The Contribution of United States Aircraft Reconnaissance Data to the China Meteorological Administration Tropical Cyclone Intensity Data:An Evaluation of Homogeneity

This paper investigates the homogeneity of United States aircraft reconnaissance data and the impact of these data on the homogeneity of the tropical cyclone(TC)best track data for the seasons 1949-1987 generated by the China Meteorological Administration(CMA).The evaluation of the reconnaissance data shows that the minimum central sea level pressure(MCP)data are relatively homogeneous,whereas the maximum sustained wind(MSW)data show both overestimations and spurious abrupt changes.Statistical comparisons suggest that both the reconnaissance MCP and MSW were well incorporated into the CMA TC best track dataset.Although no spurious abrupt changes were evident in the reconnaissance-related best track MCP data,two spurious changepoints were identified in the remainder of the best-track MCP data.Furthermore,the influence of the reconnaissance MSWs seems to extend to the best track MSWs unrelated to reconnaissance,which might reflect the optimistic confidence in making higher estimates due to the overestimated extreme wind“observations”.In addition,the overestimation of either the reconnaissance MSWs or the best track MSWs was greater during the early decades compared to later decades,which reflects the important influence of reconnaissance data on the CMA TC best track dataset.The wind-pressure relationship(WPR)used in the CMA TC best track dataset is also evaluated and is found to overestimate the MSW,which may lead to inhomogeneity within the dataset between the aircraft reconnaissance era and the satellite era.

Support vector regression-based operational effectiveness evaluation approach to reconnaissance satellite system

As one of the most important part of weapon system of systems(WSoS),quantitative evaluation of reconnaissance satellite system(RSS)is indispensable during its construction and application.Aiming at the problem of nonlinear effectiveness evaluation under small sample conditions,we propose an evaluation method based on support vector regression(SVR)to effectively address the defects of traditional methods.Considering the performance of SVR is influenced by the penalty factor,kernel type,and other parameters deeply,the improved grey wolf optimizer(IGWO)is employed for parameter optimization.In the proposed IGWO algorithm,the opposition-based learning strategy is adopted to increase the probability of avoiding the local optima,the mutation operator is used to escape from premature convergence and differential convergence factors are applied to increase the rate of convergence.Numerical experiments of 14 test functions validate the applicability of IGWO algorithm dealing with global optimization.The index system and evaluation method are constructed based on the characteristics of RSS.To validate the proposed IGWO-SVR evaluation method,eight benchmark data sets and combat simulation are employed to estimate the evaluation accuracy,convergence performance and computational complexity.According to the experimental results,the proposed method outperforms several prediction based evaluation methods,verifies the superiority and effectiveness in RSS operational effectiveness evaluation.

Investigation multi-target synergistic mechanism of Choerospondias axillaris in the treat of cerebral ischemia based on systems pharmacology and experimental verification

Background:Choerospondias axillaris(CA)is a traditional Mongolian medicine that has been proven to have a good therapeutic effect on cerebrovascular disease.Cerebral Ischemia(CI)is a severe and life-threatening cerebrovascular disease.However,the specific mechanism of action of CA in the treatment of CI is still unclear.Methods:In this study,the related targets and pathways of CA in the treatment of CI were first predicted by system pharmacology and then verified by relevant experiments.Results:The results showed that 12 active ingredients and 208 targets were selected.Further validation through protein-protein interaction(PPI)network analysis and active ingredients-target-pathway(A-T-P)network analysis has confirmed the pivotal roles of the main bioactive constituents,including quercetin,kaempferol,naringin,β-sitosterol,and gallic acid.These components exert their anti-ischemic effects by modulating key targets such as IL6,TNF,MAPK3,and CASP3,thereby regulating the PI3K-Akt,HIF-1,and MAPK signaling pathways,which are integral to processes like inflammation,apoptosis,and oxidative stress.More importantly,through experimental verification,this study confirmed our prediction that CAE significantly reduced neurological function scores,infarct volume,and the percentage of apoptosis neurons.Conclusion:This indicates that CA acts on CI through multi-target synergistic mechanism,and this study provides theoretical basis for the clinical application of CA.

Research on Rotating Machinery Fault Diagnosis Based on Improved Multi-target Domain Adversarial Network

Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.

Analysis of detection capabilities of LEO reconnaissance satellite constellation based on coverage performance

In the design problem of low earth orbit(LEO) reconnaissance satellite constellation, optimization of coverage performance is the design goal in most current methods. However,in the using process, the user only concerns with the detection capabilities rather than coverage performance. To establish the relationship between these two aspects, the reconnaissance processes of normal stochastic targets are considered and the mathematic models of detection processes are built. The indicators of coverage performance are used to evaluate the detection probability and expectation of detection time delay, which are important factors in reconnaissance constellation estimation viewed from military intelligence discipline. The conclusions confirmed by the final simulation will be useful in LEO reconnaissance constellation design, optimization and evaluation.

