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Piezotronic neuromorphic devices:principle,manufacture,and applications

International Journal of Extreme Manufacturing 2024年第3期6卷 363-385页

作者： Xiangde Lin Zhenyu Feng Yao Xiong Wenwen Sun Wanchen Yao Yichen Wei Zhong Lin Wang Qijun Sun Department of Research Shanghai University of Medicine and Health Sciences Affiliated Zhoupu HospitalShanghai 201318People’s Republic of China beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400People’s Republic of China Center on Nanoenergy Research School of Chemistry and Chemical EngineeringGuangxi UniversityNanning 530004People’s Republic of China Shandong Zhongke Naneng Energy technology co. LtdDongying 257061People’s Republic of China Georgia Institute of technology AtlantaGA 30332-0245United States of America

With the arrival of the era of artificial intelligence(AI)and big data,the explosive growth of data has raised higher demands on co.puter hardware and *** techniques inspired by biological nervous systems are expected to be one of the approaches to breaking the von Neumann *** neuromorphic devices modulate electrical transport characteristics by piezopotential and directly associate external mechanical motion with electrical output signals in an active manner,with the capability to sense/store/process information of external *** this review,we have presented the piezotronic neuromorphic devices(which are classified into strain-gated piezotronic transistors and piezoelectric nanogenerator-gated field effect transistors based on device structure)and discussed their operating mechanisms and related manufacture ***,we summarized the research progress of piezotronic neuromorphic devices in recent years and provided a detailed discussion on multifunctional applications,including bionic sensing,information storage,logic co.puting,and electrical/optical artificial ***,in the co.text of future development,challenges,and perspectives,we have discussed how to modulate novel neuromorphic devices with piezotronic effects more *** is believed that the piezotronic neuromorphic devices have great potential for the next generation of interactive sensation/memory/co.putation to facilitate the development of the Internet of Things,AI,biomedical engineering,etc.

关键词： piezotronics neuromorphic devices strain-gated transistors piezoelectric nanogenerators synaptic transistors

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Significant genomic introgression from grey junglefowl(Gallus sonneratii)to domestic chickens(Gallus gallus domesticus)

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Journal of Animal Science and Biotechnology 2024年第4期15卷 1482-1493页

作者： Xiurong Zhao Junhui Wen Xinye Zhang Jinxin Zhang Tao Zhu Huie Wang Weifang Yang Guomin Cao Wenjie Xiong Yong Liu Changqing Qu Zhonghua Ning Lujiang Qu National Engineering Laboratory for Animal Breeding College of Animal Science and TechnologyChina Agricultural UniversityBeijing 100193China Key Laboratory of Protection and Utilization of Biological Resources in Tarim Basin Xinjiang Production and Construction CorpsTarim UniversityAlar 843300China beijing Municipal General Station of Animal Science Beijing 100107China Animal husbandry station of Fangchenggang FangchenggangGuangxi Province 538001China Animal Disease Prevention and co.trol Center of Fangchenggang FangchenggangGuangxi Province 538001China beijing Agricultural Effect Poultry Industry co. Ltd.Beijing 101100China Engineering technology Research Center of Anti‑aging Chinese Herbal Medicine of Anhui Province Fuyang Normal UniversityFuyangAnhui 236037China

Background Chicken is one of the most numerous and widely distributed species around the world,and many studies support the multiple ancestral origins of domestic *** research regarding the yellow skin phenotype in domestic chickens(regulated by Bco.)likely originating from the grey junglefowl serves as crucial evidence for demonstrating the multiple origins of ***,beyond the Bco. gene region,much remains unknown about the introgression from the grey junglefowl into domestic ***,in this study,based on wholegenome data of 149 samples including 4 species of wild junglefowls and 13 local domestic chicken breeds,we explored the introgression events from the grey junglefowl to domestic *** We successfully detected introgression regions besides Bco.,including two associated with growth trait(IGFBP2 and TKT),one associated with angiogenesis(TIMP3)and two members of the heat shock protein family(HSPB2 and CRYAB).Our findings suggest that the introgression from the grey junglefowl may impact the growth performance of ***,we revealed introgression events from grey junglefowl at the Bco. region in multiple domestic chicken breeds,indicating a phenomenon where the yellow skin phenotype likely underwent strong selection and was ***,our haplotype analysis shed light on Bco. introgression event from different sources of grey junglefowl into domestic chickens,possibly suggesting multiple genetic flows between the grey junglefowl and domestic *** In summary,our findings provide evidences of the grey junglefowl co.tributing to the genetic diversity of domestic chickens,laying the foundation for a deeper understanding of the genetic co.position within domestic chickens,and offering new perspectives on the impact of introgression on domestic chickens.

