Risk assessment of an oil depot using the improved multi-sensor fusion approach based on the cloud model and the belief Jensen-Shannon divergence

At present, enterprises have introduced the Internet of Things (IoT) technology to monitor and evaluate the safety status of oil depots, allowing for the collection of a substantial amount of multi-source monitoring data from factories. However, sensor monitoring data is often inaccurate and fuzzy. To improve the reliability of risk prevention and control based on multi-source sensor data, this study proposed a CM-BJS-DS model based on the cloud model (CM), the Belief Jensen-Shannon (BJS) divergence and Dempster-Shafer(D-S) evidence theory. First, the relevant evaluation factors of the accident and their threshold intervals of different risk levels were determined, and the fuzzy cloud membership functions (FCMFs) corresponding to different risk levels were constructed. Then, the sensor monitoring data were processed using the correlation measurement of the FCMF, and basic probability assignments (BPAs) were generated under the risk assessment frame of discernment. Finally, the BPAs were pre-processed by the improved evidence fusion model and the accident risk level was evaluated. Based on the monitoring data, a case study was performed to assess the risk level of vapor cloud explosion (VCE) accidents due to liquid petroleum gas (LPG) tank leaks. The results show that the proposed method presents the following characteristics: (i) The BPAs were constructed based on the monitoring data, which reduced the subjectivity of the construction process; (ii) Compared with single sensors, the multiple sensor fusion evaluation yielded more specific results; (iii) When dealing with highly conflicting evidence, the evaluation results of the proposed method exhibited a higher belief degree. This method can be used as a decision-making tool to detect potential risks and identify critical risk spots to improve the specificity and efficiency of emergency response.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

非营养缺陷型原生质体融合选育角蛋白酶生产菌

以两性霉素B抗性作为标记,通过单株灭活,进行栖土曲霉种内非营养缺陷型原生质体融合.在40%的PEG(Mr＝6000)促融下,融合频率为1.62×10-5.连续传接10代后,获得稳定的融合子.对孢子体积、核DNA含量、生长速度进行了测定,并利用RAPD技术分析比较了亲本及融合子的基因组DNA指纹多态性,证明融合子为杂合二倍体,产角蛋白酶活力较亲本显著提高.图3表4参17     

北京平原区夏垫断裂的多源遥感影像特征

1679年9月2日三河—平谷8.0级地震在地表形成总体走向N40°~45°E,倾角60°~70°,长达10 km的破裂带。该文采用Landsat-8、SPOT-5、SAR、GDEM、Google Earth多源遥感影像研究夏垫断裂的遥感影像特征。利用ERDAS软件,对Landsat-8影像按7∶4∶2依次为R、G、B分量作彩色合成增强处理,突出影像的线性构造特征;对Landsat-8和SPOT-5影像、Landsat-8和SAR影像分别进行融合处理,丰富影像的光谱信息;对GDEM与SPOT影像进行三维叠加,突出影像的三维特征;对SAR影像进行斑点压缩、方向滤波处理,突出影像平行断裂方向的线性特征。文中分析、对比了Google Earth和SPOT-5影像上地震陡坎的特征,陡坎在SPOT-5影像上表现较明显,但在影像上表现不出较低陡坎,同时,在两种影像上容易将绿色植被、狭窄道路或其它地物误认为陡坎;SAR滤波后的影像表现出暗色的线性条带;Landsat-8和SAR融合后的影像表现出较宽的异常条带,该条带指示断裂带所在位置。     

基于多源遥感影像融合的武汉市土地利用分类方法研究

准确高效的获取土地利用信息,对于合理利用和开发土地资源具有十分重要的意义。在快速城镇化地区,土地利用活动频繁且密集,土地利用格局演变十分剧烈,增加了城市土地利用精准分类的不确定性;且受环境气候和云雨天气影响增加了有效光学影像获取的难度。为提高城市土地分类精度,该文选取武汉市中心城区为研究案例,以Sentinel-1A和Landsat8 OLI影像为数据源,采用Gram-Schmidt变换方法进行影像融合,选取最大似然、支持向量机、CART决策树、BP神经网络等4种分类方法对融合的影像进行分类,提取了研究区土地利用信息,并对其进行分析。进一步,通过与光学影像的分类结果对比,探究了Sentinel-1A和Landsat8 OLI融合影像在土地利用信息提取方面是否具有优势。研究结果表明:(1)对比其他3种方法,CART决策树分类方法对于融合后的影像分类精度最高,总体分类精度和Kappa系数分别达到88.55%和0.841 4;(2)与光学影像相比,Sentinel-1A和Landsat8 OLI融合影像可以更有效地获取高精度城市土地利用信息;(3)基于多源遥感影像融合的CART决策树分类方法是获取研究区高精度土地利用信息的一种行之有效的技术手段。研究成果可为快速城镇化区域的土地利用分类提供参考。     

