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.

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.

The Combination Operator of Information Sources by a New Expressive Matrix

The multi-sensors fusion refers to the synergistic combination of sensory data from multiple sensors to provide more accurate and reliable information. The potential benefits of the Fusion are multi-sensors’ redundancy and extra information acquired. The fusion of redundant information can reduce the overall uncertainty and thus helps to provide information specified more precisely. Several sensors providing redundant information can also be used to increase reliability in the case of error, omission or failure of sensors. The combination operators are exponential and are more complex in terms of calculation;the Dempster-Shafer operator is exponential for more than three (3) information sources?[1] [2]. Our work focuses on the definition of another formulation of this operation, and puts it in a matrix form to illuminate the computational complexity, more precision guaranty and a minimal execution time. We propose to use each information source in a form of a matrix, which contains 0 value in lines that do not contain the masses (m(Ai) = 0) or once m(Ai) is not null (m(Ai) ≠ 0). The use of this expressed matrix attempts to ameliorate Dempster-Shafer operator via initialing either a criterion or criteria sources’ solution, increasing the efficiency of the Dempster-Shafer operator and facilitates the combination among the sources. We evaluate our approach by conducting a case study for showing the effectiveness of this matrix.

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.

Extending Qualitative Probabilistic Network with Mutual Information Weights

Bayesian network (BN) is a well-accepted framework for representing and inferring uncertain knowledge. As the qualitative abstraction of BN, qualitative probabilistic network (QPN) is introduced for probabilistic inferences in a qualitative way. With much higher efficiency of inferences, QPNs are more suitable for real-time applications than BNs. However, the high abstraction level brings some inference conflicts and tends to pose a major obstacle to their applications. In order to eliminate the inference conflicts of QPN, in this paper, we begin by extending the QPN by adding a mutual-information-based weight (MI weight) to each qualitative influence in the QPN. The extended QPN is called MI-QPN. After obtaining the MI weights from the corresponding BN, we discuss the symmetry, transitivity and composition properties of the qualitative influences. Then we extend the general inference algorithm to implement the conflict-free inferences of MI-QPN. The feasibility of our method is verified by the results of the experiment.

Combination method of conflict evidences based on evidence similarity

Aiming at the problem that the traditional Dempster Shafer (D-S) evidence theory cannot deal with conflicted evidences effectively and correctly, this paper points out that the key issue of this problem is to measure the degree of conflict between evidences correctly after analyzing various improved methods. The existing evidence conflict measure methods are analyzed, and a new evidence conflict measure method called evidence similarity measure based on the Tanimoto measure is proposed, while a new evidence combination method is proposed on the basis of evidence similarity measure. Firstly, the conflict degrees between evidences are obtained through the evidence similarity measure. Then the evidence sources are modified based on the credibility of different evidences and the weights of conflicted parts of evidences on different focal elements are determined. Finally, the fusion result is obtained by this method. Numerical examples show that the proposed method can effectively fuse evidences when evidences are consistent or highly conflicted, and it has a fast convergence speed, a high degree of accuracy and good adaptability.

New conflict representation model in generalized power space

The study on alternative combination rules in Dempster- Shafer theory （DST） when evidences are in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. The earlier researches have mainly focused on investigating the alternative which would be appropriate for the conflicting situation, under the assumption that a conflict is identified. However, the current research shows that not only the combination rule but also the classical conflict coefficient in DST are not correct to determine the conflict degree between two pieces of evidences. Most existing methods of measuring conflict do not consider the open world situation, whose frame of discernment is incomplete. To solve this problem, a new conflict representa- tion model to determine the conflict degree between evidences is proposed in the generalized power space, which contains two parameters： the conflict distance and the conflict coefficient of inconsistent evidences. This paper argues that only when the con- flict measure value in the new representation model is high, it is safe to say the evidences are in conflict. Experiments illustrate the efficiency of the proposed conflict representation model.

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.

Integration Technique of Multi-source Information Dominated by Aerial Radiometric Measure-ment and Its Application

This paper aims at exploring a digital image integration technique for multi-geoscience in formation dominated by airborne gamma-ray data, especially deeply discussing the method to secondly develop those aerial data by combining digital image processing system with the colored mapping system. Utilizing this technique , we have analyzed the geologic environment of uranium mineralization of Lianshanguan area > Liaoning Province, provided some important background information for further seeking of minerals. Meanwhile , experimental studies have been made to predict uranium mineralization , and evident results aquired. Practise shows that this new technique offers prospecting significance for mineral seeking and great practical value in survey of uranium resources.

