LAMOST的“Unknown”光谱分类研究:ODS-YOLOv7模型

天体识别是天文新发现和深入研究天体的基础。在LAMOST DR8 v1.0发布的低分辨率光谱数据中有约53万条因没有类别标签而被命名为“Unknown”的光谱,其中有88.56%的光谱信噪比在0~10之间,对这批光谱的研究分析将增加LAMOST的有效数据产出。该研究为“Unknown”光谱的分类设计了一种ODS-YOLOv7模型。它是一种端到端的类别预测模型,通过添加一维卷积注意力模块以提高光谱识别能力。在经过一批信噪比在0~10之间的已知类别光谱训练后,ODS-YOLOv7可以学习到低信噪比光谱的有效特征,进而实现对“Unknown”光谱的类别预测。实验表明,该模型在已知类别标记的低信噪比恒星、星系、类星体的光谱识别中,F1-score分别为0.98、0.95、0.95;同时在与传统算法KNN、RF、DT、SVM和深度学习算法1D CNN、1DSSCNN、ResNet、DenseNet、VIT对比实验中取得相对最好的效果。实验结果还给出了ODS-YOLOv7模型对DR8 v1.0中信噪比在0~10的“Unknown”光谱预测置信度分布,在预测类别为恒星、星系、类星体任务中,有92%的分类置信度在60%以上。为保证模型输出质量,本文只选取分类置信度大于99%的光谱类别作为输出结果。以此为依据,在DR8 v1.0和DR9 v0发布的全部“Unknown”光谱中分别有37.19%和47.03%被ODS-YOLOv7模型预测出类别。此外,还增加人工认证以检验该模型预测的正确性。为提升模型的可解释性,参照了二维图像特征可视化的Grad-CAM方法,将其改进为适合于可视化一维光谱数据特征的算法。其结果表明该模型可自动关注到不同的分类特征,使得该模型非常适用于低信噪比“Unknown”光谱的类别预测。

Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.

A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

Efficient Simulation of Stationary Multivariate Gaussian Random Fields with Given Cross-Covariance

The present paper introduces a new approach to simulate any stationary multivariate Gaussian random field whose cross-covariances are predefined continuous and integrable functions. Such a field is given by convolution of a vector of univariate random fields and a functional matrix which is derived by Cholesky decomposition of the Fourier transform of the predefined cross-covariance matrix. In contrast to common methods, no restrictive model for the cross-covariance is needed. It is stationary and can also be reduced to the isotropic case. The computational effort is very low since fast Fourier transform can be used for simulation. As will be shown the algorithm is computationally faster than a recently published spectral turning bands model. The applicability is demonstrated using a common numerical example with varied spatial correlation structure. The model was developed to support simulation algorithms for mineral microstructures in geoscience.

Practical prescribed-time tracking control for uncertain strict-feedback systems with guaranteed performance under unknown control directions

In this paper,we consider the practical prescribed-time performance guaranteed tracking control problem for a class of uncertain strict-feedback systems subject to unknown control direction.Due to the existence of unknown nonlinearities and uncertainties,it is challenging to design a controller that can ensure the stability of closed-loop system within a predetermined finite time while maintaining the specified transient performance.The underlying problem becomes further complex as the control directions are unknown.To deal with the above problems,a special translation function as well as Nussbaum type function are introduced in the prescribed performance control(PPC)framework.Finally,a PPC as well as preset finite time tracking control scheme is designed,and its effectiveness is confirmed by both theoretical analysis and numerical simulation.

Subspace Identification for Closed-Loop Systems With Unknown Deterministic Disturbances

This paper presents a subspace identification method for closed-loop systems with unknown deterministic disturbances.To deal with the unknown deterministic disturbances,two strategies are implemented to construct the row space that can be used to approximately represent the unknown deterministic disturbances using the trigonometric functions or Bernstein polynomials depending on whether the disturbance frequencies are known.For closed-loop identification,CCF-N4SID is extended to the case with unknown deterministic disturbances using the oblique projection.In addition,a proper Bernstein polynomial order can be determined using the Akaike information criterion(AIC)or the Bayesian information criterion(BIC).Numerical simulation results demonstrate the effectiveness of the proposed identification method for both periodic and aperiodic deterministic disturbances.

