear magnetization into a monolayer CrI3 is proposed to be an effective approach to modulate the local electronic properties of the two-dimensional (2D) magnetic material. Using first-principles calculation, we illustrate that both the conduction and valence bands in the monolayer CrI3 are lowered down by spin spiral states. The distinct electronic structure of the monolayer noncollinear CrI3 can be applied in nanoscale functional devices. As a proof of concept, we show that a magnetic domain wall can form a one-dimensional conducting channel in the 2D semiconductor via proper gating. This conducting channel is approximately 7 nm wide and has a carrier concentration of 1013–1014cm−2.

关键词： Domain walls Helicoidal magnetic texture Magnetic anisotropy Spin waves 2-dimensional systems Devices Magnetic semiconductors Nanowires

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HL-LSM: A LSM-Tree Combined with Read Hotness and Learned Index

HL-LSM: A LSM-Tree Combined with Read Hotness and Learned In...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tan, Hong Li, Hui Wang, Xiaopeng Huang, Weixiang Huang, Runhuai Deng, Shaowen Wu, Jianping Peking University Shenzhen Graduate School Shenzhen China Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology China FuYao Univ. of Sci. &tech School of Computer & Ai Fuzhou China China Mobile Internet Co. Ltd Capability & Platform Business Dept Guangzhou China China Telecom Co. Ltd China Telecom Cloud Computing Beijing China China Unicom Global Limited Hong Kong China Communications Services Construction Co. Ltd GuangZhou China

ISBN: (纸本)9798350362480

Log-Structured Merge Tree (LSM-Tree) is widely employed in key-value (kv) store owing to its exceptional performance in write-intensive workloads. LSMTree appends kv pairs to memory initially, and only when the memory capacity reaches a threshold does it flush the data to disk. However, in comparison to write, read operations often entail more disk access, resulting in relatively a slower response speed. Although LSM-Tree utilizes an index structure to expedite read operations, its performance enhancement remains limited. In addition, LSM-Tree fails to consider the read hotness of each sorted string table (SSTable) during compaction, leading to significant fluctuations in performance. This paper introduces HL-LSM, a novel LSM-Tree. HL-LSM utilizes learned index to replace original index to save system overhead and improve read performance. Moreover, a lightweight read hotness statistics is devised to optimize the compaction and assess the feasibility of each SSTable in constructing a learned index. In order to evaluate the performance of HL-LSM, we constructed a system prototype, HLDB, based on Wisckey. Experiment demonstrates that in read-intensive scenarios, HLDB is able to reduce the overall system latency by nearly 10% compared to Wisckey. Furthermore, it was observed that the optimization effect of HL-LSM improves as the variance of hotness value distribution increases. © 2024 IEEE.

关键词： Compaction

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Stable Attribute Group Editing for Reliable Few-shot Image Generation

arXiv

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arXiv 2023年

作者： Ding, Guanqi Han, Xinzhe Wang, Shuhui Wu, Shuzhe Jin, Xin Tu, Dandan Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China Huawei Cloud EI Innovation Lab China

Few-shot image generation aims to generate data of an unseen category based on only a few samples. Apart from basic content generation, a bunch of downstream applications hopefully benefit from this task, such as low-data detection and few-shot classification. To achieve this goal, the generated images should guarantee category retention for classification beyond the visual quality and diversity. In our preliminary work, we present an "editing-based" framework Attribute Group Editing (AGE) for reliable few-shot image generation, which largely improves the performance compared with existing methods that require re-training a GAN with limited data. Nevertheless, AGE's performance on downstream classification is not as satisfactory as expected. This paper investigates the class inconsistency problem and proposes Stable Attribute Group Editing (SAGE) for more stable class-relevant image generation. Different from AGE which directly edits from a one-shot image, SAGE takes use of all given few-shot images and estimates a class center embedding based on the category-relevant attribute dictionary. Meanwhile, according to the projection weights on the category-relevant attribute dictionary, we can select category-irrelevant attributes from the similar seen categories. Consequently, SAGE injects the whole distribution of the novel class into StyleGAN's latent space, thus largely remains the category retention and stability of the generated images. Going one step further, we find that class inconsistency is a common problem in GAN-generated images for downstream classification. Even though the generated images look photo-realistic and requires no category-relevant editing, they are usually of limited help for downstream classification. We systematically discuss this issue from both the generative model and classification model perspectives, and propose to boost the downstream classification performance of SAGE by enhancing the pixel and frequency components. Extensive experime

关键词： Generative adversarial networks

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Selective-Opening Security for Public-key Encryption in the Presence of Parameter Subversion

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Security and Communication Networks 2021年第1期2021卷

作者： Kang, Burong Huang, Zhengan Zhang, Lei Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Guangxi Key Laboratory of Cryptography and Information Security Guangxi Guilin541000 China Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education East China Normal University Shanghai200062 China Peng Cheng Laboratory Shenzhen518000 China

In public-key encryption (PKE), ciphertexts received by a receiver may be possibly correlated and the security of a PKE relies on honestly generated system parameters. Security against selective opening attacks (SOA) for PKE guarantees that even when an attacker has broken into a subset of honestly generated ciphertexts and opened them (i.e., seeing plaintexts and random bits), the unopened ciphertexts remain secure. While security against parameter subversion attacks (PSA) for PKE requires that even when the public system parameters are maliciously generated, a PKE scheme should be secure. In this paper, we initiate the study of PKE secure against both SOA and PSA. To capture SOA and PSA simultaneously, we formulate a new security notion called indistinguishability under selective opening attacks and parameter subversion attacks (IND-SO-PSA). Further, we define the lossy trapdoor function and all-but-many lossy trapdoor function in the presence of PSA (LTF-PSA and ABM-LTF-PSA correspondingly) and propose an instantiation with the efficiently-embeddable group (EG). Applying these new primitives, we construct a PKE scheme that is proven to be IND-SO-PSA secure. © 2021 Burong Kang et al.

关键词： Public key cryptography

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