Indexing habitat suitability and human-elephant conflicts using GIS-MCDA in a human-dominated landscape

Concerns for biodiversity loss, wildlife conservation, and habitat destruction have dominated the policy agenda worldwide for decades. Unsustainable human-induced development and negative interaction between humans and wildlife have emerged as predominant issues globally. The present study deals with human and elephant conflicts (HEC) in the Polpitigama Divisional Secretariat, Sri Lanka, which is located in the Kahalla-Pallekele elephant corridor and connects Wilpattu and Kaudulla wildlife sanctuaries. The research objectives are identifying spatial patterns of elephant habitat suitability and probable risk zones for HEC. The elephant habitat suitability and HEC risk zones were identified on spatial and temporal scales using Geographic Information System integrating Multi-Criteria Decision Analysis. Different factors, including habitat suitability, distance to roads, distance to croplands, distance to forests and protected areas, settlements, and population density, were considered to determine HEC risk zones in the area. Topography, water, and vegetation criteria are considered when determining elephant habitat suitability. The results of the Analytic Hierarchy Process run the spatially explicit model. The results revealed that of the total area, 15.3% is very highly suitable for elephant habitats, while the least suitable areas contribute only 4%. About 33.8% of the area is moderately suitable for elephants. The risk map indicates that 23.7% of the total area is under very high risk for HEC, and the least risk areas only account for 5.4%. About 26.2% of the area falls under the moderate risk zone for HEC. Since the model considered three aspects of HEC, it will help policymakers in wildlife conservation to avoid and minimize the HEC.

冲突性健康信息研究:定义、产生原因、类型及未来展望

[目的/意义]冲突性健康信息对公众健康的影响愈发凸显,但鲜有研究对其展开系统性梳理与分析,本文跟踪国内外冲突性健康信息研究进展,发现现有研究已经取得的成绩,提出未来值得关注的潜在研究方向,为后续开展相关研究提供参考。[方法/过程]通过分析和总结近年来国内外冲突性健康信息相关研究的主要观点和论述,对冲突性健康信息的定义、产生原因与类型进行了较为概括性的归纳。[结果/结论]未来可考虑从跨学科理论整合开展冲突性健康信息研究、冲突性健康信息背景下健康信息采纳行为研究和冲突性健康信息的干预策略研究3个方面对冲突性健康信息做进一步研究。

顾及空间冲突与逻辑一致的不动产登记数据事中监管方法

为提高不动产登记数据的规范性和准确性、避免问题数据逐级积累、提升不动产登记应用服务成效,本文在顾及空间冲突与数据逻辑一致性的条件下,分析设计了不动产登记数据的监管规则和监管技术流程,开发了基于省级不动产登记数据库的统一监管服务,设计了具体部署架构,并在广东省省域范围进行了具体应用实践。通过持续应用与优化,已形成了省域不动产登记数据的事中监管机制,在不动产登记业务办理过程中通过监管服务加强数据监管从而提升数据质量。应用证明,本文提出的技术方法和应用路径能够有效提升不动产登记数据的完整性、规范性和逻辑性。

信息沟通技术(ICT)的休闲使用对员工工作冲突的影响:休闲溢出和重塑的中介作用

随着信息沟通技术的迅猛发展,信息沟通技术被广泛应用于员工的工作和生活之中,对员工的工作绩效与身心健康的影响日益受到研究者的重视。此次研究基于边界理论,以员工休闲重塑与休闲溢出为中介机制,探讨信息沟通技术的休闲使用对员工工作和家庭冲突的影响机制。通过对300名员工的调研数据分析,结果表明,信息沟通技术的休闲使用正向影响休闲重塑,休闲重塑正向影响员工休闲溢出,休闲溢出负向影响员工的工作-家庭冲突、任务冲突、关系冲突;休闲重塑在信息沟通技术的休闲使用和休闲溢出之间起中介作用;休闲溢出在休闲重塑与工作-家庭冲突、任务冲突、关系冲突之间起中介作用;此外,休闲重塑、休闲溢出在信息沟通技术的休闲使用和工作-家庭冲突之间起链式中介效应。本文对员工应对家庭和工作中的冲突提供一定理论指导。

