Network analysis and spatial agglomeration of China’s high-speed rail: A dual network approach

China has the largest high-speed railway(HSR) system in the world, and it has gradually reshaped the urban network.The HSR system can be represented as different types of networks in terms of the nodes and various relationships(i.e.,linkages) between them. In this paper, we first introduce a general dual network model, including a physical network(PN)and a logical network(LN) to provide a comparative analysis for China’s high-speed rail network via complex network theory. The PN represents a layout of stations and rail tracks, and forms the basis for operating all trains. The LN is a network composed of the origin and destination stations of each high-speed train and the train flows between them. China’s high-speed railway(CHSR) has different topological structures and link strengths for PN in comparison with the LN. In the study, the community detection is used to analyze China’s high-speed rail networks and several communities are found to be similar to the layout of planned urban agglomerations in China. Furthermore, the hierarchies of urban agglomerations are different from each other according to the strength of inter-regional interaction and intra-regional interaction, which are respectively related to location and spatial development strategies. Moreover, a case study of the Yangtze River Delta shows that the hub stations have different resource divisions and are major contributors to the gap between train departure and arrival flows.

On Study of Solutions of Kac-van Moerbeke Lattice and Self-dual Network Equations

Kac 货车 Moerbeke 格子和 self-dualnetwork 方程的答案的关上的形式被基于 Riccati 方程建议转变考虑，用符号的计算。与微分差别方程的旅行波浪解决方案的数字计算相对照，我们的方法获得有物理关联的准确答案。

Smart Lung Tumor Prediction Using Dual Graph Convolutional Neural Network

A significant advantage of medical image processing is that it allows non-invasive exploration of internal anatomy in great detail.It is possible to create and study 3D models of anatomical structures to improve treatment outcomes,develop more effective medical devices,or arrive at a more accurate diagnosis.This paper aims to present a fused evolutionary algorithm that takes advantage of both whale optimization and bacterial foraging optimization to optimize feature extraction.The classification process was conducted with the aid of a convolu-tional neural network(CNN)with dual graphs.Evaluation of the performance of the fused model is carried out with various methods.In the initial input Com-puter Tomography(CT)image,150 images are pre-processed and segmented to identify cancerous and non-cancerous nodules.The geometrical,statistical,struc-tural,and texture features are extracted from the preprocessed segmented image using various methods such as Gray-level co-occurrence matrix(GLCM),Histo-gram-oriented gradient features(HOG),and Gray-level dependence matrix(GLDM).To select the optimal features,a novel fusion approach known as Whale-Bacterial Foraging Optimization is proposed.For the classification of lung cancer,dual graph convolutional neural networks have been employed.A com-parison of classification algorithms and optimization algorithms has been con-ducted.According to the evaluated results,the proposed fused algorithm is successful with an accuracy of 98.72%in predicting lung tumors,and it outper-forms other conventional approaches.

A novel pore-fracture dual network modeling method considering dynamic cracking and its applications

Unconventional reservoirs are normally characterized by dual porous media, which has both multi-scalepore and fracture structures, such as low permeability or tight oil reservoirs. The seepage characteristicsof such reservoirs is mainly determined by micro-fractures, but conventional laboratory experimentalmethods are difficult to measure it, which is attribute to the dynamic cracking of these micro-fractures.The emerging digital core technology in recent years can solve this problem by developing an accuratepore network model and a rational simulation approach. In this study, a novel pore-fracture dualnetwork model was established based on percolation theory. Fluid flow in the pore of two scales, microfracture and matrix pore, were considered, also with the impact of micro-fracture opening and closingduring flow. Some seepage characteristic parameters, such as fluid saturations, capillary pressure, relative permeabilities, displacement efficiency in different flow stage, can be predicted by proposedcalculating method. Through these work, seepage characteristics of dual porous media can be achieved.