关键词： Bco. Domestic chickens Grey junglefowl Introgression

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Detection of UAV Target Based on co.tinuous Radon Transform and Matched Filtering Process for Passive Bistatic Radar

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Journal of beijing Institute of technology 2024年第1期33卷 9-18页

作者： Luo Zuo Yuefei Yan Jun Wang Xin Sang Yan Wang Dongming Ge Lihao Ping Zhihai Wang co.gsi Wang Guangzhou Institute of technology Xidian UniversityGuangzhou 510555China National Laboratory of Radar Signal Processing Xidian UniversityXi’an710071China School of Information and co.trol Engineering Xi’an University of Architecture and TechnologyXi’anShaanxi 710055China CETC Wuhu Diamond Aircraft Manufacturing co. LtdWuhuAnhui 241000China beijing Space Vehicle General Design Department Beijing 100094China CETC No.38 Research Institute Hefei 230088China

Long-time integration technique is an effective way of improving target detection performance for unmanned aerial vehicle(UAV)in the passive bistatic radar(PBR),while range migration(RM)and Doppler frequency migration(DFM)may have a major effect due to the target *** paper proposed an innovative long-time co.erent integration approach,regarded as co.tinuous Radon-matched filtering process(CRMFP),for low-observable UAV target in passive bistatic *** not only mitigates the RM by co.laborative research in range and velocity dimensions but also co.pensates the DFM and ensures the co.erent integration through the matched filtering process(MFP).Numerical and real-life data following detailed analysis verify that the proposed method can overco.e the Doppler mismatch influence and acquire co.parable detection performance.

关键词： passive bistatic radar unmanned aerial vehicle long-time co.erent integration Radon-matched filtering process

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Overhead-free Noise-tolerant Federated Learning: A New Baseline

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Machine Intelligence Research 2024年第3期21卷 526-537页

作者： Shiyi Lin Deming Zhai Feilong Zhang Junjun Jiang Xianming Liu Xiangyang Ji Department of co.puter Science and technology Harbin Institute of TechnologyHarbin150000China Department of Automation Tsinghua UniversityBeijing100084China

Federated learning (FL) is a promising decentralized machine learning approach that enables multiple distributed clients to train a model jointly while keeping their data private. However, in real-world scenarios, the supervised training data stored in local clients inevitably suffer from imperfect annotations, resulting in subjective, inco.sistent and biased labels. These noisy labels can harm the co.laborative aggregation process of FL by inducing inco.sistent decision boundaries. Unfortunately, few attempts have been made towards noise-tolerant federated learning, with most of them relying on the strategy of transmitting overhead messages to assist noisy labels detection and co.rection, which increases the co.munication burden as well as privacy risks. In this paper, we propose a simple yet effective method for noise-tolerant FL based on the well-established co.training framework. Our method leverages the inherent discrepancy in the learning ability of the local and global models in FL, which can be regarded as two co.plementary views. By iteratively exchanging samples with their high co.fident predictions, the two models “teach each other” to suppress the influence of noisy labels. The proposed scheme enjoys the benefit of overhead co.t-free and can serve as a robust and efficient baseline for noise-tolerant federated learning. Experimental results demonstrate that our method outperforms existing approaches, highlighting the superiority of our method.

关键词： Federated learning noise-label learning privacy-preserving machine learning edge intelligence distributed machine learning

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Enhancing XRF sensor-based sorting of porphyritic co.per ore using particle swarm optimization-support vector machine(PSO-SVM)algorithm

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International Journal of Mining Science and technology 2024年第4期34卷 545-556页

作者： Zhengyu Liu Jue Kou Zengxin Yan Peilong Wang Chang Liu Chunbao Sun Anlin Shao Bern Klein School of Civil and Resources Engineering University of Science and TechnologyBeijing 100083China Institute of Minerals Research University of Science and TechnologyBeijing 100083China State Key Laboratory of Mineral Processing Beijing 102628China Ansteel Group Mining co.Ltd. Anshan 114000China Norman B.Keevil Institute of Mining Engineering University of British ColumbiaVancouverBC V6T 1Z4Canada

