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In the era of paper-based contract, a lot of time, human labor and expenses are required to handle the process of contract drafting, contract signing, contract execution, and payment settlement. The emerging of electronic contract enhances the tedious signing process of paper-based contract and improves the efficiency of contract management. However, due to the centralized system architecture and the database-based storage schema, the stored contract data is at high risk of information leaking, data tampering and hacker attacks. In this study, we introduce Blockchain technology to the contract management, and develop a process-oriented contract management system (BEcontractor) for a Hangzhou-located power grid enterprise X, aiming to solve a series of security issues existing in the traditional electronic contract system. By deploying BEcontractor, procurement activities could be resumed online among X and its nation-widely commodity suppliers during COVID-19 epidemic. Up to September 2020, 6336 electronic contracts have been signed, with an accumulated amount of $\yen ~6.5$ billion. It is showed that the cost for accomplishing the contract signing process was significantly reduced, and the payment period was shortened from three months to around one month.

Deep reinforcement learning algorithms have made great progress in video games. However, there are still some problems, such as sample inefficiency and poor generalization. In this paper, we highlight that these problems are partially caused by the inability of convolutional neural networks (CNNs) to reason with the underlying relations between the objects in the image observations. Based on this point, we try to alleviate these problems in a more efficient and explainable way, including learning the representations of objects and reasoning the relations between them with a relation network (RN). Each pixel in the feature maps is treated as an object and our model explicitly learns the relations between object pairs. The relations are summarized through an attention mechanism and then fed into the downstream fully-connected layers. In the experiments, our model is compared with baseline models in three typical object based Atari games. Under the same hyperparameter settings, our model still achieves better sample efficiency and generalization capability. Further studies throw light on the impact of hyperparameters and verify the interpretability of the model.

How to accurately cut the handwritten string especially the sticky string, has become a key part of recognizing handwritten strings. Aiming at the traditional segmentation algorithm has some problems, such as more complex､the segmentation effect is not good and so on, this paper proposes a segmentation algorithm based on background domain recursive training. This algorithm is a common algorithm for handwritten digit string segmentation and English string segmentation. The principle is the use of handwritten string at the adhesion of the background domain features and recursive neural network RNN fusion of special mechanisms. It completes by modeling, training and implementation of three steps. RNN modeling is the core of the algorithm, it contain two important parts: ①Assignment for the background domain, extracting the eigenvector value of the depression area by the principle of adjacent matching, the connection weights of the RNN input layer are calculated.② Using the minimum area selection principle to modify eigenvector values in the RNN's acceptance layer, the connection weight of the layer are calculated agin. After modeling completion, RNN is training samples､studying and remembering. Finally, use the knowledge that RNN has learned to complete real segmentation, the effect is satisfactory.

The M-Rife algorithm is widely used in frequency estimation field. By using the interpolation and spectrum shift it can achieve high-precision frequency estimation. In this paper, a new frequency estimation method based on the Multi-M-Rife algorithm is proposed. First M-Rife algorithm is used to estimate the intra pulses’ rough-estimated frequency, then an inter-pulse sinusoidal wave sequence is constructed, after that by using maximum likelihood estimation (MLE) the frequency offset between signal and sequence has been detected. Experimental results show that by using the new algorithm the frequency estimation accuracy of pulse signals is better than 0.3Hz, and the algorithm can also achieve a high detection rate under the condition of low signal-to-noise ratio.

In online advertising systems, having a good knowledge of user behavior is crucial for click-through rate (CTR) prediction. In recent years, many researchers turn to seek a better way of user representation by modeling the behavior sequences with recurrent neural network (RNN). However, recurrent layers implicitly adopt the assumption that elements with different orders are fundamentally different, which is inefficient in many practical scenarios with much uncertainty and complicated hidden states. In this paper, we follow the paradigm of Relation Network (RN), and propose a new model called Similitude Attentive Relation Network (SARN). The user behavior is modeled as a graph, where nodes correspond to the visited items and edges correspond to the relations. To capture the latent user interest better, the model concentrates on the relations between items, rather than the translation on the time series. More specifically, the model tries to learn the similarity between items in a semantic space through a learnable dot-product operation and blend both of the item representations and relational information together as the final relations. We define our user representation on an attentive pooling of the relations directly. To verify the effectiveness of our method, extensive experiments on two public datasets and one real-world online advertising dataset are conducted. Experimental results show that our methods achieve usually better performance than others. Besides, we explore the properties of our model by controlled experiments and show the learned relational knowledge by visualizing the inner states of SARN.

