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| 1 | Template matching for simple waveforms with low signal-to-noise ratio and its application to icequake detection显示文摘Template matching is a useful method to detect seismic events through waveform similarity between two signals.The traditional template matching method works well in detecting small tectonic earthquakes.However,the method has some difficulty when the signals have relatively low signal-to-noise ratios(SNRs)and simple shapes,e.g.a sinusoidal function.In this study,we modify the traditional template matching approach for this situation.We first construct a virtual three-component seismic station using vertical-component waveforms recorded by three stations.Next,we select a template event from the virtual station,and apply the traditional template matching.We then verify this method by detecting icequakes with simple waveforms on the Urumqi Glacier No.1 and compare the results with those from the short-term-averages over long-term-average(STA/LTA),the REST method,and traditional template matching method.It can be concluded that the modified template matching method using virtual stations has some advantages for seismic data with low SNRs. | Haichao Ma Risheng Chu Minhan Sheng Ziye Yu | 2020 | Earthquake Science2020，33，5： | 3 |
| 2 | A Survey of Underwater Multi-Robot Systems显示文摘As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent decades.In this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new functions.More specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their combination.Further,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of MRS.To conclude,we have discussed related promising directions for future research.This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios. | Ziye Zhou Jincun Liu Junzhi Yu | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，1： | 3 |
| 3 | The high-resolution community velocity model V2.0 of southwest China,constructed by joint body and surface wave tomography of data recorded at temporary dense arrays显示文摘The Sichuan-Yunnan area is located at the southeastern margin of the Tibetan Plateau,where tectonic movement is strong with deep and large faults distributed in a staggered manner,which results in strong seismic activities and severe earthquake hazards.Since the 21st century,several earthquakes of magnitude 7.0 or above occurred in this region,which have caused huge casualties and economic losses,especially the 2008 M_(s)8.0 Wenchuan earthquake.At present,earthquake monitoring and source parameter inversion,strong earthquake hazard analysis and disaster assessment are still the focus of seismological researches in the Sichuan-Yunnan region.Regional high-precision 3D community velocity models are fundamental for these studies.In this paper,by assembling seismic observations at permanent seismic stations and several temporary dense seismic arrays in this region,we obtained about 7.06 million body wave travel time data(including absolute and differential travel times)using a newly developed artificial intelligence body wave arrival time picking method and about 100,000 Rayleigh wave phase velocity dispersion data in the period range of 5-50 s from ambient noise cross-correlation technique.Based on this abundant dataset,we obtained the three-dimensional high resolution V_p and V_(s)model in the crust and uppermost mantle of southwest(SW)China by adopting the joint body and surface wave travel time tomography method considering the topography effect starting from the first version of community velocity model in SW China(SWChina CVM-1.0).Compared to SWChina CVM-1.0,this newly determined velocity model has higher resolution and better data fitness.It is accepted by the China Seismic Experimental Site as the second version of the community velocity model in SW China(SWChina CVM-2.0).The new model shows strong lateral heterogeneities in the shallow crust.Two disconnected low velocity zones are observed in the middle to lower crust,which is located in the Songpan-Ganzi block and the northern Chuandian block to the west of the Longmenshan-Lijiang-Xiaojinhe fault,and beneath the Xiaojiang fault zone,respectively.The inner zone of the Emeishan large igneous province(ELIP)exhibits a high velocity anomaly,which separates the two aforementioned low velocity anomalies.Low velocity anomaly is also shown beneath the Tengchong volcano.The velocity structures in the vicinity of the 2008 M_(s)8.0 Wenchuan earthquake,the 2013 M_(s)7.0Lushan earthquake and the 2017 M_(s)7.0 Jiuzhaigou earthquake mainly show high V_(p)and V_(s)anomalies and the mainshocks are basically located at the transition zone between the high and low velocity anomalies.Along with the segmentation characteristics of seismic activity,we suggest that areas with significant changes in velocity structures,especially in active fault zones,might have a greater potential to generate moderate to strong earthquakes. | Ying LIU Ziye YU Zhiqi ZHANG Huajian YAO Weitao WANG Haijiang ZHANG Hongjian FANG Lihua FANG | 2023 | Science China Earth Sciences2023，66，10： | 2 |
