Introduction: The objective of this work was to study maternal deaths noted on arrival in the Gynecology and Obstetrics Department at Fousseyni Daou Hospital in Kayes over a period of 10 years. Materials and Methods: ...Introduction: The objective of this work was to study maternal deaths noted on arrival in the Gynecology and Obstetrics Department at Fousseyni Daou Hospital in Kayes over a period of 10 years. Materials and Methods: This was a cross-sectional, descriptive study with data collection over a period of 10 years;The data collection was retrospective over nine years from January 1, 2013 to December 31, 2021 and prospective over one year from January 1, 2022 to December 31, 2022. This study focused on all patients whose death was noted on arrival during pregnancy, labor or in the postpartum period in the Gynecology-Obstetrics Department of Fousseyni Daou Hospital. Confidentiality and anonymity were respected. The processing and analysis of statistical data were carried out using SPSS 20.0 software. Results: During the study period, we recorded 93 cases of death noted on arrival out of a total of 606 maternal deaths, i.e., a frequency of 15.34%. The average age was 27 years with the extremes of 20 years and 34 years. They came mainly from rural areas at 74%, were married at 82%, uneducated at 51.6%, housewives at 87.1%. The profession of the spouses is worker at 37.6%. In our sample, evacuated patients were the most represented with 75.3%. Postpartum hemorrhage was the most frequent reason for admission with 22.6%. The deceased patients had no medical history at 86%. In our series, 59.5% of the deceased patients had not had antenatal consultations (CPN). Patients who died on arrival and who had given birth at home were the most represented with 54.8%. Deaths from immediate postpartum hemorrhage complicated by shock were the most frequent with 25.8% followed by severe anemia 8.6%. Deaths were mainly due to direct obstetric causes at 76.3%. In these deaths observed on arrival, the 2nd delay was identified at 48.4%. Conclusion: Maternal deaths observed on arrival remain frequent in the Kayes region. The main causes are immediate postpartum hemorrhage and anemia, which are almost all preventable causes of maternal death following the 1st and 2nd delay.展开更多
Accurately picking P-and S-wave arrivals of microseismic(MS)signals in real-time directly influences the early warning of rock mass failure.A common contradiction between accuracy and computation exists in the current...Accurately picking P-and S-wave arrivals of microseismic(MS)signals in real-time directly influences the early warning of rock mass failure.A common contradiction between accuracy and computation exists in the current arrival picking methods.Thus,a real-time arrival picking method of MS signals is constructed based on a convolutional-recurrent neural network(CRNN).This method fully utilizes the advantages of convolutional layers and gated recurrent units(GRU)in extracting short-and long-term features,in order to create a precise and lightweight arrival picking structure.Then,the synthetic signals with field noises are used to evaluate the hyperparameters of the CRNN model and obtain an optimal CRNN model.The actual operation on various devices indicates that compared with the U-Net method,the CRNN method achieves faster arrival picking with less performance consumption.An application of large underground caverns in the Yebatan hydropower station(YBT)project shows that compared with the short-term average/long-term average(STA/LTA),Akaike information criterion(AIC)and U-Net methods,the CRNN method has the highest accuracy within four sampling points,which is 87.44%for P-wave and 91.29%for S-wave,respectively.The sum of mean absolute errors(MAESUM)of the CRNN method is 4.22 sampling points,which is lower than that of the other methods.Among the four methods,the MS sources location calculated based on the CRNN method shows the best consistency with the actual failure,which occurs at the junction of the shaft and the second gallery.Thus,the proposed method can pick up P-and S-arrival accurately and rapidly,providing a reference for rock failure analysis and evaluation in engineering applications.展开更多