Recently, software-defined Industrial Internet of Things (SDIIoT), the integration of software-defined networking (SDN) and Industrial Internet of Things (IIoT), has emerged. It is perceived as an effective way to manage IIoT dynamically. Aiming to improve scalability and flexibility of SDIIoT, multi-SDN has been applied to form a physically distributed control plane to handle the large amount of data generated by industrial devices. However, as the core of multi-SDN, reaching consensus among multiple SDN controllers is a thorny issue. To meet the required design principle, this paper proposes a blockchain-enabled distributed architecture with SDIIoT to synchronize local views between distinct SDN controllers and finally reach the consensus of global view. On the other hand, both the cryptographic operations of blockchain and the noncryptographic computational tasks have access to the same computational resource pool of mobile edge cloud (MEC). In order to simultaneously optimize the throughput of blockchain and the energy consumption caused by computing, we adaptively allocate computational resources and the block size by jointly considering the trust features of SDN controllers and the resource requirements of non-cryptographic operations. To implement the truly distributed manner of blockchain, we describe our problem as a partially observable Markov decision process (POMDP) and propose a novel deep recurrent Q-network (DRQN) approach to solve it. In the simulation results, we compare two different protocols of blockchain and show the effectiveness of our scheme in either of them.

This paper is a comprehensive review on the applications of Model Predictive Control (MPC) on wind turbines and the Field-Programmable Gate Array (FPGA) implementations of MPC. MPC is an optimal control algorithm that can effectively deal with system constraints, which has been extensively applied to various aspects. FPGA, with the advantages of parallel processing capability, re-programmability, and flexible design, is one of the suitable implementations of MPC. In this paper, the applications of MPC on wind turbines are carried out in four aspects according to the four control objectives: maximum wind power extraction, regulation capability enhancement, mechanical loads minimization and weighting coefficients optimization. The FPGA implementations of MPC primarily focus on two aspects: the algorithms for Quadratic Programming (QP) problem and the FPGA-based implementations.

Both mobile edge cloud (MEC) and software-defined networking (SDN) are technologies for next generation mobile networks. In this paper, we simultaneously optimize energy consumption and quality of experience (QoE) in video streaming over software-defined mobile networks (SDMN) with MEC. Specifically, we propose to jointly consider buffer dynamics, video quality adaption, edge caching, video transcoding and transmission. We formulate two optimization problems which can be depicted as a constrained Markov decision process (CMDP) and a Markov decision process (MDP). Then we transform the CMDP problem into regular MDP by deploying Lyapunov technique. We utilize asynchronous advantage actor-critic (A3C) algorithm, one of the deep reinforcement learning (DRL) methods, to solve the corresponding MDP problems. Simulation results are presented to show that the proposed scheme can achieve the goal of energy saving and QoE enhancement with the corresponding constraints satisfied.

Gaseous nitrous acid (HONO) is a relatively active nitrogen oxides in the atmosphere. In this work, the incoherent broadband cavity-enhanced absorption spectroscopy (IB-BCEAS) and long path absorption photometer (LOPAP) was both used to measure HONO. The measurement results were compared and verified. Under a variety of different relative humidity (RH = 0, 28, 54, 69%), flow rate (flow rate = 2, 4, 6, 8 LPM) and NO 2 concentration (NO 2 = 50, 80, 100 ppbV), LOPAP and IBBCEAS simultaneously measured the HONO to characterize the uncertainty and relationship between the two instruments. The results showed uptake of NO 2 produced HONO by the extractive tubing surface of IBBCEAS instrument. The measured concentration of IBBCEAS needs to correct the influence of the tubing. And the LOPAP instrument could have positive HONO readings while high NO 2 were introduced. The IBBCEAS and LOPAP instruments were highly consistent in measuring HONO.

