Spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

This paper proposes a novel open set recognition method,the Spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open environment.The spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center vectors.The designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during training.Consequently,this method allows unknown classes to occupy a larger space in the feature space.This reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more distinct.Additionally,the feature comparator threshold can be used to reject unknown samples.For signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world emitter.The experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open environment.Specifically,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

ON THE EXACT HAUSDORFF MEASURE OF A CLASS OF SELF-SIMILAR SETS SATISFYING OPEN SET CONDITION

In this paper, we provide a new effective method for computing the exact value of Hausdorff measures of a class of self-similar sets satisfying the open set condition （OSC）. As applications, we discuss a self-similar Cantor set satisfying OSC and give a simple method for computing its exact Hausdorff measure.

γ-（α,β）-Open Sets and γ-（α,β）-Ti Spaces

We introduce the concept of γ-(α, β)-open sets and the concepts of γ-(α, β)-T_i spaces(i = 0, 1, 2, 3, 4) based on the concept of(α, β)-open sets. We obtain some properties of γ-(α, β)-Tispaces. Furthermore, we study their essential characterization in topological space.

On <i>M</i>-Asymmetric Semi-Open Sets and Semicontinuous Multifunctions in Bitopological Spaces

The purpose of this paper is to introduce the notions of m-asymmetric semiopen sets and M-asymmetric semicontinuous multifunctions defined between asymmetric sets satisfying certain minimal conditions in the framework of bitopological spaces. Some new characterizations of m-asymmetric semiopen sets and M-asymmetric semicontinuous multifunctions will be investigated and several fundamental properties will be obtained.

Weak Form of Fuzzy Open Sets and Their Related Continuity

The aim of this paper is to give a new class of fuzzy open sets called fuzzy pre-γ-open sets in fuzzy topological spaces. Also,a new type of fuzzy continuous functions called,fuzzy pre-γ-continuous functions is introduced and studied.

Pointwise dimensions of general Moran measures with open set condition

Recently Lou and Wu obtained the formulas of pointwise dimensions of some Moran measures on Moran sets in Rd under the strong separation condition.In this paper,we prove that the result is still true under the open set condition.Due to the lack of the strong separation condition,our approach is essentially different from that used by Lou and Wu.We also obtain the formulas of the Hausdorff and packing dimensions of the Moran measures and discuss some interesting examples.

Improving Open Set Domain Adaptation Using Image-to-Image Translation and Instance-Weighted Adversarial Learning

We propose to address the open set domain adaptation problem by aligning images at both the pixel space and the feature space.Our approach,called Open Set Translation and Adaptation Network(OSTAN),consists of two main components:translation and adaptation.The translation is a cycle-consistent generative adversarial network,which translates any source image to the“style”of a target domain to eliminate domain discrepancy in the pixel space.The adaptation is an instance-weighted adversarial network,which projects both(labeled)translated source images and(unlabeled)target images into a domain-invariant feature space to learn a prior probability for each target image.The learned probability is applied as a weight to the unknown classifier to facilitate the identification of the unknown class.The proposed OSTAN model significantly outperforms the state-of-the-art open set domain adaptation methods on multiple public datasets.Our experiments also demonstrate that both the image-to-image translation and the instance-weighting framework can further improve the decision boundaries for both known and unknown classes.

THE EXPLICIT SOLUTIONS OF C_(p,q)^(k+α)-FORM FOR THE -EQUATIONS ON PSEUDOCONVEX OPEN SETS

In this paper, we study the integral solution operators for the -equations on pseudoconvex domains. As a generalization of [1] for the -dequations on pseudoconvex domains with boundary of class C∞, we obtain the explicit integral operator solutions of C -form for the -equations on pseudoconvex open sets with boundary of Ck (k≥0) and the sup-norm estimates of which solutions have similar as that [1] in form.

基于YOLO-OpenMax的水声通信信号开集识别方法

水声通信信号调制方式随着技术进步从典型的传统调制方式发展为水声通信机厂商私有开发的新型调制方式,这使得现有的水声通信信号闭集调制识别技术实用性受到制约,但现阶段鲜有研究基于深度学习的水声通信信号调制方式的开集识别方法。针对此现状,提出一种基于YOLO图像检测识别网络和OpenMax模型的开集识别方法。利用YOLOv5网络的输出特点,将传统OpenMax模型的处理方法进行改进,提出一种两步测试方法,实现了在多径信道下的水声通信信号的开集识别。仿真实验表明:在归一化系数为0.5,信噪比等于10 dB条件下的归一化开集识别准确率达到90%以上,实测数据同样验证了本文方法的有效性。

低空智联网中基于多质心OpenMax的无人机开集识别方法

随着网络化、智能化的发展,无人机(Unmanned aerial vehicles, UAVs)逐渐成为低空智联网(Low-altitude intelligent network, LAIN)的重要组成部分,但如何对低空智联网中的无人机平台进行有效的管理仍面临严峻挑战。基于无人机信号中的细微特征可对无人机进行个体识别,并检测是否为非法无人机,从而实现低空智联网中无人机的身份识别和管理。针对低空领域信道环境复杂且无法提前获取非法无人机信号样本的问题,本文提出了基于差值时频和多质心OpenMax的无人机开集识别方法。首先,提出了与信道无关的差值时频特征来降低多径信道环境对射频指纹(Radio frequency fingerprinting, RFF)特征的影响,并利用数据增强提高了识别模型的准确率和鲁棒性。其次,利用多质心OpenMax替代神经网络Softmax层,以实现无人机个体的开集识别。最后,对神经网络的损失函数进行了改进,提高了开集识别准确率。本文利用真实环境采集的数据对所提算法进行了验证,在多径信道环境中开放度为0.087时,开集识别准确率达到了93.23%,与基准算法相比,准确率分别提高了7.61%和13.4%。本文提出的算法可在复杂信道环境中有效识别无人机个体并检测出首次出现的非法无人机。

