Feed-Forward Neural Network Based Petroleum Wells Equipment Failure Prediction

In the oil industry, the productivity of oil wells depends on the performance of the sub-surface equipment system. These systems often have problems stemming from sand, corrosion, internal pressure variation, or other factors. In order to ensure high equipment performance and avoid high-cost losses, it is essential to identify the source of possible failures in the early stage. However, this requires additional maintenance fees and human power. Moreover, the losses caused by these problems may lead to interruptions in the whole production process. In order to minimize maintenance costs, in this paper, we introduce a model for predicting equipment failure based on processing the historical data collected from multiple sensors. The state of the system is predicted by a Feed-Forward Neural Network (FFNN) with an SGD and Backpropagation algorithm is applied in the training process. Our model’s primary goal is to identify potential malfunctions at an early stage to ensure the production process’ continued high performance. We also evaluated the effectiveness of our model against other solutions currently available in the industry. The results of our study show that the FFNN can attain an accuracy score of 97% on the given dataset, which exceeds the performance of the models provided.

Computing Power Network:A Survey

With the rapid development of cloud computing,edge computing,and smart devices,computing power resources indicate a trend of ubiquitous deployment.The traditional network architecture cannot efficiently leverage these distributed computing power resources due to computing power island effect.To overcome these problems and improve network efficiency,a new network computing paradigm is proposed,i.e.,Computing Power Network(CPN).Computing power network can connect ubiquitous and heterogenous computing power resources through networking to realize computing power scheduling flexibly.In this survey,we make an exhaustive review on the state-of-the-art research efforts on computing power network.We first give an overview of computing power network,including definition,architecture,and advantages.Next,a comprehensive elaboration of issues on computing power modeling,information awareness and announcement,resource allocation,network forwarding,computing power transaction platform and resource orchestration platform is presented.The computing power network testbed is built and evaluated.The applications and use cases in computing power network are discussed.Then,the key enabling technologies for computing power network are introduced.Finally,open challenges and future research directions are presented as well.

Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks

A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.

Near-infrared Spectral Detection of the Content of Soybean Fat Acids Based on Genetic Multilayer Feed forward Neural Network

In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.

International Freight Forwarding Services Network in the Yangtze River Delta, 2005–2015: Patterns and Mechanisms

This study examined the spatio-temporal trajectories of the international freight forwarding service(IFFS) in the Yangtze River Delta(YRD) and explored the driving mechanisms of the service. Based on a bipartite network projection from an IFFS firm-city data source, we mapped three IFFS networks in the YRD in 2005, 2010, and 2015. A range of statistical indicators were used to explore changes in the spatial patterns of the three networks. The underlying influence of marketization, globalization, decentralization, and integration was then explored. It was found that the connections between Shanghai and other nodal cities formed the backbones of these networks. The effects of a city's administrative level and provincial administrative borders were generally obvious. We found several specific spatial patterns associated with IFFS. For example, the four non-administrative centers of Ningbo, Suzhou, Lianyungang, and Nantong were the most connected cities and played the role of gateway cities. Furthermore, remarkable regional equalities were found regarding a city's IFFS network provision, with notable examples in the weakly connected areas of northern Jiangsu and southwestern Zhejiang. Finally, an analysis of the driving mechanisms demonstrated that IFFS network changes were highly sensitive to the influences of marketization and globalization, while regional integration played a lesser role in driving changes in IFFS networks.

A Kind of Second-Order Learning Algorithm Based on Generalized Cost Criteria in Multi-Layer Feed-Forward Neural Networks

A kind of second order algorithm--recursive approximate Newton algorithm was given by Karayiannis. The algorithm was simplified when it was formulated. Especially, the simplification to matrix Hessian was very reluctant, which led to the loss of valuable information and affected performance of the algorithm to certain extent. For multi layer feed forward neural networks, the second order back propagation recursive algorithm based generalized cost criteria was proposed. It is proved that it is equivalent to Newton recursive algorithm and has a second order convergent rate. The performance and application prospect are analyzed. Lots of simulation experiments indicate that the calculation of the new algorithm is almost equivalent to the recursive least square multiple algorithm. The algorithm and selection of networks parameters are significant and the performance is more excellent than BP algorithm and the second order learning algorithm that was given by Karayiannis.

Combined Signal Processing Based Techniques and Feed Forward Neural Networks for Pathological Voice Detection and Classification

This paper presents the pathological voice detection and classification techniques using signal processing based methodologies and Feed Forward Neural Networks(FFNN).The important pathological voices such as Autism Spectrum Disorder(ASD)and Down Syndrome(DS)are considered for analysis.These pathological voices are known to manifest in different ways in the speech of children and adults.Therefore,it is possible to discriminate ASD and DS children from normal ones using the acoustic features extracted from the speech of these subjects.The important attributes hidden in the pathological voices are extracted by applying different signal processing techniques.In this work,three group of feature vectors such as perturbation measures,noise parameters and spectral-cepstral modeling are derived from the signals.The detection and classification is done by means of Feed For-ward Neural Network(FFNN)classifier trained with Scaled Conjugate Gradient(SCG)algorithm.The performance of the network is evaluated by finding various performance metrics and the the experimental results clearly demonstrate that the proposed method gives better performance compared with other methods discussed in the literature.