目的通过评价中药复方防治胃癌前病变(PLGC)动物实验研究的方法学质量及报告质量,分析实验过程中的偏倚风险及研究报告的不足,为提高中药复方防治PLGC动物实验研究质量提供参考。方法计算机检索中国知识资源总库(CNKI)、万方数据知识服...目的通过评价中药复方防治胃癌前病变(PLGC)动物实验研究的方法学质量及报告质量,分析实验过程中的偏倚风险及研究报告的不足,为提高中药复方防治PLGC动物实验研究质量提供参考。方法计算机检索中国知识资源总库(CNKI)、万方数据知识服务平台(WanfangData)、中文科技期刊数据库(VIP)、中国生物医学文献数据库(CBM)、PubMed、Cochrane Library、Web of Science和Embase数据库2014年1月1日-2024年2月23日发表的关于中药复方防治PLGC动物实验文献,采用SYRCLE工具和ARRIVE2.0指南对纳入文献进行评分并计算各条目“低风险”符合率。结果共纳入文献213篇,其中中文文献189篇、英文文献24篇。SYRCLE工具评分为(12.86±1.29)分,“低风险”符合率为32.79%。ARRIVE2.0指南必备条目评分为(24.15±2.80)分,“低风险”符合率为49.08%;推荐条目评分为(11.28±3.40)分,“低风险”符合率为30.27%。SYRCLE工具评价中,144项(67.61%)研究未详细阐述分配序列产生的方法,所有研究均未描述分配隐藏充分与否及实施偏倚过程中的盲法,7项(3.29%)研究描述对结果评价者施盲。ARRIVE2.0指南中,所有研究均未报告样本量的确定方法、均未提供用于确定样本量的结局指标及实验方案注册声明,51项(23.94%)研究明确提出PLGC造模成功标准,66项(30.96%)研究提供所使用统计方法的详细信息,29项(13.62%)研究提供完整的伦理声明,22项(10.33%)报告了利益冲突。结论2014-2024年发表的中药复方防治PLGC动物实验文献方法学质量及报告质量存在较多问题,尤其是在实验过程中随机盲法策略的实施、样本量计算细节及纳入排除标准报告等方面存在缺陷,建议今后研究参考SYRCLE工具及ARRIVE2.0指南清单,以优化研究方案和报告,提高PLGC动物实验研究结果的可信度与规范性。展开更多
In underwater acoustic applications,the conventional cyclic direction of arrival algorithm faces challenges,including a low signal-to-noise ratio and high bandwidth when compared with modulated frequencies.In response...In underwater acoustic applications,the conventional cyclic direction of arrival algorithm faces challenges,including a low signal-to-noise ratio and high bandwidth when compared with modulated frequencies.In response to these issues,this paper introduces a novel,robust,and broadband cyclic beamforming algorithm.The proposed method substitutes the conventional cyclic covariance matrix with the variance of the cyclic covariance matrix as its primary feature.Assuming that the same frequency band shares a common steering vector,the new algorithm achieves superior detection performance for targets with specific modulation frequencies while suppressing interference signals and background noise.Experimental results demonstrate a significant enhancement in the directibity index by 81%and 181%when compared with the traditional Capon beamforming algorithm and the traditional extended wideband spectral cyclic MUSIC(EWSCM)algorithm,respectively.Moreover,the proposed algorithm substantially reduces computational complexity to 1/40th of that of the EWSCM algorithm,employing frequency band statistical averaging and covariance matrix variance.展开更多
为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用...为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用Aberth-Newton迭代法来迭代求解方程。通过计算机仿真验证了基于多根长馈线天线基站的TDOA定位模型和解法的有效性,并对该模型的多解问题进行了分析,用优化基站布局的方案,解决了定位模型的唯一解问题。本定位模型在覆盖范围数百米时,定位精度可达分米级。展开更多
This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfac...This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.展开更多
Purpose-The design goal for the tracking interval of high-speed railway trains in China is 3 min,but it is difficult to achieve,and it is widely believed that it is mainly limited by the tracking interval of train arr...Purpose-The design goal for the tracking interval of high-speed railway trains in China is 3 min,but it is difficult to achieve,and it is widely believed that it is mainly limited by the tracking interval of train arrivals.If the train arrival tracking interval can be compressed,it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval.The goal of this article is to study how to compress the train arrival tracking interval.Design/methodologylapproach-By simulating the process of dense train groups arriving at the station and stopping,the headway between train arrivals at the station was calculated,and the pattern of train arrival headway was obtained,changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.Findings-When the running speed of trains is high,the headway between trains is short,the length of the station approach throat area is considerable and frequent train arrivals at the station,the arrival headway for the first group or several groups of trains will exceed the headway,but the subsequent sets of trains will havea headway equal to the arrival headway.This convergence characteristic is obtained by appropriately increasing the running time.Originality/value-According to this pattern,there is no need to overly emphasize the impact of train arrival headway on the headway.This plays an important role in compressing train headway and improving high-speedrailwaycapacity.展开更多
Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based ...Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.展开更多
Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suf...Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.展开更多