Field reconnaissance of the 2007 Niigata-Chuetsu Oki earthquake

As part of the 2007 Tri-Center Field Mission to Japan, a reconnaissance team comprised of fourteen graduate students and three faculty members from three U.S. earthquake engineering research centers, namely, Multidisciplinary Center for Earthquake Engineering Research （MCEER）, Mid-America Earthquake Center （MAE）, and Pacific Earthquake Engineering Research Center （PEER）, undertook a reconnaissance visit to the affected area shortly after the 2007 Niigata- Chuetsu Oki earthquake. This mission provided an opportunity to review the nature of the earthquake damage that occurred, as well as to assess the significance of the damage from an educational perspective. This paper reports on the seismological characteristics of the earthquake, preliminary findings of geotechnical and structural damage, and the causes of the observed failures or collapses. In addition, economic and socio-economic considerations and experiences to enhance earthquake resilience are presented.

Re-examining Tropical Cyclone Fullness Using Aircraft Reconnaissance Data

We use FLIGHT+ aircraft reconnaissance data for tropical cyclones(TCs) in the North Atlantic and Eastern Pacific from 1997 to 2015 to re-examine TC fullness(TCF) characteristics at the flight level.The results show a strong positive correlation between the flight-level TCF and the intensity of TCs,with the flight-level TCF increasing much more rapidly than the near-surface TCF with increasing intensity of the TCs.The tangential wind in small-TCF hurricanes is statistically significantly stronger near the eye center than that in large-TCF hurricanes.Large-TCF hurricanes have a ring-like vorticity structure.No significant correlation is observed between the flight-level TCF and the comparative extent of the vorticityskirt region occupied in the outer core skirt.The proportion of the rapid filamentation zone in the outer core skirt increases with increasing flight-level TCF.The differences in entropy between the radius of the maximum wind and the outer boundary of the outer core skirt also increase with increasing flight-level TCF.

A multiple heterogeneous UAVs reconnaissance mission planning and re-planning algorithm

Reconnaissance mission planning of multiple unmanned aerial vehicles(UAVs)under an adversarial environment is a discrete combinatorial optimization problem which is proved to be a non-deterministic polynomial(NP)-complete problem.The purpose of this study is to research intelligent multiUAVs reconnaissance mission planning and online re-planning algorithm under various constraints in mission areas.For numerous targets scattered in the wide area,a reconnaissance mission planning and re-planning system is established,which includes five modules,including intelligence analysis,sub-mission area division,mission sequence planning,path smoothing,and online re-planning.The intelligence analysis module depicts the attribute of targets and the heterogeneous characteristic of UAVs and computes the number of sub-mission areas on consideration of voyage distance constraints.In the sub-mission area division module,an improved K-means clustering algorithm is designed to divide the reconnaissance mission area into several sub-mission areas,and each sub-mission is detected by the UAV loaded with various detective sensors.To control reconnaissance cost,the sampling and iteration algorithms are proposed in the mission sequence planning module,which are utilized to solve the optimal or approximately optimal reconnaissance sequence.In the path smoothing module,the Dubins curve is applied to smooth the flight path,which assure the availability of the planned path.Furthermore,an online re-planning algorithm is designed for the uncertain factor that the UAV is damaged.Finally,reconnaissance planning and re-planning experiment results show that the algorithm proposed in this paper are effective and the algorithms designed for sequence planning have a great advantage in solving efficiency and optimality.

Projectile-Borne Video Reconnaissance System

Aiming at applications as a projectile-borne video reconnaissance system, the overall design and prototype in principle of a mortar video reconnaissance system bomb were developed. Mortar launched test results show that the initial integrated system was capable of transmitting images through tens of kilometers with the image resolution identifying effectively tactical targets such as roads, hills, caverns, trees and rivers. The projectile-borne video reconnaissance system is able to meet the needs of tactical target identification and battle damage assessment for tactical operations. The study will provide significant technological support for further independent development.

