Pore network modeling of water block in low permeability reservoirs

A pore network model was used in this paper to investigate the factors, in particular, throat radius, wettability and initial water saturation, causing water block in low permeability reservoirs. A new term - ＇relative permeability number＇ （RPN） was firstly defined, and then used to describe the degree of water block. Imbibition process simulations show that the RPN drops in accordance with the extension of the averaged pore throat radius from 0.05 to 1.5 μm, and yet once beyond that point of 1.5 μm, the RPN reaches a higher value, indicating the existence of a critical pore throat radius where water block is the maximum. When the wettability of the samples changes from water-wet to weakly water-wet, weakly gas-wet, or gas（oil）-wet, the gas RPN increases consistently, but this consistency is disturbed by the RPN dropping for weakly water-wet samples for water saturations less than 0.4, which means weakly waterwet media are more easily water blocked than water-wet systems. In the situation where the initial water saturation exceeds 0.05, water block escalates along with an increase in initial water saturation.

RESEARCH ON THE BLOCKING FLOW IN A TRANSPORTATION NETWORK──THE GENERAL CONCEPTS AND THEORY OF THE BLOCKING FLOW

Blockage is a kind of phenomenon occurring frequently in modern transportation network. This paper deals with the research work on the blocking now in a network with the help of network flow theory. The blockage phenomena can be divided intO local blockage and network blockage. In this paper, which deals mainly with the latter, the fundamental concepts and definitions of network blocking flow, blocking outset are presented and the related theorems are proved. It is proved that the sufficient and necessary condition for the emergence of a blocking now in a network is the existence of the blocking outset. The necessary conditions for the existence of the blocking outset in a network are analysed and the characteristic cutset of blockage which reflects the all possible situation of blocking nows in the network is defined.In the last part of the paper the mathematical model of the minimum blocking now is developed and the solution to a small network is given.

A novel image block cryptosystem based on a spatiotemporal chaotic system and a chaotic neural network

In this paper, we propose a novel block cryptographic scheme based on a spatiotemporal chaotic system and a chaotic neural network （CNN）. The employed CNN comprises a 4-neuron layer called a chaotic neuron layer （CNL）, where the spatiotemporal chaotic system participates in generating its weight matrix and other parameters. The spatiotemporal chaotic system used in our scheme is the typical coupled map lattice （CML）, which can be easily implemented in parallel by hard- ware. A 160-bit-long binary sequence is used to generate the initial conditions of the CML. The decryption process is symmetric relative to the encryption process. Theoretical analysis and experimental results prove that the block cryptosys- tem is secure and practical, and suitable for image encryption.

RESEARCH ON THE LOCAL BLOCKAGE OF A TRANSPORTATION NETWORK AND ITS MINIMUM FLOW CAPACITY

This paper deals with the research work on the phenomena of local blockage in a transportation network. Onthe basis of introducing the research results in [1], theminimum now capacity problem of a network in the mostseriously blocked situation is studied. With the conceptof complete outset presented in [1], the relationship between the minimum now capacity of a network and its minimum complete cut capacity is discussed, and the reasons for the difference betweent the minimum now capacity of a network and its minimum complete cut capa-city are analysed. In order to get the solution to the problem, the concepts of normalization of a network and its blocking path graph are presented. In the paper it is proved that the necessary and sufficient conditions for the equality between the minumum now capacity and its minumum complete cut capacity are the existence of a feasible flow in the blocking path graph. For the reason that there are some dependent production points in the blocking path graph of a network, the proof about the tenability of the Gale's Theorm for the planat normalized network without circuit is made.

Predictive Block-Matching Algorithm for Wireless Video Sensor Network Using Neural Network

This paper proposed a back propagation neural network model for predictive block-matching. Predictive block-matching is a way to significantly decrease the computational complexity of motion estimation, but the traditional prediction model was proposed 26 years ago. It is straight forward but not accurate enough. The proposed back propagation neural network has 5 inputs, 5 neutrons and 1 output. Because of its simplicity, it requires very little calculation power which is negligible compared with existing computation complexity. The test results show 10% - 30% higher prediction accuracy and PSNR improvement up to 0.3 dB. The above advantages make it a feasible replacement of the current model.

Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:A case study

In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.