| 4 | Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network显示文摘Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore lacking.Here,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU devices.Evaluations based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive fields.Similar performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most efficient.The RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and PhaseNet.UNet++requires the most computational effort among the pickers.As all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their applications.For beginners,we provide a tutorial on training and validating the pickers using the DiTing dataset.We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users.All of our models are open-source and publicly accessible. | Ziye Yu Weitao Wang Yini Chen | 2023 | Earthquake Science2023，36，2： | 1 |
| 5 | CREDIT-X1local:A reference dataset for machine learning seismology from ChinArray in Southwest China显示文摘High-quality datasets are critical for the development of advanced machine-learning algorithms in seismology.Here,we present an earthquake dataset based on the ChinArray Phase I records(X1).ChinArray Phase I was deployed in the southern north-south seismic zone(20°N-32°N,95°E-110°E)in 2011-2013 using 355 portable broadband seismic stations.CREDIT-X1local,the first release of the ChinArray Reference Earthquake Dataset for Innovative Techniques(CREDIT),includes comprehensive information for the 105,455 local events that occurred in the southern north-south seismic zone during array observation,incorporating them into a single HDF5 file.Original 100-Hz sampled three-component waveforms are organized by event for stations within epicenter distances of 1,000 km,and records of≥200 s are included for each waveform.Two types of phase labels are provided.The first includes manually picked labels for 5,999 events with magnitudes≥2.0,providing 66,507 Pg,42,310 Sg,12,823 Pn,and 546 Sn phases.The second contains automatically labeled phases for 105,442 events with magnitudes of−1.6 to 7.6.These phases were picked using a recurrent neural network phase picker and screened using the corresponding travel time curves,resulting in 1,179,808 Pg,884,281 Sg,176,089 Pn,and 22,986 Sn phases.Additionally,first-motion polarities are included for 31,273 Pg phases.The event and station locations are provided,so that deep learning networks for both conventional phase picking and phase association can be trained and validated.The CREDIT-X1local dataset is the first million-scale dataset constructed from a dense seismic array,which is designed to support various multi-station deep-learning methods,high-precision focal mechanism inversion,and seismic tomography studies.Additionally,owing to the high seismicity in the southern north-south seismic zone in China,this dataset has great potential for future scientific discoveries. | Lu Li Weitao Wang Ziye Yu Yini Chen | 2024 | Earthquake Science2024，37，2： | 0 |
| 6 | Collaborative Caching in Vehicular Edge Network Assisted by Cell-Free Massive MIMO显示文摘The 6G mobile communications demand lower content delivery latency and higher quality of service for vehicular edge network.With the popularity of content-centric networks,mobile users are paying more and more attention to the delay and reliability of fetching cached content.For reducing communication costs,increasing network capacity and improving the content delivery,we propose a collaborative caching scheme based on deep reinforcement learning for vehicular edge network assisted by cell-free massive multiple-input multipleoutput(MIMO)system,in which the macro base station is considered as the central processor unit,and the roadside units are treated as roadside access points(RSAPs).The proposed scheme can effectively cache contents in edge nodes,i.e.,RSAPs and vehicles with caching capability.We jointly consider the mobility of vehicles and the content request preferences of users,then we use deep Qnetworks algorithm to optimize the caching decisions.Simulation results show that the proposed scheme can significantly reduce the content delivery average latency and increase the content cache hit ratio. | WANG Chaowei WANG Ziye XU Lexi YU Xiaofei ZHANG Zhi WANG Weidong | 2023 | Chinese Journal of Electronics2023，32，6： | 0 |
| 7 | High-sensitivity and fast-response fiber optic temperature sensor using an anti-resonant reflecting optical waveguide mechanism显示文摘Temperature sensing is essential for human health monitoring.High-sensitivity(>1 nm∕℃)fiber sensors always require long interference paths and temperature-sensitive materials,leading to a long sensor and thus slow response(6–14 s).To date,it is still challenging for a fiber optic temperature sensor to have an ultrafast(~ms)response simultaneously with high sensitivity.Here,a side-polished single-mode/hollow/single-mode fiber(SPSHSF)structure is proposed to meet the challenge by using the length-independent sensitivity of an anti-resonant reflecting optical waveguide mechanism.With a polydimethylsiloxane filled sub-nanoliter volume cavity in the SP-SHSF,the SP-SHSF exhibits a high temperature sensitivity of 4.223 nm/℃ with a compact length of 1.6 mm,allowing