Fecal microbial biomarkers combined with multi-target stool DNA test improve diagnostic accuracy for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a major global health burden.The current diagnostic tests have shortcomings of being invasive and low accuracy.AIM To explore the combination of intestinal microbiome composition and multi-target stool DNA(MT-sDNA)test in the diagnosis of CRC.METHODS We assessed the performance of the MT-sDNA test based on a hospital clinical trial.The intestinal microbiota was tested using 16S rRNA gene sequencing.This case-control study enrolled 54 CRC patients and 51 healthy controls.We identified biomarkers of bacterial structure,analyzed the relationship between different tumor markers and the relative abundance of related flora components,and distinguished CRC patients from healthy subjects by the linear discriminant analysis effect size,redundancy analysis,and random forest analysis.RESULTS MT-sDNA was associated with Bacteroides.MT-sDNA and carcinoembryonic antigen(CEA)were positively correlated with the existence of Parabacteroides,and alpha-fetoprotein(AFP)was positively associated with Faecalibacterium and Megamonas.In the random forest model,the existence of Streptococcus,Escherichia,Chitinophaga,Parasutterella,Lachnospira,and Romboutsia can distinguish CRC from health controls.The diagnostic accuracy of MT-sDNA combined with the six genera and CEA in the diagnosis of CRC was 97.1%,with a sensitivity and specificity of 98.1%and 92.3%,respectively.CONCLUSION There is a positive correlation of MT-sDNA,CEA,and AFP with intestinal microbiome.Eight biomarkers including six genera of gut microbiota,MT-sDNA,and CEA showed a prominent sensitivity and specificity for CRC prediction,which could be used as a non-invasive method for improving the diagnostic accuracy for this malignancy.

Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

The netted radar system(NRS)has been proved to possess unique advantages in anti-jamming and improving target tracking performance.Effective resource management can greatly ensure the combat capability of the NRS.In this paper,based on the netted collocated multiple input multiple output(CMIMO)radar,an effective joint target assignment and power allocation(JTAPA)strategy for tracking multi-targets under self-defense blanket jamming is proposed.An architecture based on the distributed fusion is used in the radar network to estimate target state parameters.By deriving the predicted conditional Cramer-Rao lower bound(PC-CRLB)based on the obtained state estimation information,the objective function is formulated.To maximize the worst case tracking accuracy,the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes,subject to some resource constraints.Since the formulated JTAPA is non-convex,we propose an efficient two-step solution strategy.In terms of the simulation results,the proposed algorithm can effectively improve tracking performance in the worst case.

Geographical Reconnaissance of Household in Northern Nigeria towards Optimizing Indoor Residual Spraying Method for Malaria Elimination

As a part of an effort to roll back malaria in Nigeria, exploring the use of geographically related tools triggered the use of modern approaches of knowing the spatial distribution of target populations to attain significant malaria elimination intervention. GIS tool was used for geographical reconnaissance (GR), providing demographic data on respondents’ household and spatial information on the distribution of households in the selected location. A cross-sectional study design was used to collect spatial data in the two locations, while a quantitative questionnaire was used to collect the household data. The analysis of field data indicated that 49,500 unique households were enumerated and thus included in the Indoor Residual Spraying to prevent malaria infection, covering 424 towns in the two Local Government Areas (LGAs). 383,301 persons were recorded during the GR exercise in Doma and Nassarawa Eggon LGAs out of which 79,339 were children of agesless than five years, with 13,526 pregnant women. Further data analysis revealed that the average number of persons per household in both LGAs was approximately eight. The spatial information from the GR provides a foundation for an updateable database for any future survey for developmental activities in Nigeria. The use of modern GR approach has proved to be accurate, reliable and more cost effective and less cumbersome than the traditional approach in the collection and geo-positioning of household data. Use of Garmin e-Trex GPS handheld instruments to collect household data in the designated areas removed the constraints of expensive Personal Digital Assistants and reduced errors of wrong location coordinates. Several African countries which did not use GR or applied the use of Geospatial tool appropriately had setbacks. The previous study in other countries showed limitations which was characterized by substantial inherent logistical and technical challenges culminating in missed targets. This setback was addressed in our study.

Multi-Target Tracking of Person Based on Deep Learning

To improve the tracking accuracy of persons in the surveillance video,we proposed an algorithm for multi-target tracking persons based on deep learning.In this paper,we used You Only Look Once v5(YOLOv5)to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric(DeepSORT)to do cascade matching and Intersection Over Union(IOU)matching of person targets between different frames.To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification(ReID)network in the process of cascade matching,we introduced Spatial Relation-aware Global Attention(RGA-S)and Channel Relation-aware Global Attention(RGA-C)attention mechanisms into the network structure.The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03.To enhance the discrimination performance of the network,we proposed a new loss function design method,which introduces the Hard-Negative-Mining way into the benchmark triplet loss.To improve the classification accuracy of the network,we introduced a Label-Smoothing regularization method to the cross-entropy loss.To facilitate the model’s convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training,this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay.The experimental results on CUHK03 show that the mean Average Precision(mAP)of the improved ReID network is 76.5%.The Top 1 is 42.5%,the Top 5 is 65.4%,and the Top 10 is 74.3%in Cumulative Matching Characteristics(CMC);Compared with the original algorithm,the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%,the tracking precision is improved by 3.8%.The number of identity switching is reduced by 25%.The algorithm effectively alleviates the IDSwitch problem,improves the tracking accuracy of persons,and has a high practical value.

