Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Research on a Monte Carlo global variance reduction method based on an automatic importance sampling method

Global variance reduction is a bottleneck in Monte Carlo shielding calculations.The global variance reduction problem requires that the statistical error of the entire space is uniform.This study proposed a grid-AIS method for the global variance reduction problem based on the AIS method,which was implemented in the Monte Carlo program MCShield.The proposed method was validated using the VENUS-Ⅲ international benchmark problem and a self-shielding calculation example.The results from the VENUS-Ⅲ benchmark problem showed that the grid-AIS method achieved a significant reduction in the variance of the statistical errors of the MESH grids,decreasing from 1.08×10^(-2) to 3.84×10^(-3),representing a 64.00% reduction.This demonstrates that the grid-AIS method is effective in addressing global issues.The results of the selfshielding calculation demonstrate that the grid-AIS method produced accurate computational results.Moreover,the grid-AIS method exhibited a computational efficiency approximately one order of magnitude higher than that of the AIS method and approximately two orders of magnitude higher than that of the conventional Monte Carlo method.

Association between triglyceride-glucose index and colorectal polyps:A retrospective cross-sectional study

BACKGROUND Colorectal polyps(CPs)are frequently occurring abnormal growths in the colorectum,and are a primary precursor of colorectal cancer(CRC).The triglyceride-glucose(TyG)index is a novel marker that assesses metabolic health and insulin resistance,and has been linked to gastrointestinal cancers.AIM To investigate the potential association between the TyG index and CPs,as the relation between them has not been documented.METHODS A total of 2537 persons undergoing a routine health physical examination and colonoscopy at The First People's Hospital of Kunshan,Jiangsu Province,China,between January 2020 and December 2022 were included in this retrospective cross-sectional study.After excluding individuals who did not meet the eligibility criteria,descriptive statistics were used to compare characteristics between patients with and without CPs.Logistic regression analyses were conducted to determine the associations between the TyG index and the prevalence of CPs.The TyG index was calculated using the following formula:Ln[triglyceride(mg/dL)×glucose(mg/dL)/2].The presence and types of CPs was determined based on data from colonoscopy reports and pathology reports.RESULTS A nonlinear relation between the TyG index and the prevalence of CPs was identified,and exhibited a curvilinear pattern with a cut-off point of 2.31.A significant association was observed before the turning point,with an odds ratio(95% confidence interval)of 1.70(1.40,2.06),P<0.0001.However,the association between the TyG index and CPs was not significant after the cut-off point,with an odds ratio(95% confidence interval)of 0.57(0.27,1.23),P=0.1521.CONCLUSION Our study revealed a curvilinear association between the TyG index and CPs in Chinese individuals,suggesting its potential utility in developing colonoscopy screening strategies for preventing CRC.

Multivariate form of Hermite sampling series

In this paper,we establish a new multivariate Hermite sampling series involving samples from the function itself and its mixed and non-mixed partial derivatives of arbitrary order.This multivariate form of Hermite sampling will be valid for some classes of multivariate entire functions,satisfying certain growth conditions.We will show that many known results included in Commun Korean Math Soc,2002,17:731-740,Turk J Math,2017,41:387-403 and Filomat,2020,34:3339-3347 are special cases of our results.Moreover,we estimate the truncation error of this sampling based on localized sampling without decay assumption.Illustrative examples are also presented.

Association between Residential Greenness and Cardiometabolic Risk Factors among Adults in Rural Xinjiang Uygur Autonomous Region,China:A Cross-Sectional Study

Objective This study aimed to explore the relationships between residential greenness and cardiometabolic risk factors among rural adults in Xinjiang Uygur Autonomous Region(Xinjiang)and thus provide a theoretical basis and data support for improving the health of residents in this region.Methods We recruited 9,723 adult rural residents from the 51st Regiment of the Third Division of the Xinjiang Production and Construction Corps in September 2016.The normalized difference vegetation index(NDVI)was used to estimate residential greenness.The generalized linear mixed model(GLMM)was used to examine the association between residential greenness and cardiometabolic risk factors.Results Higher residential greenness was associated with lower cardiometabolic risk factor prevalence.After adjustments were made for age,sex,education,and marital status,for each interquartile range(IQR)increase of NDVI500-m,the risk of hypertension was reduced by 10.3%(OR=0.897,95%CI=0.836-0.962),the risk of obesity by 20.5%(OR=0.795,95%CI=0.695-0.910),the risk of type 2 diabetes by 15.1%(OR=0.849,95%CI=0.740-0.974),and the risk of dyslipidemia by 10.5%(OR=0.895,95%CI=0.825-0.971).Risk factor aggregation was reduced by 20.4%(OR=0.796,95%CI=0.716-0.885)for the same.Stratified analysis showed that NDVI500-m was associated more strongly with hypertension,dyslipidemia,and risk factor aggregation among male participants.The association of NDVI500-m with type 2 diabetes was stronger among participants with a higher education level.PM10 and physical activity mediated 1.9%-9.2%of the associations between NDVI500-m and obesity,dyslipidemia,and risk factor aggregation.Conclusion Higher residential greenness has a protective effect against cardiometabolic risk factors among rural residents in Xinjiang.Increasing the area of green space around residences is an effective measure to reduce the burden of cardiometabolic-related diseases among rural residents in Xinjiang.

Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull distribution.The control chart developed supports the examination of the mean lifespan variation for a particular product in the process of manufacturing.Three control limit levels are used:the warning control limit,inner control limit,and outer control limit.Together,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control chart.The control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control charts.Finally,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain.

A Path Planning Algorithm Based on Improved RRT Sampling Region

For the problem of slow search and tortuous paths in the Rapidly Exploring Random Tree(RRT)algorithm,a feedback-biased sampling RRT,called FS-RRT,is proposedbasedon RRT.Firstly,toimprove the samplingefficiency of RRT to shorten the search time,the search area of the randomtree is restricted to improve the sampling efficiency.Secondly,to obtain better information about obstacles to shorten the path length,a feedback-biased sampling strategy is used instead of the traditional random sampling,the collision of the expanding node with an obstacle generates feedback information so that the next expanding node avoids expanding within a specific angle range.Thirdly,this paper proposes using the inverse optimization strategy to remove redundancy points from the initial path,making the path shorter and more accurate.Finally,to satisfy the smooth operation of the robot in practice,auxiliary points are used to optimize the cubic Bezier curve to avoid path-crossing obstacles when using the Bezier curve optimization.The experimental results demonstrate that,compared to the traditional RRT algorithm,the proposed FS-RRT algorithm performs favorably against mainstream algorithms regarding running time,number of search iterations,and path length.Moreover,the improved algorithm also performs well in a narrow obstacle environment,and its effectiveness is further confirmed by experimental verification.

Improving Generalization for Hyperspectral Image Classification:The Impact of Disjoint Sampling on Deep Models

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art(SOTA)models e.g.,Attention Graph and Vision Transformer.When training,validation,and test sets overlap or share data,it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new examples.This paper presents an innovative disjoint sampling approach for training SOTA models for the Hyperspectral Image Classification(HSIC).By separating training,validation,and test data without overlap,the proposed method facilitates a fairer evaluation of how well a model can classify pixels it was not exposed to during training or validation.Experiments demonstrate the approach significantly improves a model’s generalization compared to alternatives that include training and validation data in test data(A trivial approach involves testing the model on the entire Hyperspectral dataset to generate the ground truth maps.This approach produces higher accuracy but ultimately results in low generalization performance).Disjoint sampling eliminates data leakage between sets and provides reliable metrics for benchmarking progress in HSIC.Disjoint sampling is critical for advancing SOTA models and their real-world application to large-scale land mapping with Hyperspectral sensors.Overall,with the disjoint test set,the performance of the deep models achieves 96.36%accuracy on Indian Pines data,99.73%on Pavia University data,98.29%on University of Houston data,99.43%on Botswana data,and 99.88%on Salinas data.

Invasive Procedures for Prenatal Diagnosis in Salmaniya Medical Complex in Bahrain: A Retrospective Cross-Sectional Descriptive Study

Background: Prenatal diagnosis is the process of evaluating the presence of disease or potential disease in the fetus, this enables families to be better prepared before the birth of the baby. There are non-invasive prenatal diagnosis procedures and invasive prenatal diagnosis procedures. The invasive prenatal diagnosis procedures are CVS (chorionic villus sampling) and amniocentesis. The American College of Obstetricians and Gynecologists states that invasive diagnostic testing should be available to all women, regardless of age or risk. Objective: To determine the indications, outcome and results of diagnostic invasive prenatal procedures. Study setting: The obstetrics and Gynecology Department in Salmaniya Medical Complex in Kingdom of Bahrain. Study design: Retrospective descriptive study. Study subjects and Methods: This retrospective descriptive study was conducted on 175 pregnant women who underwent invasive prenatal procedures (CVS and amniocentesis) between January 2013 and December 2018 at SMC in Kingdom of Bahrain. All medical records of the participants were reviewed and entered the study. According to the implemented procedures, medical records were categorized into two chorionic villus sampling (CVS) and amniocentesis groups. The study subject will include indications of the procedures which are advanced maternal age, hematological disorders, genetic disorders, metabolic disorders, abnormal structural findings in fetal ultrasound and previous child with aneuploidy. In addition, the study will address the complications, outcome and results of procedures. Results: About half of our indications of the procedures were due to hematological disorders (47.6%) followed by abnormal structural findings in fetal ultrasound (30.1%) then genetic disorders (15.7%), metabolic disorders (4.8%) and advanced maternal age (1.8%). Regarding complications of the procedure;threatened miscarriage or loss of pregnancy within 3 weeks was (2.3%), amniotic fluid leakage (0.7%), abdominal cramps (0.7%) and Insufficient or contaminated sample (6.2%). Regarding outcome of the pregnancy, our results showed that the loss of pregnancy was (4.8%), intrauterine fetal death or still birth was (13.9%), live birth was (63.9%), preterm delivery was (7.8%), preterm premature rupture of membrane (PPROM) was (1.8%), limbs reduction was (0.0%). Termination of pregnancy outside the country was (7.8%) of chorionic villus sampling and amniocentesis. Conclusion: CVS and amniocentesis are useful outpatient procedures to detect diagnosis or to assess whether a patient is at increased risk of having an affected fetus and that will minimize the psychological impact on the patient and to provide a proper antenatal care to the pregnant women by her obstetrician and follow up to the baby by pediatrician. In this study it was observed that most of the patients who underwent the procedure were couples either carrier or affected to sickle cell disease or Beta thalassemia.

