Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

Fault location of distribution networks based on multi-source information

In order to promote the development of the Internet of Things(IoT),there has been an increase in the coverage of the customer electric information acquisition system(CEIAS).The traditional fault location method for the distribution network only considers the information reported by the Feeder Terminal Unit(FTU)and the fault tolerance rate is low when the information is omitted or misreported.Therefore,this study considers the influence of the distributed generations(DGs)for the distribution network.This takes the CEIAS as a redundant information source and solves the model by applying a binary particle swarm optimization algorithm(BPSO).The improved Dempster/S-hafer evidence theory(D-S evidence theory)is used for evidence fusion to achieve the fault section location for the distribution network.An example is provided to verify that the proposed method can achieve single or multiple fault locations with a higher fault tolerance.

A multi-source information fusion method for tool life prediction based on CNN-SVM

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

Information freshness optimization of multiple status update streams in Internet of things:Generation rate control and service rate reservation

The Internet of things(IoT)has become a key infrastructure providing up-to-date and fresh information for policy analysis and decision-making of upper-layer applications.However,there are limited sensing and communication resources in IoT devices,which significantly affects the timeliness and freshness of the updated status.This work proposes two schemes,namely,the generation rate control and service rate reservation schemes,to improve the overall information freshness of multiple status update streams at the receiver.Specifically,using the recently proposed Age of Information(AoI)as the metric for evaluating information freshness,we characterized the overall information freshness,i.e.,the overall average AoI at the receiver for both schemes,by considering the urgency difference of status update and streams.Both schemes for status updates and streams,respectively,were formulated as two optimization problems.We proved that both problems are convex and the optimal generation and service rates for different streams are found by the standard convex optimization algorithm.Moreover,we proposed both approximate optimal generation and approximate optimal service rate for fast deployment in heavy and light load cases.Numerical results verify the theoretical findings and accuracy of the proposed approximate solutions,guiding the design and deployment of IoT.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Fine mapping of multiple interacting quantitative trait loci using combined linkage disequilibrium and linkage information

Quantitative trait loci (QTL) and their additive, dominance and epistatic effects play a critical role in complex trait variation. It is often infeasible to detect multiple interacting QTL due to main effects often being confounded by interaction effects. Positioning interacting QTL within a small region is even more difficult. We present a variance component approach nested in an empirical Bayesian method, which simultaneously takes into account additive, dominance and epistatic effects due to multiple interacting QTL. The covariance structure used in the variance component approach is based on combined linkage disequilibrium and linkage (LDL) information. In a simulation study where there are complex epistatic interactions between QTL, it is possible to simultaneously fine map interacting QTL using the proposed approach. The present method combined with LDL information can efficiently detect QTL and their dominance and epistatic effects, making it possible to simultaneously fine map main and epistatic QTL.

Structural damage detection method based on information fusion technique

Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.

Enhancing train position perception through Al-driven multi-source information fusion

This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

An Evidence Combination Method based on DBSCAN Clustering

Dempster-Shafer(D-S)evidence theory is a key technology for integrating uncertain information from multiple sources.However,the combination rules can be paradoxical when the evidence seriously conflict with each other.In the paper,we propose a novel combination algorithm based on unsupervised Density-Based Spatial Clustering of Applications with Noise(DBSCAN)density clustering.In the proposed mechanism,firstly,the original evidence sets are preprocessed by DBSCAN density clustering,and a successfully focal element similarity criteria is used to mine the potential information between the evidence,and make a correct measure of the conflict evidence.Then,two different discount factors are adopted to revise the original evidence sets,based on the result of DBSCAN density clustering.Finally,we conduct the information fusion for the revised evidence sets by D-S combination rules.Simulation results show that the proposed method can effectively solve the synthesis problem of high-conflict evidence,with better accuracy,stability and convergence speed.

Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems

The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.

