亨普尔科学理论思想的演进、特征及缺陷

科学理论问题是科学哲学研究的中心问题,对于科学解释、理论评价等问题的探究起至关重要的作用。亨普尔科学理论思想从初期到晚期有一个明显的演进过程,即初期站在逻辑经验主义立场坚持科学理论的"标准观念",后期则放弃了这一观念,提出包括内部原理和桥梁原理的科学理论"网络结构"模型。总体上,他的科学理论思想的本质特征主要有三个方面:一是以科学概念作为科学理论形成的基础;二是强调科学理论中理论实体的重要地位;三是把解释与理解看做科学理论的主要功能。此外,亨普尔的科学理论思想也存在不可避免的理论缺陷。

Gray correction method of X-ray fusion image

The conventional X-ray gray weighted image fusion method based on variable energy cannot characterize the phys- ical properties of complicated objects correctly, therefore, the gray correction method of X-ray fusion image based on neural network is proposed. The conventional method acquires 12 bit images on variable energy, and then fuses the images in a tra- ditional way. While the new method takes the fusion image as the input of neural network simulation system and takes the acquired 16 bit image as the output of neural network. The X-ray image physical characteristic model based on neural net- work is obtained through training. And then it takes steel ladder block as the test object to verify the feasibility of the mod- el. In the end, the gray curve of output image is compared with the gray curve of 16 bit real image. The experiment results show that this method can fit the nonlinear relationship between the fusion image and the real image, and also can expand the scope of application of low dynamic image acquisition equipment.

RESEARCH AND APPLICATION OF A NEURAL NETWORK CLASSIFIER BASED ON DYNAMIC THRESHOLD

In this study, a Multi-Layer BP neural network(MLBP) with dynamic thresholds is employed to build a classifier model.As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers.The classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level.Finally, the classifier is applied to forecast box office revenue of a movie before its theatrical release.The comparison results with the MLP method show that the MLBP classifier model achieves more satisfactory results, and it is more reliable and effective to solve the problem.

A hybrid approach for evaluating CPT-based seismic soil liquefaction potential using Bayesian belief networks

Discernment of seismic soil liquefaction is a complex and non-linear procedure that is affected by diversified factors of uncertainties and complexity.The Bayesian belief network(BBN)is an effective tool to present a suitable framework to handle insights into such uncertainties and cause–effect relationships.The intention of this study is to use a hybrid approach methodology for the development of BBN model based on cone penetration test(CPT)case history records to evaluate seismic soil liquefaction potential.In this hybrid approach,naive model is developed initially only by an interpretive structural modeling(ISM)technique using domain knowledge(DK).Subsequently,some useful information about the naive model are embedded as DK in the K2 algorithm to develop a BBN-K2 and DK model.The results of the BBN models are compared and validated with the available artificial neural network(ANN)and C4.5 decision tree(DT)models and found that the BBN model developed by hybrid approach showed compatible and promising results for liquefaction potential assessment.The BBN model developed by hybrid approach provides a viable tool for geotechnical engineers to assess sites conditions susceptible to seismic soil liquefaction.This study also presents sensitivity analysis of the BBN model based on hybrid approach and the most probable explanation of liquefied sites,owing to know the most likely scenario of the liquefaction phenomenon.

Energy-absorption forecast of thin-walled structure by GA-BP hybrid algorithm

In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.

Service-Oriented Network Virtualization Architecture for Internet of Things

As a key technology to realize smart services of Internet of Things（IoT）, network virtualization technology can support the network diversification and ubiquity, and improve the utilization rate of network resources. This paper studies the service-ori- ented network virtualization architecture for loT services. Firstly the semantic description method for loT services is proposed, then the resource representation model and resource management model in the environment of network virtualization are presented. Based on the above models, the service-oriented virtual network architecture for loT is established. Finally, a smart campus system is designed and deployed based on the service-oriented virtual network architecture. Moreover, the proposed architecture and models are verified in experiments.

Simulation of Morphologies and Mechanical Properties of A/B Polymer Blend Film

The effects of blend composition and micro-phase structure on the mechanical behavior of A/B polymer blend film are studied by coupling the Monte Carlo(MC) simulation of morphology with the lattice spring model(LSM) of micro mechanics of materials.The MC method with bond length fluctuation and cavity diffusion algorithm on cubic lattice is adopted to simulate the micro-phase structure of A/B polymer blend.The information of morphology and structure is then inputted to the LSM composed of a three-dimensional network of springs to obtain the mechanical properties of polymer blend film.Simulated results show that the mechanical response is mainly affected by the density and the composition of polymer blend film through the morphology transition.When a force is applied on the outer boundary of polymer blend film,the vicinity of the inner cavities experiences higher stresses and strains responsible for the onset of crack propagation and the premature failure of the entire system.

