The Expansion of the Function with Two Unknowns on the Reproducing Kernel Space

In this paper we make use of a special procedure on the repro ducing kernel space to give an expansion theorem for the function with two unkno wns and a surface approximation formula. The error of the surface possesses mono tonically decreasing and uniformly convergent characteristics in the sense of t he norm on the space.

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.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

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.

INCREMENTAL UNKNOWNS FOR THE HEAT EQUATION WITH TIME-DEPENDENT COEFFICIENTS: SEMI-IMPLICIT θ-SCHEMES AND THEIR STABILITY

Based on the finite difference discretization of partial differential equations, we propose a kind of semi-implicit θ-schemes of incremental unknowns type for the heat equation with time-dependent coefficients. The stability of the new schemes is carefully studied. Some new types of conditions give better stability when θ is closed to 1/2 even if we have variable coefficients.

Preparing For Unknowns Many changes will occur after the global battle against the novel coronavirus

The novel coronavirus disease(COVID-19)outbreak is a major public health crisis unseen in about 100 years.There is also a possibility that the disease may continue to exist for a long time to come.One of the most effective approaches to tackling the spread of the virus is to adopt widespread lockdowns.

Distributed fault diagnosis observer for multi-agent system against actuator and sensor faults

Component failures can cause multi-agent system(MAS)performance degradation and even disasters,which provokes the demand of the fault diagnosis method.A distributed sliding mode observer-based fault diagnosis method for MAS is developed in presence of actuator and sensor faults.Firstly,the actuator and sensor faults are extended to the system state,and the system is transformed into a descriptor system form.Then,a sliding mode-based distributed unknown input observer is proposed to estimate the extended state.Furthermore,adaptive laws are introduced to adjust the observer parameters.Finally,the effectiveness of the proposed method is demonstrated with numerical simulations.

Adaptive Fault Estimation for Dynamics of High Speed Train Based on Robust UKF Algorithm

This paper proposes an adaptive unscented Kalman filter algorithm(ARUKF)to implement fault estimation for the dynamics of high⁃speed train(HST)with measurement uncertainty and time⁃varying noise with unknown statistics.Firstly,regarding the actuator and sensor fault as the auxiliary variables of the dynamics of HST,an augmented system is established,and the fault estimation problem for dynamics of HST is formulated as the state estimation of the augmented system.Then,considering the measurement uncertainties,a robust lower bound is proposed to modify the update of the UKF to decrease the influence of measurement uncertainty on the filtering accuracy.Further,considering the unknown time⁃varying noise of the dynamics of HST,an adaptive UKF algorithm based on moving window is proposed to estimate the time⁃varying noise so that accurate concurrent actuator and sensor fault estimations of dynamics of HST is implemented.Finally,a five-car model of HST is given to show the effectiveness of this method.

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.

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial ones.Since nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes.The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate.With this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for WSN.The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes.In addition,the IAOAB-NLS model is stimulated by the behaviour of Aquila.The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network.For guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS model.The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

May 2022 acute hepatitis outbreak,is there a role for COVID-19 and other viruses?

There has been an increasing number of reported cases of acute hepatitis of unknown origin in previously healthy children since first reported on March 31,2022.This clinical syndrome is identified by jaundice and markedly elevated liver enzymes with increased aspartate transaminase and/or alanine aminotransa-minase(greater than 500 IU/L).We conducted an inclusive literature review with respect to acute hepatitis outbreaks in children using the search terms acute hepatitis,outbreak,children,severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),coronavirus disease 2019(COVID-19),and adenovirus.According to the cumulative data presented in four main studies,the median age is 4 years,with a male predominance(1.3:1).Jaundice was the most common clinical manifestation(69%),followed by vomiting(63%),anorexia(52.9%),diarrhea(47.2%),abdominal pain(39%),pyrexia(33.3%),pale stool(30%),and dark urine(30%).Coryza and lethargy were reported in 16.6%,while pruritus was reported in 2%of cases.Acute liver failure was observed in 25%of cases.The exact mechanism of this acute hepatitis outbreak is still not entirely clear.Adenoviruses and SARS-CoV-2 were detected in a significant number of patients.Coinfection with adenovirus and SARS-CoV-2 could be a possible underlying mechanism.However,other possible infections and mechanisms must be considered in the pathogenesis of this condition.Acute hepatitis of unknown origin in children has been a serious problem since the start of the COVID-19 pandemic but has not yet been sufficiently addressed.Many questions remain regarding the underlying mechanisms leading to acute liver failure in children,and it is likely that extensive future research is needed.

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.