Quantitative Expression of Heat Flow versus Tectonic Deformation in the China Continent: The Effects of Plastic-Flow Network and Stable Block

Based on the heat flow data published in 1990 and 2001, a study of the factors influencing the terrestrial heat flow distribution in the China continent and its quantitative expression is carried out using the ＂Netlike Plastic-Flow＂ continental dynamics model and the methods of statistic analysis and optimum fitting. The result indicates that the factors influencing the heat flow distribution is classified into two groups, i.e. background and tectonic ones, in which the former mainly involves the non- uniform distribution of mantle heat flow, heat production of radioactive dements in the crust, heattransfer media and hydrothermal circulation, while the latter mainly involves plastic-flow networks and relatively-stable blocks. The plastic-flow network is a manifestation of shear localization in the netlike plastic-flow process in the lower lithosphere, which is composed of two sets of plastic-flow belts （PFBs） intersecting each other and, as one of the basic action regimes, controls the intraplate tectonic deformation. Relatively stable blocks （RSBs）, which are the tectonic units with relatively-high viscosities existing in the netlike plastic-flow field, as one of the principal origins, result in the development of large-seale compressional basins. PFB and RSB, as the active and quiet states of tectonic deformation, give rise to the higher and lower heat flow values, respectivdy. The provincial average heat flow in continent can be estimated using the expression qav = q0 ＋ a Pbt-c Pbk, where the three terms of the right side are background heat flow, PFB-positive contribution and RSB-negative contribution, Pbt and Pbk are the PFB- and RSB-coverage ratios, respectively, a is the coefficient of PFB- positive contribution depending mainly on the strain in the lower lithosphere, and c is the coefficient of RSB-negative contribution related mainly to the thickness of the lithosphere, the aseismic-area ratio and the tectonic age. For the major portion of the China continent excluding some of the southeastern region of China, the confidence interval of the provincial average background heat flow is qo=57.25±24.8 mW/m^2 and the PFB-positive- and RSB-negative-contribution coefficients are a=14.8-71.9 mW/m^2 and c=0-25.6 mW/m^2, respectively. The concepts of PFB and RSB effects and the heat flow expression suggested provide a new choice of the approach to the quantitative description of the characteristics of heat flow distribution in continent and their physical mechanisms.

Blocking Probability in All-Optical WDM Network Using IMCA

The analysis of WDM (Wavelength-Division Multiplexing) optical network is essential to have the routed wavelength blocking probability with the conversion of wavelength using techniques. In this paper, an enhanced analytical model is proposed to evaluate the blocking performances in topology network and to improve the performances of reduction of blocking probability. The variation of probability is based on the wavelength and load used in the network. The conversion is carried out with the support of optical backbone of the inherent flexibility of the network using the proposed IMCA in Sparse-Partial Wavelength Conversion (SPWC) architecture. It reduces the number of converters significantly with efficient process and provides placement scheme of wavelength converters in the network. The proposed model utilizes the network with the assignment and routing of wavelength using dynamic process of assignment algorithm. The proposed model provides dynamic and static routing process with the range limit to have a minimum conversion for the same probabilities of blocking. The proposed system analysis and the simulation results show the better performances in faster coverage, minimum number of conversions, blocking probability improvement for high load.

Effects of information transmission delay and channel blocking on synchronization in scale-free Hodgkin-Huxley neuronal networks

In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the underlying model of neuronal dynamics,we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise.It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks.In particular,regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases.Moreover,the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks.As the fraction of blocked sodium channels increases,the frequency of excitatory events decreases,which in turn manifests as an increase in the neuronal synchrony that,however,is dysfunctional due to the virtual absence of large-amplitude excitations.Expectedly,we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking.The presented results are also robust against the variation of the network size,thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.

An Advanced Probabilistic Neural Network for the Design of Breakwater Armor Blocks

In this study, an advanced probabilistic neural network （APNN） method is proposed to reflect the global probability density function （PDF） by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables. The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of＇ van der Meet, and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network （ANN） model. The APNN shows better results in predicting the stability number of armor bilks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable.

一种面向语义重叠社区发现的Link-Block算法

语义社会网络是一种由信息节点及社会关系构成的新型复杂网络,传统语义社会网络分析算法在进行社区挖掘时需要预先设定社区个数,且无法发现重叠社区.针对这一问题,提出一种面向语义社区发现的link-block算法.该算法首先以LDA模型为语义信息模型,创新性地建立了以link为核心的block区域LBT(link-block-topic)取样模型;其次,根据link-block语义分析结果,建立可度量link-block区域的语义链接权重方法,实现了语义信息的可度量化;最后,根据语义链接权重建立了以link-block为单位的聚类算法以及可评价语义社区的SQ模型,并通过实验分析,验证了该算法及SQ模型的有效性及可行性.

