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.

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.

Development of an Asynchronous Web Based E-Learning System

Advancements in Information Communication Technology (ICT) have led to several opportunities especially the ones provided by the Internet. Several people are now taking advantage of distance learning courses and in the past few years huge research efforts have been dedicated to the development of distance learning systems. So far, many e-learning systems are proposed and used practically. This paper focused on the development of an asynchronous and interactive Web-based e-learning system. Its primary objective is to develop a fast, reliable, effective and efficient web-based e-learning system that will address the problems associated with the traditional learning system. Succinctly, the paper discusses the design of a system that enhances e-learning where course lecturers can set their courses, tests and quizzes at their convenient time and can track the activities and performance of their students and guide them to acquire knowledge without being obliged to be physically present on the institution campus. The system was designed using PHP as the scripting language, Macromedia Dreamweaver for the web page, MySQL as the database and Apache as the web server. The system was implemented using real data and the result was successful. This system is no doubt a solution to the constraints of the classical learning system and can be used successfully in distance learning, training, and various educational institutions.

Simultaneous wireless information and power transfer transmitting architecture based on asynchronous space-time-coding digital metasurface[Invited]

Simultaneous wireless information and power transfer(SWIPT)architecture is commonly applied in wireless sensors or Internet of Things(IoT)devices,providing both wireless power sources and communication channels.However,the traditional SWIPT transmitter usually suffers from cross-talk distortion caused by the high peak-to-average power ratio of the input signal and the reduction of power amplifier efficiency.This paper proposes a SWIPT transmitting architecture based on an asynchronous space-time-coding digital metasurface(ASTCM).High-efficiency simultaneous transfer of information and power is achieved via energy distribution and information processing of the wireless monophonic signal reflected from the metasurface.We demonstrate the feasibility of the proposed method through theoretical derivations and experimental verification,which is therefore believed to have great potential in wireless communications and the IoT devices.

城市轨道交通区域控制器内通信列车联合筛选方法研究

在城市轨道交通中,为实现列车的紧密追踪,CBTC系统需要识别两列通信列车之间没有非通信车。本文提出一种区域控制器内通信列车联合筛选方法。该方法通过ZC(区域控制器)综合两相邻通信列车位置信息并消除通信列车信息异步的影响,在确保安全的前提下完成通信列车间列车筛选,从而提高CBTC系统的运行效率。该方法具有通用性,可适用于两通信列车头对头、头对尾、尾对尾的中间筛选场景。

Asynchronism of the spreading dynamics underlying the bursty pattern

The potential mechanisms of the spreading phenomena uncover the organizations and functions of various systems.However,due to the lack of valid data,most of early works are limited to the simulated process on model networks.In this paper,we track and analyze the propagation paths of real spreading events on two social networks:Twitter and Brightkite.The empirical analysis reveals that the spreading probability and the spreading velocity present the explosive growth within a short period,where the spreading probability measures the transferring likelihood between two neighboring nodes,and the spreading velocity is the growth rate of the information in the whole network.Besides,we observe the asynchronism between the spreading probability and the spreading velocity.To explain the interesting and abnormal issue,we introduce the time-varying spreading probability into the susceptible-infected(SI)and linear threshold(LT)models.Both the analytic and experimental results reproduce the spreading phenomenon in real networks,which deepens our understandings of spreading problems.

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.

海上动目标身份置信度融合计算模型

为了支持海上动目标实时打击决策,提出基于D-S(Dempster-Shafer)证据理论的移动目标身份置信度动态演化与融合框架,构建海上动目标身份置信度多源融合计算模型,实现目标身份置信度连续可靠评估。针对移动目标的多源异步信息,建立目标身份辨识空间,构建历史与当前目标信息的基本概率分配函数,表征历史信息作为证据随时间推演而不断增加的不确定性,利用历史与当前二元证据合成规则计算目标身份融合置信度。仿真结果表明,所提方法能够提高目标身份置信度、降低信息融合系统目标识别的不确定性,扩展了目标打击窗口,提升了决策可靠性和打击灵活性。

An information-volume-based distance measure for decision-making

D-S evidence theory,as a general framework for reasoning with uncertainty,allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy measure.However,the mass assignments given by unknown information sources are disordered.How to measure the difference between the mass assignments has aroused people’s interest.In this paper,inspired by the information volume,a novel distance-based measure is proposed to measure the difference between mass assignments.The method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass assignments.At the same time,it is verified that the measure not only meets the properties of distance,but also proves the superiority of the proposed Information Volume Distance(IVD)through simulation experiments.Meanwhile,in the process of information fusion,the reliability of each source could be quantified through IVD.Therefore,based on IVD,a new multi-source information algorithm is proposed to solve the problem of multi-source information fusion.Moreover,algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.

