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SSRN 2024年

作者： Liu, Yu Chen Hua, Qiang Chen, Lin Lin Dong, Chun Ru Zhang, Feng Zhang, Yong Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Photovoltaic power generation is a key component of the industrial field, whose power generation efficiency is affected by surface defects of photovoltaic cells. The recent success in defect detection is largely attributed to the widespread application of deep learning detection models. However, because of the various sizes of defects on the photovoltaic cells, it is difficult to effectively detect multi-scale defects, especially small defects. Additionally, a large amount of noise information in images brings more difficulties in extracting distinguishable features. To address these issues, a novel one-stage Multi-Scale Neighborhood Feature Interaction Network (MNFI-Net) is proposed in this study to identify defects of various sizes in complex backgrounds. The MNFI-Net consists of the following components: 1) a Ghost Cross Stage Module (GCSM) that is devised to reduce redundant information;2) a Neighborhood Feature Interaction Model (NFIM), which is used to improve the capability of detecting defects with various size;3) a Global Attention Mechanism (GAM) that is devised to emphasize the key features of the fused feature maps. Furthermore, we introduce a novel Balanced Efficient (BE) loss function, which integrates the efficient iou loss and normalized weighted distance loss. Extensive comparison experiments and ablation studies on public photovoltaic electroluminescence images dataset are performed and the experimental results show that the defect classification and detection results of MNFI-Net in photovoltaic cell images achieve 94.0% precision and 95.5% mAP, outperforming the state-of-the-art methods. The code and proposed models in this study can be accessed at https://***/lyc686/MNFI-Net. © 2024, The Authors. All rights reserved.

关键词： Photovoltaics

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In-Plane Dual-Gated Spin-Valve Device Based on the Zigzag Graphene Nanoribbon

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Physical Review Applied 2020年第4期13卷 044006-044006页

作者： Min Zhou Hao Jin Yanxia Xing Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement Ministry of Education Micronano Centre Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing 100081 China Shenzhen Key Laboratory of Advanced Thin Films and Applications College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 China

Using the nonequilibrium Green’s function combined with density-functional theory, we systematically study the transport properties of the zigzag graphene nanoribbon (ZGNR). When an external transverse electric field is applied, a ZGNR can exhibit half-metallic characteristics with its conductive channel localized at the edge positions. Accordingly, an in-plane dual-gated spin-valve device is proposed. Thanks to its unique design, the proposed device overcomes the bottleneck of current leakage and avoids contact issues. Remarkably, a 100% spin injection efficiency and a giant tunnel magnetoresistance of up to 107 are realized, which represent much better performance than that of traditional magnetic tunnel junctions. In addition, we also explore the effect of architecture reconstruction and impurity doping on spin-polarized transport. It is found that, in general, the tunneling process is hindered if disorder occurs at the conductive edge. By contrast, if we put the deformation or impurities away from the conductive edge, the transport properties may be barely degraded or even improved, depending on the type of disorder. It should be possible to apply these ubiquitous underlying principles to other materials, which could inspire the design of alternative spintronic devices with high performance.

关键词： Edge states Quantum transport Spintronics Graphene Nanoribbon Spin valves Density functional calculations Nonequilibrium Green's function

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The effects of different training modes on the performance of silent speech recognition based on high-density sEMG

The effects of different training modes on the performance o...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Pi, Yao Zhu, Mingxing Yang, Zijian Wang, Xin Wang, Cheng Zhang, Haoshi Wang, Mingjiang Wan, Feng Chen, Shixiong Li, Guanglin Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Shenzhen518055 China School of Information Science and Engineering Wuhan University of Science and Technology Hubei Wuhan430081 China School of Electronics and Information Engineering Harbin Institute of Technology Guangdong Shenzhen518055 China University of Macau Department of Science and Technology China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Guangdong Shenzhen518055 China