A Direction-Based Data Forwarding Algorithm for Opportunistic Networks

In this paper, the forwarding objective and mobility law of nodes in opportunistic networks are first investigated to establish a mathematical model for further analysis, then a gradually accelerated data forwarding algorithm is proposed. In this algorithm, according to the distance between data carriers （nodes） and the destination, some intermediate nodes are selected to relay the data. Especially, the forwarded copies can be increased when the delay reaches a threshold, to guarantee the required delivery ratio. The simulation results show that the proposed algorithm can effectively reduce the storage occupancies of nodes and forwarding delay, and guarantee the delivery ratio simultaneously.

Trajectory-Based Data Forwarding Schemes for Vehicular Networks

This paper explains trajectory-based data forwarding schemes for multihop data delivery in vehicular networks where the trajectory is the GPS navigation path for driving in a road network. Nowadays, GPS-based navigation is popular with drivers either for efficient driv- ing in unfamiliar road networks or for a better route, even in familiar road networks with heavy traffic. In this paper, we describe how to take advantage of vehicle trajectories in order to design data-forwarding schemes for information exchange in vehicular networks. The design of data-forwarding schemes takes into account not only the macro-scoped mobility of vehicular traffic statistics in road net- works, but also the micro-scoped mobility of individual vehicle trajectories. This paper addresses the importance of vehicle trajectory in the design of multihop vehicle-to-infrastructure, infrastructure-to-vehicle, and vehicle-to-vehicle data forwarding schemes. First, we explain the modeling of packet delivery delay and vehicle travel delay in both a road segment and an end-to-end path in a road net- work. Second, we describe a state-of-the-art data forwarding scheme using vehicular traffic statistics for the estimation of the end-to- end delivery delay as a forwarding metric. Last, we describe two data forwarding schemes based on both vehicle trajectory and vehicu- lar traffic statistics in a privacy-preserving manner.

Adaptive Jointing Superposition and Denoising-and-Forward Physical Layer Network Coding

The existing physical-layer network coding(PNC) can be grouped into three generic schemes,which are XOR-based PNC,superposition-based PNC,and denoising-and-forward(DNFbased) PNC.Generally speaking,DNF-based PNC has better performance of rate pair region compared with the other two schemes when the transmission is symmetric.When the transmission is asymmetric,its performance is degraded severely.However,superposition-based PNC does not have that limitation even if its rate pair region performance is inferior to that of DNF-based PNC and XOR-based PNC.In this paper,we focus on the combined use of the two PNC schemes,superposition-based PNC and DNFbased PNC,and present a novel PNC scheme called joint superposition and DNF physical-layer network coding(JSDNF-based PNC) as well as the information theory analysis of the achievable rate pair region.At the same time,in the proposed scheme,an adaptive power allocation factor is introduced.By changing the power factor,the system can adapt its rate pair region flexibly.The numerical results show that the proposed scheme achieves the largest rate pair region when the rate difference of two source signals is very large.At the same time,the support on asymmetric transmission is also an important profit of the scheme.

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

多层级视频会议系统跨网段融合技术的应用

通过介绍天津市地震局应急视频会议系统接入中国地震局视频会议系统、天津市政府视频系统和天津应急管理局视频系统的基本情况,结合地震应急视频会议系统现状,分析多类型、多层级、多网段视频会议系统的架构特点,着重介绍了多网段、多视频源视频转发优化技术在视频会议系统融合中的应用。通过该技术的应用,实现了天津市地震应急视频会议系统与各相关单位视频会议系统的全部连通。

基于模糊评判的无线传感器网络簇间数据转发算法

由于无线传感器网络在簇间数据转发过程中消耗能量较大、转发效率低,导致数据在转发过程中容易失效。为此,提出基于模糊评判的无线传感器网络簇间数据转发算法。建立无线传感器网络模型和通信模型,明确节点的运行方式和转发数据所消耗的能量,构建模糊综合评判模型,选取梯形隶属度函数计算传感器节点的相似度和活跃度,找出最佳转发节点,完成数据转发任务。仿真结果表明,所提算法的第一个节点死亡轮数和网络失效轮数最高为167和178,在仿真轮数达到1000轮时,该算法剩余的网络能量和节点平均能量分别为4 J和0.01 J,证明所提算法在数据转发过程中可消耗最少的能量,高效率地完成数据转发任务。

面向无人机无人船自组网的NDN智能数据转发策略

无人机和无人船组成的移动自组织网络存在通信环境恶劣和网络拓扑结构变化频繁等挑战,导致网络性能变差。针对这一问题,建立以数据为中心的命名数据网络(Named Data Networking, NDN)网络架构,在此基础上提出基于深度强化学习的智能数据转发策略。利用深度强化学习实时感知网络动态变化,优化数据转发策略,设计优先采样和双重Q网络算法,加快深度强化学习收敛速度。实验结果表明,该策略可以有效降低时延并提高兴趣包满足率。

