An anti-main-lobe jamming algorithm for airborne early warning radar based on APC-SVRGD joint optimization

Main lobe jamming seriously affects the detection performance of airborne early warning radar.The joint processing of polarization-space has become an effective way to suppress the main lobe jamming.To avoid the main beam distortion and wave crest migration caused by the main lobe jamming in adaptive beamforming,a joint optimization algorithm based on adaptive polarization canceller(APC)and stochastic variance reduction gradient descent(SVRGD)is proposed.First,the polarization plane array structure and receiving signal model based on primary and auxiliary array cancellation are established,and an APC iterative algorithm model is constructed to calculate the optimal weight vector of the auxiliary channel.Second,based on the stochastic gradient descent principle,the variance reduction method is introduced to modify the gradient through internal and external iteration to reduce the variance of the stochastic gradient estimation,the airspace optimal weight vector is calculated and the equivalent weight vector is introduced to measure the beamforming effect.Third,by setting up a planar polarization array simulation scene,the performance of the algorithm against the interference of the main lobe and the side lobe is analyzed,and the effectiveness of the algorithm is verified under the condition of short snapshot number and certain signal to interference plus noise ratio.

Designing a Secure and Scalable Data Sharing Mechanism Using Decentralized Identifiers(DID)

Centralized storage and identity identification methods pose many risks,including hacker attacks,data misuse,and single points of failure.Additionally,existing centralized identity management methods face interoperability issues and rely on a single identity provider,leaving users without control over their identities.Therefore,this paper proposes a mechanism for identity identification and data sharing based on decentralized identifiers.The scheme utilizes blockchain technology to store the identifiers and data hashed on the chain to ensure permanent identity recognition and data integrity.Data is stored on InterPlanetary File System(IPFS)to avoid the risk of single points of failure and to enhance data persistence and availability.At the same time,compliance with World Wide Web Consortium(W3C)standards for decentralized identifiers and verifiable credentials increases the mechanism’s scalability and interoperability.

Re-identifying beef cattle using improved AlignedReID++

Accurate and continuous identification of individual cattle is crucial to precision farming in recent years.It is also the prerequisite to monitor the individual feed intake and feeding time of beef cattle at medium to long distances over different cameras.However,beef cattle can tend to frequently move and change their feeding position during feeding.Furthermore,the great variations in their head direction and complex environments(light,occlusion,and background)can also lead to some difficulties in the recognition,particularly for the bio-similarities among individual cattle.Among them,AlignedReID++model is characterized by both global and local information for image matching.In particular,the dynamically matching local information(DMLI)algorithm has been introduced into the local branch to automatically align the horizontal local information.In this research,the AlignedReID++model was utilized and improved to achieve the better performance in cattle re-identification(ReID).Initially,triplet attention(TA)modules were integrated into the BottleNecks of ResNet50 Backbone.The feature extraction was then enhanced through cross-dimensional interactions with the minimal computational overhead.Since the TA modules in AlignedReID++baseline model increased the model size and floating point operations(FLOPs)by 0.005 M and 0.05 G,the rank-1 accuracy and mean average precision(mAP)were improved by 1.0 percentage points and 2.94 percentage points,respectively.Specifically,the rank-1 accuracies were outperformed by 0.86 percentage points and 0.12 percentage points,respectively,compared with the convolution block attention module(CBAM)and efficient channel attention(ECA)modules,although 0.94 percentage points were lower than that of squeeze-and-excitation(SE)modules.The mAP metric values were exceeded by 0.22,0.86 and 0.12 percentage points,respectively,compared with the SE,CBAM,and ECA modules.Additionally,the Cross-Entropy Loss function was replaced with the CosFace Loss function in the global branch of baseline model.CosFace Loss and Hard Triplet Loss were jointly employed to train the baseline model for the better identification on the similar individuals.AlignedReID++with CosFace Loss was outperformed the baseline model by 0.24 and 0.92 percentage points in the rank-1 accuracy and mAP,respectively,whereas,AlignedReID++with ArcFace Loss was exceeded by 0.36 and 0.56 percentage points,respectively.The improved model with the TA modules and CosFace Loss was achieved in a rank-1 accuracy of 94.42%,rank-5 accuracy of 98.78%,rank-10 accuracy of 99.34%,mAP of 63.90%,FLOPs of 5.45 G,frames per second(FPS)of 5.64,and model size of 23.78 M.The rank-1 accuracies were exceeded by 1.84,4.72,0.76 and 5.36 percentage points,respectively,compared with the baseline model,part-based convolutional baseline(PCB),multiple granularity network(MGN),and relation-aware global attention(RGA),while the mAP metrics were surpassed 6.42,5.86,4.30 and 7.38 percentage points,respectively.Meanwhile,the rank-1 accuracy was 0.98 percentage points lower than TransReID,but the mAP metric was exceeded by 3.90 percentage points.Moreover,the FLOPs of improved model were only 0.05 G larger than that of baseline model,while smaller than those of PCB,MGN,RGA,and TransReID by 0.68,6.51,25.4,and 16.55 G,respectively.The model size of improved model was 23.78 M,which was smaller than those of the baseline model,PCB,MGN,RGA,and TransReID by 0.03,2.33,45.06,14.53 and 62.85 M,respectively.The inference speed of improved model on a CPU was lower than those of PCB,MGN,and baseline model,but higher than TransReID and RGA.The t-SNE feature embedding visualization demonstrated that the global and local features were achieve in the better intra-class compactness and inter-class variability.Therefore,the improved model can be expected to effectively re-identify the beef cattle in natural environments of breeding farm,in order to monitor the individual feed intake and feeding time.