Self-organized Criticality in an Integrate-and-Fire Neuron Model Based on Modified Aging Networks

Based on an integrate-and-fire mechanism, we investigate self-organized criticality of a simple neuron model on a modified BA scale-free network with aging nodes. In our model, we find that the distribution of avalanche size follows power-law behavior. The critical exponent τ depends on the aging exponent α. The structures of the network with aging of nodes change with an increase of α. The different topological structures lead to different behaviors in models of integrate-and-fire neurons.

A Hierarchical Virus Immunization Method for Community Networks

Due to the enormous harm of virus propagation,research regarding virus immunizations still absolutely necessary.In comparison to current researches,a new virus immunization method the hierarchical virus immunization method(HVIM) for community networks is proposed.Based on the virus transmission dynamic model SusceptibleInfectious-Removed and SusceptibleRemoved(SIRSR),HVIM considered the influence of external factors on the spread of viruses and only needs a portion of the network structure to be able to carry out immunization.Another pro for HVIM is that it is scalable and suitable for parallel computing which is a requirement in the big data era.Finally,a simulation dataset and a real dataset were used to run experiments,and the results of simulation showed that HVIM obviously is superior to others on the aspect of immunity.

Study on Microblog Dissemination Law in View ol Forwarding

Forwarding is a major means of information dissemination on the Microblog platform.The article,combining static analysis and dynamic analysis,takes Microblog forwarding as the object of study,and studies the network topology of grass-roots Microblog forwarding users.It also studies the correlation between characteristic quantity and forwarding times of Microblog network topology.Furthermore,it conducts modification on virus transmission model,builds and verifies the Microblog forwarding dynamical model.The study finds out that Microblog postings present qute strong dissemination capacity on the initial stage,and some Microblog postings with many forwarding times and long duration of forwarding process due to the dynamic growth of the forwarding user network and the joining of strong nodes make network infection density decrease in some phases.

STUDY ON THE OPTIMUM PATTERN OF DAQIN G URBAN SPATIAL STRUCTURE

Daqing is a mining city that was set up on wetland by exploiting and processing petroleum.This paper points out that net-group urban system is the optimization mode for Daqin g urban spatial structure through an alyzing and appraising the present situation,c haracteristics,advantages and dis advantages of Daqing spatial structure.And the best way of optimizing Daqing urban spatial structure is to adopt sustainable development strategy,establish th e coordinated grade structure of urban system,con struct developed towns net system,p refect the function structure of the towns at all levels,make full use of resources an d strengthen environmental protection.Spatial structure of Daqing must be according-ly adjusted in order to adapt to the tr ansformation of future economy types and functions.Based on the foundation of keep-ing net group,the development shoul d be from disperse to moderate centra lization in order to give prominence to the multi-function of the central city.Constr ucting ruralizing city should be the future goal of Daqing City.

Modeling of Malicious Code Propagations in Internet of Things

Nowadays, the main communication object of Internet is human-human. But it is foreseeable that in the near future any object will have a unique identification and can be addressed and con- nected. The Internet will expand to the Internet of Things. IPv6 is the cornerstone of the Internet of Things. In this paper, we investigate a fast active worm, referred to as topological worm, which can propagate twice to more than three times faster tl^an a traditional scan-based worm. Topological worm spreads over AS-level network topology, making traditional epidemic models invalid for modeling the propagation of it. For this reason, we study topological worm propagation relying on simulations. First, we propose a new complex weighted network mod- el, which represents the real IPv6 AS-level network topology. And then, a new worm propagation model based on the weighted network model is constructed, which descries the topological worm propagation over AS-level network topology. The simulation results verify the topological worm model and demonstrate the effect of parameters on the propagation.

Topological Properties of Complex Networks with Different Types of Nodes

Nodes play different roles or have different functions in many natural and social networks.In this paper,a simple model with different types of nodes and deterministic selective linking rule is proposed.The structural properties by theoretical predictions are investigated that the given model exhibits a power-law distribution.

