Performance of real‑time neutron/gamma discrimination methods

Nuclear security usually requires the simultaneous detection of neutrons and gamma rays.With the development of crystalline materials in recent years,Cs2LiLaBr6(CLLB)dual-readout detectors have attracted extensive attention from researchers,where real-time neutron/gamma pulse discrimination is the critical factor among detector performance parameters.This study investigated the discrimination performance of the charge comparison,amplitude comparison,time comparison,and pulse gradient_(m)ethods and the effects of a Sallen–Key filter on their performance.Experimental results show that the figure of merit(FOM)of all four methods is improved by proper filtering.Among them,the charge comparison method exhibits excellent noise resistance;moreover,it is the most_(s)uitable method of real-time discrimination for CLLB detectors.However,its discrimination performance depends on the parameters t_(s),t_(m),and t_(e).When t_(s)corresponds to the moment at which the pulse is at 10%of its peak value,t_(e)requires a delay of only 640–740 ns compared to t_(s),at which time the potentially optimal FOM of the charge comparison method at 3.1–3.3 MeV is greater than 1.46.The FOM obtained using the t_(m)value calculated by a proposed maximized discrimination difference model(MDDM)and the potentially optimal FOM differ by less than 3.9%,indicating that the model can provide good guidance for parameter selection in the charge comparison method.

Misclassification analysis of discriminant model

This paper extends the criterion of the misclassification ratio of discriminant model and presents a new selection method of discriminant model.For selecting the discriminant model,this method establishes the rule of misclassification degree ratio through misclassification ratio of the discriminant model and misclassification degree of the samples.To test the effect of this method,this work uses seven UCI data sets.Numerical experiments on these examples indicate that this method has certain rationality and has a better effect to select a discriminant model.

A Multi-Task Motion Generation Model that Fuses a Discriminator and a Generator

The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.

Effects of Differential Item Discriminations between Individual-Level and Cluster-Level under the Multilevel Item Response Theory Model

This study attempted to interpret differential item discriminations between individual and cluster levels by focusing on patterns and magnitudes of item discriminations under 2PL multilevel IRT model through a set of variety simulation conditions. The consistency between the mean of individual-level ability estimates and cluster-level ability estimates was evaluated by the correlations between them. As a result, it was found that they were highly correlated if the patterns of item discriminations were the same for both individual and cluster levels. The magnitudes of item discriminations themselves did not affect much on correlations, as far as the patterns were the same at the two levels. However, it was found that the correlation became lower when the patterns of item discriminations were different between the individual and cluster levels. Also, it was revealed that the mean of the estimated individual-level abilities would not be necessarily a good representation of the cluster-level ability, if the patterns were different at the two levels.

Discriminative tone model training and optimal integration for Mandarin speech recognition

Two discriminative methods for solving tone problems in Mandarin speech recognition are presented. First, discriminative training on the HMM （hidden Markov model） based tone models is proposed. Then an integration technique of tone models into a large vocabulary continuous speech recognition system is presented. Discriminative model weight training based on minimum phone error criteria is adopted aiming at optimal integration of the tone models. The extended Baum Welch algorithm is applied to find the model-dependent weights to scale the acoustic scores and tone scores. Experimental results show that tone recognition rates and continuous speech recognition accuracy can be improved by the discriminatively trained tone model. Performance of a large vocabulary continuous Mandarin speech recognition system can be further enhanced by the discriminatively trained weight combinations due to a better interpolation of the given models.

Fisher discriminant analysis model and its application for prediction of classification of rockburst in deep-buried long tunnel

A Fisher discriminant analysis （FDA） model for the prediction of classification of rockburst in deep-buried long tunnel was established based on the Fisher discriminant theory and the actual characteristics of the project. First, the major factors of rockburst, such as the maximum tangential stress of the cavern wall σθ, uniaxial compressive strength σc, uniaxial tensile strength or, and the elastic energy index of rock Wet, were taken into account in the analysis. Three factors, Stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet, were defined as the criterion indices for rockburst prediction in the proposed model. After training and testing of 12 sets of measured data, the discriminant functions of FDA were solved, and the ratio of misdiscrimina- tion is zero. Moreover, the proposed model was used to predict rockbursts of Qinling tunnel along Xi＇an-Ankang railway. The results show that three forecast results are identical with the actual situation. Therefore, the prediction accuracy of the FDA model is acceptable.

