UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is proposed.The clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid assessment.Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is proposed.The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics.The LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization sensitivity.Comparing experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient classification.Compared with the five kinds of assess-ments models,the SAE-LVQ model has the highest accuracy.Finally,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation assessment.The assessment results are in line with the actual situation.

基于SA-Apriori的道路事故风险评估研究

道路事故风险评估是提高道路安全水平的有效方式。为实现在道路网络特征视角下的道路事故风险评估,融合线段分析与频繁集算法,提出SA-Apriori的特征提取算法,进而构建SA-Apriori道路事故风险评估模型,以重庆市大渡口区作为分析对象,建立以道路网络特征变量为指标的道路事故风险等级划分体系,评估其道路事故风险水平,结合商业点位分布情况对该评估方法的有效性进行验证。并针对大渡口区道路事故风险情况,提出道路事故风险的管理对策与建议。结果表明:该道路事故风险评估方法具有较高的可靠性,可为基于路网特征的道路事故风险评估提供参考。

A New Model for Network Security Situation Assessment of the Industrial Internet

To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.

A cloud Bayesian network approach to situation assessment of scouting underwater targets with fixed-wing patrol aircraft

The battlefield situation changes rapidly because underwater targets'are concealment and the sea environment is uncertain.So,a great number of situation information greatly increase,which need to be dealt with in the course of scouting underwater targets.Situation assessment in sea battlefield with a lot of uncertain information is studied,and a new situation assessment method of scouting underwater targets with fixed-wing patrol aircraft is proposed based on the cloud Bayesian network,which overcomes the deficiency of the single cloud model in reasoning ability and the defect of Bayesian network in knowledge representation.Moreover,in the method,the cloud model knowledge deal with the input data of Bayesian network reasoning,and the advantages in knowledge representation of cloud theory and reasoning of Bayesian network are applied;also,the fuzziness and stochasticity of cloud theory in knowledge expression,the reasoning ability of Bayesian network,are combined.Then,the situation assessment model of scouting underwater targets with fixed-wing patrol aircraft is established.Hence,the directed acyclic graph of Bayesian network structure is constructed and the assessment index is determined.Next,the cloud model is used to deal with Bayesian network,and the discrete Bayesian network is obtained.Moreover,after CPT of each node and the transformation between certainty degree and probability are accomplished;the final situation level is obtained through a probability synthesis formula.Therefore,the target type and the operational intention of the other side are deduced to form the battlefield situation.Finally,simulations are carried out,and the rationality and validity of the proposed method are testified by simulation results.By this method,the battlefield situation can be gained.And this method has a wider application range,especially for large sample data processing,and it has better practicability.

Aerial target threat assessment based on gated recurrent unit and self-attention mechanism

Aerial threat assessment is a crucial link in modern air combat, whose result counts a great deal for commanders to make decisions. With the consideration that the existing threat assessment methods have difficulties in dealing with high dimensional time series target data, a threat assessment method based on self-attention mechanism and gated recurrent unit(SAGRU) is proposed. Firstly, a threat feature system including air combat situations and capability features is established. Moreover, a data augmentation process based on fractional Fourier transform(FRFT) is applied to extract more valuable information from time series situation features. Furthermore, aiming to capture key characteristics of battlefield evolution, a bidirectional GRU and SA mechanisms are designed for enhanced features.Subsequently, after the concatenation of the processed air combat situation and capability features, the target threat level will be predicted by fully connected neural layers and the softmax classifier. Finally, in order to validate this model, an air combat dataset generated by a combat simulation system is introduced for model training and testing. The comparison experiments show the proposed model has structural rationality and can perform threat assessment faster and more accurately than the other existing models based on deep learning.

Situation assessment for air combat based on novel semi-supervised naive Bayes

A method is proposed to resolve the typical problem of air combat situation assessment. Taking the one-to-one air combat as an example and on the basis of air combat data recorded by the air combat maneuvering instrument, the problem of air combat situation assessment is equivalent to the situation classification problem of air combat data. The fuzzy C-means clustering algorithm is proposed to cluster the selected air combat sample data and the situation classification of the data is determined by the data correlation analysis in combination with the clustering results and the pilots＇ description of the air combat process. On the basis of semi-supervised naive Bayes classifier, an improved algorithm is proposed based on data classification confidence, through which the situation classification of air combat data is carried out. The simulation results show that the improved algorithm can assess the air combat situation effectively and the improvement of the algorithm can promote the classification performance without significantly affecting the efficiency of the classifier.

A Novel Situation Assessment Method for Network Survivability

Survivability has emerged as a new phase for the development of network security technique, and quantifying survivability for network system helps to evaluate it exactly for the system in different environments. In this paper, we adopt a stochastic method called sequential Monte Carlo and try to reflect dynamic evolvement process of network survivability situation from several time sequences. The experiment results show that this method has the features of quantitative description, real-time calculation and dynamic tracking, and it is a good situation assessment solution for network survivability.

