A time-space network-based model for transportation service optimization of China Railway Express

This paper describes the development and optimization plans for the China Railway Express(CR Express).As a new type of international land transport organization,CR Express has emerged with the continuous expansion of China toward European investment and trade,and in particular,has expanded with the continuous progress of the One Belt and One Road(OBOR)initiative.In addition to improving the service quality of CR Express,the operating costs must be reduced for developing“smart railways”that serve“smart cities”.We propose a dualobjective-based function mathematical optimization model;the satisfaction of the cargo owner is considered,and the timeliness,transportation capacity,and goods category constraints of CR Express transportation are designed.Moreover,we present the normalized equivalent method of the two-objective function of the model.Finally,a case study is conducted against the background of certain trains in the western corridor of CR Express to validate the effectiveness of the model and research methods proposed in this study.

A traffic model of optical networks based on time-space complexity and traffic grooming

This paper researched the traffic of optical networks in time-space complexity,proposed a novel traf-fic model for complex optical networks based on traffic grooming,designed a traffic generator GTS(gener-ator based on time and space)with 'centralized+distributed' idea,and then made a simulation in Clanguage.Experiments results show that GTS can produce the virtual network topology which can changedynamically with the characteristic of scaling-free network.GTS can also groom the different traffic andtrigger them under real-time or scheduling mechanisms,generating different optical connections.Thistraffic model is convenient for the simulation of optical networks considering the traffic complexity.

Holographic alloy positioning design system and holographic network phase diagrams of Au-Cu system

Taking Au?Cu system as an example, three discoveries and two methods were presented. First, a new way for boosting sustainable progress of systematic metal materials science (SMMS) and alloy gene engineering (AGE) is to establish holographic alloy positioning design (HAPD) system, of which the base consists of measurement and calculation center, SMMS center, AGE center, HAPD information center and HAPD cybernation center; Second, the resonance activating-sychro alternating mechanism of atom movement may be divided into the located and oriented diffuse modes; Third, the equilibrium and subequilibrium holographic network phase diagrams are blueprints and operable platform for researchers to discover, design, manufacture and deploy advanced alloys, which are obtained respectively by the equilibrium lever numerical method and cross point numerical method of isothermal Gibbs energy curves. As clicking each network point, the holographic information of three structure levels for the designed alloy may be readily obtained: the phase constitution and fraction, phase arranging structure and properties of organization; the composition, alloy gene arranging structure and properties of each phase and the electronic structures and properties of alloy genes. It will create a new era for network designing advanced alloys.

Alloy gene Gibbs energy partition function and equilibrium holographic network phase diagrams of AuCu_3-type sublattice system

Taking AuCu3-type sublattice system as an example, three discoveries have been presented: First, the third barrier hindering the progress in metal materials science is that researchers have got used to recognizing experimental phenomena of alloy phase transitions during extremely slow variation in temperature by equilibrium thinking mode and then taking erroneous knowledge of experimental phenomena as selected information for establishing Gibbs energy function and so-called equilibrium phase diagram. Second, the equilibrium holographic network phase diagrams of AuCu3-type sublattice system may be used to describe systematic correlativity of the composition?temperature-dependent alloy gene arranging structures and complete thermodynamic properties, and to be a standard for studying experimental subequilibrium order-disorder transition. Third, the equilibrium transition of each alloy is a homogeneous single-phase rather than a heterogeneous two-phase, and there exists a single-phase boundary curve without two-phase region of the ordered and disordered phases; the composition and temperature of the top point on the phase-boundary curve are far away from the ones of the critical point of the AuCu3 compound.