Adaptive Object Tracking Discriminate Model for Multi-Camera Panorama Surveillance in Airport Apron

Autonomous intelligence plays a significant role in aviation security.Since most aviation accidents occur in the take-off and landing stage,accurate tracking of moving object in airport apron will be a vital approach to ensure the operation of the aircraft safely.In this study,an adaptive object tracking method based on a discriminant is proposed in multi-camera panorama surveillance of large-scale airport apron.Firstly,based on channels of color histogram,the pre-estimated object probability map is employed to reduce searching computation,and the optimization of the disturbance suppression options can make good resistance to similar areas around the object.Then the object score of probability map is obtained by the sliding window,and the candidate window with the highest probability map score is selected as the new object center.Thirdly,according to the new object location,the probability map is updated,the scale estimation function is adjusted to the size of real object.From qualitative and quantitative analysis,the comparison experiments are verified in representative video sequences,and our approach outperforms typical methods,such as distraction-aware online tracking,mean shift,variance ratio,and adaptive colour attributes.

Water resistant features of high-risk outburst coal seams and standard discriminant model of mining under water-pressure

It is important to emphasize the value of research in safe mining technology of high-risk water outburst coal seams. We describe briefly current conditions abroad and in China. Based on an Ordovician limestone aquifer with high-risk water outburst seams in the Feicheng coal field, we analyzed the water-resistant characteristics of a coal floor aquifuge and the behavior of water head intrusion of a confined aquifer and propose a safe criterion model and relevant technology of mining above aquifers. This has brought satisfactory results in engineering practice.

Entity Burst Discriminative Model for Cumulative Citation Recommendation

Knowledge base acceleration-cumulative citation recommendation(KBA-CCR)aims to detect citation-worthiness documents from a chronological stream corpus for a set of target entities in a knowledge base.Most previous works only consider a number of semantic features between documents and target entities in the knowledge base,and then use powerful machine learning approaches such as logistic regression to classify relevant documents and non-relevant documents.However,the burst activities of an entity have been proved to be a significant signal to predict potential citations.In this paper,an entity burst discriminative model(EBDM)is presented to substantially exploit such burst features.The EBDM presents a new temporal representation based on the burst features,which can capture both temporal and semantic correlations between entities and documents.Meanwhile,in contrast to the bag-of-words model,the EBDM can significantly decrease the number of non-zero entries of feature vectors.An extensive set of experiments were conducted on the TREC-KBA-2012 dataset.The results show that the EBDM outperforms the performance of the state-of-the-art models.

Prognostic and diagnostic scoring models in acute alcoholassociated hepatitis:A review comparing the performance of different scoring systems

Alcohol-associated hepatitis(AAH)is a severe form of liver disease caused by alcohol consumption.In the absence of confounding factors,clinical features and laboratory markers are sufficient to diagnose AAH,rule out alternative causes of liver injury and assess disease severity.Due to the elevated mortality of AAH,assessing the prognosis is a radical step in management.The Maddrey discriminant function(MDF)is the first established clinical prognostic score for AAH and was commonly used in the earliest AAH clinical trials.A MDF>32 indicates a poor prognosis and a potential benefit of initiating corticosteroids.The model for end stage liver disease(MELD)score has been studied for AAH prognostication and new evidence suggests MELD may predict mortality more accurately than MDF.The Lille score is usually combined to MDF or MELD score after corticosteroid initiation and offers the advantage of assessing response to treatment a 4-7 d into the course.Other commonly used scores include the Glasgow Alcoholic Hepatitis Score and the Age Bilirubin international normalized ratio Creatinine model.Clinical AAH correlate adequately with histologic severity scores and leave little indication for liver biopsy in assessing AAH prognosis.AAH presenting as acute on chronic liver failure(ACLF)is so far prognosticated with ACLF-specific scoring systems.New artificial intelligence-generated prognostic models have emerged and are being studied for use in AAH.Acute kidney injury(AKI)is one possible complication of AAH and is significantly associated with increased AAH mortality.Predicting AKI and alcohol relapse are important steps in the management of AAH.The aim of this review is to discuss the performance and limitations of different scoring models for AAH mortality,emphasize the most useful tools in prognostication and review predictors of recurrence.

