一、请根据录音,填写单词。二、听对话,选择正确的答案。1.Who is coming for dinner?A.Friends.B.Aunt and uncle.C.Grandma and grandpa.2.What does Dad offer to do at the beginning?A.Go shopping.B.Set the table.C.Cook the fish.
Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd cou...Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering behaviors.We believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and emergencies.Therefore,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective responses.To address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first time.Traditional image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval accuracy.While local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model performance.To overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event type.By calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval efficiency.To validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent studies.We conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of EDAN.In the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,while in the text-to-image retrieval task,it showed superior results on both R@10 and R@5 metrics.Additionally,EDAN excelled in the overall Rsummetric,achieving the best performance.Finally,ablation studies further verified the effectiveness of event-driven attention module.展开更多
In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clu...In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.展开更多
On 5 March,2025,the"Spring Blossoms"ASEAN-China Lady Cultural Gathering was held at the White Pagoda Academy in Beijing.The event was co-hosted by the ASEAN-China Centre and the Beijing People's Associat...On 5 March,2025,the"Spring Blossoms"ASEAN-China Lady Cultural Gathering was held at the White Pagoda Academy in Beijing.The event was co-hosted by the ASEAN-China Centre and the Beijing People's Association for Friendship with Foreign Countries.展开更多
叠前偏移技术在地震数据处理中发挥着非常重要的作用。然而,利用偏移速度分析方法处理复杂地质构造时,往往难以精准重建速度模型,导致共成像点道集的同相轴发生弯曲,影响构造的成像精度。基于动态时间规整算法的地震数据叠加方法通过匹...叠前偏移技术在地震数据处理中发挥着非常重要的作用。然而,利用偏移速度分析方法处理复杂地质构造时,往往难以精准重建速度模型,导致共成像点道集的同相轴发生弯曲,影响构造的成像精度。基于动态时间规整算法的地震数据叠加方法通过匹配参考地震道与其他地震道的相似性来校正同相轴的一致性,但当各地震道之间的幅值差异较大,或者地震数据信噪比较低时,该方法易出现错误匹配的情况,进而导致波形发生畸变,因而影响了其在道集校正中的应用效果。针对上述问题,提出了一种基于迭代光滑动态时间规整算法(iterative smooth dynamic time warping,ISDTW)的共成像点道集叠加方法,该方法引入平滑约束和迭代策略对规整路径进行优化,提高了道集的校正精度和同相轴的一致性。特别是在复杂地质条件下,ISDTW算法取得了较好的成像效果。合成数据和实际数据的测试结果验证了ISDTW算法在减少局部匹配误差、提升地震成像质量方面的显著优势。展开更多
This is a case study of the application of pre-stack inverted elastic parameters to tight-sand reservoir prediction. With the development of oil and gas exploration, pre-stack data and inversion results are increasing...This is a case study of the application of pre-stack inverted elastic parameters to tight-sand reservoir prediction. With the development of oil and gas exploration, pre-stack data and inversion results are increasingly used for production objectives. The pre-stack seismic property studies include not only amplitude verse offset （AVO） but also the characteristics of other elastic property changes. In this paper, we analyze the elastic property parameters characteristics of gas- and wet-sands using data from four gas-sand core types. We found that some special elastic property parameters or combinations can be used to identify gas sands from water saturated sand. Thus, we can do reservoir interpretation and description using different elastic property data from the pre-stack seismic inversion processing. The pre- stack inversion method is based on the simplified Aki-Richard linear equation. The initial model can be generated from well log data and seismic and geologic interpreted horizons in the study area. The input seismic data is angle gathers generated from the common reflection gathers used in pre-stack time or depth migration. The inversion results are elastic property parameters or their combinations. We use a field data example to examine which elastic property parameters or combinations of parameters can most easily discriminate gas sands from background geology and which are most sensitive to pore-fluid content. Comparing the inversion results to well data, we found that it is useful to predict gas reservoirs using λ, λρ, λ/μ, and K/μ properties, which indicate the gas characteristics in the study reservoir.展开更多