an ultrafast response(16 ms)and fast recovery time(176 ms).The figure of merit(FOM),defined as the absolute ratio of sensitivity to response time,is proposed to assess the comprehensive performance of the sensor.The FOM of the proposed sensor reaches up to 263.94(nm/℃)∕s,which is more than two to three orders of magnitude higher than those of other temperature fiber optic sensors reported previously.Additionally,a threemonth cycle test shows that the sensor is highly robust,with excellent reversibility and accuracy,allowing it to be incorporated with a wearable face mask for detecting temperature changes during human breathing.The high FOM and high stability of the proposed sensing fiber structure provide an excellent opportunity to develop both ultrafast and highly sensitive fiber optic sensors for wearable respiratory monitoring and contactless in vitro detection. | ZHIBIN LI ZIYE WU ZHUOQI LI LIANGXUN OU WENXIANG ZHANG ZHICONG LAI YU ZHANG MENGYUAN XIE JIEYUAN TANG WENGUO ZHU HUADAN ZHENG YONGCHUN ZHONG XIONG DENG XIHUA ZOU ZHE CHEN JIANHUI YU | 2023 | Photonics Research2023，11，8： | 0 |
| 8 | A survey of the pursuit-evasion problem in swarm intelligence显示文摘For complex functions to emerge in artificial systems,it is important to understand the intrinsic mechanisms of biological swarm behaviors in nature.In this paper,we present a comprehensive survey of pursuit–evasion,which is a critical problem in biological groups.First,we review the problem of pursuit–evasion from three different perspectives:game theory,control theory and artificial intelligence,and bio-inspired perspectives.Then we provide an overview of the research on pursuit–evasion problems in biological systems and artificial systems.We summarize predator pursuit behavior and prey evasion behavior as predator–prey behavior.Next,we analyze the application of pursuit–evasion in artificial systems from three perspectives,i.e.,strong pursuer group vs.weak evader group,weak pursuer group vs.strong evader group,and equal-ability group.Finally,relevant prospects for future pursuit–evasion challenges are discussed.This survey provides new insights into the design of multi-agent and multi-robot systems to complete complex hunting tasks in uncertain dynamic scenarios. | Zhenxin MU Jie PAN Ziye ZHOU Junzhi YU Lu CAO | 2023 | Frontiers of Information Technology & Electronic Engineering2023，24，8： | 0 |
| 9 | Application of Machine Learning Methods in Arrival Time Picking of P Waves from Reservoir Earthquakes显示文摘Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods.However,methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking.The present study establishes a deep learning network model combining a convolutional neural network(CNN) and recurrent neural network(RNN).The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time.The neural network automatically picks the P-wave arrival time,providing a strong constraint for small earthquake positioning.The model is shown to achieve an accuracy rate of 90.7 % in picking P waves of microseisms in the reservoir area,with a recall rate reaching 92.6% and an error rate lower than 2%.The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes,thus providing new technical measures for subsequent microseismic monitoring in the reservoir area. | HU Jiupeng YU Ziye KUANG Wenhuan WANG Weitao RUAN Xiang DAI Shigui | 2020 | Earthquake Research in China2020，34，3： | 0 |
| 10 | Effect of Buyanghuanwu Decoction on PI3K/AKT Signaling Pathway and Aquaporin AQP4 in Cerebral Hemorrhage Rats显示文摘[Objectives] To explore the effect of Buyanghuanwu decoction on PI3K/AKT signaling pathway and aquaporin AQP4 in cerebral hemorrhage rats and clarify the mechanism to provide clear direction and target for cerebral hemorrhage treatment caused by cerebral edema.[Methods]SD rats were randomly divided into six groups: model group,sham operation group,Buyanghuanwu decoction low,medium and high dose groups,and Ginkgo biloba group. Model group,Buyanghuanwu decoction group,G. biloba group were prepared to be intracerebral hemorrhage rat models by referring to Rosenberg law. While the expression of ' polarity' of aquaporin AQP4 was detected by immunofluorescence labeling method,the Evans blue( Evans Blue,EB) content of brain tissue was determined by Spectrophotometry. In addition,the water content of brain tissue was detected by wet and dry weight method. [Results] When compared to the model group,the Buyang Huanwu decoction group,G. biloba group of PI3K and AKT proteins expression increased significantly( P < 0. 05) and AQP4 in Astrocyte end feet membrane concentrated expression significantly increased( P < 0. 05),EB content and water content of brain tissue significantly reduced( P <0. 05).[Conclusions]The protective mechanisms of Buyanghuanwu decoction on cerebral hemorrhage can work might because it can activate PI3K/AKT signaling pathway,regulate AQP4 ' polar' expression,and reduce the permeability of the blood brain barrier and cerebral edema. | Qiuchen LU Jiayu PANG Bolin LI Shuai LIU Yu GONG Ziye JIA Xiaoguang WU | 2018 | Medicinal Plant2018，9，5： | 0 |
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