The Research about A Regional Drought Identifying System Based on Dynamic Drought Index

With the data of daily precipitation and daily evaporation,dynamic drought index was calculated and compared with the identification standard of drought grade to qualify the severity of drought.According to the dynamic drought index,a regional drought identifying system was developed for the watershed between the reach of the Yangtze River and Huaihe River in Anhui Province by using VC++ working platform and Access database.This drought identifying system would be very useful to forecast and early warn the happening of drought in this area.

利用Identifiler分型系统推断同胞关系

目的探讨自主开发的同胞关系鉴定自动分析软件（ASI）对Identifiler分型系统进行同胞关系鉴定的可行性。方法应用本课题组所开发的软件ASI,对151对同胞及31 224对人工模拟无关个体进行Identifiler系统的15个常染色体STR基因座分型进行分析,计算亲权指数（PI）、同胞关系概率（WFS）和等位基因匹配情况,所获数据进行统计分析,自动计算排序。结果当WFS大于99.999%时,同胞个体占39.07%,无关个体占0%,两组具有显著差异,可以推断两个体同胞关系。当WFS介于1%~99.999%范围内,同胞个体和无关个体有部分重叠,同胞个体占60.93%,无关个体占21.3%,两者具有一定差异,可以通过增加检测STR基因座,再结合案情加作Y-STR、m tDNA检测,以推断两个体是否具有同胞关系。当WFS小于1%时,同胞个体占0%,无关个体占78.7%,可以推断两个体不具有同胞关系。个体间等位基因匹配结果表明：在检测Identifiler体系15个STR基因座时,当两个体常染色体STR基因座的全相同数目≥5时,或全不同数目≤1时,提示为同胞关系;当两个体全不同数目≥6时,或全相同数目≤1时,提示为无关个体,以此作为预测有无同胞关系的界值。结论Identifiler系统及同胞关系鉴定自动分析软件ASI可用于推断同胞关系。