Acute hepatitis of unknown etiology in an adult female:A case report

BACKGROUND Acute liver injury(ALI)refers to inflammation of the hepatic parenchyma without hepatic encephalopathy that lasts less than 6 mo.When the etiology is unknown,Acute Hepatitis of Unknown Origin(AHUO)can present as a diagnostic and treatment challenge.AHUO in the adult population is unusual and poorly documented.It has an incidence between 11%and 75%.Currently,no treatment guidelines exist.With no identified cause,treatment is often blind,and the wrong treatment plan may have unintended consequences.CASE SUMMARY We present the case of a 58-year-old woman who presented to the emergency room for elevated liver function tests(LFTs).Her symptoms started 10 d prior to admission and included nausea,vomiting,jaundice,decreased appetite,weight loss of 10 lbs,and dark urine.She denied drinking alcohol or taking any hepatotoxic agents,including acetaminophen,statins,vitamins,or supplements.She was admitted to the hospital,and an etiologic work-up was carried out.Her initial bloodwork revealed elevated liver enzymes(alanine aminotransferase 2500 U/L,aspartate aminotransferase 3159 U/L,and alkaline phosphatase 714 U/L)and elevated total bilirubin of 6.4 mg/dL.She tested negative for common infectious etiologies such as hepatotropic viruses A,B,C,and E.Further infective work-up revealed negative serology for cytomegalovirus,Epstein-Barr virus,herpes simplex virus 1&2,and human immunodeficiency virus.Her autoanti-body test results were negative,including anti-smooth muscle antibody,anti-mitochondrial antibody,and anti-liver kidney microsome 1 antibody.Magnetic resonance cholangiopancreatography ruled out biliary causes of elevated LFTs,and her core liver biopsy proved inconclusive.Over the course of her hospital stay,the patient's LFTs improved with supportive care and without steroids.CONCLUSION Idiopathic hepatitis makes treatment challenging.It can leave patients feeling confused and unfulfilled.Thus,educating the patient thoroughly for shared decision-making and management becomes essential.

基于超声子宫内膜容受性参数和淋巴细胞免疫表型对复发性流产再孕早期流产的影响因素分析

目的基于超声子宫内膜容受性参数和淋巴细胞免疫表型探究不明原因复发性流产患者再妊娠后妊娠结局的影响因素。方法选择2022年4-8月南通市妇幼保健院收治的不明原因复发性自然流产再次妊娠患者166例。根据妊娠结局分为成功分娩组和再次流产组,检测子宫内膜容受性参数及外周血T淋巴细胞,筛查不明原因复发性流产患者再次妊娠流产的影响因素。结果166例患者中成功分娩102例(61.45%),再次流产64例(38.55%)。成功分娩组患者的子宫内膜厚度、内膜容积、血管化血流指数(VFI)均高于再次流产组(P<0.05)。成功分娩组患者的CD4^(+)、CD4^(+)/CD8^(+)低于再次流产组(P<0.05)。CD4^(+)、CD4^(+)/CD8^(+)是妊娠失败的危险因素(P<0.05),子宫内膜厚度、内膜容积、VFI是妊娠失败的保护因素(P<0.05)。结论不明原因复发性流产患者再妊娠的妊娠结局与子宫内膜厚度、内膜容积、VFI、CD4^(+)、CD4^(+)/CD8^(+)有关。

基于双随机森林的发热待查智能诊断方法

在机器学习预测模型中,不平衡数据集会降低少数类的预测准确性。针对发热待查数据集的不平衡特性,该文提出了一种基于K-Means聚类欠采样的双随机森林病因预测方法。首先通过K-Means聚类欠采样构建一个平衡数据集,并在此基础上创建一个基于CART投票机制的随机森林预测模型。然后对初始数据集用同样的方法创建一个随机森林预测模型。最后将两个随机森林预测模型联合,使用两者的CART一起投票预测。该文提出的方法增加了CART的数量,在保持原有数据集特性的同时,提高了少数类的投票权重。在发热待查数据集上的实验表明,该文所提方法不仅改善了少数类的预测性能,对其他类别的预测性能也有一定程度的提升。

具有未知时变参数城轨列车的精准停车鲁棒控制

列车运行情况复杂多变,使得列车质量、基本运行阻力及线路状况等参数难以精确获得且随着列车运行存在明显的时变特性。列车模型参数的不确定性和时变特性会对列车的精准停车性能产生重要的影响。在动车组多质点动力学模型的基础上,根据Lyapunov再设计方法设计了一种非线性列车鲁棒控制器。该控制器将系统中的不确定性因素视为等价干扰,并设计相应的鲁棒补偿项。根据参数的先验区间,设计的鲁棒补偿项能够抑制模型估计误差,使得列车的位置和速度跟踪误差快速收敛,最终实现精准停车的控制目标;同时,也证明了系统的最终一致有界稳定性。最后,选取城市轨道交通场景进行仿真验证,仿真结果表明:本文提出的控制方法在列车模型参数未知且时变情景下也能实现对期望停车曲线的快速、精确追踪,表现出较强的鲁棒特性。

