Cooperative wireless networks for better green services

During the last two decades, mobile communication systems (such as GSM, GPRS and 3G networks), wireless broadcasting networks, wireless local area networks (WLAN or WiFi), and wireless sensor networks have been successfully developed and widely deployed through different technological routes for providing a variety of communication services in different application scenarios. While making tremendous contributions to social progress and economic growth, these heterogeneous wireless networks consume a lot of energy in achieving overlapped service coverage, and at the same time, generate strong electromagnetic interference (EMI) and radiation pollution, especially in big cities with high building density and user population. In order to guarantee the overall return on investment (ROI), improve user experience and quality of service (QoS), save energy, reduce EMI and radiation pollution, and enable the sustainable deployment of new profitable applications and services, this paper proposes a cross-network cooperation mechanism to effectively share network resources and infrastructures, and then adaptively control and match multi-network energy distribution characteristics according to actual user/service requirements in different geographic areas. Some idle or lightly-loaded Base Stations (BS or BSs) will be temporally turned off for saving energy and reducing EMI. Initial simulation results show the proposed approach can significantly improve the overall energy efficiency and QoS performance across multiple cooperative wireless networks.     

A New Equipment Management Framework Based on Grid of Things

This paper describes the core of an equipment management framework offering general functionality based on grid of things.The framework supports sensor,wireless network and intelligent control system based on services and service features.The framework is more generic than traditional hierarchical equipment management framework.It supports sensor,actuator and wireless networks.In the framework,techniques of spontaneous service provision,grid,scene-aware,semantic reasoning,and equipment service management are smoothly synthesized.It enables equipments in grid of things to interact automatically,provides semantic-integration equipment status awareness,and supports meta-service management based adaptability and scalability.     

Biomimic Vein-Like Transparent Conducting Electrodes with Low Sheet Resistance and Metal Consumption

In this contribution,inspired by the excellent resource management and material transport function of leaf veins,the electrical transport function of metallized leaf veins is mimicked from the material transport function of the vein networks.By electroless copper plating on real leaf vein networks with copper thickness of only several hundred nanometre up to several micrometre,certain leaf veins can be converted to transparent conductive electrodes with an ultralow sheet resistance 100 times lower than that of state-of-the-art indium tin oxide thin films,combined with a broadband optical transmission of above 80%in the UV–VIS–IR range.Additionally,the resource efficiency of the vein-like electrode is characterized by the small amount of material needed to build up the networks and the low copper consumption during metallization.In particular,the high current density transport capability of the electrode of>6000 A cm^−2 was demonstrated.These superior properties of the vein-like structures inspire the design of high-performance transparent conductive electrodes without using critical materials and may significantly reduce the Ag consumption down to<10%of the current level for mass production of solar cells and will contribute greatly to the electrode for high power density concentrator solar cells,high power density Li-ion batteries,and supercapacitors.     

Analytic Hierarchy Process(AHP)to Optimize the Service in a Water Supply Network

Decision making in drinking water supply networks is increasingly complex due to the large number of variables involved.In order to make better decisions it is necessary to use adequate and robust methodologies.This paper presents the application of the Analytic Hierarchy Process(AHP)related to the operation of the drinking water supply network of the city of Chihuahua,Mexico,where two possible alternatives are delineated with the objective to optimize the service.The application of AHP was carried out in 24 sectors that have substantial differences in their efficiency but with instrumentation and measurement in all the variables contemplated by the operating agency,with a population of 221,722 inhabitants which represent a 30%of the total population of the city,the results indicate that the best alternative is the one with less criteria to be controlled and fewer repercussions on the cost of operation and investment in the rehabilitation and replacement of the network.     

Computer-aided Prediction of the ZrO2 Nanoparticles’ Effects on Tensile Strength and Percentage of Water Absorption of Concrete Specimens

In the present paper,two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing ZrO2 nanoparticles have been developed at different ages of curing.For building these models,training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted.The data used in the multilayer feed forward neural networks models and input variables of genetic programming models were arranged in a format of eight input parameters that cover the cement content,nanoparticle content,aggregate type,water content,the amount of superplasticizer,the type of curing medium,age of curing and number of testing try.According to these input parameters,in the neural networks and genetic programming models,the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles were predicted.The training and testing results in the neural network and genetic programming models have shown that two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing ZrO2 nanoparticles.It has been found that neural network(NN) and gene expression programming(GEP) models will be valid within the ranges of variables.In neural networks model,as the training and testing ended when minimum error norm of network gained,the best results were obtained and in genetic programming model,when 4 genes were selected to construct the model,the best results were acquired.Although neural network have predicted better results,genetic programming is able to predict reasonable values with a simpler method rather than neural network.     

