Estimating network effects in China’s mobile telecommunications

A model is proposed along with empirical investigation to prove the existence of network effects in China’s mobile telecommunications market. Futhernore, network effects on China’s mobile telecommunications are estimated with a dynamic model. The structural parameters are identified from regression coefficients and the results are analyzed and compared with another literature. Data and estimation issues are also discussed. Conclusions are drawn that network effects are significant in China’s mobile telecommunications market, and that ignoring network effects leads to bad policy making.

Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

A case study on the shareholder network effect of stock market data:An SARMA approach

One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.

Self-organizing fuzzy clustering neural network and application to electronic countermeasures effectiveness evaluation

A self-organizing fuzzy clustering neural network by combining the self-organizing Kohonen clustering network with the fuzzy theory is proposed. This network model is designed for the effectiveness evaluation of electronic countermeasures, which not only exerts the advantages of the fuzzy theory, but also has a good ability in machine learning and data analysis. The subjective value of sample versus class is computed by the fuzzy computing theory, and the classified results obtained by self-organizing learning of Kohonen neural network are represented on output layer. Meanwhile, the fuzzy competition learning algorithm keeps the similar information between samples and overcomes the disadvantages of neural network which has fewer samples. The simulation result indicates that the proposed algorithm is feasible and effective.

Effects of Reduced Frequency on Network Configuration and Synchronization Transition

Synchronization of networked phase oscillators depends essentially on the correlation between the topological structure of the graph and the dynamical property of the elements. We propose the concept of ＇reduced frequency＇, a measure which can quantify natural frequencies of each pair of oscillators. Then we introduce an evolving network whose linking rules are controlled by its own dynamical property. The simulation results indicate that when the linking probability positively correlates with the reduced frequency, the network undergoes a first-order phase transition. Meanwhile, we discuss the circumstance under which an explosive synchronization can be ignited. The numerical results show that the peculiar butterfly shape correlation between frequencies and degrees of the nodes contributes to an explosive synchronization transition.

Analysis of coseismic effect on temperature in the Three Gorges well network

Through the Three Gorges well network, we examine different coseismic changes in water temperature caused by local earthquakes since 2008, and offer a mechanistic explanation.The relations between the coseismic changes in water temperature and the parameters of distant and local earthquakes are deduced.

Analysis of coverage problem under border effects in wireless sensor networks

Wireless sensor networks can be used to monitor the interested region by deploying dense sensornodes.Coverage is a primary metric to evaluate the capacity of monitoring.In this paper,we focus on thecoverage problem under border effects,where the sensor nodes are distributed in a circle-shaped regionrandomly.Under this scenario,we derive the expected coverage of the sensor node and the total networkcoverage provided by n sensor nodes accurately by probability.These findings are useftd to determine therelated parameters(sensing range,number of sensor nodes and radius of monitored region)for a specificnetwork coverage ratio.Simulation results demonstrate that our analysis is correct and effective.

Wavelet network based predistortion method for wideband RF power amplifiers exhibiting memory effects

RF power amplifiers (PAs) are usually considered as memoryless devices in most existing predistortion techniques. Nevertheless, in wideband communication systems, PA memory effects can no longer be ignored and memoryless predistortion cannot linearize PAs effectively. After analyzing PA memory effects, a novel predistortion method based on wavelet networks (WNs) is proposed to linearize wideband RF power amplifiers. A complex wavelet network with tapped delay lines is applied to construct the predistorter and then a complex backpropagation algorithm is developed to train the predistorter parameters. The simulation results show that compared with the previously published feed-forward neural network predistortion method, the proposed method provides faster convergence rate and better performance in reducing out-of-band spectral regrowth.

Analysis of effect factors-based stochastic network planning model

Looking at all the indeterminate factors as a whole and regarding activity durations as independent random variables, the traditional stochastic network planning models ignore the inevitable relationship and dependence among activity durations when more than one activity is possibly affected by the same indeterminate factors. On this basis of analysis of indeterminate effect factors of durations, the effect factors-based stochastic network planning （EFBSNP） model is proposed, which emphasizes on the effects of not only logistic and organizational relationships, but also the dependent relationships, due to indeterminate factors among activity durations on the project period. By virtue of indeterminate factor analysis the model extracts and describes the quantitatively indeterminate effect factors, and then takes into account the indeterminate factors effect schedule by using the Monte Carlo simulation technique. The method is flexible enough to deal with effect factors and is coincident with practice. A software has been developed to simplify the model-based calculation, in VisualStudio.NET language. Finally, a case study is included to demonstrate the applicability of the proposed model and comparison is made with some advantages over the existing models.