ISBN: (纸本)9781665436786

The convolutional neural network (CNN) is frequenctly used in silent speech recognition (SSR) based on surface electromyography (sEMG). Currently, there are two different modes when training the CNN classifier, using the sEMG signals from a single subject as the training datasets and using the mixed signals from multiple subjects as the training datasets. However, it still remains unclear how different training modes affect the performance of the CNN classifier in different classification metrics. In this study, two different training modes were used for the CNN classifier of SSR based on high-density sEMG (HD sEMG) signals. HD sEMG signals collected from six subjects were used to build two different training datasets. The HD sEMG signals from either a single subject or multiple subjects were to train the same CNN model and the performance difference was thoroughly compared in different metrics. The results showed that the CNN model trained from the signals of a single subject showed superior performance with higher average precision, average recall, and average F1 score. It also converged faster and was more stable under different signal conditions. However, it was only suitable for the SSR of the same subject, while the CNN model trained from the signals of multiple subjects showed satisfactiroy performance across all the recruited subjects. This study revealed that the CNN models trained with different training modes performed differently, and therefore the training mode could be taken into consideration in different applications of SSR based on sEMG. © 2021 IEEE.

关键词： Speech recognition

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Skeleton-Embedded Network for action recognition

Skeleton-Embedded Network for action recognition

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Luo, Li Ren, Ziliang Qin, Yong Zhang, Qieshi Gao, Xiangyang Dongguan University of Technology Department of Computer Science Dongguan52300 China Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China The Chinese University of HongKong Hong Kong Hong Kong

ISBN: (纸本)9798350327182

Multimodality-based human action recognition is becoming an increasingly attractive topic as different modalities can provide complementary information. RGB and skeleton data have their pros and limitations for action recognition because they describe the action from distinctive views. Therefore, the fusion of these two modalities using their complementarity is meaningful to action recognition. However, it is difficult to completely utilize the supplementarity of RGB and skeleton data. In this paper, to take advantage of their complementary at the semantic level, A skeleton-embedded network(SE-Net) is proposed by us. The architecture is based on integrating their feature in the network not only a post-hoc fusion. With a sparse sampling strategy, which is efficient for learning using the whole action video. To examine the performance of our method, we conducted experiments on the wildly used datasets, obtaining an accuracy of 86.4% in C-sub and 88.2% in C-view on NTU-RGB+D 60. For NTU-RGB+D 120, the accuracy of 81.3% in C-sub and 83.4% in C-set is obtained. © 2023 IEEE.

关键词： Semantics

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Global small solutions to heat conductive compressible nematic liquid crystal system: Smallness on a scaling invariant quantity

arXiv

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arXiv 2021年

作者： Li, Jinkai Tao, Qiang South China Research Center for Applied Mathematics and Interdisciplinary Studies School of Mathematical Sciences South China Normal University Guangzhou510631 China School of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

In this paper, we consider the Cauchy problem to the three dimensional heat conducting compressible nematic liquid crystal system in the presence of vacuum and with vacuum far fields. Global well-posedness of strong solutions is established under the condition that the scaling invariant quantity (||ρ0||∞ + 1)[||ρ0||3 + (||ρ0||∞ + 1)2(||√ρ0u0||22 + ||∇d0||22)][||∇u0||22 + (||ρ0||∞ + 1)(||√ρ0E0||22 + ||∇2d0||22)] is sufficiently small with the smallness depending only on the parameters appeared in the system. Copyright © 2021, The Authors. All rights reserved.

关键词： Heat conduction

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Performance Evaluation of Deep learning Techniques on Classifying Commercial Sensor-based Auditory Brainstem Response 16

Performance Evaluation of Deep Learning Techniques on Classi...

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16th International Conference on Sensing Technology, ICST 2023

作者： Huang, Kaiyang Wang, Xin He, Yuchao Ji, Junyu Xu, Yangjie Jia, Desheng Zhao, Chunrui Huang, Jinzhu Tao, Yuan Pan, Hongguang Li, Guanglin Chen, Shixiong Guangzhou University of Chinese Medicine School of Medical Information Engineering Guangzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Reliability and Trust University of Luxembourg Interdisciplinary Centre for Security Luxembourg Luxembourg Shenzhen Children's Hospital Department of Otolaryngology Shenzhen China The First Dongguan Affiliated Hospital of Guangdong Medical University Department of Endocrine Dongguan China Peking University Shenzhen Hospital Department of Otorhinolaryngology Shenzhen China