A Empirical Study on Impact Factors of Employee＇s Knowledge Capital in Supply Chain： Social NetworkPerspective

In contrast to the previous studies of knowledge capital from the perspective of enterprises, this study discusses the employee knowledge capital formation mechanism in the supply chain using social network method from the perspective of social capital s~＇ucture, taking individual employees as the study objects. 150 effective questionnaires of three groups were returned by multistage cluster sampling, and then they were analyzed through regression and the structural equation model. The results are as follows： （1） the acquisition of social capital and knowledge capital is affected by the network structure position of the employees in the supply chain; （2） the knowledge capital is affected by how much social capital the employees obtained in the supply chain; （3） social capital is an intermediary variable to affect the knowledge capital in the network structure. Finally, related suggestions for the supply chain management and the subsequent studies are proposed. 1

Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics

Before any rock engineering project,mechanical parameters of rocks such as uniaxial compressive strength and young modulus of intact rock get measured using laboratory or in-situ tests,but in some situations preparing the required specimens is impossible.By this time,several models have been established to evaluate UCS and E from rock substantial properties.Artificial neural networks are powerful tools which are employed to establish predictive models and results have shown the priority of this technique compared to classic statistical techniques.In this paper,ANN and multivariate statistical models considering rock textural characteristics have been established to estimate UCS of rock and to validate the responses of the established models,they were compared with laboratory results.For this purpose a data set for 44 samples of sandstone was prepared and for each sample some textural characteristics such as void,mineral content and grain size as well as UCS were determined.To select the best predictors as inputs of the UCS models,this data set was subjected to statistical analyses comprising basic descriptive statistics,bivariate correlation,curve fitting and principal component analyses.Results of such analyses have shown that void,ferroan calcitic cement,argillaceous cement and mica percentage have the most effect on USC.Two predictive models for UCS were developed using these variables by ANN and linear multivariate regression.Results have shown that by using simple textural characteristics such as mineral content,cement type and void,strength of studied sandstone can be estimated with acceptable accuracy.ANN and multivariate statistical UCS models,revealed responses with 0.87 and 0.76 regressions,respectively which proves higher potential of ANN model for predicting UCS compared to classic statistical models.

Research on BP-ANN Model of Semi-rigid Connection

The beam-to-column semirigid connection in a steel frame structure is represented by a zero-length rotational spring at the end of the beam element. The beam-to-column semirigid connection behavior is represented by its moment-rotation relationship. Several traditional mathematical models have been proposed to fit the moment-rotation curves from the experimental database,but they may be more reliable within certain ranges. In this paper, the intellectualized analytical model is proposed in the semirigid connections for top and seat angles with double web angles using the feed-forward back-propagation artificial neural network （BP-ANN） technique. the intellectualized analytical model from experimental results based on BP-ANN is more reliable and it is a better choice to the moment-rotation curves for beam-to-column semirigid connection. The results are found to provide effectiveness to the experimental response that is satisfactory for use in steel structural engineering design.

A Weighted Evolving Network with Community Size Preferential Attachment

Community structure is an important characteristic in real complex network.It is a network consists ofgroups of nodes within which links are dense but among which links are sparse.In this paper, the evolving network includenode, link and community growth and we apply the community size preferential attachment and strength preferentialattachment to a growing weighted network model and utilize weight assigning mechanism from BBV model.Theresulting network reflects the intrinsic community structure with generalized power-law distributions of nodes'degreesand strengths.

Properties of Bottleneck on Complex Networks

The traffic bottleneck plays a key role in most of the natural and artificial network. Here we present a simply model for bottleneck dynamical characteristics consideration the reliability on the complex network by taking into account the network topology characteristics and system size. We find that there is a critical rate of flow generation below which the network traffic is free but above which traffic congestion occurs. Also, it is found that random networks have larger critical flow generating rate than scale free ones. Analytical results may be practically useful for designing networks, especially for the urban traffic network.

A statistical end-to-end performance model for networks with complex topologies

Network calculus provides new tools for performance analysis of networks, but analyzing networks with complex topologies is a challenging research issue using statistical network calculus. A service model is proposed to characterize a service process of network with complex topologies. To obtain closed-form expression of statistical end-to-end performance bounds for a wide range of traffic source models, the traffic model and service model are expanded according to error function. Based on the proposed models, the explicit end-to-end delay bound of Fractional Brownian Motion（FBM） traffic is derived, the factors that affect the delay bound are analyzed, and a comparison between theoretical and simulation results is performed. The results illustrate that the proposed models not only fit the network behaviors well, but also facilitate the network performance analysis.

A dynamic logistic regression for network link prediction

In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.