基于KPCA与KLPP及Wilks统计量的留兰香三维荧光数据特征提取与鉴别分析

为实现留兰香产地的快速鉴别,该研究提出了一种核主成分分析(kernel principal component analysis,KPCA)与核局部保持投影(kernel locality preserving projections,KLPP)及WilksΛ统计量序贯融合的特征波长提取策略,在此基础上鉴别5个产地的留兰香。首先,在采集5个产地300个留兰香样本的三维荧光数据后,运用三角形内插值法去除原始光谱中的瑞利散射和拉曼散射,并运用SG(Savitzky-Golay)对数据进行平滑预处理。然后,对预处理后的荧光光谱数据分别利用KPCA、KPCA+KLPP、KPCA+WilksΛ统计量、 KPCA+KLPP+WilksΛ统计量4种方法提取特征激发波长和特征发射波长。接着,按特征激发波长从小到大顺序将其对应的特征发射波长光谱值首尾相连转换成行向量;4种方法从300个样本中各得到1个300行的特征波长光谱值矩阵。再者,运用Fisher判别分析(fisher discriminant analysis,FDA)对特征波长光谱值矩阵进行数据可分性融合,生成可分性FD(fisher discriminant)变量。选取前4个累计判别能力达到99%的FD变量作为鉴别模型的输入向量。最后,用支持向量机(support vector machine,SVM)算法分析4个FD变量,分别得到对应于4种特征提取波长方法的FDA+SVM鉴别结果,其正确率分别为92.00%、96.00%、94.67%、100%。结果表明,所提出的KPCA+KLPP+WilksΛ统计量序贯融合的特征波长提取策略能够有效减少三维荧光光谱数据的冗余,并能表征原始荧光数据的信息特征,实现了5种留兰香产地的正确鉴别。该研究可为后续利用三维荧光光谱开展留兰香重要组分量化分析提供一定的基础。

基于太赫兹成像检测技术与特征提取方法结合巴旦木饱满度检测方法研究

巴旦木是一种营养丰富的坚果,对巴旦木的品质进行检测具有重要的经济价值和实际意义。由于巴旦木具有较为坚硬的外壳,传统的检测手段较难实现内部检测,因此,采用新兴的太赫兹透射成像检测技术,开展巴旦木饱满度的检测研究。首先采集不同饱满度巴旦木的太赫兹透射图像,并且从太赫兹图像的感兴趣区域分别提取无样品区域、空壳区域和满仁区域的太赫兹光谱信息;为了提高模型的精度,减少计算量,采用竞争性自适应重加权算法(CARS)、无信息变量消除(UVE)、连续投影算法(SPA)、蒙特卡罗无信息变量消除法(MCUVE)和遗传算法(GA)对太赫兹光谱信息进行特征提取,建立对应的最小二乘支持向量机(LS-SVM)、随机森林(RF)和K-近邻(KNN)定性判别模型,对巴旦木的饱满和空壳区域进行检测和鉴别。此外,对太赫兹特征图像转为JPG格式,接着转化为RGB格式进行G通道提取和图像二值化分离出外壳和果仁图像,检测饱满度为太赫兹特征图像的壳仁像素点之比;对原始图像进行轮廓提取和图像二值化分离出外壳和果仁图像,实际饱满度为原始图像的壳仁像素点之比。通过计算检测饱满度和实际饱满度的误差,证明了太赫兹透射成像技术检测巴旦木饱满度的可行性。建立的KS-GA-RF模型的鉴别效果最优,准确率为98.21%;通过壳仁像素点之比分别计算出对应的检测饱满度和实际饱满度,误差为16%。研究验证了采用太赫兹图、谱相融合的方法,可以很好地实现对巴旦木内部种仁饱满度可视化检测,为巴旦木的准确分级提供了新的思路,也为太赫兹成像技术检测其他坚果饱满度提供了理论参考,具有重要的应用价值。

基于大数据分析的高速公路分车型拥堵态势评估

针对面向全车型的道路拥堵状态评估统一化和静态管控方法的局限性,围绕不同类型车辆的交通拥堵问题开展研究。首先,基于沈海高速公路福建段龙门架数据,采用聚类方法分析不同类型车辆在不同指标下的交通拥堵特征;其次,提出分车型数据驱动的模糊云模型,根据行业专家给出的数字特征计算不同车型下的拥堵等级隶属度;然后,选取拥堵指标作为模糊逻辑系统的输入,并采用Mamdani方法建立模糊关联矩阵,量化不同专家评价的可信度;引入Dempster-Shafter证据理论对所有专家的综合可信度进行模糊融合,根据隶属度最大原则综合评判道路交通状态;最后,验证了提出模型的精度及对车型间差异表征的准确性,分车型下的拥堵评价结果一致性达84.51%。结果表明:相比1~4型客车及小型货车,大型货车在移动过程中形成的瓶颈对高速公路上堵塞的消散表现出明显的迟滞作用;在低密度下,客车、少轴货车、多轴货车渗透率的提高对路况的影响依次提升;在高密度下,3~6轴货车渗透率的改变对拥堵状态的影响最为明显,1~4轴客车次之。分车型拥堵研究可为制订适应各类车型的主动交通管理提供定量的数据支撑,特别是在动态调控措施如错峰出行、可变限速等方面具有重要指导意义。