Dyslipidemia in Adults with Type 2 Diabetes in a Rural Community in Ganadougou, Mali: A Cross-Sectional Study

Dyslipidemia is a disorder where abnormally lipid concentrations circulate in the bloodstream. The disorder is common in type 2 diabetics (T2D) and is linked with T2D comorbidities, particularly cardiovascular disease. Dyslipidemia in T2D is typically characterized by elevated plasma triglyceride and low high-density lipoprotein cholesterol (HDL-C) levels. There is a significant gap in the literature regarding dyslipidemia in rural parts of Africa, where lipid profiles may not be captured through routine surveillance. This study aimed to characterize the prevalence and demo-graphic profile of dyslipidemia in T2D in the rural community of Ganadougou, Mali. We performed a cross-sectional study of 104 subjects with T2D in Ganadougou between November 2021 and March 2022. Demographic and lipid profiles were collected through cross-sectional surveys and serological analyses. The overall prevalence of dyslipidemia in T2D patients was 87.5% (91/104), which did not differ by sex (P = .368). High low-density lipoprotein cholesterol (LDL-C) was the most common lipid abnormality (78.9%, [82/104]). Dyslipidemia was associated with age and hypertension status (P = .013 and.036, respectively). High total and high LDL-C parameters were significantly associated with hypertension (P = .029 and .006, respectively). In low-resource settings such as rural Mali, there is a critical need to improve infrastructure for routine dyslipidemia screening to guide its prevention and intervention approaches. The high rates of dyslipidemia observed in Gandadougou, consistent with concomitant increases in cardiovascular diseases in Africa suggest that lipid profile assessments should be incorporated into routine medical care for T2D patients in African rural settings.

Nearest Neighbor Sampling of Point Sets Using Rays

We propose a new framework for the sampling,compression,and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces.Our approach involves constructing a tensor called the RaySense sketch,which captures nearest neighbors from the underlying geometry of points along a set of rays.We explore various operations that can be performed on the RaySense sketch,leading to different properties and potential applications.Statistical information about the data set can be extracted from the sketch,independent of the ray set.Line integrals on point sets can be efficiently computed using the sketch.We also present several examples illustrating applications of the proposed strategy in practical scenarios.

TCAS-PINN:Physics-informed neural networks with a novel temporal causality-based adaptive sampling method

Physics-informed neural networks(PINNs)have become an attractive machine learning framework for obtaining solutions to partial differential equations(PDEs).PINNs embed initial,boundary,and PDE constraints into the loss function.The performance of PINNs is generally affected by both training and sampling.Specifically,training methods focus on how to overcome the training difficulties caused by the special PDE residual loss of PINNs,and sampling methods are concerned with the location and distribution of the sampling points upon which evaluations of PDE residual loss are accomplished.However,a common problem among these original PINNs is that they omit special temporal information utilization during the training or sampling stages when dealing with an important PDE category,namely,time-dependent PDEs,where temporal information plays a key role in the algorithms used.There is one method,called Causal PINN,that considers temporal causality at the training level but not special temporal utilization at the sampling level.Incorporating temporal knowledge into sampling remains to be studied.To fill this gap,we propose a novel temporal causality-based adaptive sampling method that dynamically determines the sampling ratio according to both PDE residual and temporal causality.By designing a sampling ratio determined by both residual loss and temporal causality to control the number and location of sampled points in each temporal sub-domain,we provide a practical solution by incorporating temporal information into sampling.Numerical experiments of several nonlinear time-dependent PDEs,including the Cahn–Hilliard,Korteweg–de Vries,Allen–Cahn and wave equations,show that our proposed sampling method can improve the performance.We demonstrate that using such a relatively simple sampling method can improve prediction performance by up to two orders of magnitude compared with the results from other methods,especially when points are limited.