真空泵用屏蔽电机无传感器残压重投控制策略

针对真空干泵机组故障、生产工艺需要等原因影响真空设备产线在线运行时间的问题,研究真空干泵用屏蔽式感应电机带速重投策略,寻求重投的有利时刻以保证带速重投的成功,对电机断电后的转速和定子失电残压特性展开研究。为此,根据屏蔽感应电机空间向量模型,建立基于电压电流混合磁链观测的无速度传感器估算模型、推导电机断电期间的转速估算方法以及定子失电残压解析式。通过重点研究定子残压和虚拟重投电压间幅值特性和相角特性,确定控制系统的首次重投点和其次重投点。以4 kW真空干泵用屏蔽感应电机机组为例,通过模型仿真和失电残压实验验证了该控制方法在泵类工程中有较好的适用性和准确性,重投阶段的转速稳定时间为0.5 s以内,重投阶段的冲击电流平均减小59.18%。

结合未知类特征生成与分类得分修正的SAR目标开集识别方法

现有合成孔径雷达(SAR)目标识别方法大多局限于闭集假定,即认为训练模板库内训练目标类别包含全部待测目标类别,不适用于库内已知类和库外未知新类目标共存的真实开放识别环境。针对训练模板库目标类别非完备情况下的SAR目标识别问题,该文提出一种结合未知类特征生成与分类得分修正的SAR目标开集识别方法。该方法在利用已知类学习原型网络保证已知类识别精度的基础上结合对潜在未知类特征分布的先验认知,生成未知类特征更新网络,进一步保证特征空间中已知类、未知类特征的鉴别性。原型网络更新完成后,所提方法挑选各已知类边界特征,并计算边界特征到各自类原型的距离(极大距离),通过极值理论对各已知类极大距离进行概率拟合确定了各已知类最大分布区域。测试阶段在度量待测样本特征与各已知类原型距离预测闭集分类得分的基础上,计算了各距离在对应已知类极大距离分布上的概率,并修正闭集分类得分,实现了拒判概率的自动确定。基于MSTAR实测数据集的实验结果表明,所提方法能够有效表征真实未知类特征分布并提升网络特征空间已知类与未知类特征的鉴别性,可同时实现对库内已知类目标的准确识别和对库外未知类新目标的准确拒判。

基于启发式映射法的未知三维环境路径规划

针对全局环境的未知性和常用规划算法易得到局部最优解的特性,提出一种基于启发式映射法的未知三维环境路径规划算法。在分段规划的策略下,使用启发式映射法来获取分段的子目标,克服了常规直线映射法在一定情况下难以确定目标的缺陷,结合三维环境下的A^(*)算法完成了移动智能体的规划任务。通过MATLAB进行仿真,与直线映射法相比,得到了更短的路径长度,且运行时间更短,可以较好地解决未知三维环境的路径规划问题。

具有功率调节和容错能力的风力机自适应控制

水平轴风力机面对高风速变化工况时,极易出现转子灾难性失控超速运行,此时风能转换效率低于预期值,同时恶劣的环境也对桨距角控制提出了容错要求。针对这些问题,提出一种具有功率调节和容错能力的风力机自适应约束控制方案,通过自适应约束的桨距角控制实现安全可靠的功率调节,并集成容错能力以补偿可能的故障影响。介绍水平轴风力机的数学模型和运行要求,分析基准的非线性自适应约束控制方案的设计过程,并在此基础上讨论具有未知控制增益、任意初始条件和存在故障影响下的设计方案,最后搭建MATLAB仿真模型对所提出的方案进行验证。仿真实验结果表明:所提方案可以提高风力机的功率调节效率,同时在不同工况和不同故障条件下均能将转子速度和发电功率控制在安全范围内。

多域特征提取结合AdaBoost的含未知故障提速道岔故障诊断方法

针对提速道岔未知新故障误判影响列车安全运行及道岔检修效率的问题,提出一种基于多域特征提取和自适应提升算法(Adaptive boosting, adaboost)的信号分析及故障诊断模型。首先,为了深入挖掘道岔的故障特征,分别从时域、频域及时频域提取故障特征,构造原始特征集;然后根据AdaBoost模型获得的特征重要度排序构造不同特征数量的分类模型,并利用模型分类精度进一步获得最佳特征子集;最后将最佳特征子集输入含判定机制的AdaBoost故障诊断模型,完成对提速道岔含未知故障类型的诊断,同时,通过对模型的再训练,实现了对现有故障诊断模型的自适应更新。结果表明:本文方法在有效提取故障特征,提高道岔已知类故障诊断精度的同时,可以有效地识别出道岔之前未出现的新故障。