Focus on the energy saving problem in cloud radio access network (C-RAN), this paper jointly optimizes the system power consumption and delay performance with the consideration of quality-of-service (QoS) and fronthaul capacity. First, to make the power consumption model compatible with C-RAN, we give a modified power consumption model of base station (BS) on the basis of the well-known EARTH model. Then, to save the system power consumption, the sleeping strategy is introduced to dynamically switch ON/OFF the BS. In order to analyze BS mode under the sleeping strategy, the arriving users are modeled as a discrete-time queuing network. Based on the global balance equations of the queuing network, the probability that the BS is in active mode can be obtained. In the light of the above result, to further reduce the system power consumption, power control is introduced under the constraint of QoS and fronthaul capacity. Meanwhile, by applying Lyapunov method, a different way to consider the delay performance is provided, and at the same time, network stability is guaranteed. Simulation results show that the proposed algorithm can balance the power consumption and delay by adjusting relevant parameters, and achieve the goal of energy saving on the basis of delay control.

In this paper, a dimensionality reduction Gauss-Hermite quadrature filter (DRGHQF) is proposed. Compared with the conventional GHQF algorithm and with the Rao-Blackwellised particle filter (RBPF), the proposed DRGHQF performs better in terms of computational cost and accuracy. The simulation results indicate that the DRGHQF can significantly reduce the computational cost without incurring a loss in accuracy.

This paper proposes a high-accuracy Doppler shift Estimator based on Chinese Remainder Theorem (CRT) and spectrum correction for radar signal processing is proposed. The high accuracy is arising from the fact that the Tsui corrector almost reduces the error of the frequency offset to zero, which almost provides a noise-free remainder for the CRT reconstruction. Numerical results well verify this conclusion. To be generalized, the CRT reconstruction and the spectrum correction can be well combined in the phase unwrapping, which present vast potentials for future applications in SAR, InSAR or electronic surveillance fields.

International audience; Genetic Algorithms are commonly used to generate high-quality solutions to combinational optimization problems. However, the execution time can become a limiting factor for large and complex problems. In this paper, we propose a parallel Genetic Algorithm consisting of an island model at the upper level and a fine-grained model at the lower level. It is designed to be highly consistent with the CUDA framework to get the maximum speedup without compromising to solutions' quality. As several parameters control the performance of the hybrid method, we test them by a flexible flow shop scheduling problem and analyze their influence. Finally, numerical experiments show that our approach cannot only obtain competitive results but also reduces execution time by setting a medium size selection diameter, a relatively large island size and a wide range size migration interval.

Seasonal variations of gravity waves over the globe in the stratosphere are investigated using radio occultation data from the Constellation Observing System for Meteorology, Ionosphere and Climate mission from September 2006 to June 2016. The global distributions of 10-year averaged seasonal means of gravity waves indicate that potential energy is large over tropical and subtropical latitudes (30°S-30°N) in all seasons, as well as over middle and high latitudes in winter hemisphere. Gravity wave potential energy and the outgoing longwave radiation (OLR) which is a proxy for deep convection are negative correlated at equatorial latitudes. This suggests that convection is the primary source of gravity waves in the Tropics. At 50°N and 50°S, gravity waves are mainly generated by the diverse terrains of mountains, and either propagate upward or are filtered out under the effect of background zonal wind. Intense eastward wind, which mainly happens in winter hemisphere, makes great contributions to the upward propagation of gravity waves, while 0 m/s wind level, which is apparent in summer hemisphere, filters out most of the gravity waves. This can explain the seasonal variations of gravity waves at middle and high latitudes.