基于异步相关判别性学习的孪生网络目标跟踪算法

现有基于孪生网络的单目标跟踪算法能够实现很高的跟踪精度,但是这些跟踪器不具备在线更新的能力,而且其在跟踪时很依赖目标的语义信息,这导致基于孪生网络的单目标跟踪算法在面对具有相似语义信息的干扰物时会跟踪失败.为了解决这个问题,提出了一种异步相关响应的计算模型,并提出一种高效利用不同帧间目标语义信息的方法.在此基础上,提出了一种新的具有判别性的跟踪算法.同时为了解决判别模型使用一阶优化算法收敛慢的问题,使用近似二阶优化的方法更新判别模型.为验证所提算法的有效性,分别在Got-10k、TC128、OTB和VOT2018数据集上做了对比实验,实验结果表明,该方法可以明显地改进基准算法的性能.

基于互信息的异步电动机轴承故障特征选择方法研究

由信息论中互信息的基本概念,针对异步电动机轴承故障的特征类别,提出基于互信息的异步电动机轴承故障特征选择算法。将该方法应用于电动机轴承外圈故障诊断,提取振动信号时域统计特征并进行排序,结果表明,该算法在降维条件下,能够有效筛选数据特征集,提高诊断效率。

考虑非完美通信的高比例光伏配电网模糊电压控制策略

高比例光伏接入的配电网电压协同控制策略,能有效解决电压偏移、电压波动、功率因数降低等问题。但在当前研究中,控制策略的优良应用需在完美通信条件下实现。而实际应用中,配电网系统通信总会存在数据丢失、时延以及通信中断等情况,造成电压控制效果不理想。为解决上述问题,提出一种考虑非完美通信的高比例光伏配电网模糊电压控制策略,该策略通过构建异步迭代架构和信息校验补偿,来解决通信数据丢失以及数据时延等问题。仿真算例表明,所提方法能有效解决数据丢失、数据时延等因素对电压控制效果的影响。

基于事件驱动的异步并行自动流程设计方法

为了在确保火箭发射可靠性的基础上尽可能缩短发射准备时间,提升火箭的快速反应能力,基于事件驱动提出异步并行自动流程设计方法,针对运载火箭系统发射流程异步工作耗时长、可靠性低的工程问题,以主线任务进程耗时最短为目标,给出了完整的设计方法。将单项任务进行独立子流程切分,以关键事件为驱动,通过时间差分的校验重传机制,降低由于通信环节产生的丢帧、乱序、误码等干扰造成的系统间匹配性风险,提升并行时间序列稳定触发的可靠性,推进各分系统任务按照主线任务进程时间链多线异步并行工作,协调有序地完成流程任务,实现火箭快速可靠发射。

基于Web Service进行信息共享和集成的关键技术

由于Web Service是基于标准协议和规范(包括HTTP、SOAP、XML、WSDL、UDDI等),并且平台独立和语言独立的,因此WebService已成为基于Internet网进行信息交换、信息共享、信息集成和互操作的主流技术,在电子商务和电子政务中被广泛应用。详细介绍了基于WebService进行信息共享和集成的关键技术——WebService异步调用技术和动态调用技术。通过Web Service异步调用技术,实现了Web Service的分布式并行执行,提高了计算的效率。通过Web Service动态调用技术,使整个软件系统具有可扩充性,并且易于维护,能满足Internet环境动态性的要求。

分布式多传感信息融合系统的异步航迹关联方法

在分布式多传感信息融合系统中,由于每个局部传感器的采样频率不同以及具有不同的通信延迟,导致来自不同传感器的局部航迹往往是异步的.针对此问题,提出了一种异步航迹关联方法.该方法首先基于最小二乘法实现单传感器的时域融合,从而将多传感异步航迹同步化.接下来,将多传感多目标航迹关联问题转化为在网络中搜索总费用最小的多个互不相交的路径问题,从而获得相应于每个目标的各个传感器的局部航迹组合.仿真试验表明,算法可以有效地解决异步航迹的关联问题,且具有较高的关联成功率.

Ajax技术在农业装备信息网中的应用

针对中国农业装备信息网利用Web2.0技术优化用户使用界面,提高系统访问效率的要求,采用Ajax技术对基于MVC框架模式的系统进行优化,提出基于现有系统下的Ajax应用模型,使客户端用户操作与服务器间的请求/响应异步化。以信息查询系统为例,介绍了结合运用Ajax技术与J2EE技术,利用客户端闲置资源减轻服务器端负担,有效提高了Web应用程序的执行效率,带来丰富的呈现形式和更为流畅的用户体验。