CIRBlock:融合低代价卷积的轻量反向残差模块

针对轻量级卷积神经网络MobileNet采用的反向残差结构仍具有较多的冗余计算的问题,构建了一种更为轻量的反向残差模块(cheap inverted residuals block,CIRBlock),并设计了一种新的轻量级卷积神经网络CIRNet。通过低代价卷积操作,简化逐点卷积,并构建旁路分支进行特征复用,减少反向残差的输出通道;利用通道注意力机制和通道混洗,增强通道间信息交流;在下采样时利用旁路分支信息构建和主分支相同的拓扑结构,提高特征冗余结构的通道多样性;完成轻量化网络模块CIRBlock的设计,并通过人工堆叠CIRBlock构建不同复杂度的轻量级卷积神经网络CIRNet。在目标分类上的实验表明:在CIFAR数据集上,基于相同的VGG16架构,使用CIRBlock比使用MobileNetV2的反向残差结构FLOPs降低58.1%,参数量减少55.5%,分类精度损失小于0.4%。在Mini-ImageNet目标分类数据集上,CIRNet分类精度比MobileNetV2高0.35%,FLOPs降低69%,参数量减少77.4%。

不同镇痛方法用于髋部或股骨干骨折患者椎管内麻醉摆放体位时镇痛效果的网状Meta分析

目的采用网状Meta分析系统评价不同镇痛方法用于髋部或股骨干骨折患者椎管内麻醉摆放体位时的镇痛效果。方法计算机检索PubMed、Cochrane Library、Web of Science、EMbase、中国生物医学文献数据库(CBM)、中国知网、维普、万方,检索时间为建库至2022年8月,纳入髋部或股骨干骨折患者摆放体位和椎管内麻醉时实施镇痛的随机对照研究。由两名研究员独立进行文献筛选、资料提取和偏倚风险评价,采用Stata 17.0和RevMan 5.3软件进行统计分析。结果共纳入28篇文献,患者1773例。累计排序概率曲线下面积(SUCRA)显示,降低摆放体位时VAS疼痛评分PENG阻滞(94.4%)效果最佳,其次是FIB联合IVA(83.8%)和FIB(71.1%);降低椎管内麻醉时VAS疼痛评分PENG阻滞(98.2%)效果最佳,其次是FIB(71.1%)和FNB(55.6%);缩短椎管内麻醉操作时间PENG阻滞(84.1%)效果最佳,其次是FNB(70.7%)和FIB(68.5%);升高体位摆放质量评分PENG阻滞(99.1%)效果最佳,其次是FIB(73.1%)和FNB(52.9%)。结论神经阻滞或神经阻滞联合IVA可降低髋部或股骨干骨折患者体位摆放和椎管内麻醉时VAS疼痛评分、缩短麻醉操作时间和升高体位摆放质量评分。PENG阻滞对髋部或股骨干骨折患者摆放体位和椎管内麻醉时实施镇痛的效果最佳。

A New Traffic Model for Mobile Wireless Networks

A new traffic model for mobile wireless networks was given and analysed. In the model, it is considered of traffic characteristic for two special periods of time. A pre-emptive priority scheme combined with channel borrowing strategy according to the speciality was proposed. All the channels can be used by non-real-time services, and real-time services are given higher priority for they are allowed to pre-empt channels used by non-real-time services. Either real-time handoff requests or non-real-time handoff requests can be queued in queues when there is no channel can be used. Some channels are reserved for real-time handoff requests, which can also be used by non-real-time service when they are idle. Simulation results are also given. It is seen that our scheme performs better than other schemes when the arrival rate of real-time services is much higher than non-real-time services.

Basic Characteristics of Present Movement of Major Tectonic Blocks in China's Continent

In order to study the present crustal movement and geodynamics in China's continent, a countrywide GPS monitoring network consisting of 22 stations was set up evenly on major tectonic blocks in China's continent in the early 1990s. Three-phase observations using the network were carried out in 1992, 1994, and 1996, respectively. In this paper, the data processing and accuracy of the three-phase observations are examined and the basic characteristics of present block movement in China's continent are analyzed based on the data of three-phase repeated observations. The study result indicates that the accuracy of data obtained in three-phase observations on the GPS network reaches 10-8 ～ 10-9, which is adequate to the need of monitoring of crustal movement. A model for block movement in China's continent constructed based on the result of the three-phase observations has effectively tested the results of geological and geophysical studies. In global framework, China's continent as a whole shows its clear eastward motion and its regional movement relative to Siberian block is characterized by that the western China is mainly affected by northward subduction and pushing of Indian Plate. Qinghai-Xizang Plateau shows clear eastward lateral slip simultaneously with longitudinal compression. It is more favorable to the escape model for the continent. Block movement of eastern China is under the combined effect of Indian, Pacific,and Philippine plates, resulting in northeastern and eastern motions of eastern China up to southeastern coastal region where the effect of Philippine Plate strengthens.

