Analysis on evaluation ability of nonlinear safety assessment model of coal mines based on artificial neural network

Based on the integration analysis of goods and shortcomings of various methods used in safety assessment of coal mines, combining nonlinear feature of mine safety sub-system, this paper establishes the neural network assessment model of mine safety, analyzes the ability of artificial neural network to evaluate mine safety state, and lays the theoretical foundation of artificial neural network using in the systematic optimi- zation of mine safety assessment and getting reasonable accurate safety assessment result.

Situational Awareness Technology in Network Security

With the development and popularization of network technology, such as attacks from the network is also facing serious challenges, showing a ＂one foot in mind that＂ the situation. How can detect possible security risks and the type of attack, and provide preventive strategy is to network managers have been pursuing the goal of network security situational awareness can speak a variety of services and associated data as a highly organic whole, summarized network security and dependency relationships come more comprehensive, complete, accurate decision-making for network security assessment and countermeasures.

Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these security issues,the safety protection technology of the in-vehicle system has become a focus of research.Using the advanced autoencoder network and recurrent neural network in deep learning,we investigated the intrusion detection system based on the in-vehicle system.We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior.In order to verify the accuracy and efficiency of the proposed model,it was evaluated using real vehicle data.The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior.The parameters of the model are self-iteratively updated using the time-based back propagation algorithm.We verified that the model proposed in this study can reach a nearly 96%accurate detection rate.

Distributed Control Technology for Air and Missile Defence Operations

This paper relates to accepted presentation at international conference Air and Missile Defence Technology,November 16-17,2022,London UK(day two),reflecting contents of the presentation slides.It describes applications of the patented and internationally tested Spatial Grasp Technology(SGT)and its Spatial Grasp Language(SGL)for Integrated Air and Missile Defense(IAMD).Based on holistic space navigation and processing by recursive mobile code self-spreading in distributed words,SGT differs radically from traditional management of large systems as consisting of parts exchanging messages.The dynamic network of SGL interpreters can be arbitrarily large and cover terrestrial and celestial environments as powerful spatial engines.The paper contains an example of tracking and destruction of multiple cruise missiles by self-evolving spatial intelligence in SGL using networks of radar stations.It also briefs the growing multiple satellite constellation in Low Earth Orbits(LEO)for potential IAMD applications.Starting from Strategic Defense Initiative(SDI)of the past and then briefing the latest project of Space Development Agency,the paper shows SGL solutions for discovery,tracking,and destroying ballistic missiles and hypersonic gliders with the use of collectively behaving constellations of LEO satellites.It also shows how to organize higher levels of supervision of groups of mobile chasers fighting multiple targets(both potentially as missiles or drones),by providing their global awareness even consciousness in SGL which can drastically improve their performance.The latest version of SGT can be implemented on any platforms and put into operation in a short time,similarly to its previous versions in different countries.

Situation Awareness of Active Distribution Network:Roadmap,Technologies,and Bottlenecks

With the rapid development of local generation and demand response,the active distribution network(ADN),which aggregates and manages miscellaneous distributed resources,has moved from theory to practice.Secure and optimal operations now require an advanced situation awareness(SA)system so that operators are aware of operation states and potential risks.Current solutions in distribution supervisory control and data acquisition(DSCADA)as well as the distribution automation system(DAS)generally are not able to meet the technology requirements of SA.In this paper,the authors’participation in the project of developing an SA system as the basic component of a practical active distribution management system(ADMS)deployed in Beijing,China,is presented.This paper reviews the ADN’s development roadmap by illustrating the changes that are made in elements,topology,structure,and control scheme.Taking into consideration these hardware changes,a systematic framework is proposed for the main components and the functional hierarchy of an SA system for the ADN.The SA system’s implementation bottlenecks are also presented,including,but not limited to issues in big data platform,distribution forecasting,and security evaluation.Potential technology solutions are also provided.

Providing Global Awareness in Distributed Dynamic Systems

The paper investigates applicability of the developed high-level model and technology for solution of diverse problems in large distributed dynamic systems which can provide sufficient awareness of their structures,organization,and functionalities.After the review of meanings of awareness and existing approaches for its expression and support,the paper shows application of the Spatial Grasp Model and Technology(SGT)and its basic Spatial Grasp Language(SGL)for very practical awareness solutions in large distributed dynamic systems,with obtaining any knowledge from any point inside or outside the system.The self-evolving,self-replicating,and self-recovering scenario code in SGL can effectively supervise distributed systems under any circumstances including rapidly changing number of their elements.Examples are provided in SGL for distributed networked systems showing how in any node any information about other nodes and links,including the whole system,can be obtained by using network requesting patterns based on recursive scenarios combining forward and backward network matching and coverage.The returned results may be automatically organized in networked patterns too.The presented exemplary solutions are parallel and fully distributed,without the need of using vulnerable centralized resources,also very compact.This can be explained by fundamentally different philosophy and ideology of SGT which is not based on traditional partitioned systems representation and multiple agent communications.On the contrary,SGT and its basic language supervise and control distributed systems by holistic self-spreading recursive code in wavelike,virus-like,and even“soul-like”mode.