基于集成学习与深度学习的洪水径流预报研究

深度学习模型凭借其对水文因素间复杂作用的优秀处理能力,在水文预报领域得到了一定的应用,然而,针对集成学习与深度学习耦合模型的研究仍有所缺失。通过融合集成学习AdaBoost算法与深度学习Informer模型,提出了一种组合模型,称为AdaBoost-Informer模型,以提高洪水径流预报的精度。该模型以历史雨量和径流数据作为数据输入,将具备长时序依赖捕获能力的Informer作为集成学习的弱预测器,使用网格搜索法进行超参数调优,使用AdaBoost集成学习算法对弱预测器进行加权组合得到强预测器。在浙江省椒江流域的应用分析表明:对比Random Forest、AdaBoost、Transformer、Informer等模型,AdaBoost-Informer模型表现最佳,RMSE为62.08 m^(3)/s,MAE为23.83 m^(3)/s,NSE为0.980,预报合格率为100%。所提模型可有效提高洪水预报精度,为防汛抢险和防洪系统调度提供决策依据。

基于人工智能的信息安全防御技术研究

随着信息技术的快速发展,企业面临的网络安全威胁日益增多,传统的信息安全防御方法已难以满足当前的安全需求。基于人工智能(AI)的信息安全防御技术因其自动化、智能化的特点,成为解决信息安全问题的有效手段。文中设计了一种企业信息安全的人工智能防御方法,包括网络信息防御环境的预处理、多目标信息识别防御机制、企业信息防御模型设计以及组合式加密处理技术。然后,通过对该技术进行测试分析,验证了其在信息安全防御中的有效性。

城市轨道交通新型检测技术及应用浅析

城市轨道交通的高质量发展对检测技术的安全性、可靠性、实时性、标准化和集成化等提出了更高的要求。激光检测技术、机器视觉检测技术、超声检测技术和红外检测技术等新型技术的出现和应用,很好地弥补了传统检测技术的不足,满足了城市轨道交通现阶段检测技术的需求。双轨机器人、检测管理平台、主动预警系统及非接触检测等方法在不同场景应用,给城市轨道交通新型检测技术的推广提供了支持。本文通过综合分析,预测综合性、基于信息化、智能化、主动检测及动态与非接触检测相结合等将成为城市轨道交通新型检测技术的方向发展。

基于信息量法的多模型对比分析石家庄西部山区滑坡易发性研究

石家庄市西部山区地质状况不良,气象方面常在雨季形成暴雨,因而地质灾害频发,其中滑坡灾害占比较重,因此针对石家庄市西部山区的滑坡灾害进行易发性分析有重要意义。现有单一模型在滑坡灾害易发性评价方面应用广泛,但仍有局限性。为提高对区域滑坡灾害易发性评价精准度,采用单一模型和复合模型等多模型对比分析研究区滑坡易发性,得到评价精度更好的评价模型。首先通过对研究区孕灾条件筛选出降雨量、坡度、高程、坡向、曲率、地质岩性、距水系距离、距断层距离、地震烈度、距道路距离、植被覆盖率和土地利用类型共12项评价因子。基于信息量法,用层次分析法、熵权法以及组合赋权法进行对比分析,最终得到易发性分区图,通过ROC曲线和灾害点密度验证其可靠性。结果表明,以上三种分析方法在ROC曲线中的AUC值分别为0.732、0.749、0.766,三种模型评价结果都有一定的可靠性,其中组合赋权法模型精度更高,可靠性更好,确定采用组合赋权模型。

基于视觉思维下用户界面信息可视化设计——以联合收割机远程故障监测系统为例

随着大数据时代发展带来的信息膨胀,如何高效、快速地实现各种信息的传递与转译是目前研究的重点。信息可视化作为传递和转译信息的重要交流手段是目前实现信息交互的重要技术,进行用户界面的图形、色彩、文字等视觉元素优化是提高信息表达能力的重要发展方向。为此,以联合收割机远程故障监测APP研发为例,基于信息可视化和视觉思维相关理论基础,运用视觉思维作用机制探究视觉思维在用户界面设计中的运用策略,进行联合收割机远程故障监测可视化APP界面视觉设计,并基于Android系统开发,实现登陆界面的登录和注册,结合Android系统的UI特性实现工作数据和预警数据的实时显示及故障反馈。研究结果可为相关智能控制平台人机交互界面的设计提供参考。