A situation awareness assessment method based on fuzzy cognitive maps

The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions.Thus the measurement method of the situation awareness status is an important topic to research.So far,there are lots of methods designed for the measurement of situation awareness status,but there is no model that can measure it accurately in real-time,so this work is conducted to deal with such a gap.Firstly,collect the relevant physiological data of operators while they are performing a specific mission,simultaneously,measure their status of situation awareness by using the situation awareness global assessment technique(SAGAT),which is known for accuracy but cannot be used in real-time.And then,after the preprocessing of the raw data,use the physiological data as features,the SAGAT’s results as a label to train a fuzzy cognitive map(FCM),which is an explainable and powerful intelligent model.Also,a hybrid learning algorithm of particle swarm optimization(PSO)and gradient descent is proposed for the FCM training.The final results show that the learned FCM can assess the status of situation awareness accurately in real-time,and the proposed hybrid learning algorithm has better efficiency and accuracy.

Monitoring and Assessment of Current Situation of Environmental Quality in Yueqing Bay

Based on the monitoring results of environmental quality in Yueqing Bay during 2007-2014,the current situation of environmental quality in the bay was analyzed and assessed. The results show that pH,DO,CODMn,petroleum,and heavy metals( Cu,Pb,Zn,Cd,Hg,As and Cr)in the seawater of Yueqing Bay did not exceed the second class standard of Seawater Quality Standard( GB 3097-1997),but inorganic nitrogen and reactive phosphate in the seawater of Yueqing Bay exceeded the second class standard seriously,and the water quality of the bay was in an eutrophic state; the standard index of evaluation factors of sediment quality was smaller than 1,meeting the demands of sediment quality for environmental protection.

The Situation of Southeast Asia in 2017 and Assessment of Its Relations with China

The situation in Southeast Asia in 2017 was generally stable,as relations with China continued to improve,and progress in maritime consultations continued to develop.I.The political situation is stable and generally manageable Stability is manifested in the following aspects.The governments of the Philippines,Myanmar,Vietnam,Laos are seeking stability and good governance.

应急情境下管制员情境意识可靠性研究

为提升管制员的应急处置能力,从管制员的个人能力、管制任务特点和管制设备3个方面构建管制员情境意识(SA)可靠性模型;利用塔台管制模拟软件和SA全面评价技术(SAGAT)测量管制员在应急情境下的SA水平;基于贝叶斯网络(BN)对SA可靠性进行定量分析,进而预测管制员的SA水平,并基于贝叶斯推理分析影响管制员SA可靠性的关键因素。研究结果表明:应急情境下,管制员SA可靠性和SA水平显著正相关,可通过管制员的SA可靠性预测SA水平;通过BN推理分析发现,对管制员的SA可靠性影响程度和敏感程度较高的因素为调配飞机可用时间、管制设备的精准度和管制员的记忆力;对管制员SA可靠性影响最大的致因链为调配飞机可用时间→任务特点→SA可靠性。

智能煤矿综采控制系统双人协同作业情境意识研究

目的探究智能煤矿综采控制系统双人协同作业中个体在不同协同条件下的情境意识(SA)的构建情况,为智能煤矿中人员协同操作模式提供管理的相关依据。方法利用E-prime软件模拟智能煤矿综采控制系统中的采煤机运行状态监控任务,实验模拟四种不同协同场景(单独作业、操作协同、工作交流协同、非工作内容交流协同),利用全局情境意识技术测度被试总体情境意识以及感知、理解、预测三阶段情境意识得分,并验证不同协同条件下双人协同作业模式中个体的情境意识影响差异。结果双人操作协同与工作内容交流协同显著提升个体情境意识水平,并取得较高的工作绩效;在情境意识感知阶段,非工作内容交流协同与其他三组对比情境意识得分显著下降;在理解阶段,工作内容交流组与其他三组均呈现情境意识得分上的显著性差异,且工作内容交流组得分最高;在预测阶段单独作业组与操作协同组和工作内容交流协同组在SA得分上存在显著性差异,单独作业在预测阶段的得分显著低于有协同组。结论相较于单独作业,操作协同、有关工作内容的交流协同能够有效维持个体情境意识和提高绩效水平,但非工作内容的交流协同反而会降低个体情境意识。

基于改进SKNet-SVM的网络安全态势评估

为提高网络安全态势评估的准确率,增强稳定性与鲁棒性,提出一种基于改进选择性卷积核卷积神经网络和支持向量机的网络安全态势评估模型。首先,使用改进选择性卷积核代替传统卷积核进行特征提取,提高卷积神经网络感受野变化的自适应性,增强特征之间关联性。然后,将提取的特征输入到支持向量机中进行分类,并使用网格优化算法对支持向量机中的参数进行全局寻优。最后,根据网络攻击影响指标计算网络安全态势值。实验表明,基于改进选择性卷积核卷积神经网络和支持向量机的态势评估模型与传统的卷积神经网络搭建的态势评估模型相比,准确率更高,并且具有更强的稳定性和鲁棒性。