非家族股东超额委派董事与企业会计信息质量——来自上市家族企业的经验证据

如何认识股东超额委派董事这一行为,切实规范董事会决策机制,已成为公司治理领域亟待解决的问题。现有研究主要探讨了控股股东超额委派董事对企业经济决策行为的影响,鲜有文献从非控股股东超额委派董事的视角展开讨论。鉴于此,本文以2008—2021年我国上市家族企业为研究对象,深入考察非家族股东超额委派董事与企业会计信息质量之间的关系。研究发现,非家族股东超额委派董事会降低企业会计信息质量。机制分析表明,非家族股东超额委派董事会加剧第一类代理冲突,进而导致企业会计信息质量下降。进一步研究发现,当内部控制质量较低、企业处于非数字化转型期、外部审计监督较弱、地区市场化进程较慢时,非家族股东超额委派董事对企业会计信息质量的负向影响更显著。经济后果分析表明,非家族股东超额委派董事降低了会计信息质量,由此增加了企业股价崩盘风险,即会计信息质量在两者之间具有部分中介作用。

数字转型背景下员工角色冲突对创新绩效的影响机制

聚焦于数字化转型情境之下,基于资源保存理论和工作要求-资源模型探析员工数字转型角色冲突影响员工创新绩效的作用机制,重点考察现场非正式学习在二者之间发挥的中介作用,以及数字技术可供性的调节作用。通过对237份问卷进行数据分析,结果表明,员工数字转型角色冲突负向影响员工创新绩效,现场非正式学习在二者间起到部分中介作用;随着数字技术可供性的提高,员工数字转型角色冲突对现场非正式学习的负向影响减弱,现场非正式学习在员工数字转型角色冲突和员工创新绩效间的中介作用同样减弱。因此,企业在推进数字化转型的同时,应关注员工角色状态,提升员工的数字技术可供性感知,从而降低员工因数字转型角色冲突对创新产生的不利影响。

基于属性值变化的动态三支冲突分析

冲突分析有利于信息系统的问题处理。为了促进相关动态学习,将冲突分析模型推广到动态形式信息系统中,并结合三支决策思想研究基于属性值变化的动态三支冲突分析。首先,考虑单行属性值变化,研究距离矩阵的变化,得到关于冲突集、中立集、联合集的三支分析模型的变化结果。其次,类似考虑多行属性值变化,得到对应的三支分析模型的变化规律,并由此设计相关的动态更新算法。进而,针对列属性值变化,基于形式信息系统来转换成行属性值变化,从而调用行值动态更新方法。最后,实例分析说明了所得动态三支冲突分析的性质与算法的有效性。

城市道路中间路段人车冲突中网联预警信息对驾驶行为的影响

本文旨在研究网联预警信息对城市道路人车冲突中驾驶人行为的影响。利用驾驶模拟器设计城市道路驾驶场景与网联预警信息系统,考虑不同类型的视觉盲区,构建了6个典型的行人与机动车冲突场景。将招募的70名驾驶人划分为实验组和对照组,进行驾驶模拟器实验,采集实验过程中的驾驶人行为和车辆轨迹数据。利用生存分析法研究了不同因素对冲突过程中驾驶人避让反应时间与制动持续时间的影响,并建立面向冲突过程的人车事故风险预测模型,评估网联预警信息对驾驶行为的影响。结果表明:由公交车、路侧停车及树木与车辆等导致的视觉盲区会降低驾驶绩效,增加冲突过程中驾驶人的避让反应时间,降低制动持续时间;人行横道的存在使平均避让反应时间减少0.90 s,使平均制动持续时间增加0.41 s,并降低人车事故风险;网联预警信息可使驾驶人的平均避让反应时间减少0.52 s,平均制动持续时间增加0.40 s,使制动过程更加平稳,显著降低冲突过程中的事故风险,保障行人安全。

国外冲突性健康信息研究热点与前沿分析

[目的/意义]系统审视并分析国外冲突性健康信息研究的现状、热点以及前沿,以全面把握该领域的发展情况,为未来研究提供有益的参考和支持。[方法/过程]在Web of Science和Elsevier ScienceDirect两个外文数据库中检索冲突性健康信息的研究文献,利用VOSviewer可视化工具分析文献的发文趋势以及研究热点和前沿,并对这些分析结果进行详细的阐释和说明。[结果/结论]国外冲突性健康信息研究正处于高速发展阶段,研究热点包括:冲突性健康信息产生原因和来源研究、冲突性健康信息对公众健康认知和行为的影响研究、冲突性健康信息下的公众健康决策研究以及冲突性健康信息应对和治理研究。研究前沿涵盖了不确定性视角下的冲突性健康信息、冲突性健康信息的延滞效应以及跨学科理论的冲突性健康信息研究。