为实现前沿阵地和地面防空两种典型应用约束条件下到达时间差(Time Difference of Arrival,TDOA)定位的自动布站优化,分析了这两种典型约束条件下的基站长度、站点间角度等对定位精度的影响。提出了基于自适应差分进化(Differential Evo...为实现前沿阵地和地面防空两种典型应用约束条件下到达时间差(Time Difference of Arrival,TDOA)定位的自动布站优化,分析了这两种典型约束条件下的基站长度、站点间角度等对定位精度的影响。提出了基于自适应差分进化(Differential Evolution,DE)算法的TDOA自动布站优化策略。采用该算法对这两种约束条件下的自动布站优化进行了仿真分析,并对自适应DE算法计算复杂度进行了分析。结果表明,自适应DE算法的自动布站复杂度低,收敛快速,与定位误差分析一致,具有较好的全局寻优能力。展开更多
Serious stretch appears in shallow long offsset signals after NMO correction. In this article we study the generation mechanism of NMO stretch, demonstrate that the conventional travel time equation cannot accurately ...Serious stretch appears in shallow long offsset signals after NMO correction. In this article we study the generation mechanism of NMO stretch, demonstrate that the conventional travel time equation cannot accurately describe the travel time of the samples within the same reflection wavelet. As a result, conventional NMO inversion based on the travel time of the wavelet＇s central point occurs with errors. In this article, a travel time equation for the samples within the same wavelet is reconstructed through our theoretical derivation （the shifted first arrival point travel time equation）, a new NMO inversion method based on the wavelet＇s first arrival point is proposed. While dealing with synthetic data, the semblance coefficient algorithm equation is modified so that wavelet first arrival points can be extracted. After that, NMO inversion based on the new velocity analysis is adopted on shot offset records. The precision of the results is significantly improved compared with the traditional method. Finally, the block move NMO correction based on the first arrival points travel times is adopted on long offset records and non-stretched results are achieved, which verify the proposed new equation.展开更多
时差定位(Time Difference of Arrival,TDOA)是一种广泛应用的被动定位技术,具有定位精度高、组网能力强、系统鲁棒性强等特点。针对运动目标定位计算复杂、精度收敛较慢等问题,在给出视距(Line of Sight,LOS)环境下定位模型的基础上,...时差定位(Time Difference of Arrival,TDOA)是一种广泛应用的被动定位技术,具有定位精度高、组网能力强、系统鲁棒性强等特点。针对运动目标定位计算复杂、精度收敛较慢等问题,在给出视距(Line of Sight,LOS)环境下定位模型的基础上,提出了定位适用于多站时差定位系统的定位方法,该方法将组群时差定位关系方程合理地线性化为统计估计问题,利用在线迭代实时求解目标位置。给出了针对目标不同运动特性条件下的多平台协同定位算法及其仿真结果,仿真结果表明所述方法可以实现对目标的精确定位,并且分析了运动形式对于定位精度的影响,仿真结果对于系统的工程设计具有指导作用。展开更多
This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the b...This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.展开更多
文摘Introduction: The objective of this work was to study maternal deaths noted on arrival in the Gynecology and Obstetrics Department at Fousseyni Daou Hospital in Kayes over a period of 10 years. Materials and Methods: This was a cross-sectional, descriptive study with data collection over a period of 10 years;The data collection was retrospective over nine years from January 1, 2013 to December 31, 2021 and prospective over one year from January 1, 2022 to December 31, 2022. This study focused on all patients whose death was noted on arrival during pregnancy, labor or in the postpartum period in the Gynecology-Obstetrics Department of Fousseyni Daou Hospital. Confidentiality and anonymity were respected. The processing and analysis of statistical data were carried out using SPSS 20.0 software. Results: During the study period, we recorded 93 cases of death noted on arrival out of a total of 606 maternal deaths, i.e., a frequency of 15.34%. The average age was 27 years with the extremes of 20 years and 34 years. They came mainly from rural areas at 74%, were married at 82%, uneducated at 51.6%, housewives at 87.1%. The profession of the spouses is worker at 37.6%. In our sample, evacuated patients were the most represented with 75.3%. Postpartum hemorrhage was the most frequent reason for admission with 22.6%. The deceased patients had no medical history at 86%. In our series, 59.5% of the deceased patients had not had antenatal consultations (CPN). Patients who died on arrival and who had given birth at home were the most represented with 54.8%. Deaths from immediate postpartum hemorrhage complicated by shock were the most frequent with 25.8% followed by severe anemia 8.6%. Deaths were mainly due to direct obstetric causes at 76.3%. In these deaths observed on arrival, the 2nd delay was identified at 48.4%. Conclusion: Maternal deaths observed on arrival remain frequent in the Kayes region. The main causes are immediate postpartum hemorrhage and anemia, which are almost all preventable causes of maternal death following the 1st and 2nd delay.
基金We acknowledge the funding support from National Natural Science Foundation of China(Grant No.42077263).