面向喷染车间的挥发性有机物质量浓度预测方法及应用研究

以喷染车间挥发性有机物为研究对象,对喷染车间挥发性有机物(Volatile Organic Compounds, VOCs)质量浓度预测方法进行研究。首先,使用随机森林(Random Forest, RF)算法对影响喷染车间挥发性有机物质量浓度的特征变量进行权重分析。同时,构建基于长短期记忆神经网络(Long-Term and Short-Term Memory Neural Network, LSTM)的挥发性有机物质量浓度预测模型,并在此基础上引入麻雀搜索算法(Sparrow Search Algorithm, SSA)进行参数优化选择。最后,以浙江省杭州市某汽车喷染车间7月29日—10月28日的数据为样本,将温度、相对湿度、室内大气压、室外大气压作为模型输入变量,并与LSTM模型、随机森林-长短期记忆神经网络(Random Forest-Long Short-Term Memory neural network, RF-LSTM)模型、随机森林-反向传播神经网络(Random Forest-BP neural network, RF-BP)模型进行对比试验。结果显示,基于随机森林-麻雀搜索算法-长短期记忆神经网络(Random Forest-Sparrow Search Algorithm-Long Short-Term Memory neural network, RF-SSA-LSTM)模型的预测效果最佳,平均绝对误差、均方根误差和决定系数分别为2.812 2、3.457 4、0.988。同时,为验证RF-SSA-LSTM模型性能,通过不同时间步长实现对喷染车间VOCs质量浓度预测,结果显示预测误差较小,在可接受范围内。RF-SSA-LSTM预测模型提高了挥发性有机物质量浓度的预测精度,为减少挥发性有机物排放提供科学依据。

态势感知技术在智慧校园安全中的应用研究

随着网络安全等级保护制度2.0标准的实施,主动防御已成为校园网络安全防护的重要策略,进而也推动着智慧校园安全体系的建设。通过对以态势感知技术在信息资产、业务逻辑、数据采集、架构设计以及安全预警等方面的深入研究,搭建出智慧校园态势感知平台的应用场景,及时捕获校园网络中的漏洞或异常事件,增强对校外攻击行为的主动防御能力,为学校提供精准的网络安全态势信息,从而提高智慧校园安全状况的控制力,进而降低其安全风险,高质量实现智慧校园安全防御目标。

基于大数据技术的网络安全态势感知平台设计

在网络安全威胁不断演变的背景下,构建一套基于大数据技术的网络安全态势感知平台至关重要。本文介绍了网络安全态势感知平台的设计框架,包括系统架构、数据采集与处理、安全事件检测与分析以及实时可视化等关键模块。通过应用案例分析,突显平台在实际环境中的应用效果。实时可视化与报告和自动化响应与持续改进两个章节详细探讨了用户界面设计、报告定制化和自动响应机制。最后,总结了平台设计的关键要点,并展望了未来网络安全领域的发展趋势。

基于无线传感器网络技术的智能物联施工安全防护系统设计

为解决传统公路施工现场安全防护系统不能实现远程监控的问题,提出了一种基于无线传感器网络技术的智能物联施工安全防护系统,阐述了该安全防护系统的总体结构及工作原理。该系统以安全帽为智能穿戴载体,采用基于ZigBee技术的无线自组网方式,实现了施工现场的实时环境数据采集,通过无线传感器网络技术将环境数据采集至物联网平台进行分析和处理,完成对施工现场的安全监控。实验结果表明,该系统可实现对施工现场安全情况的实时监控,其传输数据稳定可靠,且具备较强的抗干扰能力。

基于人工智能技术的配网用户侧负荷态势感知方法

为解决现有配网用户侧负荷态势感知方法感知误差大的问题,提出了基于人工智能技术的配网用户侧负荷态势感知方法。分析了配网用户侧负荷异常情况,筛选出零电量用户,构建人工智能技术负荷态势感知模型,对负荷群态势进行量化分析。使用机器学习方法划分负荷空域集合,计算每一个集合的异常得分,使用雷达图法评估零电量电力用户异常程度。以态势负荷时间序列能量值为基准进行归一化处理,结合Fisher判别法对待感知态势进行动态预测,获取态势感知类别。实验结果表明,该方法与实际最高感知负荷相差0.05 MW,最高感知误差为0.20,说明使用该技术能够得到精准的感知结果。

基于SDN和集成学习的工业控制网络安全防护系统

针对工业控制网络通信信息安全与稳定问题,设计一种基于SDN和集成学习的工业控制网络安全防护系统。该系统采用SDN技术,分为物理层、现场层、转发层、控制层和应用层等5个层次。物理层包含现场终端设备;现场层通过控制模块与操作员站实现对现场终端的控制;转发层使用SDN交换机进行通信数据传输,并将数据镜像传输至应用层进行安全分析;控制层中的SDN控制器管理和控制SDN交换机,并执行应用层下发的安全防护策略;应用层利用集成学习算法对工业控制网络进行入侵行为检测,通过安全响应模块分析入侵信息并选择相应的防御机制。实验结果表明,所设计系统满足工业控制网络通信的实时性要求,能准确地实施入侵检测,从而保障工业控制网络的安全性和正常通信。

