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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Wang, Haolin Xu, Wei Li, Zezhao Yang, Yanni Huang, Zhijian Shandong University School of Computer Science and Technology Qingdao China National Key Laboratory of Science and Technology on Information System Security China

ISBN: (纸本)9798331515966

Two-dimensional vibration measurement is essential for monitoring structure and machine health, especially providing more information than those 1D solutions. However, most existing 2D vibration monitoring methods require multi-dimension sensors, which can be costly for large-scale deployment. In this paper, we propose m^2 Vib, the first work that can realize accurate 2D vibration measurement using a single mmWave radar. Our system is achieved by creating a virtual radar with non-line-of-sight (NLoS) signals from a nearby reflector (e.g., wall) to offer the 2nd-dimension vibration observations. We further establish a geometric model to unveil the relationship between 2D vibration displacement and mmWave signal. Such an idea still encounters challenges from significantly attenuated NLoS signals and low-resolution geometric parameters from the commercial mmWave radar. Thus, we design a new retroreflector to enhance NLoS signals and advanced signal processing methods to obtain fine-grained geometric parameters. Extensive experimental results show that we can achieve sub-mm level 2D vibration measurement with an average vibration displacement error of 62.2 um and frequency error of 0.01 Hz in the X-axis and Y-axis. © 2024 IEEE.

关键词： Vibration measurement

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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Weighted sum power maximization for STAR-RIS-aided SWIPT systems with nonlinear energy harvesting

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science China(information sciences) 2024年第10期67卷 343-358页

作者： Weiping SHI Cunhua PAN Feng SHU Yongpeng WU Jiangzhou WANG Yongqiang BAO Jin TIAN School of Network and Communication Nanjing Vocational College of Information Technology National Mobile Communications Research Laboratory Southeast University School of Information and Communication Engineering Hainan University School of Electronic and Optical Engineering Nanjing University of Science and Technology Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Jiao Tong University School of Engineering University of Kent School of Information and Communication Engineering Nanjing Institute of Technology Engineering School of Networks and Telecommunications Jinling Institute of Technology

The conventional reconfigurable intelligent surface(RIS) is limited to reflecting incident signals,thereby imposing constraints on the placement of the transmitter and receiver, which hinders achieving comprehensive signal coverage across an entire area. This paper investigates a simultaneously transmitting and reflecting(STAR)-RIS-aided simultaneous wireless information and power transfer(SWIPT) system with a nonlinear energy harvesting model under three different RIS transmission protocols: energy splitting(ES),time switching(TS), and mode switching(MS). The objective of this paper is to maximize the weighted sum power(WSP) of all energy harvesting receivers(EHRs) while ensuring fairness in the collected power among them. This is achieved by jointly optimizing the transmit beamforming at the base station(BS)and the transmission and reflection coefficients at the STAR-RIS, subject to rate constraints for information decoding receivers(IDRs), transmit power constraint at the BS, and coefficient constraints of each element at the STAR-RIS corresponding to the three protocols. Solving this optimization problem poses challenges because of the complicated objective function and numerous coupled optimization variables of the ES STAR-RIS. To address this complexity, an efficient alternating optimization(AO) approach is proposed as an iterative solution method that achieves suboptimal results. The AO algorithm is then extended to MS STAR-RIS and TS STAR-RIS. Specifically, for MS STRA-RIS, binary constraints in the STAR-RIS coefficient optimization subproblem are handled using the first-order approximation technique along with the penalty function method. For TS STAR-RIS, apart from optimizing BS transmit beamforming and STAR-RIS coefficients subproblems, the transmission and reflection time allocation of STAR-RIS also needs *** findings demonstrate that compared to conventional RIS-aided systems, utilizing three different protocols in a STAR-RIS-aided sy

关键词： STAR-RIS beamforming SWIPT alternating optimization nonlinear energy harvesting

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Discriminative feature encoding for intrinsic image decomposition

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Computational Visual Media 2023年第3期9卷 597-618页

作者： Zongji Wang Yunfei Liu Feng Lu Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518000China

Intrinsic image decomposition is an important and long-standing computer vision *** an input image,recovering the physical scene properties is *** physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image *** work takes advantage of deep learning,and shows that it can solve this challenging computer vision problem with high *** focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input *** achieve this goal,we explore the distinctive characteristics of different intrinsic components in the high-dimensional feature embedding *** define feature distribution divergence to efficiently separate the feature vectors of different intrinsic *** feature distributions are also constrained to fit the real ones through a feature distribution *** addition,a data refinement approach is provided to remove data inconsistency from the Sintel dataset,making it more suitable for intrinsic image *** method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent *** results indicate that our proposed network structure can outperform the existing state-of-the-art.