Neural network-based matrix effect correction in EDXRF analysis

In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect effectively through classifying the samples automatically,and influence of X-ray absorption and enhancement by major elements of the samples is reduced.Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.

Neural Network Based Terminal Sliding Mode Control for WMRs Affected by an Augmented Ground Friction With Slippage Effect

Wheeled mobile robots(WMRs) encounter unavoidable slippage especially on the low adhesion terrain such that the robots stability and accuracy are reduced greatly.To overcome this drawback,this article presents a neural network(NN) based terminal sliding mode control(TSMC) for WMRs where an augmented ground friction model is reported by which the uncertain friction can be estimated and compensated according to the required performance.In contrast to the existing friction models,the developed augmented ground friction model corresponds to actual fact because not only the effects associated with the mobile platform velocity but also the slippage related to the wheel slip rate are concerned simultaneously.Besides,the presented control approach can combine the merits of both TSMC and radial basis function(RBF) neural networks techniques,thereby providing numerous excellent performances for the closed-loop system,such as finite time convergence and faster friction estimation property.Simulation results validate the proposed friction model and robustness of controller;these research results will improve the autonomy and intelligence of WMRs,particularly when the mobile platform suffers from the sophisticated unstructured environment.

Measuring Effectiveness in E-Commerce Networks

The revolution of information technology within the p ast twenty years has dramatically changed the picture of our economy. Numerous n ew possibilities of communication have let competition advantages for many compa nies and even advantageous macroeconomic consequences emerge on national and international level. Through newly developed information technologies the knowl edge base of market participants improves with a concurrent reduction of the inf ormation obtaining costs. As a result considerable competition advantages develo p for those companies acting in E-commerce networks. These advantages of the la test development lead to macroeconomic effects on national level, if the effecti veness and efficiency increasing possibilities are used more strongly than in ot her countries. Positive international effects arise since the allocation effic iency is increased through intensified competition between different market pa rticipants in various countries. This in turn leads to an increase in wo rldwide prosperity. This causal chain however is not yet realistic to the whole extent, as such an i ncreased transparency of information is not necessarily accepted by all market p articipants. Otherwise a considerable productivity increase would already have o ccurred in industrial countries. Overall the question arises, whether the change s in the competition situation make single enterprises technically more effectiv e, concurrently however deteriorate the efficiency of the entire market through informational asymmetries. To answer these and further questions and to measure the effectiveness and effic iency of various E-commerce networks an interdisciplinary analysis platform is to be developed. With the help of this platform, it should be possible to examin e single and macroeconomic questions, reveal temporal connections and to analyse aspects of business management and national economy, information management, em ployment politics and finance politics. For this, various part-models for the i ndividual knowledge disciplines have to be generated and brought together in the platform. This platform allows various users to make the right decisions (effec tiveness) with the help of the developed models and to competently estimate the effects (efficiency). Currently models of the individual knowledge disciplines (business management, e conomics, computer science) are being developed within the research project EEE. con. This project deals with the question of Supply Chain Management (SCM), E-P rocurement, with the implementation of inter-organisational information systems , as well as various market, competition and organisation models. The department of economics and computer science from Prof. Dr.-Ing. habil. W. Dangelmaier particularly deals with the development of an agent controlled SCM- communication model which is part of the E-commerce analysis platform. Both are described in this paper. Furthermore, a unified modelling language in order to allow a prototypic implementation of the analysis tool and to make the work with other project participants and external participants easier is decided upon wit hin this project.

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.

A Study of New Method for Weapon System Effectiveness Evaluation Based on Bayesian Network

As weapon system effectiveness is affected by many factors,its evaluation is essentially a multi-criterion decision making problem for its complexity.The evaluation model of the effectiveness is established on the basis of metrics architecture of the effectiveness.The Bayesian network,which is used to evaluate the effectiveness,is established based on the metrics architecture and the evaluation models.For getting the weights of the metrics by Bayesian network,subjective initial values of the weights are given,gradient ascent algorithm is adopted,and the reasonable values of the weights are achieved.And then the effectiveness of every weapon system project is gained.The weapon system,whose effectiveness is relative maximum,is the optimization system.The research result shows that this method can solve the problem of AHP method which evaluation results are not compatible to the practice results and overcome the shortcoming of neural network in multilayer and multi-criterion decision.The method offers a new approach for evaluating the effectiveness.