Chord length sampling correction analysis for dispersion fuel in Monte Carlo simulation

Dispersion fuels,knowned for their excellent safety performance,are widely used in advanced reactors,such as hightemperature gas-cooled reactors.Compared with deterministic methods,the Monte Carlo method has more advantages in the geometric modeling of stochastic media.The explicit modeling method has high computational accuracy and high computational cost.The chord length sampling(CLS)method can improve computational efficiency by sampling the chord length during neutron transport using the matrix chord length?s probability density function.This study shows that the excluded-volume effect in realistic stochastic media can introduce certain deviations into the CLS.A chord length correction approach is proposed to obtain the chord length correction factor by developing the Particle code based on equivalent transmission probability.Through numerical analysis against reference solutions from explicit modeling in the RMC code,it was demonstrated that CLS with the proposed correction method provides good accuracy for addressing the excludedvolume effect in realistic infinite stochastic media.

Failure-Informed Adaptive Sampling for PINNs,Part II:Combining with Re-sampling and Subset Simulation

This is the second part of our series works on failure-informed adaptive sampling for physic-informed neural networks(PINNs).In our previous work(SIAM J.Sci.Comput.45:A1971–A1994),we have presented an adaptive sampling framework by using the failure probability as the posterior error indicator,where the truncated Gaussian model has been adopted for estimating the indicator.Here,we present two extensions of that work.The first extension consists in combining with a re-sampling technique,so that the new algorithm can maintain a constant training size.This is achieved through a cosine-annealing,which gradually transforms the sampling of collocation points from uniform to adaptive via the training progress.The second extension is to present the subset simulation(SS)algorithm as the posterior model(instead of the truncated Gaussian model)for estimating the error indicator,which can more effectively estimate the failure probability and generate new effective training points in the failure region.We investigate the performance of the new approach using several challenging problems,and numerical experiments demonstrate a significant improvement over the original algorithm.

Mental health and insomnia problems in healthcare workers after the COVID-19 pandemic:A multicenter cross-sectional study

BACKGROUND Healthcare workers(HCWs)are at increased risk of contracting coronavirus disease 2019(COVID-19)as well as worsening mental health problems and insomnia.These problems can persist for a long period,even after the pandemic.However,less is known about this topic.AIM To analyze mental health,insomnia problems,and their influencing factors in HCWs after the COVID-19 pandemic.METHODS This multicenter cross-sectional,hospital-based study was conducted from June 1,2023 to June 30,2023,which was a half-year after the end of the COVID-19 emergency.Region-stratified population-based cluster sampling was applied at the provincial level for Chinese HCWs.Symptoms such as anxiety,depression,and insomnia were evaluated by the Generalized Anxiety Disorder-7,Patient Health Questionnaire-9,and Insomnia Severity Index.Factors influencing the symptoms were identified by multivariable logistic regression.RESULTS A total of 2000 participants were invited,for a response rate of 70.6%.A total of 1412 HCWs[618(43.8%)doctors,583(41.3%)nurses and 211(14.9%)nonfrontline],254(18.0%),231(16.4%),and 289(20.5%)had symptoms of anxiety,depression,and insomnia,respectively;severe symptoms were found in 58(4.1%),49(3.5%),and 111(7.9%)of the participants.Nurses,female sex,and hospitalization for COVID-19 were risk factors for anxiety,depression,and insomnia symptoms;moreover,death from family or friends was a risk factor for insomnia symptoms.During the COVID-19 outbreak,most[1086(76.9%)]of the participating HCWs received psychological interventions,while nearly all[994(70.4%)]of them had received public psychological education.Only 102(7.2%)of the HCWs received individual counseling from COVID-19.CONCLUSION Although the mental health and sleep problems of HCWs were relieved after the COVID-19 pandemic,they still faced challenges and greater risks than did the general population.Identifying risk factors would help in providing targeted interventions.In addition,although a major proportion of HCWs have received public psychological education,individual interventions are still insufficient.

Wideband spectrum sensing using step-sampling based on the multipath nyquist folding receiver

Wideband spectrum sensing with a high-speed analog-digital converter(ADC) presents a challenge for practical systems.The Nyquist folding receiver(NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing,which is subject to the complexity of processing the modulated outputs.In this case,a multipath NYFR architecture with a step-sampling rate for the different paths is proposed.The different numbers of digital channels for each path are designed based on the Chinese remainder theorem(CRT).Then,the detectable frequency range is divided into multiple frequency grids,and the Nyquist zone(NZ) of the input can be obtained by sensing these grids.Thus,high-precision parameter estimation is performed by utilizing the NYFR characteristics.Compared with the existing methods,the scheme proposed in this paper overcomes the challenge of NZ estimation,information damage,many computations,low accuracy,and high false alarm probability.Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.