IDentifier DNA分型盒(炎黄34)的法医学验证及应用评估

目的 测试IDentifier DNA分型盒(炎黄34)的技术性能指标,评估其法医学应用价值。方法 根据《法庭科学人类荧光标记STR复合扩增检测试剂质量基本要求》(GB/T 37226—2018),从种属特异性、分型准确性、灵敏度、适应性、耐受性、一致性、均衡性、反应条件验证、混合样本、稳定性、批间差11个方面对IDentifier DNA分型盒(炎黄34)进行测试。比较IDentifier DNA分型盒(炎黄34)与Power Plex~?Fusion 6C系统、Versa Plex~?27PY系统、Veri Filer~(TM) Plus PCR扩增试剂盒的系统效能。使用IDentifier DNA分型盒(炎黄34)检测日常案件中的拭子类生物检材,观察其STR检验结果 。结果 IDentifier DNA分型盒(炎黄34)具有良好的种属特异性、分型准确性、适应性、耐受性和均衡性,灵敏度可达0.062 5 ng,能检测案件中不同类型的检材、降解检材及混有抑制剂的检材,对混合比例为4∶1以内的样本均能获得完整分型。该试剂盒的系统效能非常高,TDP可达1-1.08×10~(-37),CPE_(trio)和CPE_(duo)分别可达1-5.47×10~(-14)和1-6.43×10~(-9)。针对案件中的接触类生物检材,其有效检出率可达21.05%。针对亲缘关系鉴定,单亲鉴定和全同胞鉴定的系统效能均得到了有效提高。结论 IDentifier DNA分型盒(炎黄34)的上述性能指标符合要求,可用于个体识别及亲权鉴定,适合在法庭科学领域应用。

DC中Identifier项的模式体系标准化探析

网络时代 ,元数据 DC是组织网络信息资源的重要手段 ,为了增强互操作性其元素项的限定是必要的。本文在分析了 DC中 Identifier项的模式体系的基础上 ,探讨了 URI、URL 和 URN三者之间的关系 ,认为 U RN将成为该项模式体系的国际标准。

Structural modal parameter identifi cation and damage diagnosis based on Hilbert-Huang transform

Traditional modal parameter identifi cation methods have many disadvantages,especially when used for processing nonlinear and non-stationary signals.In addition,they are usually not able to accurately identify the damping ratio and damage.In this study,methods based on the Hilbert-Huang transform(HHT) are investigated for structural modal parameter identifi cation and damage diagnosis.First,mirror extension and prediction via a radial basis function(RBF) neural network are used to restrain the troublesome end-effect issue in empirical mode decomposition(EMD),which is a crucial part of HHT.Then,the approaches based on HHT combined with other techniques,such as the random decrement technique(RDT),natural excitation technique(NExT) and stochastic subspace identifi cation(SSI),are proposed to identify modal parameters of structures.Furthermore,a damage diagnosis method based on the HHT is also proposed.Time-varying instantaneous frequency and instantaneous energy are used to identify the damage evolution of the structure.The relative amplitude of the Hilbert marginal spectrum is used to identify the damage location of the structure.Finally,acceleration records at gauge points from shaking table testing of a 12-story reinforced concrete frame model are taken to validate the proposed approaches.The results show that the proposed approaches based on HHT for modal parameter identifi cation and damage diagnosis are reliable and practical.

Dimensional Synthesis for 3-PRS Mechanism Based on Identifiability Performance

Many performance indices for parallel mechanism are put forward in the phase of dimensional synthesis,except for identifiability index,which determines the difficulty of kinematical calibration.If the dimensional parameters are inappropriately selected,the existing methods for optimizing identifiability will not effectively work.Thus,with the aim of studying identifiability optimization in dimensional synthesis for 3-PRS mechanism,kinematics with structural errors is analyzed to provide theoretical bases for kinematical model.Then through a comparison of two 3-PRS mechanisms with different dimensional parameters,identifiability performance is proved to be necessary and feasible for optimization in the phase of dimensional design.Finally,an index δ is proposed to scale the identifiability performance.With the index,identifiability analysis and dimensional synthesis simulation in the whole workspace is completed.The index is verified to be correct and feasible,and based on the index,a procedure of dimensional synthesis,as well as an example set of non-dimensional parameters of 3-PRS mechanism,is proposed.The proposed identifiability index and design method can effectively introduce identifiability optimization into dimensional synthesis,and will obviously benefit later kinematical calibration.