基于网络效应与行动者效应的专利转让及受让行为研究

专利交易是技术创新扩散的主要渠道,是实现科技成果转化的有效途径。研究专利转让与受让行为可以识别影响专利交易的主要因素,为政府制定技术交易政策提供科学依据。以2002—2019年中国集成电路产业专利转让记录作为数据源,利用Siena模型,结合网络效应和行动者效应,对专利转让与受让行为影响因素进行实证检验。研究发现:①专利技术交易以单向交易为主,随着集成电路产业从萌芽阶段步入成长阶段,产业内技术交易主体增加,网络规模扩大,网络信息传输效率有所提升,但仍处于较低水平;②网络初期产生的锁定效应明显得以缓解,技术交易网络内组织社团化特征明显,技术交易社团规模呈扩张态势且社团内紧密程度不断提高;③网络效应方面,转让者更倾向于与技术交易伙伴进行交易,频繁发生受让行为的企业会吸引更多新的交易伙伴,企业间不存在明显的互惠交易;④行动者效应方面,随着产业进入成长阶段,成熟企业更倾向于专利转让行为,初创企业更倾向于专利受让行为,占据高结构洞位置的企业更倾向于专利转让行为。企业间技术创新能力差距越小越容易发生技术交易,企业倾向于选择同一国别或同一企业集团内伙伴进行交易。

Effective Life and Area Based Data Storing and Deployment in Vehicular Ad-Hoc Networks

In vehicular ad-hoc networks (VANETs), store-carry-forward approach may be used for data sharing, where moving vehicles carry and exchange data when they go by each other. In this approach, storage resource in a vehicle is generally limited. Therefore, attributes of data that have to be stored in vehicles are an important factor in order to efficiently distribute desired data. In VANETs, there are different types of data which depend on the time and location. Such kind of data cannot be deployed adequately to the requesting vehicles only by popularity-based rule. In this paper, we propose a data distribution method that takes into account the effective life and area in addition to popularity of data. Our extensive simulation results demonstrate drastic improvements on acquisition performance of the time and area specific data.

Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

场景赋能驱动下的工业互联网平台成长演化——“柠檬豆”案例研究

工业互联网平台是工业领域的数字新经济,是数字经济与实体经济融合的关键载体。以“柠檬豆”平台为案例研究对象分析工业互联网平台成长演化机理,研究发现,工业互联网平台场景赋能以平台用户需求场景为牵引,开发冗余数据资源,整合异质资源,实现资源共享、知识传导和数字协同;工业价值网络中的资源映射到工业互联网平台上成为可调用的数据资源,数字孪生产生更丰富的场景,驱动网络效应强化;工业互联网平台成长演化的本质是基于场景赋能体系构建的价值创造过程与基于网络效应形成的生态构建过程之间的交互共演。响应场景驱动创新范式,研究有助于完善平台组织理论,丰富网络效应研究情境,为我国工业互联网平台高质量发展提供理论依据。

Predicting Ground Effects of Omnidirectional Antennas in Wireless Sensor Networks

Omnidirectional antennas are often used for radio frequency (RF) communication in wireless sensor networks (WSNs). Outside noise, electromagnetic interference (EMI), overloaded network traffic, large obstacles (vegetation and buildings), terrain and atmospheric composition, along with climate patterns can degrade signal quality in the form of data packet loss or reduced RF communication range. This paper explores the RF range reduction properties of a particular WSN designed to operate in agricultural crop fields to collect aggregate data composed of subsurface soil moisture and soil temperature. Our study, using simulation, anechoic and field measurements shows that the effect of antenna placement close to the ground (within 10 cm) signi?cantly changes the omnidirectional transmission pattern. We then develop and propose a prediction method that is more precise than current practices of using the Friis and Fresnel equations. Our prediction method takes into account environmental properties for RF communication range based on the height of nodes and gateways.