多源信息融合技术在内陆湖库水华预警中的研究进展与展望

面对水体富营养化愈来愈严重，水华爆发越来越频繁的严峻形式；为减少水华发生频率及由此造成的损失，开展水华预警已成当务之急。水华爆发是水体中营养盐的累积、气候条件与水力条件等众多因素非线性共同作用的产物；为此，有必要在众多水华影响因素的动态监测信息的融合基础上，寻求水华爆发与这些影响因素间的影射关系，这就需要信息融合技术。通过归纳总结多源信息融合技术在内陆湖库水华预警中应用的研究进展，结合目前我国水华预警的具体需求，提出建立基于多源信息融合技术的水华预警决策支持系统的研究前景与初步设想。信息融合技术为水华预警提供了一个良好的平台，它将与水华相关的不同信息源（水文、气象、水环境质量与环境遥感）所提供的局部不完整的观测信息加以集成与互补，消除多源信息之间存在的冗余和矛盾，形成对水华爆发环境相对完整的感知与描述；从而提高水华预警与应急响应决策的效率，提高预警信息的时间与空间分辨率，扩展信息的时空监测范围。     

一种面向灾害应急的UAV影像快速拼接方法

洪涝、地震等自然灾害发生突然,特别是对城郊设防水平较低地区,短时间内能造成巨大的损失.及时、准确、快速地获得足够的灾情信息是减灾救灾的前提；依靠无人机采集的灾区遥感影像越来越成为减灾部门提取第一手灾情信息的首选数据源.结合无人机影像特点,优化利用影像局部不变特征进行特征匹配,通过RANSAC算子剔除匹配粗差,并用变换优化法求取最佳变换模型参数,然后采用基于动态规划的最佳拼接缝搜索策略和加权平均相结合的融合策略,在保证灾害应急精度要求的前提下,很好地消除了拼接缝和“融合鬼影”现象,为城郊区应急情况下的灾情信息获取提供了新的技术手段和支持.     

基于数据挖掘法的矿井瓦斯联动监测

为解决传统矿井监测技术存在的监测效果差、数据利用率低、预警能力不足等问题,提出采用多点及多因素融合的联动监测方法。采用计算机数据挖掘、多级数据融合处理技术建立基于实际预警、规则评判、历史数据检测等方法的矿井故障分析及瓦斯浓度预警数据处理模型。该技术在临汾市尧都区5个煤矿的安全生产监控中试验应用,结果表明联动分析法有助于分析矿井故障原因、提高瓦斯浓度预警能力,且数据挖掘及多级数据融合技术的应用有助于规范样本,提高分析预警规则的稳定性、适应性。     

内外场综合可靠性试验方法分析与探讨

介绍了常规内场试验采用的一些可靠性评价标准,概括总结了内场与外场可靠性分析与评价中运用的数据处理方法,提出了利用内场数据,收集和分析外场数据的信息融合技术,即内外场可靠性综合分析法,阐述了该技术方法的相关研究内容,最后通过实际应用指出了计算的有关方法和步骤.     

基于融合权集对云的煤矿安全评价及应用*

为合理评价煤矿安全状态,针对煤矿安全影响因素多、指标体系关联度复杂等问题,提出融合权与集对云的安全评价模型。运用云理论特征值修饰集对分析联系度,兼顾系统的模糊性与不确定性,并引入融和后的各指标最优权重,得出系统综合云联系度;绘制等级云图判定系统安全状态及偏向趋势;结合煤矿实例进行验证。结果表明:案例煤矿所处安全状态为较安全,有偏向一般安全状态微势。分析结果与实际相符,且充分考虑煤矿安全评价体系的随机性,该模型可为煤矿安全评价提供理论指导。