Application of the wavelet packet and its energy spectrum to identify nugget splash during the aluminum alloys spot welding

Nugget splash during aluminum alloys spot welding has a detrimental effect on weld nugget integrity, strength and durability of the welded joints. This investigation is performed to identify nugget splash from voltage signals because these are easily accessible on-line. In the present work, we propose a novel method based on the wavelet packet transform and its energy spectrum for pattern recognition of splash signal. The result demonstrates that this novel method is more accuracy and a useful way of monitoring the spot welding quality.

Identification of variable coefficients for vibrating systems by boundary control and observation

We consider the identification problem of coefficients for vibrating systems described by a Euler-Bernoulli beam eq~. ation Or a string equation, with one end clamped and with an input exerted on the other end. For the beam equation, the observations are the velocity and the angle velocity at the free end, while for the string equation, the observation is the velocity at the free end. In the framework of well-posed linear system theory, we show that both the density and the flexural rigidity of the beam, and the tension of the string, can be uniquely determined by the observations for all positive times. Moreover, a general constructive method is developed to show that the mass density and the elastic modulus of the string are not determined by the observation.

Identification of Conserved Cotton MicroRNAs and Their Targets

No study has been performed on identifying microRNAs(miRNAs) and their targets in cotton although cotton is one of the most important fiber and economic crops around the world.In this study,

Two novel gastric cancer-associated genes identified by differential display

AIM To clone novel gastric cancer-associated genes and investigate their roles in gastric cancer occurrence.METHODS A method called differential display was used which allows the identification of differentially expressed genes by using PAGE to display PCR-amplified cDNA fragments between gastric cancer cells and normal gastric mucosa cells. These fragments were cloned into plasmid vector pUC18. Homology analysis was made after sequencing these fragments.RESULTS Two novel genes were identified compared with sequences from GenBank. One was registered with the AD number AF 051783. In situ hybridization showed that these two novel genes expressed specifically in gastric cancer tissues.CONCLUSION The two novel genes obtained by differential display were confirmed to be gastric cancer-associated genes using in situ hybridization.

A circular zone counting method of identifying a Duffing oscillator state transition and determining the critical value in weak signal detection

Identifying state transition and determining the critical value of the Duffing oscillator are crucial to indicating external signal existence and have a great influence on detection accuracy in weak signal detection. A circular zone counting （CZC） method is proposed in this paper, by combining the Duffing oscillator＇s phase trajectory feature and numerical calculation for quickly and accurately identifying state transition and determining the critical value, to realize a high- efficiency weak signal detection. Detailed model analysis and method construction of the CZC method are introduced. Numerical experiments into the reliability of the proposed CZC method compared with the maximum Lyapunov exponent （MLE） method are carried out. The CZC method is demonstrated to have better detecting ability than the MLE method, and furthermore it is simpler and clearer in calculation to extend to engineering application.

Modal parameter identification of flexible spacecraft using the covariance-driven stochastic subspace identification(SSI-COV) method

In this paper, the on-orbit identification of modal parameters for a spacecraft is investigated. Firstly, the coupled dynamic equation of the system is established with the Lagrange method and the stochastic state-space model of the system is obtained. Then, the covariance-driven stochastic subspace identification（SSI-COV） algorithm is adopted to identify the modal parameters of the system. In this algorithm, it just needs the covariance of output data of the system under ambient excitation to construct a Toeplitz matrix, thus the system matrices are obtained by the singular value decomposition on the Toeplitz matrix and the modal parameters of the system can be found from the system matrices. Finally,numerical simulations are carried out to demonstrate the validity of the SSI-COV algorithm. Simulation results indicate that the SSI-COV algorithm is effective in identifying the modal parameters of the spacecraft only using the output data of the system under ambient excitation.