基于完形填空的方面级情感四元组预测

方面情感四元组预测(ASQP)任务旨在从给定的评论语句中提取所有方面词以及相应的方面类别、观点表达和情感极性,有助于全面了解用户对产品或服务不同方面的评价情况.现有情感四元组预测方法主要存在以下局限:(1)判别式模型没有利用prompt捕获情感元素之间的语义关系;(2)生成式模型要么简单地将情感元素类型标签组合形成prompt,缺乏理解标签类型涵义的语境;要么将离散模板作为解码器的输入,而编码器则无法捕获到模板中情感元素之间的语义关系.为了缓解这些问题,本文首先基于完形填空思想研制离散和连续2类prompt,提供理解4个情感元素类型涵义的语境,帮助模型更好地捕获情感元素之间的语义关系;然后,基于设计的prompt,提出C-ASQP框架,包含判别式模型DC-ASQP和生成式模型GC-ASQP.在DC-ASQP中,采用2阶段策略,先预测4个情感元素中2个较为容易的情感元素,再将预测结果嵌入到设计的prompt中,帮助模型理解情感元素类型的涵义,从而有效预测另外2个情感元素.在GC-ASQP中,将设计的prompt作为编码器的输入,借助预训练模型的学习模式,充分利用预训练模型蕴含的知识提升四元组的生成效果.实验结果显示,DC-ASQP模型在4个常用数据集上的F1值相比同类判别式最优模型分别提高4.70%、6.48%、6.97%和2.60%,GC-ASQP模型的F1值比最优基准模型分别提高0.86%、1.67%、0.15%和1.02%,验证了将ASQP建模为完形填空任务的有效性,所设计的2类prompt以及C-ASQP框架是有效的.

基于综合赋权与云熵优化的园区综合能源系统评价

综合能源系统(integrated energy system,IES)作为能源转型中的重要环节已得到越来越多国家的广泛关注。构建一套匹配中国国情的综合能源系统评价体系和评价方法不仅能够为综合能源系统规划后评价打下基础,以此对规划方案进行优劣排序;还能够提高综合能源系统项目的管理水平,在制定统一、完整的综合能源系统综合评价标准时提供参考。为此,首先结合园区IES基本特征以及运行特性,构建包含经济性、可靠性、环保性以及智能友好性4个方面的综合评价指标体系;然后为解决IES在运行中的不确定性问题,对基于传统云物元模型的综合评价体系提出云熵优化,即考虑不同评价者对模糊性的可接受程度;为解决单一赋权方法可能导致的评价结果过于主观或过于客观的问题,选择基于最小鉴别信息原理将决策实验室法与熵权法相结合的综合赋权法,并采用变权法进一步完善综合评价指标;最后通过算例分析,验证所提综合评价体系的科学正确性。

基于高频激光除漆的LIBS监测平台设计与应用研究

基于激光诱导击穿光谱(LIBS)技术的飞机漆层清洗监测,需要对峰值功率密度范围进行限定,以确保等离子体激发和漆层清洗的稳定性。然而,对于广泛使用的高频(kHz级)脉冲激光除漆技术,其峰值功率密度相对偏低,除漆过程中等离子体激发受到限制;且高频激光烧蚀材料产生的强连续背景光谱干扰了等离子体光谱采集。论文依据蒙皮功能漆层可控清洗需求,基于LabVIEW嵌入式开发系统的控制软件编写及激光清洗、光谱采集、控制与显示模块的集成,设计了一款适用于高频激光除漆的LIBS监测平台。选取2024-T3铝合金双漆层试样作为研究对象,采集波长介于360~700nm范围内的漆层/基体体系光谱(面漆层:Tc;底漆层:Pr;基体:As)。采用平滑滤波,基线校正和归一化对原始光谱进行预处理,并选取12条特征谱线进行主成分分析(PCA),其降维数据作为线性判别分析(LDA)的输入变量,以此建立PCA-LDA判别模型。最后将所建模型导入LIBS监测平台,通过试验验证了高频激光除漆LIBS监测平台的分类准确性。结果表明:仅以累积方差解释率大于85%作为主成分选取原则,不能满足除漆过程LDA的分类需要;通过优化LDA的主成分个数,最终选取前9个主成分作为LDA的输入,显著提升了LIBS平台的检测准确率,此时基于LIBS光谱的PCA-LDA模型分类准确率达92.5%。由此可见,设计的高频激光除漆LIBS监测平台能够完成漆层/基体体系不同结构层的材料识别,从而实现了高频脉冲激光可控除漆的有效监测。

