联合作战中光电对抗系统作战效能评估

针对联合作战中光电对抗系统作战效能评估问题,用联合指数法对联合作战中光电对抗系统中的单件光电对抗装备、光电对抗平台、光电对抗基本作战单位的作战能力进行了量化,对系统作战能力标量指数进行了聚合,并对光电对抗系统基本编组和光电对抗系统的作战能力进行了量化分析,最后用指数一兰彻斯特方程对光电对抗系统的作战效能进行了评估,其结果可为评判联合作战行动方案的完成情况提供参考。     

光电对抗装备的分类及其分析

光电对抗装备现已大量装备各国的陆、海、空三军,成为军事装备现代化的一个重要标志。光电对抗装备具有手段多样的特点,为了加深对光电对抗装备的理解,适当的分类是必须的。主要介绍了光电对抗装备的发展和应用,讨论了已有光电对抗装备的分类方法,并从装备效能评估的角度出发,提出了按功能对光电对抗装备进行分类的方法。     

本期重点推荐论文北大核心CSCD

《航空光电侦察技术发展研究》是航空光电侦察领域的综述型文章。作者对航空光电侦察的优点进行了分析和总结,对凝视观瞄、情报侦察监视、红外搜跟、告警与对抗四大类载荷进行了态势分析,列举了典型产品,分析了其特点及技术途径,从多个方面对国内外航空光电侦察技术和装备的发展情况及发展趋势进行了综述,为国内同行科研工作中不可多得的参考资料。     

激光对风标式激光制导炸弹干扰效能分析

针对光电对抗系统中烟幕无源、激光角度欺骗、激光阻塞、激光压制4种干扰方式,以风标式激光制导炸弹作为对抗目标,建立了激光对风标式激光制导炸弹的光电对抗仿真系统。阐述了系统的仿真流程,重点研究了激光能量大气传输、烟幕透过率、导引头信号处理、干扰效能计算、炸弹控制与运动等数学模型,进行了仿真试验及数据分析。根据仿真结果,分析了各干扰方式的主要影响因素,将影响干扰效果的主要因素分为瞬变型和渐变型两种,并给出二者对干扰效果影响的差异。该系统已应用于某光电对抗内场半实物仿真项目中,为光电对抗武器作战效能的分析与评估提供平台和依据。     

光电对抗仿真系统研究

介绍了光电对抗在信息化战争中的地位与作用,阐述了光电对抗仿真系统研究的必要性,针对国内外光电对抗仿真的研究现状,给出了光电对抗仿真系统的总体框架,研究了光电对抗仿真需解决的关键技术和需要完成的主要工作,最后对我国光电对抗仿真系统的研究进行了总结。     

微光夜视对抗初探

本文从微光夜视仪器工作原理出发,分析了微光夜视无源干扰、光电对抗的依据、方法,并给出试验结果。对光电对抗中微光象增强器的破坏部位做了探讨,同时指出微光夜视光电对抗的难点,并作了初步分析。     

海洋维权执法光电装备概况与发展思路初探

目前我国海洋安全形势非常严峻,维权执法面临极大的挑战。通过分析光电装备在维权执法中的独特优势,对海警光电装备进行了归纳分类。调研了国外海警光电装备的应用实例并综述其发展趋势。详细分析了我国海警光电装备的发展现状,通过对比指明与国外发达国家的差距。为达到世界先进水平,针对我国海警舰艇、飞机等维权执法平台的实际应用需求,探讨了我国海警光电装备的发展目标及思路。     

Research on atmospheric refraction correction of airborne electro-optical system target location北大核心CSCD

目标定位精度是评判机载光电系统性能的一项重要指标。对于高空机载光电系统,目标定位精度除受传感器、测距仪、载荷平台稳定精度、飞行平台稳定精度和位置精度等综合因素影响外,还受大气折射的影响,对于高空机载光电系统远距离对地观测,大气折射对目标定位的影响尤为严重。该文从大气折射对目标定位的影响机理出发,给出大气模型,分析大气折射的影响因素,并基于介于圆球体和参考旋转椭球体之间的地球模型给出了大气折射误差模型;基于该文提出的近似参考旋转椭球体地球模型,仿真分析了大气折射对目标定位的影响结果。分析结果对于机载光电系统远距离目标定位大气折射修正具有重要指导意义。     