义务教育英语学科核心素养评价模型设计

“证据中心”评价设计模式是一种系统化进行评价设计的模式,具有整合性、可视性、灵活性、重复性,适用于评价复杂、抽象的英语学科核心素养。以“证据中心”模式为基础,《义务教育英语课程为标准(2022版)》为依据,建构主体多元、方式多样的素养导向评价模型。该模型依托真实情景,借助立体化的核心素养质量标准和扎根真实情景的任务,以纸笔、活动、现代信息技术等手段为呈现方式,在合理范围内系统评估不同学段学生的英语学科核心素养,可改善命题质量、提升教师评价素养、促进教学评一体化等。

融合神经网络和优化算法的网络安全态势评估及预测模型研究

针对网络安全防护,提出一种融合神经网络与优化算法的态势评估模型。利用深度神经网络进行网络态势评估模型构建,并通过长短时记忆构建网络态势预测模型,利用遗传算法实现模型参数调优。结果表明,评估模型的平均误差率相比于浅层神经网络模型降低了3.88%。因此,研究设计的基于优化深层神经网络与长短时记忆网络的态势感知模型,具有较好的评估预测性能。

关于电信网络诈骗案件侦查用谋的思考

应对电信网络诈骗犯罪的高发态势,仅从技术层面创新侦查技战法、优化侦查技术策略存在一定局限。公安机关应秉持“两手抓,两手都要硬”的思路,在进一步强化信息技术作用的前提下,高度重视侦查谋略在此类案件中的运用和创新。运用侦查谋略,能够弥补信息技术手段在侦查中的不足,充分发挥侦查主体智慧优势,进一步查明案情、获取证据、抓获犯罪嫌疑人。侦查此类犯罪案件,侦查人员应审时度势、因势设谋,通过攻心夺气、奇正相合、示形用佯、迂直相寓等谋略,积极主动开展谋势、谋人、谋事、谋证等方面工作。

MEWS联合SBAR沟通模式在血液内科护理中的应用效果

目的分析改良早期预警评分(MEWS)联合现状-背景-评估-建议(SBAR)沟通模式在血液内科护理中的应用效果。方法选择2019年4月至2021年4月收治的100例患者,采用随机数字表法将其分为对照组和研究组,每组50例。对照组接受常规护理,研究组接受MEWS联合SBAR沟通模式。比较两组的护理效果。结果研究组的风险事件总发生率低于对照组,遵医行为总依从率高于对照组(P<0.05)。护理后,研究组的焦虑自评量表(SAS)、抑郁自评量表(SDS)评分低于对照组,血小板计数、血红蛋白、白细胞计数、中性粒细胞计数高于对照组(P<0.05)。结论MEWS联合SBAR沟通模式在血液内科患者护理中的应用效果显著,能够改善患者心理状态,提高遵医依从性,利于促进疾病转归。

基于变权物元可拓的中小机场空管运行安全态势评估

在建立了由人员因素、设备因素、环境因素、管理因素4个一级指标和16个二级指标的评估指标体系的基础上,根据层次分析法确定各指标的初始常权权重,引入变权向量后计算得到变权权重;结合物元可拓评估模型,得到量化的安全态势值。以某空管运行单位为例,得到的结果为“一般”,与专家评估结果和常权法模型评估结果相比较,证明了该评估模型的可行性,并且评估准确度优于传统模型。

新形势下网络教学模式在环境影响评价课程中的应用

环境影响评价是环境专业的核心课程,其内容涉及广泛、实用性强,但传统教学模式很难达到高标准的教学目的。该文对传统教学模式存在的问题进行梳理分析及讨论,同时“互联网+”教育模式逐渐出现在大众视野。该文基于“互联网+”技术和思维,探讨有效的新型教学模式,该模式激发同学们的学习热情,重新构建教学过程。结合新形势下网络发展,加强环境影响评价课程的教学建设,加强学生对课程深度及广度的认知与延伸,进而提高学生能力和课堂教学质量。

基于人工免疫的城轨列控系统信息安全态势评估方法

针对城轨列控系统面临的信息安全风险日益突出的问题,提出基于人工免疫系统的态势评估(AISSA)方法,即利用城轨列控系统数据特性,设计检测器成熟机制和攻击检测方法,实时感知城轨列控系统遭受的网络攻击;设计检测器克隆和变异机制,进一步丰富检测器种群,提高城轨列控系统感知网络攻击的能力;仿真试验模拟城轨列控系统遭受不同强度身份认证拒绝服务攻击和TCP拒绝服务攻击,采用AIS-SA方法感知网络攻击并实时量化系统的安全态势。结果表明:AIS-SA方法感知网络攻击的能力较强,当检测器进化至25代时,对身份认证拒绝服务攻击的检测率为96.81%、误报率为0.25%,对TCP拒绝服务攻击的检测率为98.46%、误报率为1.32%,与其他方法相比检测率较高且误报率较低;此外,AIS-SA方法能表征不同攻击强度下城轨列控系统安全态势,当攻击强度增大时实时态势量化值升高,反之减小;仿真结果验证了AIS-SA方法的有效性和准确性。