关键词： intrinsic image decomposition deep learning feature distribution data refinement

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Prospects on mangrove knowledge services in the smart era:From plant atlas to knowledge graphs

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science China Earth sciences 2025年第1期68卷 111-127页

作者： Zhi-Wei HOU Wenlong JING Cheng-Zhi QIN Ji YANG Qing XIA Xiaoling YIN Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou510070China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou511458China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing100049China School of Traffic&Transportation Engineering Changsha University of Science and TechnologyChangsha410114China

Mangroves are crucial to the ecological security of the Earth and human *** management,conservation,and restoration are of great importance and necessitate the support of spatio-temporal information and multidisciplinary knowledge such as biology and *** knowledge services such as plant atlas provide illustrated textual knowledge of ***,this kind of service is oriented to information retrieval and is incapable of effectively mining and utilizing fragmented knowledge from multi-source heterogeneous data,facing the problem of“massive data,rare knowledge”.Knowledge graphs are capable of extracting,organizing,and fusing the knowledge contained in massive data into semantic networks that can be understood and computed by *** provide a solution for the realization of intelligent knowledge *** on the urgent need for mangrove knowledge acquisition,formal representation,and intelligent services,this paper proposes a research prospect on mangrove knowledge graphs and knowledge *** first analyze the similarities and differences between various domain-specific concepts of *** this basis,we define the mangrove knowledge graph as a large-scale knowledge base that integrates multi-disciplinary knowledge and spatio-temporal information with mangrove ecosystems as the ***,we propose a research framework for mangrove knowledge services that can realize the transformation from multi-modal data to intelligent knowledge services,including multiple research levels such as ubiquitous data sensing and aggregation,knowledge organization and graph construction,and intelligent mangrove knowledge ***,the methods and workflow for constructing mangrove knowledge graphs are ***,we discuss the challenges and possible future directions of mangrove knowledge services in the smart era,including the construction of a mangrove knowledge system that integrates the domain-specific charac

关键词： Mangrove Tupu Knowledge graph Multi-modal Knowledge service

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Highly stabilized and selective ammonia electro-oxidation over CuNi metallic glass nanoarray

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Carbon Energy 2025年第2期7卷 131-143页

作者： Yunrui Tian Rui Chen Xiaoqing Liu Zixian Mao Haotian Tan De'an Yang Feng Hou Xiaoguang Liu Lichang Yin Xiao Yan Ji Liang Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education School of Materials Science and Engineering Tianjin UniversityTianjinChina Shenzhen Institute of Information Technology ShenzhenGuangdongChina Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyangLiaoningChina

Electrochemical nitrogen looping represents a promising carbon-free and sustainable solution for the energy transition,in which electrochemical ammonia oxidation stays at the central ***,the various nitrogen-containing intermediates tend to poison and corrode the electrocatalysts,even the state-of-the-art noble-metal ones,which is worsened at a high applied ***,we present an ultrarapid laser quenching strategy for constructing a corrosion-resistant and nanostructured CuNi alloy metallic glass *** this material,single-atom Cu species are firmly bonded with the surrounding Ni atoms,endowing exceptional resistance against ammonia corrosion relative of conventional CuNi ***,a record-high durability for over 300 h is *** quenching also allows a much higher Cu content than typical single-atom alloys,simultaneously yielding a high rate and selectivity for ammonia oxidation reaction(AOR).Consequently,an outstanding ammonia conversion rate of up to 95%is achieved with 91.8%selectivity toward nitrite after 8 *** simulations reveal that the structural amorphization of CuNi alloy could effectively modify the electronic configuration and reaction pathway,generating stable singleatom Cu active sites with low kinetic barriers for *** ultrarapid laser quenching strategy thus provides a new avenue for constructing metallic glasses with well-defined nanostructures,presenting feasible opportunities for performance enhancement for AOR and other electrocatalytic processes.

关键词： ammonia oxidation reaction CuNi laser quenching metallic glasses

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Deep-learning-assisted online surface roughness monitoring in ultraprecision fly cutting

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science China(Technological sciences) 2024年第5期67卷 1482-1497页

作者： SHEHZAD Adeel RUI XiaoTing DING YuanYuan ZHANG JianShu CHANG Yu LU HanJing CHEN YiHeng Institute of Launch Dynamics Nanjing University of Science and TechnologyNanjing210094China National Key Laboratory of Complex Multibody System Dynamics Nanjing210094China Automotive Engineering Centre University of Engineering and TechnologyLahore54890Pakistan School of Science Nanjing University of Science and TechnologyNanjing210094China College of Engineering Peking UniversityBeijing100871China

Surface roughness is one of the most critical attributes of machined components,especially those used in high-performance *** surface roughness monitoring offers advancements comparable to post-process inspection methods,reducing inspection time and costs and concurrently reducing the likelihood of ***,online monitoring approaches for surface roughness are constrained by several limitations,including the reliance on handcrafted feature extraction,which necessitates the involvement of human experts and entails time-consuming ***,the prediction models trained under one set of cutting conditions exhibit poor performance when applied to different experimental *** address these challenges,this work presents a novel deep-learning-assisted online surface roughness monitoring method for ultraprecision fly cutting of copper workpieces under different cutting *** acceleration signals were acquired during each cutting experiment to develop two datasets,and no handcrafted features were *** deep learning models were developed and evaluated using standard performance metrics.A convolutional neural network stacked on a long short-term memory network outperformed all other network models,yielding exceptional results,including a mean absolute percentage error as low as 1.51%and an R2value of 96.6%.Furthermore,the robustness of the proposed model was assessed via a validation cohort analysis using experimental data obtained using cutting parameters different from those previously *** performance of the model remained consistent and commendable under varied conditions,asserting its applicability in real-world scenarios.