Brucellosis,a diagnostic dilemma,presenting atypically in a child with terminal ileitis:A case report

BACKGROUND Brucellosis is endemic in India with seropositivity rates as high as 10%in children in the eastern states,yet the disease is not on the radar when a differential diagnosis of pyrexia of unknown origin(PUO)is being considered,especially in children in urban set-up.This may be because of the non-specific multitude of systemic symptoms seen in this disease and the lack of awareness among clinicians.CASE SUMMARY We present a case of a 13-year-old boy,who came with a history of undulating fever for the past three and a half months,loss of appetite,and abdominal pain.The child had visited several pediatricians and was even admitted to a tertiary care hospital for PUO evaluation,but to no avail.He presented to us after three and half months of suffering and weight loss of more than 10%of body weight.His ultrasonography revealed thickening of the terminal ileum.His blood culture grew Brucella melitensis.A diagnosis of Brucellosis with terminal ileitis was made.Brucella serology by enzyme-linked immunoassay(ELISA)was positive for both IgG and IgM.He was treated with doxycycline and Rifampicin along with syrup multivitamin and zinc,for 6 wk.There was remarkable improvement with gain in 4 kg body weight within 2 mo of completing treatment.History revealed consumption of unpasteurized milk and contact with cattle.CONCLUSION Clinical suspicion,detailed history,appropriate laboratory investigations are the three pillars for diagnosing Brucellosis in patients presenting with vague symptoms.

The Diagnostic and Therapeutic Challenges of Fabry Nephropathy—A Review of the Literature, Illustrated by a Clinical Case

Fabry Disease (FD) is a rare lysosomal storage disorder characterized by α-galactosidase A (α-Gal A) enzyme deficiency, resulting in glycosphingolipid accumulation. Its clinical spectrum ranges from severe classical to milder nonclassical or late-onset phenotypes. Renal involvement, termed Fabry Nephropathy (FN), can vary from mild proteinuria to kidney failure. FN diagnosis, especially in nonclassical cases with a genetic Variant of Unknown Significance (VUS) in the GLA gene, poses challenges. Measurement of plasma lyso-Gb3 levels is gaining importance in FN diagnosis, while renal biopsy with electron microscopy remains the gold standard in equivocal cases. Treatment options include Enzyme Replacement Therapy (ERT) and chaperone therapy, demanding careful candidate selection due to high treatment costs. Research has predominantly focused on classical FD, revealing modest treatment benefits. However, evidence for treating patients, especially females, with milder nonclassical or late-onset phenotypes is scarce, emphasizing the necessity for placebo-controlled clinical trials in these subgroups. Meanwhile, participation in global FD registries can improve our understanding of disease management. Case Presentation: A woman in her late sixties presented with moderate chronic kidney disease, mild proteinuria, and microscopic hematuria. Her family history included a prevalence of renal, cardiac and cerebrovascular diseases. Kidney biopsy revealed characteristic myelin figures and zebra bodies in podocytes, strongly suggestive of FN. Genetic analysis identified a VUS in the GLA gene (c.655A > C, p.Ile219Leu), introducing diagnostic uncertainty. Further investigations revealed severe cardiac involvement. Considering the recurring difficulty presented by the finding of a VUS in the GLA gene during FN assessments, along with the uncertainty regarding the need for treatment in nonclassical or late-onset FD phenotypes, especially in women, this case becomes a central focus for a thorough review of the literature. This review aims to propose a practical algorithm that integrates clinical, biochemical, and genetic markers for FN screening and diagnosis. Additionally, it explores treatment benefits in nonclassical or late-onset FD phenotypes, with a focus on female patients.

基于混频器的非线性矢量网络分析仪双端口校准方法

基于混频器的非线性网络分析仪(NVNA)是一种新型的非线性网络测量平台。介绍了该平台的结构、原理、误差模型和经典校准流程。结合NVNA的时不变测量特性,提出了2种新的双端口校准方案——Absent Thru和Blind Thru,并给出了具体的校准流程。在新研制的NVNA原型样机上的实验表明,新方案与经典Unknown Thru方法的校准结果相一致。新方案丰富了双端口NVNA校准手段,为NVNA的误差模型合理性以及校准结果准确性提供了有效的检验手段。

On the Pair of Equations x+y=z+w,y+z=(x-w)^2

The system of double equations given by x+y=z+w,y+z=(x-w)^2,is studied for obtaining its non-zero distinct solutions in integers.

On How to Guess Meanings of Unknown Words in Reading Materials the Importance of Context

While reading plays a very important role in English study, many English language learners find it very difficult to fully comprehend some reading materials on account of many unknown words in them. This essay mainly discusses one technique of guessing meanings of new words according to context clues, thus emphasizing the importance of context.