无人机光电对抗技术研究

无人机以其机动性强、高效费比、无人化等独特优势搭载光电对抗系统应用于无人化电子战,夺取战场主动权,是目前光电对抗发展的热点。分析光电侦察告警、电子压制与干扰、光电隐身等光电对抗技术在无人机平台的应用,重点从小型化、轻型化、标准化等方面论述无人机平台光电技术发展的趋势,展望了无人机蜂群技术,以及智能化光电对抗体系在未来复杂战争环境中,发挥光电对抗作战效用的前景。     

信息化条件下光电装备保障信息系统技术研究

随着信息化的不断深化和我军光电装备建设实现历史性突破,急需把握信息化条件下光电装备保障信息系统的新特点和规律。深入揭示了光电装备保障信息系统是提升光电装备保障能力水平的根本保证。在明确信息化光电装备保障内涵的基础上,分析了信息化光电装备保障系统的军事需求及体系能力,给出了信息化条件下光电装备保障信息对象、系统组成和基本功能,以及使用计算机和信息化技术设计光电装备保障信息系统的过程和关键技术。     

色貌模型的人工神经网络方法的研究

色貌模型(CAM)主要解决不同观察条件、不同背景和不同环境下的颜色真实再现问题。采用人工神经网络(ANN)的方法来实现目前最新的色貌模型CIECAM02的预测,包括正向预测(从色度参数到色貌属性参数)和逆向预测(从色貌属性参数到色度参数),应用自然色系统(NCS)中的部分色样作为神经网络的训练和测试样本。由于正向输出色貌属性参数空间不是均匀的,对于网络预测精度用特殊方法评估,而对于逆向模型则可直接利用LAB色差公式评价。测试的结果表明:用神经网络对CIECAM02模型的预测达到了较高的精度。     

Microstructure Measurement Techniques for Studying Electromigration in ULSI Interconnects

Electromigration reliability remains a major threat to microelectronic circuits. Microstructure of a thin film conductor used in integrated circuit affects the electromigration lifetime significantly. A wealth of knowledge is acquired on thin film microstructure and electromigration in metallic interconnects from relevant studies in past few decades. However, it is noticed that the various techniques to measure microstructure-related attributes of thin film metallization are not presented in the context of electromigration, since these measurement techniques have their own importance. On the other hand, aggressive scaling of interconnect line-width down to nano regime, poses new challenges to microstructure characterization techniques. This article connects these two aspects of electromigration study, e.g., the characterization of microstructure and measurement techniques for the influential microstructural attributes especially for Cu-based interconnects. The microstructure-related parameters, attributes, and their impacts on electromigration lifetime are discussed. The sample preparation and various techniques to measure attributes of microstructure are presented in detail. This article describes the current state-of-the-art for the advancement of studying microstructure dependent electromigration reliability.     

Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding

Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks, solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking.     

The Attributes of Nonclassical Light and their Mutual Relationship

In the present paper the attributes of nonclassical light with respect to the theoretical basis, the experimental verification, and the applications are discussed and compared. If one ore more of the corresponding statistical measures (fluctuation excess, bunching excess, Poisson excess) attain negative values, surprising new properties of photon states occur. The analysis shows that under certain temporal and interaction conditions squeezing, antibunching, and sub-Poisson behavior can accompany one or more of the other attributes. Also the connection of the attributes with the Glauber-Sudarshan P-representation of the corresponding states is studied and yields important assertions.     

On the perception of similarity among talkers

A listener who recognizes a talker notices characteristic attributes of the talker's speech despite the novelty of each utterance. Accounts of talker perception have often presumed that consistent aspects of an individual's speech, termed indexical properties, are ascribable to a talker's unique anatomy or consistent vocal posture distinct from acoustic correlates of phonetic contrasts. Accordingly, the perception of a talker is acknowledged to occur independently of the perception of a linguistic message. Alternatively, some studies suggest that attention to attributes of a talker includes indexical linguistic attributes conveyed in the articulation of consonants and vowels. This investigation sought direct evidence of attention to phonetic attributes of speech in perceiving talkers. Natural samples and sinewave replicas derived from them were used in three experiments assessing the perceptual properties of natural and sine-wave sentences; of temporally veridical and reversed natural and sine-wave sentences; and of an acoustic correlate of vocal tract scale to judgments of sine-wave talker similarity. The results revealed that the subjective similarity of individual talkers is preserved in the absence of natural vocal quality; and that local phonetic segmental attributes as well as global characteristics of speech can be exploited when listeners notice characteristics of talkers.     