文摘Accurately picking P-and S-wave arrivals of microseismic(MS)signals in real-time directly influences the early warning of rock mass failure.A common contradiction between accuracy and computation exists in the current arrival picking methods.Thus,a real-time arrival picking method of MS signals is constructed based on a convolutional-recurrent neural network(CRNN).This method fully utilizes the advantages of convolutional layers and gated recurrent units(GRU)in extracting short-and long-term features,in order to create a precise and lightweight arrival picking structure.Then,the synthetic signals with field noises are used to evaluate the hyperparameters of the CRNN model and obtain an optimal CRNN model.The actual operation on various devices indicates that compared with the U-Net method,the CRNN method achieves faster arrival picking with less performance consumption.An application of large underground caverns in the Yebatan hydropower station(YBT)project shows that compared with the short-term average/long-term average(STA/LTA),Akaike information criterion(AIC)and U-Net methods,the CRNN method has the highest accuracy within four sampling points,which is 87.44%for P-wave and 91.29%for S-wave,respectively.The sum of mean absolute errors(MAESUM)of the CRNN method is 4.22 sampling points,which is lower than that of the other methods.Among the four methods,the MS sources location calculated based on the CRNN method shows the best consistency with the actual failure,which occurs at the junction of the shaft and the second gallery.Thus,the proposed method can pick up P-and S-arrival accurately and rapidly,providing a reference for rock failure analysis and evaluation in engineering applications.
文摘目的通过评价中药复方防治胃癌前病变(PLGC)动物实验研究的方法学质量及报告质量,分析实验过程中的偏倚风险及研究报告的不足,为提高中药复方防治PLGC动物实验研究质量提供参考。方法计算机检索中国知识资源总库(CNKI)、万方数据知识服务平台(WanfangData)、中文科技期刊数据库(VIP)、中国生物医学文献数据库(CBM)、PubMed、Cochrane Library、Web of Science和Embase数据库2014年1月1日-2024年2月23日发表的关于中药复方防治PLGC动物实验文献,采用SYRCLE工具和ARRIVE2.0指南对纳入文献进行评分并计算各条目“低风险”符合率。结果共纳入文献213篇,其中中文文献189篇、英文文献24篇。SYRCLE工具评分为(12.86±1.29)分,“低风险”符合率为32.79%。ARRIVE2.0指南必备条目评分为(24.15±2.80)分,“低风险”符合率为49.08%;推荐条目评分为(11.28±3.40)分,“低风险”符合率为30.27%。SYRCLE工具评价中,144项(67.61%)研究未详细阐述分配序列产生的方法,所有研究均未描述分配隐藏充分与否及实施偏倚过程中的盲法,7项(3.29%)研究描述对结果评价者施盲。ARRIVE2.0指南中,所有研究均未报告样本量的确定方法、均未提供用于确定样本量的结局指标及实验方案注册声明,51项(23.94%)研究明确提出PLGC造模成功标准,66项(30.96%)研究提供所使用统计方法的详细信息,29项(13.62%)研究提供完整的伦理声明,22项(10.33%)报告了利益冲突。结论2014-2024年发表的中药复方防治PLGC动物实验文献方法学质量及报告质量存在较多问题,尤其是在实验过程中随机盲法策略的实施、样本量计算细节及纳入排除标准报告等方面存在缺陷,建议今后研究参考SYRCLE工具及ARRIVE2.0指南清单,以优化研究方案和报告,提高PLGC动物实验研究结果的可信度与规范性。
基金supported by the IOA Frontier Exploration Project (No.ZYTS202001)the Youth Innovation Promotion Association CAS。
文摘In underwater acoustic applications,the conventional cyclic direction of arrival algorithm faces challenges,including a low signal-to-noise ratio and high bandwidth when compared with modulated frequencies.In response to these issues,this paper introduces a novel,robust,and broadband cyclic beamforming algorithm.The proposed method substitutes the conventional cyclic covariance matrix with the variance of the cyclic covariance matrix as its primary feature.Assuming that the same frequency band shares a common steering vector,the new algorithm achieves superior detection performance for targets with specific modulation frequencies while suppressing interference signals and background noise.Experimental results demonstrate a significant enhancement in the directibity index by 81%and 181%when compared with the traditional Capon beamforming algorithm and the traditional extended wideband spectral cyclic MUSIC(EWSCM)algorithm,respectively.Moreover,the proposed algorithm substantially reduces computational complexity to 1/40th of that of the EWSCM algorithm,employing frequency band statistical averaging and covariance matrix variance.
文摘为解决多基站定位模型中基站之间同步代价高的问题,提出了一种基于多根长馈线天线基站的到达时间差(Time Difference of Arrival,TDOA)定位模型,给出了模型方程和求解方法,该方法将复杂的3对距离差方程组转化为1个一元八次方程,然后采用Aberth-Newton迭代法来迭代求解方程。通过计算机仿真验证了基于多根长馈线天线基站的TDOA定位模型和解法的有效性,并对该模型的多解问题进行了分析,用优化基站布局的方案,解决了定位模型的唯一解问题。本定位模型在覆盖范围数百米时,定位精度可达分米级。
文摘This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.