X-ray fluorescence(XRF)sensor-based ore sorting enables efficient beneficiation of heterogeneous ores,while intraparticle heterogeneity can cause significant grade detection errors,leading to misclassifications and hindering widespread technology *** classification models are crucial to determine if actual grade exceeds the sorting threshold using localized XRF *** studies mainly used linear regression(LR)algorithms including simple linear regression(SLR),multivariable linear regression(MLR),and multivariable linear regression with interaction(MLRI)but often fell short attaining satisfactory *** study employed the particle swarm optimization support vector machine(PSO-SVM)algorithm for sorting porphyritic co.per ore ***-scale results showed PSO-SVM out-performed LR and raw data(RD)models and the significant interaction effects among input features was *** poor input data quality,PSO-SVM demonstrated exceptional ***-scale sorting achieved 93.0%accuracy,0.24%grade increase,84.94%reco.ery rate,57.02%discard rate,and a remarkable 39.62 yuan/t net smelter return(NSR)increase co.pared to no *** improvements were achieved by the PSO-SVM model with optimized input co.binations and highest data quality(T=10,T is XRF testing times).The unsuitability of LR methods for XRF sensor-based sorting of investigated sample is *** element selection and mineral association analysis elucidate element importance and influence mechanisms.

关键词： XRF sensor-based sorting PSO-SVM algorithm co.per ore pebble Receiver operating curve(ROC) Net smelter return(NSR)

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Reinforcement Learning-Based Dynamic Order Reco.mendation for On-Demand Food Delivery

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Tsinghua Science and technology 2024年第2期29卷 356-367页

作者： Xing Wang Ling Wang Chenxin Dong Hao Ren Ke Xing Department of Automation Tsinghua UniversityBeijing 100080China School of Mechanical and Automotive Engineering Qingdao Hengxing University of Science and TechnologyQingdao 266100China Meituan Beijing 100015China

On-demand food delivery(OFD)is gaining more and more popularity in modern *** a kernel order assignment manner in OFD scenario,order reco.mendation directly influences the delivery efficiency of the platform and the delivery experience of *** paper addresses the dynamism of the order reco.mendation problem and proposes a reinforcement learning solution *** actor-critic network based on long short term memory(LSTM)unit is designed to deal with the order-grabbing co.flict between different ***,three rider sequencing rules are acco.dingly proposed to match different time steps of the LSTM unit with different *** test the performance of the proposed method,extensive experiments are co.ducted based on real data from Meituan delivery *** results demonstrate that the proposed reinforcement learning based order reco.mendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing co.flicts,resulting in better delivery efficiency and experience for the platform and riders.

关键词： on-demand food delivery order reco.mendation reinforcement learning actor-critic network long short term memory

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data mining accelerated the design strategy of high-entropy alloys with the largest hardness based on genetic algorithm optimization

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Materials Genome Engineering Advances 2024年第2期2卷 108-125页

作者： Xianzhe Jin Hong Luo Xuefei Wang Hongxu Cheng Chunhui Fan Xiaogang Li Xiongbo Yan Key Laboratory for co.rosion and Protection of The Ministry of Education(MOE) Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijingChina National Materials co.rosion and Protection data Center Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijingChina beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijingChina

This article proposed a design strategy that integrated machine learning models based on random forest and genetic algorithm(GA)for the rapid screening of hardness in the Alco.rCuFeMoNiTi high-entropy alloys *** feature engineering and modeling,valence electron co.centration,atomic size difference(δr),Pauling electronegativity difference(Δχ),geometric parameters(Λ),and the Cr co.tent were identified as the five key features in the *** GAwas employed to search for alloys with superior hardness and guided *** three itera-tions,the HEA Al_(18)co.(21)Cr_(23)Fe_(23)Mo_(15)exhibiting the highest predicted hardness(868.8 HV)was *** alloy was predominantly co.posed of BCC,ordered B2,andσphases,with an experimental hardness of 899.8±9.9 HV,which as approximately 5.38%greater than the maximum hardness observed in the original *** design strategy can also solve other regression problems and pave the way for optimizing material performance in various engineering applications.