基于点云分割网络的雷达信号分选方法

针对现有基于图像分割的端到端雷达信号分选方法存在的像素点重叠与处理效率不高的问题,该文提出一种基于点云分割网络的端到端分选方法。首先将雷达脉冲流的脉冲描述字(PDW)映射为点云;之后利用点云分割网络(PointNet++)对该点云中各点依据其所属辐射源进行分割;最后将具有相同标签的点聚类形成脉冲集合,分别提取各脉冲集合所包含的辐射源并形成相应的辐射源描述字。仿真结果表明:所提方法能够有效对未知雷达信号进行分选,在脉冲丢失和虚假脉冲干扰的分选环境下也表现出较强的可靠性与稳定性,并且由于采用具有轻量化特点的模型使得该方法的执行效率更高。

无人机载光电载荷自主侦察的参数计算与分析

自主侦察是无人机载光电载荷的一种自动化、智能化侦察模式,参数计算则是自主侦察应用的前提。首先介绍了无人机载光电载荷的自主侦察模式;然后对该模式无人机自动巡航飞行速度和飞行高度,光电载荷的传感器视场角、俯仰角、扫描角速度、扫描角度范围等参数进行了计算,并分析了各个参数的物理意义及相互关系,以及对目标识别率、侦察时间等自主侦察效果的影响;最后举例进行仿真计算和分析,并指出在实际应用中存在的问题,为该模式的实际应用提供了理论依据。

跨境电信网络诈骗犯罪的发展与对策

跨境电信网络诈骗作为高发犯罪之一,具有犯罪组织稳定化、诈骗手段多样化、主犯的隐蔽性与从犯的胁迫性等特征。近年来,我国持续加强跨境电信网络诈骗犯罪的打击力度,然而,目前对于打击跨境电信网络诈骗犯罪还存在着许多问题,例如国家之间的法律不一致、调查取证难度大,追缴资金困难,等等。为了有效遏制跨境电信网络犯罪,应当加强与各国的执法合作,加强边境管理,引导出境人员回流,完善赃款追缴机制,进行全链条打击,实施打击与预防犯罪并重等综合策略。

面向对海监视的舰船目标跟踪与航迹融合数据集

对海监视中航迹实时关联与轨迹融合任务是安全防控、区域态势监视、远程精确打击等军民应用领域的热点和难点问题,高质量的数据集对推动目标跟踪与融合技术在该领域的研究有重要作用。本研究针对目标跟踪与融合领域技术研究的数据需求以及目前公开数据集所存在的数据缺乏、场景设计针对性差、数据格式单一、数据描述不全等问题,通过仿真软件对复杂场景中多传感器多目标探测数据进行仿真,提供了一套面向典型对海监视场景(以舰船为探测对象的2D雷达与侦察传感器﹝ESM﹞)的目标跟踪与航迹融合数据集。其中仿真软件包括剧情发生器和传感器模拟器两部分,是一套成熟的目标跟踪场景仿真环境,提供逼真的探测数据模拟能力。本数据集涵盖的传感器对象包括2D雷达与侦察传感器,目标包括典型的海上舰船类别,并支持携带辐射源配置,设计了高速运动、密集交通、多传感器数据融合、特定舰船侦测和交叉定位等多种典型场景。本数据集中共包含368155条目标点迹,舰船数量为101条,时间范围15000秒,数据格式符合实际设备上报情景、探测误差模型符合实际。本数据集通过对数据误差进行正态性检验、对检测率、虚警率的场景检验以及实地调研,完成了对数据的准确性评估与数据完备性控制,可为舰船目标跟踪、轨迹融合等算法研究与验证提供基础数据。

面向无人机海上侦察任务的自适应粒度分解策略研究

无人机海上侦察作战任务自动规划的前提是任务分解,分解策略的通用性和分解粒度的合理性直接影响任务规划的速度和实际效果。对无人机海上侦察任务具有随机性强、复杂度高、动态性明显等特点,提出了一种基于军事领域知识库的无人机海上侦察任务自适应粒度分解策略,构建了无人机海上侦察领域知识图谱和自适应粒度任务分解模型,并以海上编队护航背景下的无人机侦察任务为例,实施了任务分解策略验证,结果表明分解策略较传统任务分解方法具有更佳的稳定性和适应性。

雷达波形对抗技术发展预见

随着雷达、电子侦察等传感器技术的进步,对电磁空间的操控从能量、频率向更高维度的波形发展,雷达波形对抗应运而生。阐述雷达波形对抗技术产生的背景、概念内涵和发展现状,同时提出可利用博弈思维建立雷达波形“OODA”回路,通过对雷达波形进行伪装、扰乱和利用以达成制造战场迷雾、创造新质杀伤链的效果。此外,介绍实现雷达波形对抗的波形隐匿、波形重建与利用、波形捷变反侦察等关键技术及原理。从波形管控等3个方面提出雷达波形对抗制胜电磁空间的发展建议。