基金State Railway Corporation of China Limited under the Science and Technology Research and Development Programme(2021X007)China Academy of Railway Research(2021YJ012)+1 种基金National Natural Science Foundation of China(52302417)Natural Science Foundation of Sichuan Province of China(2023NSFSC0906).
文摘Purpose-The design goal for the tracking interval of high-speed railway trains in China is 3 min,but it is difficult to achieve,and it is widely believed that it is mainly limited by the tracking interval of train arrivals.If the train arrival tracking interval can be compressed,it will be beneficial for China's high-speed railway to achieve a 3-min train tracking interval.The goal of this article is to study how to compress the train arrival tracking interval.Design/methodologylapproach-By simulating the process of dense train groups arriving at the station and stopping,the headway between train arrivals at the station was calculated,and the pattern of train arrival headway was obtained,changing the traditional understanding that the train arrival headway is considered the main factor limiting the headway of trains.Findings-When the running speed of trains is high,the headway between trains is short,the length of the station approach throat area is considerable and frequent train arrivals at the station,the arrival headway for the first group or several groups of trains will exceed the headway,but the subsequent sets of trains will havea headway equal to the arrival headway.This convergence characteristic is obtained by appropriately increasing the running time.Originality/value-According to this pattern,there is no need to overly emphasize the impact of train arrival headway on the headway.This plays an important role in compressing train headway and improving high-speedrailwaycapacity.
基金supported by the National Natural Science Foundation of China(No.51279033).
文摘Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.
基金National Natural Science Foundation of China(61973037)National 173 Program Project(2019-JCJQ-ZD-324)。
文摘Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.
文摘为实现前沿阵地和地面防空两种典型应用约束条件下到达时间差(Time Difference of Arrival,TDOA)定位的自动布站优化,分析了这两种典型约束条件下的基站长度、站点间角度等对定位精度的影响。提出了基于自适应差分进化(Differential Evolution,DE)算法的TDOA自动布站优化策略。采用该算法对这两种约束条件下的自动布站优化进行了仿真分析,并对自适应DE算法计算复杂度进行了分析。结果表明,自适应DE算法的自动布站复杂度低,收敛快速,与定位误差分析一致,具有较好的全局寻优能力。
基金sponsored by the National Natural Science Foundation of China (No. 41074075)
文摘Serious stretch appears in shallow long offsset signals after NMO correction. In this article we study the generation mechanism of NMO stretch, demonstrate that the conventional travel time equation cannot accurately describe the travel time of the samples within the same reflection wavelet. As a result, conventional NMO inversion based on the travel time of the wavelet＇s central point occurs with errors. In this article, a travel time equation for the samples within the same wavelet is reconstructed through our theoretical derivation （the shifted first arrival point travel time equation）, a new NMO inversion method based on the wavelet＇s first arrival point is proposed. While dealing with synthetic data, the semblance coefficient algorithm equation is modified so that wavelet first arrival points can be extracted. After that, NMO inversion based on the new velocity analysis is adopted on shot offset records. The precision of the results is significantly improved compared with the traditional method. Finally, the block move NMO correction based on the first arrival points travel times is adopted on long offset records and non-stretched results are achieved, which verify the proposed new equation.
文摘时差定位(Time Difference of Arrival,TDOA)是一种广泛应用的被动定位技术,具有定位精度高、组网能力强、系统鲁棒性强等特点。针对运动目标定位计算复杂、精度收敛较慢等问题,在给出视距(Line of Sight,LOS)环境下定位模型的基础上,提出了定位适用于多站时差定位系统的定位方法,该方法将组群时差定位关系方程合理地线性化为统计估计问题,利用在线迭代实时求解目标位置。给出了针对目标不同运动特性条件下的多平台协同定位算法及其仿真结果,仿真结果表明所述方法可以实现对目标的精确定位,并且分析了运动形式对于定位精度的影响,仿真结果对于系统的工程设计具有指导作用。
基金supported by the National Natural Science Foundation of China(Grant Nos.61071163,61271327,and 61471191)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics,China(Grant No.BCXJ14-08)+2 种基金the Funding of Innovation Program for Graduate Education of Jiangsu Province,China(Grant No.KYLX 0277)the Fundamental Research Funds for the Central Universities,China(Grant No.3082015NP2015504)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PADA),China
文摘This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.