ISBN: (纸本)9798350395341

To objectively evaluate the health condition of the auditory pathway, Auditory Brainstem Response (ABR) is used in the clinic. The clinical diagnosis is concluded by the audiologist according to the morphology and characteristic waves of ABR through manual screening. It is a time-consuming and subjective task that not only reduces the efficiency of the physicians but also introduces subjective error which might cause an incorrect diagnosis. Therefore, it is necessary to propose an objective method that can solve the abovementioned problems. Deep learning (DL) technique develops fast and is widely used in many fields, including biomedical engineering. Hence, DL might help with the objective classification of ABR and release the physicians from the heavy workload. In this paper, we evaluated the classification performance of three commonly used DL methods, namely EEGNet, DeepConvNet and ShallowConvNet, on several metrics. The results showed that on the task of classifying normal and abnormal ABR signals, EEGNet had the best performance, with the accuracy of 0.9995 ± 0.0011, the precision of 0.9995 ± 0.0011, the recall of 0.9995 ± 0.0011, the F1-score of 0.9995 ± 0.0011, and the AUC of 1.0000 ± 0.0000. It is believed that DL can help with objective ABR classification and release the physicians' workload. © 2023 IEEE.

关键词： Biomedical engineering

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A Novel Multi-Stream Informer Used for Lower Extremity Joint Angle Estimation 12

A Novel Multi-Stream Informer Used for Lower Extremity Joint...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2022

作者： Zhou, Xin Zhang, Liming Liu, Jiaqing Ye, Jiancong Wang, Can Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China University of Science and Technology of China China University of Chinese Academy of Sciences China South China University of Technology China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

In lower extremity exoskeleton rehabilitation systems, synergy and proportionality between the human lower extremity and the exoskeleton robot have been a critical goal to pursue. In recent years, changeable deep learning models based on multi-source signals have been used for lower limb continuous motion estimation. During the process, multimode signals are fed into the time processing model as time series. However, there are some deficiencies in this framework, on the one hand, what kind of multi-source combination can be more efficient;on the other hand, most of the existing models need several iterations or network stacking to complete the extraction of temporal information. This study set out to address the above problems, we put forward Multi-Stream Informer (MS-Informer), using Multi-Stream combination and sparse attention mechanisms for lower limb continuous motion estimation. In the experiment, we compare MS-Informer and Temporal Convolutional Network (TCN) and Long Short-term Memory (LSTM) under distinctive multi-source combinations. In summary, MS-informer performs well in multiple evaluations compared with other models. Experimental results illustrate that the MS-Informer is more efficacious in lower limb joint angle estimation and is a promising method for achieving coordinated control of lower limb exoskeletons. © 2022 IEEE.

关键词： Motion estimation

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A differential public PUF design for lightweight authentication 16

A differential public PUF design for lightweight authenticat...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Duan, Shengyu Sai, Gaole School of Computer Engineering and Science Shanghai University Shanghai200444 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (纸本)9783800755899

Physical Unclonable Functions (PUFs) have emerged as a promising primitive to provide a hardware keyless security mechanism for integrated circuit applications. Public PUFs (PPUFs) address the crucial PUF vulnerability of requiring databases to store the secrete reference information. This paper investigates one of the PPUF families, differential PPUF (dPPUF), which exploits complexly interacted logic to create time gap between actual execution and simulation, and to prevent simulation attacks. Conventional dPPUFs have high costs. We observe the repressers have large impact on simulation time, but take a great proportion in current dPPUF structure. We thereby present a represser network with specially designed interconnection and shared logic, to maintain a high circuit complexity and to reduce the area cost. We show the proposed dPPUF has similar resilience over simulation attacks as the conventional design. Area cost can be reduced by up to 44%, and thus the proposed design can be used for lightweight authentication. © VDE VERLAG GMBH.