Research on Applying Bluetooth to an Elevator Wireless Control System

Compared with other elevator control systems,the wireless control system has many advanta-ges such as easy to install and maintain Bluetooth ia a new technology of short-range wireless commu-nication,and the idea of applying Bluetooth to the elevator wireless control system iy expected to get wide application,In this paper,a wireless control prototype system is yntroduced,and the experiments of this system proved the feasibility of this idea.     

A VIBRATION RECOGNITION METHOD BASED ON DEEP LEARNING AND SIGNAL PROCESSING

Effective vibration recognition can improve the performance of vibration control and structural damage detection and is in high demand for signal processing and advanced classification.Signal-processing methods can extract the potent time-frequency-domain characteristics of signals;however,the performance of conventional characteristics-based classification needs to be improved.Widely used deep learning algorithms(e.g.,convolutional neural networks(CNNs))can conduct classification by extracting high-dimensional data features,with outstanding performance.Hence,combining the advantages of signal processing and deep-learning algorithms can significantly enhance vibration recognition performance.A novel vibration recognition method based on signal processing and deep neural networks is proposed herein.First,environmental vibration signals are collected;then,signal processing is conducted to obtain the coefficient matrices of the time-frequency-domain characteristics using three typical algorithms:the wavelet transform,Hilbert-Huang transform,and Mel frequency cepstral coefficient extraction method.Subsequently,CNNs,long short-term memory(LSTM)networks,and combined deep CNN-LSTM networks are trained for vibration recognition,according to the time-frequencydomain characteristics.Finally,the performance of the trained deep neural networks is evaluated and validated.The results confirm the effectiveness of the proposed vibration recognition method combining signal preprocessing and deep learning.     

A Wireless Distributed Condition Monitoring System Based on Bluetooth Technology

Based on the discussion of bluetooth and network technology,this paper proposed an entire framework of a wireless distributed monitoring system by combining the characteristics of industry application.The feasibility of putting this kind of system in practice is discussed.The wireless distributed monitoring system can enhance the performance of condition monitoring more than the traditional one used now.     

Identification model of multi-layered neural network parameters and its applications in the petroleum production

This paper creates a LM (Levenberg-Marquardt) algorithm model which is appropriate to solve the problem about weights value of feedforward neural network. On the base of this model, we provide two applications in the oilfield production. Firstly, we simulated the functional relationships between the petrophysical and electrical properties of the rock by neural networks model, and studied oil saturation. Under the precision of data is confirmed, this method can reduce the number of experiments. Secondly, we simulated the relationships between investment and income by the neural networks model, and studied invest saturation point and income growth rate. It is very significant to guide the investment decision. The research result shows that the model is suitable for the modeling and identification of nonlinear systems due to the great fit characteristic of neural network and very fast convergence speed of LM algorithm.     

The Diagnosis of Reciprocating Machinery by Bayesian Networks

A Bayesian Network is a reasoning tool based on probability theory and has many advantages that other reasoning tools do not have,This paper discusses the basic theory of Bayesian networks and studies the problems in constructing Bayesian networks.The paper also consturcts a Bayesian diagnosis network of a reciprocating compressor.The example helps us to draw a conclusion that Bayesian diagnosis networks can diagnose reciprocating machinery effectively.     

Nano-functionalized Silk for Smart Wearable Devices

正With rapid advances of miniaturization and wireless technologies,electronics has been associated with traditional textiles to develop smart wearable devices for daily health monitoring and fitness tracking 1-2.It is of great demand to fabricate flexible materials that possess both wearability and prac-     

Comprehensive analysis of two-dimensional charge transport mechanism in thin-film transistors based on random networks of single-wall carbon nanotubes using transient measurements

Understanding charge transport mechanisms in thin-film transistors based on random networks of single-wall carbon nanotubes(SWCNT-TFTs)is essential for further advances to improve the potential for various nanoelectronic applications.Herein,a comprehensive investigation of the two-dimensional(2D)charge transport mechanism in SWCNT-TFTs is reported by analyzing the temperature-dependent electrical characteristics determined from the direct-current and non-quasi-static transient measurements at 80-300 K.To elucidate the time-domain charge transport characteristics of the random networks in the SWCNTs,an empirical equation was derived from a theoretical trapping model,and a carrier velocity distribution was determined from the differentiation of the transient response.Furthermore,charge trapping and de-trapping in shallow-and deep-traps in SWCNT-TFTs were analyzed by investigating charge transport based on their trapping/de-trapping rate.The comprehensive analysis of this study provides fundamental insights into the 2D charge transport mechanism in TFTs based on random networks of nanomaterial channels.     

Recognition of a Life Distribution Based on a Neural Network

In general, we describe three different methods to select an appropriate distribution form:histogram, probability plots, and hypothesis test. The life distribution is recognized by a neural network method. The relationship among life distribution with life data is described through threshold and weight of neural networks. The method is convenient to use. An example is presented to validate this method, and the results are satisfactory.     