ZNF772甲基化及HR-HPV检测在意义不明的非典型鳞状细胞病人分流中的意义

目的:探讨锌指蛋白(ZNF)772甲基化及高危型人乳头瘤病毒(HR-HPV)检测在意义不明的非典型鳞状细胞(ASCUS)病人分流中的意义。方法:选取初筛ASCUS病人195例,均接受HC2-HPV-DNA检测及ZNF772基因DNA甲基化检测;比较宫颈上皮内瘤样变(CIN)Ⅱ及以上与CINⅡ以下、CINⅢ及以上及CINⅢ以下病人ZNF772基因位点甲基化水平的差异,并评估ZNF772甲基化水平及HR-HPV对ASCUS病人CINⅡ及以上、CINⅢ及以上的分流价值。结果:病理学检查显示,195例ASCUS病人中炎症80例(41.03%)、CINⅠ64例(32.82%)、CINⅡ29例(14.87%)、CINⅢ/CIS/AIS 18例(9.23%)、SCC/AC 4例(2.05%)。HR-HPV(HC2)阳性141例(72.31%)、HR-HPV(HC2)阴性54例(27.69%);CINⅡ及以上病人HR-HPV(HC2)阳性检出率高于CINⅡ以下(P<0.05);CINⅢ及以上病人HR-HPV(HC2)阳性检出率高于CINⅢ以下(P<0.05)。CINⅡ及以上、CINⅢ及以上病人ZNF772基因-420、-422位点甲基化水平分别高于CINⅡ以下、CINⅢ以下病人(P<0.05)。ZNF772基因-420、-422位点甲基化水平及HR-HPV诊断CINⅡ及以上的AUC(95%CI)分别为0.867(0.811~0.911)、0.806(0.743~0.859)、0.777(0.712~0.834),诊断CINⅢ及以上的AUC(95%CI)分别为0.891(0.838~0.931)、0.868(0.813~0.912)、0.780(0.716~0.836)。ZNF772基因-420位点甲基化水平诊断CINⅡ及以上、CINⅢ及以上的AUC面积最大。结论:相较于HC2-HPV-DNA检测,ZNF772甲基化检测更能准确地识别ASCUS中CINⅡ及以上、CINⅢ及以上病变病人,有望成为分流ASCUS的有效替代手段。

基于未知风荷载作用下拉索振动位移的拉索MR阻尼器失效破坏的原位识别方法

MR阻尼器在长期服役过程中可能由于各种因素导致阻尼器失效破坏。现有的对MR阻尼器的性能识别多为离线方法,需将阻尼器从斜拉索上拆卸下来后进行人工检测。该文提出一种基于斜拉索在未知风荷载作用下振动位移,对在役失效无模型的MR阻尼器作用力进行原位在线识别的方法。首先,建立模态坐标下斜拉索-阻尼器系统运动方程及观测方程,并进行模态截断以减少未知变量的数量。然后,将模态风荷载建模为随机游走过程并加入系统的增广状态中,并视MR阻尼器的作用力视为作用在斜拉索上的‘附加未知输入’,采用最近提出的无直接反馈的平滑最小方差无偏Bayesian滤波,对结构增广状态和MR阻尼器作用力进行识别。通过识别数值模拟的MR阻尼器三种典型的失效破坏模式,验证所提方法的有效性。

基于未知输入观测器的电液执行器容错控制研究

电液执行器(EHA)中的传感器或执行器故障,会导致系统不稳定,存在安全隐患。为了解决这些问题,提出并研发了一种用于EHA的容错控制补偿技术。建立其数学模型,利用PID控制器进行位置跟踪控制,通过重构的未知输入观测器实现对干扰和传感器故障的诊断和误差补偿;利用容错控制补偿技术,可以提高EHA运行的可靠性和稳定性,确保控制器的稳定性及其跟踪性能。通过数值模拟和实验,证明了所研发的容错控制补偿技术的有效性。

神经网络阻尼比模型及工业机器人导纳控制

在工业机器人打磨过程中,环境刚度随未知环境的变化,将对力控制精度产生不利的影响,针对环境刚度变化的问题,该文提出一种基于神经网络阻尼比模型的自适应导纳控制方法。在导纳控制设计中,根据力误差与系统阻尼比之间的机理关系,设计激励函数,构造神经网络阻尼比模型;通过该模型使阻尼比在线调整,适应末端环境的刚度变化,实现力到位置自适应转换的导纳控制。与常规导纳控制进行仿真比较,结果表明所提出的力控制策略力误差更小,响应速度更快,能适应变刚度的未知打磨环境。