Pre-stack depth migration velocity analysis is one of the keys to influencing the imaging quality of pre-stack migration.In this paper we cover a residual curvature velocity analysis method on angle-domain common imag...Pre-stack depth migration velocity analysis is one of the keys to influencing the imaging quality of pre-stack migration.In this paper we cover a residual curvature velocity analysis method on angle-domain common image gathers（ADCIGs） which can depict the relationship between incident angle and migration depth at imaging points and update the migration velocity.Differing from offset-domain common image gathers（ODCIGs）,ADCIGs are not disturbed by the multi-path problem which contributes to imaging artifacts,thus influencing the velocity analysis.On the basis of horizontal layers,we derive the residual depth equation and also propose a velocity analysis workflow for velocity scanning.The tests to synthetic and field data prove the velocity analysis methods adopted in this paper are robust and valid.展开更多
Angle-domain common-image gathers（ADCIGs） are the basic data in migration velocity analysis（MVA） and amplitude variation with angle（AVA） analysis. We propose a common-angle gather-generating scheme using Kirchho...Angle-domain common-image gathers（ADCIGs） are the basic data in migration velocity analysis（MVA） and amplitude variation with angle（AVA） analysis. We propose a common-angle gather-generating scheme using Kirchhoff PSDM based on the traveltime gradient field. The scheme includes three major operations：（1） to calculate the traveltime field of the source and the receiver based on the dynamic programming approach;（2） to obtain the refl ection angle according to the traveltime gradient field in the image space; and（3） to generate the ADCIGs during the migration process. Because of the computation approach, the method for generating ADCIGs is superior to conventional ray-based methods. We use the proposed ADCIGs generation method in 3D large-scale seismic data. The key points of the method are the following.（1） We use common-shot datasets for migration,（2） we load traveltimes based on the shot aperture, and（3） we use the MPI and Open Mp memory sharing to decrease the amount of input and output（I/O）. Numerical examples using synthetic data suggest that the ADCIGs improve the quality of the velocity and the effectiveness of the 3D angle-gather generation scheme.展开更多
文摘一、请根据录音,填写单词。二、听对话,选择正确的答案。1.Who is coming for dinner?A.Friends.B.Aunt and uncle.C.Grandma and grandpa.2.What does Dad offer to do at the beginning?A.Go shopping.B.Set the table.C.Cook the fish.
基金sponsored by Natural Science Foundation of Xinjiang Uygur Autonomous Region(2024D01A19).
文摘Research on mass gathering events is critical for ensuring public security and maintaining social order.However,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering behaviors.We believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and emergencies.Therefore,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective responses.To address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first time.Traditional image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval accuracy.While local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model performance.To overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event type.By calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval efficiency.To validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent studies.We conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of EDAN.In the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,while in the text-to-image retrieval task,it showed superior results on both R@10 and R@5 metrics.Additionally,EDAN excelled in the overall Rsummetric,achieving the best performance.Finally,ablation studies further verified the effectiveness of event-driven attention module.
基金partially supported by the National Natural Science Foundation of China(62161016)the Key Research and Development Project of Lanzhou Jiaotong University(ZDYF2304)+1 种基金the Beijing Engineering Research Center of Highvelocity Railway Broadband Mobile Communications(BHRC-2022-1)Beijing Jiaotong University。
文摘In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.
文摘On 5 March,2025,the"Spring Blossoms"ASEAN-China Lady Cultural Gathering was held at the White Pagoda Academy in Beijing.The event was co-hosted by the ASEAN-China Centre and the Beijing People's Association for Friendship with Foreign Countries.