Degenerate Solutions of the Nonlinear Self-Dual Network Equation

The N-fold Darboux transformation(DT) T_n^([N]) of the nonlinear self-dual network equation is given in terms of the determinant representation. The elements in determinants are composed of the eigenvalues λ_j(j = 1, 2..., N)and the corresponding eigenfunctions of the associated Lax equation. Using this representation, the N-soliton solutions of the nonlinear self-dual network equation are given from the zero "seed" solution by the N-fold DT. A general form of the N-degenerate soliton is constructed from the determinants of N-soliton by a special limit λ_j →λ_1 and by using the higher-order Taylor expansion. For 2-degenerate and 3-degenerate solitons, approximate orbits are given analytically,which provide excellent fit of exact trajectories. These orbits have a time-dependent "phase shift", namely ln(t^2).

A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

融合时间序列趋势的Dual-ESN机组负荷预测模型

针对传统模型在机组负荷预测中无法充分捕获内部多变量演化模式的问题,提出了一种基于时间序列的趋势和数值信息融合的双重回声状态网络Dual-ESN(dual-echo state network)机组负荷动态预测模型。首先,引入最小二乘法,对相关的多元历史信息按照局部时间跨度进行趋势拟合。进一步,得到有关过程变化的模式序列,并和原本的数值分别被送入两个独立的储备池,以并行的时间维度进行特征学习。其次,将隐层的高维空间状态送入输出层,融合信息,得到所需要的预测结果。最后,基于山西某工厂660 MW机组装置的真实数据集,进行验证。对比已有预测方法,结果表明所提预测模型在多种性能指标上均有提升。

Study of CNG／diesel dual fuel engine＇s emissions by means of RBF neural network

Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission performance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFE main performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resum6, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.

Application of neural network in the study of combustion rate of natural gas／diesel dual fuel engine

In order to predict and improve the performance of matural gas/diesel dual fuel engine(DFE),a combustion rate model based on forward meural network was built to study the combustion process of the DFE.The effect of the operating parameters on combustion rate was also studied by means of this model.The study showed that the predicted results were good agreement with the experimental data.It was proved that the de-veloped combustion rate model could be used to successfully predict and optimize the combustion process of dual fuel engine.

Dual Power Allocation Optimization Based on Stackelberg Game in Heterogeneous Network with Hybrid Energy Supplies

In heterogeneous network with hybrid energy supplies including green energy and on-grid energy, it is imperative to increase the utilization of green energy as well as to improve the utilities of users and networks. As the difference of hybrid energy source in stability and economy, thus, this paper focuses on the network with hybrid energy source, and design the utility of each user in the hybrid energy source system from the perspective of stability, economy and environment pollution. A dual power allocation algorithm based on Stackelberg game to maximize the utilities of users and networks is proposed. In addition, an iteration method is proposed which enables all players to reach the Stackelberg equilibrium(SE). Simulation results validate that players can reach the SE and the utilities of users and networks can be maximization, and the green energy can be efficiently used.

Feature-Based Fusion of Dual Band Infrared Image Using Multiple Pulse Coupled Neural Network

To improve the quality of the infrared image and enhance the information of the object,a dual band infrared image fusion method based on feature extraction and a novel multiple pulse coupled neural network(multi-PCNN)is proposed.In this multi-PCNN fusion scheme,the auxiliary PCNN which captures the characteristics of feature image extracting from the infrared image is used to modulate the main PCNN,whose input could be original infrared image.Meanwhile,to make the PCNN fusion effect consistent with the human vision system,Laplacian energy is adopted to obtain the value of adaptive linking strength in PCNN.After that,the original dual band infrared images are reconstructed by using a weight fusion rule with the fire mapping images generated by the main PCNNs to obtain the fused image.Compared to wavelet transforms,Laplacian pyramids and traditional multi-PCNNs,fusion images based on our method have more information,rich details and clear edges.