Alloy gene Gibbs energy partition function and equilibrium holographic network phase diagrams of Au_3Cu-type sublattice system

Taking Au3Cu-type sublattice system as an example, three discoveries have been presented. First, the fourth barrier to hinder the progress of metal materials science is that today’s researchers do not understand that the Gibbs energy function of an alloy phase should be derived from Gibbs energy partition function constructed of alloy gene sequence and their Gibbs energy sequence. Second, the six rules for establishing alloy gene Gibbs energy partition function have been discovered, and it has been specially proved that the probabilities of structure units occupied at the Gibbs energy levels in the degeneracy factor for calculating configuration entropy should be degenerated as ones of component atoms occupied at the lattice points. Third, the main characteristics unexpected by today’s researchers are as follows. There exists a single-phase boundary curve without two-phase region coexisting by the ordered and disordered phases. The composition and temperature of the top point on the phase-boundary curve are far away from those of the critical point of the Au3Cu compound; At 0 K, the composition of the lowest point on the composition-dependent Gibbs energy curve is notably deviated from that of the Au3Cu compounds. The theoretical limit composition range of long range ordered Au3Cu-type alloys is determined by the first jumping order degree.

Study of macroscopic fundamental diagram on Shanghai urban expressway network in China

The macroscopic fundamental diagram（ MFD） is studied to obtain the aggregate behavior of traffic in cities. This paper investigates the existence and the characteristics of different types of daily MFD for the Shanghai urban expressway network. The existence of MFD in the Shanghai urban expressway network is proved based on two weeks＇ data.Moreover, the hysteresis phenomena is present in most days and the network exhibits different hysteresis loops under different traffic situations. The relationship between the hysteresis phenomena and the inhomogeneity of traffic distribution is verified. The MFDs in the years of 2009 and 2012 are compared. The hysteresis loop still exists in 2012, which further verifies the existence of the hysteresis phenomenon. The direct relationship between the length of the hysteresis loop（ ΔO） and the congestion is proved based on sufficient data. The width of the hysteresis loop, i. e., the drop in network flow（ ΔQ） has no relationship with the congestion, and it varies from day to day under different traffic situations.

Application of Artificial Neural Network in Indicator Diagram

Indicator diagram plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in shape identification. This paper illuminates ANN realization in identifying fault kinds of indicator diagrams, including a back-propagation algorithm, characteristics of the indicator diagram and some examples. It is concluded that the buildup of a neural network and the abstract of indicator diagrams are important to successful application.

Effect of Chromium on CCT Diagrams of Novel Air-Cooled Bainite Steels Analyzed by Neural Network

The quantitative effects of chromium content on continuous cooling transformation （CCT） diagrams of novel air-cooled bainite steels were analyzed using artificial neural network models. The results showed that the chromium may retard the high and medium-temperature martensite transformation.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS PART 3.ESTIMATION OF LIQUIDUS TEMPERATURE AND EXPERT SYSTEM

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Application of Windows Socket Technique to Communication Process of the Train Diagram Network System Based on Client/Server Structure

This paper is focused on the technique for de si gn and realization of the process communications about the computer-aided train diagram network system. The Windows Socket technique is adopted to program for the client and the server to create system applications and solve the problems o f data transfer and data sharing in the system.

A Study on Selecting the Shortest Routes by Voronoi Diagram in Route Networks of GIS

The problems of fast determining shortest paths through a polygonal subdivision planar with n vertices are considered in GIS. Distances are measured according to an Euclidean metric. A geographical information system (GIS) has a collection of nearest neighborhood operations and this collection serves as a useful toolbox for spatial analysis. These operations are undertaken through the Voronoi diagrams. This paper presents a novel algorithm that constructs a' shortest route set' with respect to a given source point and a target point by Voronoi diagrams. It will help to improve the efficiency of traditional algorithms, e. g., Djkstra algorithm, on selecting the shortest routes. Moreover, the novel algorithm can check the connectivity in a complex network between the source point and target one.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS

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Quantitative analysis of Ni effect on CCT diagrams of novel air-cooled bainite steels using artificial neural network models

The quantitative effect of Ni content on continuous cooling transformation （CCT） diagrams of novel air-cooled bainite steels was analyzed using artificial neural network models. The results showed that Ni may retard the high- and medium-temperature transformation and martensite transformation. The results conform to the materials science theories.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

Quantification of the Hysteresis of Macroscopic Fundamental Diagrams and Its Relationship with the Congestion Heterogeneity and Performance of a Multimodal Network