The admittance feature representation and impact sound feature extraction in the material identification of ribbed plates

The admittance features representing the physical attributes are used as the intermediates to extract the material-attributes-related impact sound features of ribbed plates.Firstly, the admittance feature representations of metal ribbed plates attributes are obtained and the relationship between the admittance features and the impact sound features are established via correlation analysis method. Then, material-attributes-related impact sound features are obtained indirectly. Finally, the performances of different sound features for the material recognition of ribbed-metal plates are verified through the Support Vector Machine classifier.The results indicate that the obtained four sets of features can effectively identify the materials of the metal ribbed plates, while the accuracy of a single feature depends on the separable degree of the corresponding material attribute. And the features extracted based on admittance functions have higher average accuracy than that of timbre features. Therefore, the proposed sound feature extraction method based on admittance features is valid, and the extracted sound features can effectively reflect the physical attributes.     

SORTING SYSTEMS BASED ON OPTICAL METHODS FOR DETECTING AND REMOVING SEEDS INFESTED INTERNALLY BY INSECTS OR FUNGI: A REVIEW*

Sorting systems based on optical methods have the potential to rapidly detect and physically remove seeds severely contaminated by fungi, or infested internally by insect larvae or pupae. Thus, the literature on sorting systems based on optical methods for detecting and sorting seeds with these attributes was reviewed. Sorting indices based on wavelengths useful for detecting these attributes were emphasized. Surface characteristics of seeds, like discoloration caused by fungi, are generally detectable in the visible range of the electromagnetic spectrum, whereas internal attributes are detectable in the near-infrared range. The spectral differences between sound and infested seeds are usually subtle, but full-spectrum and two-wavelength classification models have succeeded in detecting and classifying seeds based on these attributes. For high sorting accuracies, wavelength identification and proper selection of a sorting criterion are important. Color, chitin, ergosterol, or hydrolysis of triglycerides have been identified as indicators of seed fungal contamination whereas chitin, protein, phenolic compounds, or changes in starch have been useful indicators of internal insects in seeds.

     

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However,node heterogeneity(i.e., different attributes such as interest, energy, age, and so on) ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal processing based centrality(GSPC) method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.     

Thermomagnetic determination of Fe3O4 magnetic nanoparticle diameters for biomedical applications

The utility and promise of magnetic nanoparticles (MagNPs) for biomedicine rely heavily on accurate determination of the particle diameter attributes. While the average functional size and size distribution of the magnetic nanoparticles directly impact the implementation and optimization of nanobiotechnology applications in which they are employed, the determination of these attributes using electron microscopy techniques can be time-consuming and misrepresentative of the full nanoparticle population. In this work the average particle diameter and distribution of an ensemble of Fe₃O₄ ferrimagnetic nanoparticles are determined solely from temperature-dependent magnetization measurements; the results compare favorably to those obtained from extensive electron microscopy observations. The attributes of a population of biocompatible Fe₃O₄ nanoparticles synthesized by a thermal decomposition method are obtained from quantitative evaluation of a model that incorporates the distribution of superparamagnetic blocking temperatures represented through thermomagnetization data. The average size and size distributions are determined from magnetization data via temperature-dependent zero-field-cooled magnetization. The current work is unique from existing approaches based on magnetic measurement for the characterization of a nanoparticle ensemble as it provides both the average particle size as well as the particle size distribution.     

基于联合矩阵分解的节点多属性网络社团检测

发现复杂网络中的社团结构在社会网络、生物组织网络和在线网络等复杂网络中具备十分重要的意义. 针对社交媒体网络的社团检测通常需要利用两种信息源: 网络拓扑结构特征和节点属性特征, 丰富的节点内容属性信息为社团检测的增加了灵活性和挑战. 传统方法是要么仅针对这两者信息之一进行单独挖掘, 或者将两者信息得到的社团结果进行线性叠加判决, 不能有效进行信息源的融合. 本文将节点的多维属性特征作为社团划分的一种有效协同学习项进行研究, 将两者信息源进行融合分析, 提出了一种基于联合矩阵分解的节点多属性网络社团检测算法CDJMF, 提高了社团检测的有效性和鲁棒性. 实验表明, 本文所提的方法能够有效利用节点的属性信息指导社团检测, 具备更高的社团划分质量.