Exploring the Multi-Layer Structural Properties of the Bus-Subway Transportation Network of Shanghai

Buses and subways are essential to urban public transportation systems and an important engine for activating high-quality urban development. Traditional multi-modal transportation networks focus on the structural feature mining of single-layer networks or each layer, ignoring the structural association of multi-layer networks. In this paper, we examined the multi-layer structural property of the bus-subway network of Shanghai at both global and nodal scales. A dual-layer model of the city’s bus and subway system was built. Single-layer complex network indicators were also extended. The paper also explored the spatial coupling properties of the city’s bus and subway system and identified its primary traffic nodes. It was found that 1) the dual-layer network increased the network’s connectivity to a certain extent and broke through the spatial limitation in terms of physical structure, making the connection between any two locations more direct. 2) The dual-layer network changed the topological characteristics of the transit network, increasing the centrality value and bit order in degree centrality, betweenness centrality, and closeness centrality to different degrees, and making each centrality tend to converge to the city center in spatial distribution. Enhancing the management of critical network nodes would help the integrated public transportation system operate more effectively and provide higher-quality services.

Building Bayesian Network(BN)-Based System Reliability Model by Dual Genetic Algorithm(DGA)

A system reliability model based on Bayesian network(BN)is built via an evolutionary strategy called dual genetic algorithm(DGA).BN is a probabilistic approach to analyze relationships between stochastic events.In contrast with traditional methods where BN model is built by professionals,DGA is proposed for the automatic analysis of historical data and construction of BN for the estimation of system reliability.The whole solution space of BN structures is searched by DGA and a more accurate BN model is obtained.Efficacy of the proposed method is shown by some literature examples.

Axial Micro-Strain Sensor Based on FM-FBG via Dual-Mode ML-FMF in Sensor Networks

An in-fiber axial micro-strain sensor based on a Few Mode Fiber Bragg Grating (FM-FBG) is proposed and experimentally characterized. This FM-FBG is in inscribed in a multi-layer few-mode fiber (ML-FMF), and could acquire the change of the axial strain along fibers, which depends on the transmission dips. On account of the distinct dual-mode property, a good stability of this sensor is realized. The two transmission dips could have the different sensing behaviors. Both the propagation characteristics and operation principle of such a sensor are demonstrated in detail. High sensitivity of the FM-FBG, ~4 pm/με and ~4.5 pm/με within the range of 0 με - 1456 με, is experimentally achieved. FM-FBGs could be easily scattered along one fiber. So this sensor may have a great potential of being used in sensor networks.

基于A-BiLSTM和CNN的文本分类

为解决单一神经网络不能获取准确全局文本信息的问题,提出一种基于A-BiLSTM双通道和优化CNN的文本分类模型。A-BiLSTM双通道层使用注意力机制关注对文本分类贡献值较大的部分,并用BiLSTM提取文本中上下文语义信息;A-BiLSTM双通道层中将两者输出的特征信息融合,得到高级语义;A-BiLSTM双通道层后,使用优化CNN的强学习能力提取关键局部特征,得到最终文本特征表示。分类器输出文本信息的类别。实验结果表明,该模型分类效果优于其它对比模型,具有良好的泛化能力。

用户资源创新能力、创业模式与创业绩效关系研究——基于众创空间的双重网络嵌入的调节作用

资源对用户开展创业活动至关重要,但对用户创业者资源创新能力影响创业模式选择以及关于创业绩效理论机制的探讨较少。通过构建不同资源创新能力、创业模式与创新绩效之间的匹配关系模型,对221家嵌入众创空间的用户创业企业进行实证研究。结果发现:①资源获取创新能力有助于用户创业者选择协同型创业模式并促进生存绩效提升;②资源利用创新能力有助于用户创业者选择自主型创业模式并促进成长绩效提升;③创业模式在资源创新能力对创业绩效影响中发挥部分中介作用;④众创空间双重网络嵌入对资源创新能力与创业模式关系以及创业模式与创业绩效关系发挥正向调节作用。