Recent studies have observed hysteresis loops in the macroscopic fundamental diagram (MFD). In particular, for the same network density, higher network flows occur during congestion onset than during congestion offset. To evaluate management strategies using the MFD, investigating the relationship between the size of these loops and network performance is needed. The existing literature has mainly discussed correlating loop width (difference in density) and height (capacity drop) with congestion heterogeneity, but has failed to prove a relationship between the capacity drop and traffic conditions. Moreover, quantification of the MFD loop in complex multimodal networks has not been investigated. The objective of this paper covers these aspects. We simulated the Sioux Falls network with different mode-share ratios (car and bus users) based on a multi-agent simulation, MATSim. We investigated the relationships between MFD loop size and congestion heterogeneity (standard deviation of density) and network performance (average passenger travel time), and found that both were directly correlated with loop width, while weakly correlated with loop height. Moreover, we divided the MFD loop into two parts according to congestion onset and offset periods and found that the heights of the two parts had opposite effects. Accordingly, we show why the relationship between capacity drop and congestion heterogeneity is not found in the literature. We also found that network performance inversely affected the height of part of the loop while the height of its other part increased with an increase in congestion heterogeneity. These results help to evaluate network performance in the presence of MFD hysteresis, leading to elaborated management decisions.

Voronoi-Based Coverage Optimization for Directional Sensor Networks

Sensing coverage is a fundamental problem in sensors networks. Different from traditional isotropic sensors with sensing disk, directional sensors may have a limited angle of sensing range due to special applications. In this paper, we study the coverage problem in directional sensor networks (DSNs) with the rotatable orientation for each sensor. We propose the optimal coverage in directional sensor networks (OCDSN) problem to cover maximal area while activating as few sensors as possible. Then we prove the OCDSN to be NP-complete and propose the Voronoi-based centralized approximation (VCA) algorithm and the Voronoi-based distributed approximation (VDA) algorithm of the solution to the OCDSN problem. Finally, extensive simulation is executed to demonstrate the performance of the proposed algorithms.

Phenomenological Simulation Study of Neuronal Activity Synchronization in 110 Elements Network

The phenomenon of activity synchronization in biological neural network is considered. Simulation of neurons dynamics in the 6-layer neural network with 110 elements in different regimes： regular spikes, chaotic spikes, regular and chaotic bursting, etc was performed. Izhykevich＇s phenomenological model that displays different types of activity inherent for real biological neurons was used for simulation. Space-time diagram for the entire network and raster plots for the whole structure and for each layer separately were built for visual inspection of neural network activity synchronization. Synchronization coefficients based on cross-correlation times of action potentials for all neurons pairs were calculated for the whole neural system and for each layer separately.

Intelligent Recognition Method of Insufficient Fluid Supply of Oil Well Based on Convolutional Neural Network

Traditional methods for judging the degree of insufficient fluid supply in oil wells have low efficiency and limited accuracy. To address this problem, a method for intelligently identifying the degree of insufficient fluid supply in oil wells based on convolutional neural networks is proposed in this paper. Firstly, 5000 indicator diagrams with insufficient liquid supply were collected from the oilfield site, and a sample set was established after preprocessing;then based on the AlexNet model, combined with the characteristics of the indicator diagram, a convolutional neural network model including 4 layers of convolutional layers, 3 layers of down-pooling layers and 2 layers of fully connected layers is established. The backpropagation, ReLu activation function and dropout regularization method are used to complete the training of the convolutional neural network;finally, the performance of the convolutional neural network under different iteration times and network structure is compared, and the super parameter optimization of the model is completed. It has laid a good foundation for realizing the self-adaptive and intelligent matching of oil well production parameters and formation fluid supply conditions. It has certain application prospects. The results show that the accuracy of training and verification of the method exceeds 98%, which can meet the actual application requirements on site.

A New Synthetical Knowledge Representation Model and Its Application in Data Flow Diagram

A new synthetical knowledge representation model that integrates the attribute grammar model with the semantic network model was presented. The model mainly uses symbols of attribute grammar to establish a set of syntax and semantic rules suitable for a semantic network. Based on the model,the paper introduces a formal method defining data flow diagrams (DFD) and also simply explains how to use the method.