基于迭代协作学习框架的信誉医学参与方选择

联邦学习和群智学习等协作学习技术,能够在保护数据隐私的条件下充分利用分布在各地的分布式数据深度挖掘数据中所蕴含的知识,拥有非常广阔的应用前景,尤其是在强调隐私惯例和道德约束的医疗健康领域.任何协作工作都需要选择可靠的参与方,协作学习中全局模型的性能在很大程度上取决于参与方的选择.然而,现有研究在选择参与方时都没有对不同机构医疗数据中存在的异质性加以直接关注.导致包含稳定性在内的全局模型的性能难以得到保障.提出了从信誉的角度尝试探索求解该问题.以迭代协作学习的方式尽可能挑选出具有良好信誉的参与方进行协作学习,以获得稳定可靠的高性能全局模型.首先,提出了一个描述医疗机构数据质量的AI信誉值指标AMP(AI medical promise),以帮助其在医疗领域中形成良好的AI生态.其次,建立了一个基于后向选择的迭代协作学习(colback-learning)框架.在单次协作学习任务中,利用后向选择方法以多项式时间复杂度迭代计算出性能良好且稳定的全局模型,完成AMP计算和积累.在AMP信誉值计算中,制定了一个综合考虑全局性能指标的评分函数,以针对医疗领域更有效地指导全局模型的训练.最后,使用真实医疗数据模拟多样化的协作学习场景.实验表明,colback-learning能够选择可靠参与方训练得到性能良好的全局模型,模型的性能稳定性比现有最好的参与方选择方法提高了1.3~6倍.全局模型的可解释性与集中式学习保持了较高的一致性.

Adaptive Terminal-Modality-Based Joint Call Admission Control for Heterogeneous Cellular Networks

The coexistence of different Radio Access Technologies (RATs) requires a need for Common Radio Resource Management (CRRM) to support the provision of Quality of Service (QoS) and the efficient utilization of radio resources. The provision of QoS is an important and challenging issue in the design of integrated services packet networks. Call admission control (CAC) is an integral part of the problem. Clearly, without CAC, providing QoS guarantees will be impossible. There is unfairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous wireless networks. In this paper, an Adaptive-Terminal Modality-Based Joint Call Admission Control (ATJCAC) algorithm is proposed to enhance connection-level QoS and reduce call blocking/dropping probability. The proposed ATJCAC algorithm makes call admission decisions based on mobile terminal modality (capability), network load, adaptive the bandwidth of ongoing call and radio access technology (RAT) terminal support index. Simulation results show that the proposed ATJCAC scheme reduces call blocking/dropping probability.

基于端口注意力与通道空间注意力的网络异常流量检测

网络异常流量检测是网络安全保护重要组成部分之一。目前,基于深度学习的异常流量检测方法都是将端口号属性与其他流量属性同等对待,忽略了端口号的重要性。为了提高异常流量检测性能,借鉴注意力思想,提出一个卷积神经网络(CNN)结合端口注意力模块(PAM)和通道空间注意力模块(CBAM)的网络异常流量检测模型。首先,将原始网络流量作为PAM的输入,分离得到端口号属性送入全连接层,得到学习后的端口注意力权重值,并与其他流量属性点乘,输出端口注意力后的流量数据;其次,将流量数据转换成灰度图,利用CNN和CBAM更充分地提取特征图在通道和空间上的信息;最后,使用焦点损失函数解决数据不平衡的问题。所提PAM具有参数量少、即插即用和普遍适用的优点。在CICIDS2017数据集上,所提模型的异常流量检测二分类任务准确率为99.18%,多分类任务准确率为99.07%,对只有少数训练样本的类别也有较高的识别率。

Unified Performance and Availability Model for Call Admission Control in Heterogeneous Wireless Networks

The system capacity of wireless networks varies temporally. This may be due to the dynamic allocation of the channels and also the mobility of the users. The change in capacity will create greater impact on the system performance parameters. This variation of capacity particularly poses a greater challenge to the research community to ensure the Quality of Service (QoS) as it affects the call blocking probability which is one of the important QoS parameters. This paper proposes a performance model for call admission control and the availability model for a heterogeneous wireless network environment. The proposed model is able to handle three types of traffic considered for the study includes conversation traffic, interactive traffic and background traffic. The unified performance-availability model is developed using the Stochastic Area Networks (SAN). The performance of both analytical models and the SAN based performance-capacity models are verified by taking the call blocking probabilities for all the three types of traffics.

融合CoT Block的人物图像生成算法

目前,学者们对人物图像生成技术的研究主要集中在对人物姿势的编辑方面,忽略了身体的外观特征,导致所生成人物图像的质量不够理想。鉴于此,提出一种融合CoT Block的人物图像生成方法,即在已有PG2模型的基础上,将改进后的CoT Block引入到生成对抗网络中,通过对上下文语义信息的挖掘以及结合自注意力学习机制,更好地捕获人体姿态特征;然后利用PGGAN中的鉴别器进一步增强对图像真伪的鉴别能力。实验结果表明,改进后的算法有效提高了人物图像的生成质量。