Experimentation with Personal Identifiable Information

In this paper, actual personal identifiable information (PII) texts are analyzed to capture different types of PII sensitivities. The sensitivity of PII is one of the most important factors in determining an individual’s perception of privacy. A “gradation” of sensitivity of PII can be used in many applications, such as deciding the security level that controls access to data and developing a measure of trust when self-disclosing PII. This paper experiments with a theoretical analysis of PII sensitivity, defines its scope, and puts forward possible methodologies of gradation. A technique is proposed that can be used to develop a classification scheme of personal information depending on types of PII. Some PII expresses relationships among persons, some specifies aspects and features of a person, and some describes relationships with nonhuman objects. Results suggest that decomposing PII into privacy-based portions helps in factoring out non-PII information and focusing on a proprietor’s related information. The results also produce a visual map of the privacy sphere that can be used in approximating the sensitivity of different territories of privacy-related text. Such a map uncovers aspects of the proprietor, the proprietor’s relationship to social and physical entities, and the relationships he or she has with others.

Sliding mode identifier for parameter uncertain nonlinear dynamic systems with nonlinear input

This paper presents a sliding mode (SM) based identifier to deal with the parameter identification problem for a class of parameter uncertain nonlinear dynamic systems with input nonlinearity. A sliding mode controller (SMC) is used to ensure the global reaching condition of the sliding mode for the nonlinear system; an identifier is designed to identify the uncertain parameter of the nonlinear system. A numerical example is studied to show the feasibility of the SM controller and the asymptotical convergence of the identifier.

High-throughput screening of mouse gene knockouts identifies established and novel skeletal phenotypes

Screening gene function in vivo is a powerful approach to discover novel drug targets. We present high-throughput screening （HTS） data for 3 762 distinct global gene knockout （KO） mouse lines with viable adult homozygous mice generated using either gene-trap or homologous recombination technologies. Bone mass was determined from DEXA scans of male and female mice at 14 weeks of age and by microCT analyses of bones from male mice at 16 weeks of age. Wild-type （WT） cagemates/littermates were examined for each gene KO. Lethality was observed in an additional 850 KO lines. Since primary HTS are susceptible to false positive findings, additional cohorts of mice from KO lines with intriguing HTS bone data were examined. Aging, ovariectomy, histomorphometry and bone strength studies were performed and possible non-skeletal phenotypes were explored. Together, these screens identified multiple genes affecting bone mass： 23 previously reported genes （Calcr, Cebpb, Crtap, Dcstamp, Dkkl, Duoxa2, Enppl, Fgf23, Kissl/Kisslr, Kl （Klotho）, Lrp5, Mstn, Neol, Npr2, Ostml, Postn, Sfrp4, S1c30a5, Sic39a13, Sost, Sumf1, Src, Wnt10b）, five novel genes extensively characterized （Cldn18, Fam20c, Lrrkl, Sgpll, Wnt16）, five novel genes with preliminary characterization （Agpat2, RassfS, Slc10a7, Stc26a7, Slc30a10） and three novel undisclosed genes coding for potential osteoporosis drug targets.

Adaptive Control of a Two-Link Robot Using Batch Least-Square Identifier

We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties.Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties.Using conventional control algorithms on such systems can cause not only poor control performance,but also expensive computational costs and catastrophic instabilities.Therefore,system uncertainties need to be estimated through designing a computationally efficient adaptive control law.We focus on robot manipulators as an example of a highly nonlinear system.As a case study,a 2-DOF manipulator subject to four parametric uncertainties is investigated.First,the dynamic equations of the manipulator are derived,and the corresponding regressor matrix is constructed for the unknown parameters.For a general nonlinear system,a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters'estimations.Finally,simulation results are discussed for a two-link manipulator,and the performance of the proposed scheme is thoroughly evaluated.

THE QUASI-BOUNDARY VALUE METHOD FOR IDENTIFYING THE INITIAL VALUE OF THE SPACE-TIME FRACTIONAL DIFFUSION EQUATION

In this article,we consider to solve the inverse initial value problem for an inhomogeneous space-time fractional diffusion equation.This problem is ill-posed and the quasi-boundary value method is proposed to deal with this inverse problem and obtain the series expression of the regularized solution for the inverse initial value problem.We prove the error estimates between the regularization solution and the exact solution by using an a priori regularization parameter and an a posteriori regularization parameter choice rule.Some numerical results in one-dimensional case and two-dimensional case show that our method is efficient and stable.