Robust Set Stabilization and Its Instances for Open Quantum Systems

We propose and discuss a novel concept of robust set stabilization by permissible controls; this concept is helpful when dealing with both a priori information of model parameters and different permissible controls including quantum measurements. Both controllability and stabilization can be regarded as the special case of the novel concept. An instance is presented for a kind of uncertain open quantum systems to further justify this gen- eralized concept. It is underlined that a new type of hybrid control based on periodically perturbed projective measurements can be the permissible control of uncertain open quantum systems when perturbed projective measurements are available. The sufficient conditions are given for the robust set stabilization of uncertain quantum open systems by the hybrid control, and the design of the hybrid control is reduced to selecting the period of measurements.

Exclusive Fibula Osteosynthesis for Treating Open Fractures Gustillo I-III B of the Distal Half of the Leg Bones in a Resources-Limited Setting

Introduction: Management of open leg bones fractures is a challenging health issue for the surgeon, particularly true in resource-limited settings. In this study, we evaluate exclusive fibular osteosynthesis in the treatment of open fractures of the distal half of the leg bones as a therapeutic option in our context. Methods: This is a prospective, experimental, multicenter study of 30 open fractures of the distal half of the leg bones treated with exclusive fibula osteosynthesis, conducted in 3 hospitals in the DRC from January 1, 2013 to September 30, 2016. Results: The age range of 20 to 40 years grouped 22 (73.4%) patients, the sex ratio was 1:1 and the unemployed were the most involved with 16 (53.3%) cases. The Gustilo II, I, III B and III A types represented 40%, 33.3%, 20% and 6.7%, respectively. The fractures were located in the distal third in 12 (40%) cases, in the middle third in 11 (36.7%) cases, and in both malleoli in 7 cases (23.3%). Osteosynthesis of the fibula by screw plate was applied in 22 (73.3%) patients and pinning in 8 (26.7%). Satisfactory reduction of the tibial fracture site was achieved in 29 (96.7%) cases and 100% bone healing was achieved within an average of 10 weeks. Four (13.3%) loss of alignment, 1 (3.3%) infection, 1 (3.3%) skin necrosis and 2 (6.7%) ankle stiffness complicated our fractures. Conclusion: Exclusive osteosynthesis of the fibula as a common technique for fractures of the distal half of the leg bones allowed us to reduce, immobilize and consolidate the tibial fracture in the required time and to preserve the mobility of the ankle.

Aspen Open Solver接口技术研究

以Aspen Open Solver接口集中的非线性代数方程组(NLA)部分作为研究对象,在对接口集进行系统地分析之后,利用AspenTech提供的接口代码将分别基于梯度和非基于梯度的四种求解算法嵌入生成solver组件,并实现用Aspen Plus调用该solver组件观察各种算法嵌入的结果。

A THEORETICAL FRAMEWORK FOR THE CALCULATION OF HAUSDORFF MEASURE-SELF—SIMILAR SET SATISFYING OSC

A theoretical framework for the calculation of Hausdorff measure of self-similar sets satisfying OSC has been established.

面向SAR解译的OpenSAR数据开放共享平台

大量带有真值标注的数据集是SAR图像解译研究的重要基础和支撑。在推动SAR图像解译研究发展面临的诸多困难之中,缺少一个开放共享的数据集用于解译算法的研究和公平测试比较,是其中一个非常重要的因素。文中主要介绍构建的国内第一个面向SAR图像解译的数据开放共享在线平台:OpenSAR。详细介绍了OpenSAR平台的设计思想、功能组成、关键技术和已有数据集。通过该平台的构建,对于SAR解译研究能够取得良好的促进作用。

OpenFlow交换机协议一致性测试集模型研究

针对当前的Open Flow交换机协议测试技术的研究现状,介绍了两个Open Flow交换机协议一致性测试的测试集模型。

函数空间C_k(X)上的T-Tightness和Set-Tightness

讨论了函数空间Ck(X)在赋予紧开拓扑下的T-tightness和set-tightness性质,利用开k覆盖获得了Ck(X)是T-tightness空间和set-tightness空间的两个对偶定理,将点态收敛拓扑函数空间Cp(X)的相关结论推广到紧开拓扑函数空间Ck(X)上.

Open-MUSIC:基于度量学习与特征子空间投影的电磁目标开集识别算法

在越来越复杂的电磁频谱环境中,要想实现对频谱资源的管控,首先要判断发送信号的辐射源是否是己方已知的.针对此问题,本文提出了一种基于计算特征子空间投影比值的算法Open-MUSIC(MUltiple SIgnal Classification),通过神经网络获得已知类特征表示;进而得到已知类特征矩阵的两个正交子空间;以特征在两个子空间内的投影比值为指标,对辐射源信号样本是否为已知做判决.在3个数据集上的仿真表明,Open-MUSIC算法的性能在电磁数据集上较其他方法提升了3%以上.

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。

公共数据开放如何促进数字化发展——基于模糊集定性比较分析的研究

随着新一代信息通信技术和计算技术的迅速发展,公共数据在经济、社会、行政、科学研究等领域发挥着越来越重要的作用。为帮助人们理解公共数据开放如何促进数字化发展,文章借助中国内地31个省份的国家统计数据和部分研究者开发的评估数据,利用模糊集定性比较分析方法,研究公共数据开放与其他因素共同推动数字化发展的途径。结果表明:公共数据开放可以与其他因素相结合,共同推动数字化发展。公共数据开放主要通过3条途径促进数字化发展,即研发经费投入型路径、市场规模拉动型路径、数字技术应用型路径。