A Dual-Channel Secure Transmission Scheme for Internet-Based Networked Control Systems

Two significant issues in Internet-based networked control systems （ INCSs）, transport performance of different protocols and security breach from Internet side, are investigated. First, for improving the performance of data transmission, user datagram protocol （UDP） is adopted as the main stand for controllers and plants using INCSs. Second, a dual-channel secure transmission scheme （DCSTS）based on data transmission characteristics of INCSs is proposed, in which a raw UDP channel and a secure TCP （transmission control protocol） connection making use of SSL/TLS （secure sockets layer/transport layer security） are included. Further, a networked control protocol （NCP） at application layer for supporting DCSTS between the controllers and plants in INCSs is designed, and it also aims at providing a universal communication mechanism for interoperability of devices among the networked control laboratories in Beijing Institute of Technology of China, Central South University of China and Tokyo University of Technology of Japan. By means of a networked single-degree-of-free- dom robot arm, an INCS under the new protocol and security environment is created. Compared with systems such as IPSec or SSL/TLS, which may cause more than 91% network throughput deduction, the new DCSTS protocol may yield results ten times better, being just 5.67%.

基于双节点-双边图神经网络的茶叶病害分类方法

传统茶叶病害分类主要依赖人工方法,此类方法费工费时,同时茶叶病害样本较少使得现有的机器学习方法的模型训练不充分,病害分类准确率不够高。针对茶炭疽病、茶黑煤病、茶饼病和茶白星病4类病害,提出一种基于双节点-双边图神经网络的茶叶病害分类方法。首先通过两分支卷积神经网络提取RGB茶叶病害特征和灰度茶叶病害特征,两分支均采用ResNet12作为骨干网络,参数独立不共享,两类特征作为图神经网络的两个子节点,以获得不同域样本所包含的病害信息;其次构建相对度量边和相似性边两类边,从而强化节点对相邻节点所含病害特征的聚合能力。最后,经过双节点特征和双边特征更新模块,实现双节点和双边交替更新,提高边特征对节点距离度量的准确性,从而实现训练样本较少条件下对茶叶病害的准确分类。本文方法和小样本学习方法进行了对比实验,结果表明,本文方法获得更高的准确率,在miniImageNet和PlantVillage数据集上5way-1shot的准确率分别达到69.30%和88.42%,5way-5shot准确率分别为82.48%和93.04%。同时在茶叶数据集TeaD-5上5way-1shot和5way-5shot准确率分别达到84.74%和86.34%。

网络嵌入与双元学习如何提升供应商创新性——一个模糊集定性比较分析

供应商创新性在推动供应链创新发展中发挥重要作用。既有文献侧重研究单个或多因素对供应商创新性的净效应,鲜有分析多因素对供应商创新性的协同效应。面对日益复杂和高度不确定的外部环境,需从整体角度探索供应商创新驱动路径。基于组态视角,采用模糊集定性比较分析方法(fsQCA),从网络嵌入和双元学习两个层面探讨4个前因条件对供应商创新性的联动效应及其作用路径。研究发现:关系嵌入、结构嵌入、利用式学习和探索式学习并不是构成企业提高或导致非高水平供应商创新性的必要条件,且单个前因条件对供应商创新水平的解释力较弱,提高供应商创新性的影响因素是多方面的;存在4条驱动供应商创新性的路径,即互动—学习模式、合作共创模式、学习驱动模式、探索-引领模式;导致非高水平供应商创新性的路径有2条,且与供应商创新性提升路径具有非对称性;网络嵌入与双元学习的合理匹配,对提升供应商创新性具有重要作用。研究结论不仅可深化供应链创新、网络嵌入和双元学习相关理论,而且可为提高供应商创新性、促进供应链创新发展提供借鉴。

Adaptive neural network control for coordinated motion of a dual-arm space robot system with uncertain parameters

Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.