A BP neural network based information fusion method for urban traffic speed estimation

Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an very important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings and tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, we developed a novel method employing a back propagation (BP) neural network to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, we can use the speed of its related road sections to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the speed of this road section estimated by our method is better.     

Principle and realization method of tunnel deformation detection based on image recognition and data transmission technology

To detect the deformation of the tunnel structure based on image sensor networks is the advanced study and application of spatial sensor technology. For the vertical settlement of metro tunnel caused by internal and external stress after its long period operation, the overall scheme and measuring principle of a tunnel deformation detection system is introduced. The image data acquisition and processing of detection target are achieved by the cooperative work of image sensor, ARM embedded system. RS485 communication achieves the data transmission between ARM memory and Host Computer. The database system in station platform analyses the detection data and obtain the deformation state of tunnel inner wall, which makes it possible to early-warn the tunnel deformation and take preventive measures in time.     

Electron scattering by Friedel oscillations in carbon nanotubes

Multi-walled carbon nanotube networks were confirmed to exhibit a linear decrease in resistivity with increasing temperature from 100 to above 400 K.The linearity was explained using a defect scattering model that involved Friedel oscillations(that is,electron-electron interactions).The applicability of this model,which was originally proposed for graphene,to carbon nanotubes was assessed based on a comparison of various experimental data.Increases in the slopes of the resistivity-temperature plots following the introduction of defects,as well as an effect of charge concentration on the slope were key predictions of this model.The results obtained from few-walled carbon nanotube networks are also shown.In the literature,linear resistivity-temperature plots were obtained from other graphene derivatives,indicating that the linearity originates from the hexagonal symmetry of these materials.The present work also indicated a relationship between the appearance of linearity and negative magnetoresistance above 100 K.Based on a mechanism incorporating scattering in association with Friedel oscillations and conventional electron conduction models,the universality of resistivity-temperature plots obtained from carbon nanotube networks is introduced.     

Design of Electrode and Maximizing Surface Charges for Tribonanogenerator

正Self-powered sensing technology by collecting energy from ambience is an idea strategy to realize the application of wireless sensors in large scale.Triboelectric nanogenerator(TENG)is a device for harvesting mechanical energy from ambience to transform the energy into electricity.Since the first report of the TENG in 2012,it has been developed very rapidly.Up to now,various TENGs and     

The simulation of electrostatic coupling intra body communication based on the finite element method

Intra-body Body Communication (IBC) is a communication technology in which human body is used as a signal transmission medium. Due to its unique characters, IBC technology is proposed as a novel and promising technology for personal area network (PAN), computer network access, implant biomedical monitoring, human energy transmission, etc. In this paper, investigation has been done in the computer simulation of the electrostatic coupling IBC by using the developed finite-element models, in which (1) the incidence and reflection of electronic signal in the upper arm model were analyzed by using the theory of electromagnetic wave, (2) the finite-element models of electrostatic coupling IBC were developed by using the electromagnetic analysis package of ANSYS software, (3) the signal attenuation of electrostatic coupling IBC were simulated under the conditions of different signal frequency, electrodes direction, electrodes size and transmission distance. Finally, some important conclusions are deduced on the basis of simulation results.     

BP-Neural-Network-Based Tool Wear Monitoring by Using Wavelet Decomposition of the Power Spectrum

In a drilling process, the power spectrum of the drilling force is related to the tool wear and is widely applied in the monitoring of tool wear. But the feature extraction and identification of the power spectrum have always been an unresolved difficult problem. This paper solves it through decomposition of the power spectrum in multilayers uzing wavelet transform and extraction of the low frequency decomposition coefficient as the envelope information of the power spectrum. Intelligent identification of the tool wear statuz is achieved in the drilling process through fusing the wavelet decomposition coefficient of the power spectrum by uzing a BP( Back Propagation) neural network. The experimental results show that the features of the power spectrum can be extracted efficiently through this method, and the trained neural networks show high identification precision and the ability of extension.     

Interfacial Structure of Steel-Mushy Al-20Sn Bonding Plate

Fe-AI compound at the interface of steel-mushy AI-20Sn bonding plate was studied quantitatively. The relationship between ratio of Fe-AI compound at interface and bonding parameters (such as preheat temperature of steel plate, solid fraction of AI-20Sn slurry and rolling speed) was established by artificial neural networks perfectly. The results show that when the bonding parameters are 505℃ for preheat temperature of steel plate, 34.3% for solid fraction of AI-20Sn slurry and 10 mm/s for rolling speed, the reasonable ratio of Fe-AI compound corresponding to the largest interfacial shear strength of bonding plate is obtained. Its value is 72%. This reasonable ratio of Fe-AI compound is a quantitative criterion of interfacial embrittlement, that is, when the ratio of Fe-AI compound at interface is larger than 72%, interfacial embrittlement will occur.