关键词： Authentication

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Effects of Si substitution on the magnetic and microwave absorbing properties of Y2Fe17-xSix and their composites

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Journal of Alloys and Compounds 2025年 1031卷

作者： Zhang, Pengyu Wang, Yuankang Lin, Zhongchong Sun, Xutao Li, Kewei Qian, Huidong Tang, Shuhan Liang, Zhongyu Yun, Chao Li, Guobao Ma, Xiaobai Yang, Wenyun Wang, Changsheng Yang, Jinbo State Key Laboratory of Artificial Microstructure and Mesoscopic Physics School of Physics Peking University Beijing100871 China Beijing Key Laboratory for Magnetoelectric Materials and Devices Beijing100871 China Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials College of Physics and Energy Fujian Normal University Fuzhou350117 China School of Advanced Manufacturing Fuzhou University Jinjiang362200 China School of Materials Sun Yat-Sen University Shenzhen518107 China State Key Laboratory of Rare Earth Materials Chemistry and Applications College of Chemistry and Molecular Engineering Peking University Beijing100871 China Beijing National Laboratory for Molecular Sciences Beijing100871 China Department of Nuclear Physics China Institute of Atomic Energy Beijing102413 China

Rare-earth transition-metal (R-T) intermetallic compounds with planar magnetocrystalline anisotropy exhibit enhanced high-frequency magnetic properties and superior microwave absorption performance compared to compounds with uniaxial magnetocrystalline anisotropy, due to their higher Snoek limit resulting from high saturation magnetization (Ms) and large anisotropy factor. In this work, we investigated the structure, magnetic, and microwave absorbing properties of Y2Fe17-xSix (0≤x≤2) with planer anisotropy and their paraffin composites. Neutron powder diffraction (NPD) indicates that Y2Fe17-xSix compounds all possess a disordered Th2Ni17-type hexagonal structure as the primary phase, and the preferential order for Si substituting Fe crystallographic sites is: 12k, 6g, 12j1, 12j2, 4f, and 4e. At room temperature (RT), the 57Fe Mössbauer spectra reveal that the average hyperfine field increases with increasing Si content, which is proportional to the average atomic magnetic moment of Fe. Magnetic measurements show that Si substitution significantly raises the Curie temperature (Tc) of Y2Fe17-xSix compounds, while also increasing the Ms at RT. By studying the high-frequency electromagnetic properties of Y2Fe17-xSix/paraffin composites, it was found that the real part of permittivity (ɛ′) at 1 GHz decreases with increasing Si content, reducing by approximately 40% for x=2 compared to x=0. Meanwhile, the real part of permeability (μ′) at 1 GHz initially increases and then decreases with increasing Si content, reaching a maximum at x=1.5. Therefore, Si substitution is advantageous to enhance the impedance matching of the composites, leading to better microwave absorption performance. Among all the compositions, the Y2Fe15Si2/paraffin composite exhibits a minimum reflection loss (RL) of -53.3 dB with a thickness of 3.0 mm, and a maximum effective absorption bandwidth (EAB, RL © 2025 Elsevier B.V.

关键词： Neutron powder diffraction

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Loop closure detection based on improved hybrid deep learning architecture

Loop closure detection based on improved hybrid deep learnin...

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2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Liu, Yuan Xiang, Rong Zhang, Qieshi Ren, Ziliang Cheng, Jun School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi China Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9781728152097

Loop closure detection plays a vital role in the visual Simultaneous Localization and Mapping (SLAM) system. Traditional Bag-of-Visual-Word (BoVW) methods for loop closure detection has been successfully applied to various SLAM systems. The Convolutional Neural Networks (ConvNets) based methods can automatically learn feature representation from original image, and it is more robust to illumination changes. However, these two methods all have some limitations. In order to overcome the shortcomings of the traditional methods extract insufficient features, and the ConvNets based methods unable to adapt complex environment changes, in this paper, we propose an improved hybrid deep learning architecture (HDLA) to generate high-level semantic image features for loop closure detection specifically. This network is based on hybrid ConvNet modified by us to especially cope with robust and real-time feature extraction. We present extensive experiments on three real world datasets to evaluate each of the specific challenges in loop closure detection. The results demonstrate that the proposed method achieves superior performances and provides a feasible solution for loop closure detection. © 2019 IEEE.

关键词： Feature extraction

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