关键词： online surface roughness monitoring UPFC deep learning CNN-LSTM vibration signal

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Aggregation-Induced Emissive Scintillators: A New Frontier for Radiation Detection and Imaging

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Nano-Micro Letters 2025年第7期17卷 198-213页

作者： Xinyi Li Jiafu Yu Yinghao Fan Yuting Gao Guangda Niu Engineering Research Center of Nano-Geomaterials of Ministry of Education Faculty of Material Science and ChemistryChina University of Geosciences State Key Laboratory of Biocatalysis and Enzyme Engineering School of Life SciencesHubei University Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and Technology

Aggregation-induced emission(AIE) is a unique phenomenon where certain organic materials exhibit enhanced luminescence in their aggregated states, overcoming the typical quenching observed in conventional organic materials. Since its discovery in 2001, AIE has driven significant advances in fields like OLEDs and biological imaging, earning recognition in fundamental research. However, its application in high-energy radiation detection remains underexplored. Organic scintillators, though widely used, face challenges such as low light yield and poor radiation attenuation. AIE materials offer promising solutions by improving light yield, response speed, and radiation attenuation. This review summarizes the design strategies behind AIE scintillators and their very recent applications in X-ray, γ-ray, and fast neutron detection. We highlight their advantages in enhancing detection sensitivity, reducing background noise, and achieving high-resolution imaging. By addressing the current challenges, we believe AIE materials will play a pivotal role in advancing future radiation detection and imaging technologies.

关键词： Aggregation-induced emission Radiation detection Radiation imaging Scintillators

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Quantized anomalous Hall resistivity achieved in molecular beam epitaxy-grown Mn Bi2Te4 thin films

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national science Review 2024年第2期11卷 24-32页

作者： Yunhe Bai Yuanzhao Li Jianli Luan Ruixuan Liu Wenyu Song Yang Chen Peng-Fei Ji Qinghua Zhang Fanqi Meng Bingbing Tong Lin Li Yuying Jiang Zongwei Gao Lin Gu Jinsong Zhang Yayu Wang Qi-Kun Xue Ke He Yang Feng Xiao Feng State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Institute of Physics Chinese Academy of Sciences School of Materials Science and Engineering Tsinghua University Beijing Academy of Quantum Information Sciences Frontier Science Center for Quantum Information Hefei National Laboratory Southern University of Science and Technology

The intrinsic magnetic topological insulator MnBi2Te4provides a feasible pathway to the high-temperature quantum anomalous Hall（QAH） effect as well as various novel topological quantum phases. Although quantized transport properties have been observed in exfoliated MnBi2Te4thin flakes, it remains a big challenge to achieve molecular beam epitaxy（MBE）-grown MnBi2Te4thin films even close to the quantized regime. In this work, we report the realization of quantized anomalous Hall resistivity in MBE-grown MnBi2Te4thin films with the chemical potential tuned by both controlled in situ oxygen exposure and top gating. We find that elongated post-annealing obviously elevates the temperature to achieve quantization of the Hall resistivity, but also increases the residual longitudinal resistivity, indicating a picture of high-quality QAH puddles weakly coupled by tunnel barriers. These results help to clarify the puzzles in previous experimental studies on MnBi2Te4and to find a way out of the big difficulty in obtaining MnBi2Te4samples showing quantized transport properties.

关键词： intrinsic magnetic topological insulator quantized anomalous Hall resistivity molecular beam epitaxy quantized transport property

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Optical neural networks:progress and challenges

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Light(science & Applications) 2024年第11期13卷 2511-2535页

作者： Tingzhao Fu Jianfa Zhang Run Sun Yuyao Huang Wei Xu Sigang Yang Zhihong Zhu Hongwei Chen College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangshaChina Hunan Provincial Key Laboratory of Novel Nano-Optoelectronic Information Materials and Devices National University of Defense TechnologyChangshaChina Nanhu Laser Laboratory National University of Defense TechnologyChangshaChina Department of Electronic Engineering Tsinghua UniversityBeijingChina Beijing National Research Center for Information Science and Technology(BNRist) BeijingChina

Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources,advanced algorithms,and high-performance electronic ***,conventional computing hardware is inefficient at implementing complex tasks,in large part because the memory and processor in its computing architecture are separated,performing insufficiently in computing speed and energy *** recent years,optical neural networks(ONNs)have made a range of research progress in optical computing due to advantages such as subnanosecond latency,low heat dissipation,and high *** are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing ***,we first introduce the design method and principle of ONNs based on various optical ***,we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip ***,we summarize and discuss the computational density,nonlinearity,scalability,and practical applications of ONNs,and comment on the challenges and perspectives of the ONNs in the future development trends.

关键词： networks neural hardware

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