关键词： Alco.rCuFeMoNiTi genetic algorithm hardness high-entropy alloys machine learning

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Graded and Quantitative technology and Application of co.l-Bearing Reservoir Based on Seismic Reflection Characteristics

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Journal of Geoscience and Environment Protection 2024年第6期12卷 279-290页

作者： Hao Zhang Huan Wan Liming Lin Wenjun Xing Tiemei Yang Longgang Zhou Lijun Gao Guangchao Zhi Xin Liu Xiaowen Song CNOOC EnerTech-Drilling & Production co. Tianjin China China United co.lbed Methane co. Ltd. Beijing China

Taiyuan formation is the main exploration strata in Ordos Basin, and co.ls are widely developed. Due to the interference of strong reflection of co.ls, we cannot co.pletely identify the effective reservoir information of co.l-bearing reservoir on seismic data. Previous researchers have studied the reservoir by stripping or weakening the strong reflection, but it is difficult to determine the effectiveness of the remaining reflection seismic data. In this paper, through the establishment of 2D forward model of co.l-bearing strata, the co.responding geophysical characteristics of different reflection types of co.l-bearing strata are analyzed, and then the favorable sedimentary facies zones for reservoir development are predicted. On this basis, co.bined with seismic properties, the co.l-bearing reservoir is quantitatively characterized by seismic inversion. The above research shows that the Taiyuan formation in LS block of Ordos Basin is affected by co.ls and forms three or two peaks in different locations. The reservoir plane sedimentary facies zone is effectively characterized by seismic reflection structure. Based on the characteristics of sedimentary facies belt and petrophysical analysis, the reservoir is semi quantitatively characterized by attribute analysis and waveform indication, and quantitatively characterized by pre stack geostatistical inversion. Based on the forward analysis of co.l measure strata, this technology characterizes the reservoir facies belt through seismic reflection characteristics, and describes co.l measure reservoirs step by step. It effectively guides the exploration of LS block in Ordos Basin, and has achieved good practical application effect.

关键词： co.l-Bearing Reservoir Seismic Reflection Characteristics Waveform Indication Inversion Geostatistics Inversion

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Designing and Implementing an Advanced Big data Governance Platform

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Journal of Electronic Research and Application 2024年第3期8卷 13-19页

作者： Yekun Chen Tianqi Xu Yongjiang Xue CETC Academy of Electronics and Information technology Group co. Ltd.Beijing 100040China School of co.puter Science and technology Tiangong UniversityTianjin 300387China

co.temporary mainstream big data governance platforms are built atop the big data eco.ystem co.ponents,offering a one-stop development and analysis governance platform for the co.lection,transmission,storage,cleansing,transformation,querying and analysis,data development,publishing,and subscription,sharing and exchange,management,and services of massive *** platforms serve various role members who have internal and external data ***,in the era of big data,the rapid update and iteration of big data technologies,the diversification of data businesses,and the exponential growth of data present more challenges and uncertainties to the co.struction of big data governance *** paper discusses how to effectively build a data governance platform under the big data system from the perspectives of functional architecture,logical architecture,data architecture,and functional design.

关键词： Big data data governance Cleansing and transformation data development Sharing and exchange

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Inverting the rock mass P-wave velocity field ahead of deep buried tunnel face while borehole drilling

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International Journal of Mining Science and technology 2024年第5期34卷 681-697页

作者： Liu Liu Shaojun Li Minzong Zheng Dong Wang Minghao Chen Junbo Zhou Tingzhou Yan Zhenming Shi State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhan 430071China China Railway Eryuan Engineering Group co. LTD.Chengdu 610031China Department of Hydraulic and Hydropower Engineering Tsinghua UniversityBeijing 100084China Hubei co.munications Planning and Design Institute co. LTD.Wuhan 430051China Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education Tongji UniversityShanghai 200092China

Imaging the wave velocity field surrounding a borehole while drilling is a promising and urgently needed approach for extending the exploration range of the borehole *** paper develops a drilling process detection(DPD)system co.sisting of a multifunctional sensor and a pilot geophone installed at the top of the drilling rod,geophones at the tunnel face,a laser rangefinder,and an onsite co.puter.A weighted adjoint-state first arrival travel time tomography method is used to invert the P-wave velocity field of rock mass while borehole drilling.A field experiment in the ongoing co.struction of a deep buried tunnel in southwestern China demonstrated the DPD system and the tomography ***-frequency analysis of typical borehole drilling detection data shows that the impact drilling source is a pulse-like seismic exploration wavelet.A velocity field of the rock mass in a triangular area defined by the borehole trajectory and geophone receiving line can be *** the borehole co.e and optical image validate the inverted P-wave velocity field.A numerical simulation of a checkerboard benchmark model is used to test the tomography *** rapid co.vergence of the misfits and co.sistent agreement between the inverted and observed travel times validate the P-wave velocity imaging.

关键词： Deep buried tunnel Wave velocity field Borehole drilling Tomography Rock mass

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