基于LDA-MURE模型的背景音乐自适应推荐方法

用户的情绪状态不同,需要的背景音乐也不同,因此提出基于LDA-MURE模型的背景音乐自适应推荐方法。提取背景音乐的音频特征和社会化标签,通过Fisher线性判别分析方法融合上述数据的特征,结合投影变换方法获得不同类别背景音乐的类内离散度和类间离散度。通过现代心理学分析人类情绪的节律周期变化,在此基础上判断用户当前的情绪状态。最后在LDA模型的基础上构建LDA-MURE模型,为用户推荐不同类别的背景音乐。实验结果表明,所提方法的MEA指标值较低、P@N指标值较高、用户满意度较高。

基于Fisher判别分析可分性信息融合的马铃薯VC含量高光谱检测方法

为提高马铃薯VC含量检测结果的准确性和可靠性,提出一种基于Fisher判别分析(Fisher discriminant analysis,FDA)可分性数据融合的检测模型输入变量构建方法。首先,利用高光谱成像技术采集200个马铃薯的高光谱信息,通过对比6种预处理方法和原始数据的建模结果,确定多元散射校正为光谱数据的预处理方法;其次,采用竞争性自适应重加权采样(competitive adaptive reweighted sampling,CARS)、连续投影算法(successive projections algorithm,SPA)及CARS-SPA组合算法3种方法提取相应特征波长,通过对比分析最终确定34个有效特征波长;然后,将有效特征波长进行FDA可分性数据融合,根据融合的新变量对样本间差异性判别能力的大小进行筛选,确定构建检测模型的输入变量;最后,分别对FDA融合前后筛选的变量建立偏最小二乘模型和反向传播神经网络(back propagation neural network,BPNN)模型,并对检测结果进行对比分析。结果表明,将CARS算法提取的34个特征波长进行FDA融合,采用前3个融合变量作为构建检测模型的输入变量时,其所建BPNN模型的相关系数由0.9726提高至0.9990,均方根误差由0.7723降低至0.1727,不仅能够极大地降低数据分析维度,而且能够提高检测结果的准确性。因此,基于FDA可分性数据融合构建检测模型输入变量可以提高马铃薯VC含量检测结果的准确性。

多视图专家组区域建议预测的视觉跟踪

为解决大多数最新的目标跟踪器都面临着判别特征表示缺乏多样性、目标定位过于模糊以及正样本的数量要求问题,提出一种基于多视图的顶层特征的区域建议网络的跟踪预测学习算法。融合多种视图的特征表示方式,利用丰富多样的语义信息,有效解决判别特征过于单一的问题。在扩展的边界框上构建多个支持向量机模型并加入区域建议网络模块,精确优化边界框,预测最优的目标位置,缓解目标定位过于模糊和正样本的数量有限的问题。通过大量视频基准序列对方法的综合评价,其结果表明,提出方法融合了轻量化的深度学习模型和多视图专家组的优点,使跟踪性能有了显著提升。

低程度风化火山岩风化壳结构划分与主控因素——以准噶尔盆地西缘车排子凸起石炭系火山岩为例

为解决低风化程度的火山岩风化壳结构划分及火山岩油气储层评价与预测的难题,利用研究区28口井岩心、录井测井和地震资料,对车排子凸起石炭系火山岩风化壳的岩石学、矿物学、地球化学、储层物性进行系统研究。结果表明:研究区岩石整体风化程度偏低。在此基础上提出一种将风化壳自上而下分为土壤带、水解带、淋滤带、崩解带、蚀变带、母岩带的6层划分方案。利用研究区玄武安山岩的自然电位、自然伽马、声波、电阻率4种测井数据,结合过采样算法的决策树模型对淋滤带、崩解带、蚀变带进行分类判别,准确率达87.8%。综合分析认为:低风化程度火山岩风化壳有效储层厚度、结构带发育程度、横纵向分布等具有非均质性;有利储层发育主要受古地貌和断裂作用的控制,淋滤带、崩解带是主要有利储层发育区,常分布于古地貌的斜坡地带,一般厚度在350 m以内;受断裂作用影响,风化壳有效储层厚度可达450 m。