文摘叠前偏移技术在地震数据处理中发挥着非常重要的作用。然而,利用偏移速度分析方法处理复杂地质构造时,往往难以精准重建速度模型,导致共成像点道集的同相轴发生弯曲,影响构造的成像精度。基于动态时间规整算法的地震数据叠加方法通过匹配参考地震道与其他地震道的相似性来校正同相轴的一致性,但当各地震道之间的幅值差异较大,或者地震数据信噪比较低时,该方法易出现错误匹配的情况,进而导致波形发生畸变,因而影响了其在道集校正中的应用效果。针对上述问题,提出了一种基于迭代光滑动态时间规整算法(iterative smooth dynamic time warping,ISDTW)的共成像点道集叠加方法,该方法引入平滑约束和迭代策略对规整路径进行优化,提高了道集的校正精度和同相轴的一致性。特别是在复杂地质条件下,ISDTW算法取得了较好的成像效果。合成数据和实际数据的测试结果验证了ISDTW算法在减少局部匹配误差、提升地震成像质量方面的显著优势。
基金supported by the National Basic Priorities Program "973" Project (Grant No.2007CB209600)China Postdoctoral Science Foundation Funded Project
文摘This is a case study of the application of pre-stack inverted elastic parameters to tight-sand reservoir prediction. With the development of oil and gas exploration, pre-stack data and inversion results are increasingly used for production objectives. The pre-stack seismic property studies include not only amplitude verse offset （AVO） but also the characteristics of other elastic property changes. In this paper, we analyze the elastic property parameters characteristics of gas- and wet-sands using data from four gas-sand core types. We found that some special elastic property parameters or combinations can be used to identify gas sands from water saturated sand. Thus, we can do reservoir interpretation and description using different elastic property data from the pre-stack seismic inversion processing. The pre- stack inversion method is based on the simplified Aki-Richard linear equation. The initial model can be generated from well log data and seismic and geologic interpreted horizons in the study area. The input seismic data is angle gathers generated from the common reflection gathers used in pre-stack time or depth migration. The inversion results are elastic property parameters or their combinations. We use a field data example to examine which elastic property parameters or combinations of parameters can most easily discriminate gas sands from background geology and which are most sensitive to pore-fluid content. Comparing the inversion results to well data, we found that it is useful to predict gas reservoirs using λ, λρ, λ/μ, and K/μ properties, which indicate the gas characteristics in the study reservoir.
基金supported by the National 863 Program (Grant No.2006AA06Z206,Sustained supported)the National Science and Technology Major Project (Grant No.2008ZX05006-004)SinoPec Group Marine Facies Research (Grant No.08370502000410)
文摘Pre-stack depth migration velocity analysis is one of the keys to influencing the imaging quality of pre-stack migration.In this paper we cover a residual curvature velocity analysis method on angle-domain common image gathers（ADCIGs） which can depict the relationship between incident angle and migration depth at imaging points and update the migration velocity.Differing from offset-domain common image gathers（ODCIGs）,ADCIGs are not disturbed by the multi-path problem which contributes to imaging artifacts,thus influencing the velocity analysis.On the basis of horizontal layers,we derive the residual depth equation and also propose a velocity analysis workflow for velocity scanning.The tests to synthetic and field data prove the velocity analysis methods adopted in this paper are robust and valid.
基金funded by the National Basic Research Program of China(973 Program)(No.2011 CB201002)the National Natural Science Foundation of China(No.41374117)the great and special projects(No.2011ZX05003-003,2011ZX05005-005-008 HZ,and 2011ZX05006-002)
文摘Angle-domain common-image gathers（ADCIGs） are the basic data in migration velocity analysis（MVA） and amplitude variation with angle（AVA） analysis. We propose a common-angle gather-generating scheme using Kirchhoff PSDM based on the traveltime gradient field. The scheme includes three major operations：（1） to calculate the traveltime field of the source and the receiver based on the dynamic programming approach;（2） to obtain the refl ection angle according to the traveltime gradient field in the image space; and（3） to generate the ADCIGs during the migration process. Because of the computation approach, the method for generating ADCIGs is superior to conventional ray-based methods. We use the proposed ADCIGs generation method in 3D large-scale seismic data. The key points of the method are the following.（1） We use common-shot datasets for migration,（2） we load traveltimes based on the shot aperture, and（3） we use the MPI and Open Mp memory sharing to decrease the amount of input and output（I/O）. Numerical examples using synthetic data suggest that the ADCIGs improve the quality of the velocity and the effectiveness of the 3D angle-gather generation scheme.