结合节点特征和非合作博弈的选择性转发攻击检测

针对无线传感器网络的选择性转发攻击行为,提出了一种结合节点特征和非合作博弈的选择性转发攻击检测(node characteristics and non-cooperative game for selective forwarding attack detection,NC-NCG)方法。该方法通过设置独立监督网络环境,将节点特征中的转发率与门限阈值进行比较,计算小于阈值节点的当前转发率与T时间内平均转发率的偏离程度,根据偏离程度进行二次判定,以提高选择性转发攻击的检测率。同时为提高网络吞吐量,构建了不完全信息的非合作博弈模型,迫使可疑节点参与网络功能,实现节点快速识别。仿真实验结果表明,该方法不仅能够有效识别选择性转发攻击,而且在资源有限的情况下,可以提高网络吞吐量并延长网络生命周期。

Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia

This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O3, PM10, NO2, CH4, NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.

油田联合站水源热泵能效比软测量方法及节能效果研究

油田联合站水源热泵的应用既有效减少了以天然气为燃料的加热炉碳排放量,又解决了采暖伴热和机泵冷却的问题。但如何能精准方便测量和提高其能效比(COP)值,对水源热泵在油田推广应用有着重大意义。为此,采用BP神经网络对油田在运水源热泵的COP进行建模,测得与实际运行参数平均相对误差均小于1%,证明这种基于BP神经网络建模的水源热泵COP值软测量方法是可行的;另外,通过仿真模型分析,提出将水源热泵间接式单蒸发工艺改进为直进式双蒸发工艺,水源热泵COP值由3.6~4.2提高到4.5~4.8,年节约电耗174.1×104 kWh。研究结果对油田水源热泵的推广应用和系统节能降耗起到了积极作用。

基于U⁃Rnet的重力全张量梯度数据反演

重力反演是通过地表信息获取地下地质体空间结构与物理性质的重要手段之一。每个重力梯度分量反映不同的地质体信息,联合重力梯度分量进行重力反演能够更好地研究地下密度异常体的形态和分布。为此,提出基于神经网络的重力全张量梯度数据反演算法,将U⁃Rnet网络应用于重力全张量数据的三维反演问题。为了检验该算法的有效性,采用六种典型模型进行模拟实验,获得了具有清晰边界和稀疏的反演结果。首先,对比L2和Tversky两种损失函数的反演结果,后者的反演结果能更清晰地反映模型的边界位置;然后,对不同梯度张量组合进行反演,四组实验结果在三个方向(x、y、z)上具有不同的反演精度,组合四的误差最低;最后,将该方法应用于美国德克萨斯州文顿盐丘的FTG数据,反演结果与实际地质信息基本吻合。

一种基于空洞节点检测的可靠无人机自组网路由协议

针对高动态无人机自组网中节点之间链路生存时间(Link Live Time,LLT)短和节点遭遇路由空洞次数多的问题,提出了一种基于空洞节点检测的可靠无人机自组网路由协议——GPSR-HND(Greedy Perimeter Stateless Routing Based on Hollow Node Detection)。GPSR-HND协议中,转发节点通过空洞节点检测机制检测邻居节点状态,将有效邻居节点加入待选邻居节点集;然后基于层次分析法(Analytic Hierarchy Process,AHP)的多度量下一跳节点选择机制从待选邻居节点集中选择权重最大的邻居节点贪婪转发数据;如果待选邻居节点集为空,则从空洞邻居节点集中选择权重最大的空洞节点启动改进的周边转发机制,寻找可恢复贪婪转发模式的节点。与GPSR-NS协议和GPSR协议相比,GPSR-HND协议表现出了更好的性能,包括平均端到端时延和丢包率的改善,以及吞吐量的提高。

基于自适应小波回声神经网络的光纤陀螺测角仪温度误差补偿技术

基于光纤陀螺的测角仪可以实现对各项角运动参数的一体化动态精密测量,但在实际应用中,光纤陀螺测角仪受到温度变化的影响,导致测量精度下降。针对这一问题,本文提出了一种基于自适应小波回声神经网络的光纤陀螺测角仪温度误差补偿技术。为了提高温度误差建模的进度,提高传统神经网络的逼近能力,通过自适应前向线性预测滤波器对建模用测角仪温度漂移数据进行预处理,并采用自适应小波回声神经网络建立温度漂移模型,能够避免传统神经网络结构设计的盲目性和局部最优等问题,增强了网络学习能力和泛化能力,并利用自适应律代替神经网络梯度进行网络训练,提升神经网络的逼近精度和收敛速度。实验结果表明,该模型可以提高光纤陀螺测角仪的测量精度和环境适应性,为光纤陀螺测角仪的性能优化和实际应用提供了可靠的技术支撑。