基于邻域互信息的组合预测最优子集选择算法

为在候选模型集中高效地选择时间序列组合预测的最优子集,提出一种CSPSO-NMI-MRMR最优子集选择算法。利用邻域互信息(neighborhood mutual information, NMI)度量相关性和冗余度,避免数值型数据的离散化,按最大相关最小冗余原则(minimal redundancy and maximal relevance, MRMR)筛选最优子集;邻域互信息中的邻域参数与子集选择效果密切相关,采用CSPSO算法寻找最优邻域参数,充分利用布谷鸟算法(cuckoo search, CS)和粒子群优化算法(particle swarm optimization, PSO)的优势,兼顾搜索效率和全局搜索能力;在寻参过程中设计一种淘汰策略,优化邻域参数的寻优区间并淘汰部分单模型,减少计算量。仿真结果表明,所提方法在预测精度、运行时间和稳健性上效果更优。

基于组合赋权的聊城市洪涝灾害风险评估

为了提高城市应对洪涝灾害的应急能力,以山东省聊城市为研究区,基于洪涝灾害评估的基本原理,从洪涝灾害的致灾因子、孕灾环境及承灾体脆弱性3个方面,选择年平均降雨量、高程、坡度、与水体距离、人口密度、土地利用类型、国内生产总值等数据,构建洪涝灾害评估指标体系;利用层次分析法和熵值法分别确定各指标的主观和客观权重,并用距离函数法确定指标的主客观综合权重;利用地理信息系统对赋权后的各栅格图进行叠加计算,得到研究区洪涝灾害危险性、易损性及洪涝灾害风险等级分区。结果表明:聊城市洪涝风险较高地区主要分布在降雨量多、水系发达、人口分布密集且经济发展水平高的东昌府区;西南部地区和东北部地区降雨量少,人口稀疏,洪涝灾害风险较低;研究区洪涝风险呈现自西南向东北先增大后减小的趋势。

基于恶劣环境的通信产品可靠性研究

文中介绍了一种信息通信产品如何在复杂多变的应用环境下,实现机动便携、快速开通、稳定使用的方案,并详细阐述了方案设计,开展了模拟仿真试验。

煤矿火灾智能预警系统研发与应用

目前煤矿火灾监测系统实现了对矿井煤自燃标志性气体、温度、烟雾、火焰等部分指标的单独监测,未对煤矿火灾相关因素进行有效、全面、统一的监测。针对该问题,从内因、外因2个方面分析了煤矿火灾潜在危险因素,提出一种分源分区监测火情态势的方法。内因火灾方面,主要针对较易发生火灾的工作面采空区、密闭采空区及人工自然发火观测点等进行监测;外因火灾方面,主要针对机电硐室及其配电点、带式输送机系统、电缆等方面进行监测。建立了煤矿火灾分源分区监测指标体系,采用人工监测或在线监测的方式定期采集或更新火灾特征参量数据,按数据采集方式及影响程度,将火灾监测指标分为动态指标、静态指标和关联指标。设计了火灾智能预警系统的总体架构和业务流程,采用基于多指标联合逻辑推理的预警方法实现内因火灾预警,采用基于D-S证据理论的多参量融合预警方法实现外因火灾预警。现场试验结果表明,火灾智能预警系统实现了对矿井火灾的有效监测预警,具有煤矿火灾风险预警“一张图”可视化展示功能,同时具备火灾智能模拟演示功能及避灾路线动态规划功能。

基于AHP与熵值法构建的火灾预测组合灰色模型

火灾预测可以帮助消防部门更好地采取预防措施和制定灭火方案,减轻火灾损失。如何通过人工智能方法预测火灾数量、判断火灾发展趋势成为一项重要的研究课题。对城市消防火灾数量进行预测时,文中首先对原始数据序列进行加权滑动均值处理;其次建立了基于背景值优化的灰色模型和无偏优化灰色模型;而后引入了结合等维新息理论的马尔可夫模型,对经过改进的灰色模型进行预测值的残差修正;最后建立了基于层次分析法(AHP)与熵值法的主客观赋权组合模型。针对北京市2012—2019年火灾事故数据进行建模,并对后续两年的火灾发生数量进行数据预测与模型对比验证分析,根据预测结果判断未来火灾数据的变化趋势。实验结果显示,优化模型可以提高预测精度,其中结合AHP与熵值法的组合模型预测精度达到了相对残差最小为0.6105%,后验方差比为0.323%。实验结果证明,优化后的模型可以更好地应用于对火灾事故的预测。