新型电力系统网络安全零信任技术应用研究

为了提高电力系统网络的安全性、保障电力系统的稳定运行,提出了一种新型电力系统网络安全零信任技术。首先,分析新型电力系统网络环境图和状态转移图,以感知网络安全态势。然后,计算电力系统网络安全态势值,并根据所得结果将零信任安全组件嵌入防火墙和主机入侵检测设备,以构建零信任技术下新型电力系统网络安全防护框架体系。同时,结合电力系统的安全需求,实现差异化定制,达到对新型电力系统网络安全防护的目的。最后,通过试验验证了所提技术可有效检测到网络环境中的各类攻击,误检率和漏检率较低。该技术能够实现精准、高效的安全防护。

数据加密技术在计算机网络信息安全中的应用

随着计算机网络技术的不断发展,网络安全问题日益严重。文章分析了计算机网络信息安全防护中常见的数据加密技术,并进一步探讨数据加密技术在计算机网络信息安全中的应用,以期为科学防护计算机网络信息安全提供借鉴。

计算机网络安全技术在网络安全维护中的应用

随着计算机网络的普及和发展,网络安全问题日益引起人们的关注。网络安全问题不仅涉及个人信息的安全,还涉及国家安全和社会的稳定。因此,研究计算机网络安全技术在网络安全维护中的应用具有重要的现实意义。文章主要探讨计算机网络安全技术在网络安全维护中的应用,通过分析计算机网络安全的现状和挑战,提出相应的解决方案,提高网络的安全性。

高原瓦斯隧道“风帘+巷道式”通风方案应用研究

合理的通风方案设计是保证高原瓦斯隧道安全施工的基础。针对高原瓦斯隧道通风难度大、施工危险性高等难题,基于“风帘+巷道式”通风方案,采用通风网络设计计算、Fluent数值模拟计算与工程现场应用的方法,探究“风帘+巷道式”通风方案在高原瓦斯隧道中的应用效果。结果表明:“风帘+巷道式”通风方案不但能满足大风量通风需求,而且风帘的设计可有效解决瓦斯回流问题,保证运输安全;基于Fluent数值模拟计算,揭示了瓦斯气体具有贴壁扩散的规律,瓦斯先聚集于隧道顶部后贴壁扩散到隧道内,且回风侧的瓦斯体积分数高于风管侧,在距掌子面20 m外,所有指标均满足安全施工规范要求,各断面瓦斯平均体积分数低于0.5%,通风效果显著。研究可为类似的高原瓦斯隧道通风方案实施提供参考。

人工智能在计算机网络技术中的运用分析

计算机网络技术是一类技术的总称,在现代社会各行业中发挥着重要作用,客观要求以各类技术促进其价值发挥。本文以人工智能在计算机网络技术中的运用优势为切入点,在此基础上探讨其运用场景,并以安全防护工作为例,分析其具体运用方法,就信息采集、智能分析、数据累积和反馈等内容进行论述,为未来人工智能在计算机网络技术中的运用提供参考。

面向船舶的基于WPA-BP与虚拟现实的机舱安全监测

为了提高对船舶机舱安全运行远程故障诊断的效率,对船舶机舱的智能化诊断与监测进行了研究,设计了一种船舶机舱安全监测系统;采用优化后的狼群算法对反向传播神经网络进行改进,并创新建立了狼群算法-反向传播基础上的机舱柴油机故障诊断模型;经实验测试实现了平均诊断准确率高达99.102%、响应时间0.048 ms的验证结果;经实际应用满足了船舶安全运行的需求,提高了故障诊断有效性。

基于麻雀搜索算法改进的BP神经网络铁路技术站智能化安全评价方法研究

为了提升铁路技术站的安全性,提出一种融合主客观评价方法、麻雀搜索算法与BP神经网络模型的铁路技术站智能安全评价方法,可以有效解决铁路技术站生产作业的安全评价问题。通过熵权法确定属性权重,并建立相应的安全综合评价模型生成BP神经网络的训练和测试样本,实现对铁路技术站安全性的智能化评估。考虑到选取BP神经网络关键参数的偶然性和不确定性,借助麻雀搜索算法对网络关键参数进行全局优化求解,进一步提升其准确性。通过测试分析验证所设计方法的可行性和有效性,为改善及优化铁路技术站的安全管理提供了新的思路。

基于区块链的计算机通信网络安全风险评估

为实现计算机通信网络的全面安全管控,及时掌握风险态势,设计基于区块链技术的计算机通信网络安全风险评估模型。以区块链技术为基础生成私有化数据区块链并加密所有的数据区块,通过这些数据区块采集计算机通信网络数据后,对这些数据进行汇总并生成网络风险项目账本;依据计算机通信网络数据,构建计算机通信网络安全风险评估指标体系,评估计算机通信网络安全风险。测试结果显示:该模型具备良好的风险计算性能,可获取各个等级的风险,应用性良好,为网络风险管控提供可靠的链上溯源信息。