In this paper, we present a comparison between the ionospheric peak parameters (NmF2 and hmF2) derived from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) data and that derived from observations of ionosondes which are located in geomagnetic equatorial regions under geomagnetic disturbed conditions. In order to carry out a statistic analysis over large sample sizes, all matched pairs from COSMIC and 11 ionosonde stations between April 2006 and December 2016 under geomagnetic disturbed conditions with Kp index >3+ are studied at first. Our results demonstrate high correlations of F2 layer peak parameters obtained from the two techniques and the correlation of NmF2 is better than hmF2. We further classify the matched pairs into three groups corresponding to Kp =4, Kp=5, and Kp>=6−. It is found that as the intensity of geomagnetic activities increases, the correlation of NmF2 derived from the two techniques decreases and the relative difference of NmF2 between COSMIC and ionosondes increases. COSMIC tends to underestimate NmF2 and the amount of the underestimation increases with the increased intensity of geomagnetic activities whereas hmF2 derived from COSMIC RO are usually higher than ionosonde ones.

Future fifth generation (5G) mobile networks will greatly increase the date-rate of communication. Three-dimensional (3D) multiple input multiple output (MIMO) systems have drawn more and more attention as they meet the increasing date-rate demands in 5G mobile networks. However, in the practical system, the large overhead of the channel state information-reference signal (CSI-RS) would become a barrier for performance improvement of 3D MIMO system with the significantly increased number of transmit antennas. What's more, it will be inefficient for performance promotion that the antenna elements (AEs) in one column are usually virtualized as one antenna port to support one date stream in the vertical direction in existing 3D MIMO. To overcome these problems, on the one hand, this paper proposes linked precoding, combining separate elevation CSI-RS configuration and CSI-RS precoded with elevation beamforming vector to make a better tradeoff between resource overhead and throughput performance, and on the other hand, multi-stream precoding based on CSI-RS is also proposed to provide larger throughput performance supporting multi-stream precoding not only in horizontal direction but also in vertical direction.

By using first-principles calculations within the framework of density functional theory,[1] the magnetic properties of 3d transitional metal (TM) atoms (from Sc to Zn) adsorbed two dimensional (2D) GaAs nanosheets (GaAsNSs) are systematically investigated. The 2D GaAsNS is composed of 16 Ga and 16 As atoms, which are repeated along the x-and y-directions. 3d TM atoms adsorbed on hollow site (center of the hexagonal ring formed by 3 Ga and 3 As atoms). [2]–[3] Upon TM atom adsorption, GaAsNS, which is a nonmagnetic semiconductor, with an estimated gap of 1.29 eV[4] can be tuned into a magnetic semiconductor (Sc, V, and Fe adsorption), a half-metal (Mn adsorption), or a metal (Co and Cu adsorption). The magnetic moment of Sc, V, Fe and Mn adsorbed GaAsNS is 1 $\mu_{\text{B}}, 1\ \mu_{\text{B}}, 1\ \mu_{\text{B}}$ and 2 $\mu_{\text{B}}$ respectively. Our calculations show that the strong p-d hybridization between 3d orbit of TM atoms and 4p orbit of neighboring As atoms is responsible for the formation of chemical bonds and the origin of magnetism in the GaAsNSs with Sc, V and Fe adsorption. However, Mn 3d orbit with more unpaired electrons hybridizes with not only As 4p but also Ga 4p orbits, resulting in a stronger exchange interaction. Our results may be useful for electronic and magnetic applications of GaAsNS-based materials. Fig. 1. Bandstructure of fe and mn adsorbed gaasns

A geomechanical model and a multiphase black oilmodel are iteratively coupled in this paper. Parallel computingis employed to handle large scale problems by benefiting fromits features of distributed memory storage and efficient runtimereduction. The finite element method and the finite differencemethod are employed to discretize the two models, respectively. The geomechanical model is developed with the capability ofsimulating the rock matrix deformation with complex constitutivelaws and its effects on reservoir properties. The multiphaseflow model is modified by introducing geomechanical variablesin a conventional flow model. A coupling strategy is carefullyproposed to enable tight and dynamic interactions betweenthese two models, as well as improving parallel computationalefficiency. Example problems are presented to demonstrate theutility and efficiency of the coupled models. Expected geomechanical phenomena are illustrated by numerical experiments andvalidated by commercial software. In addition, for testing thescalability behaviour, field scale problems with millions reservoirand geomechanical grid blocks are performed. The results showencouraging speedups which indicate the integrated models canbe an efficient and useful tool for evaluating and analyzing oiland gas production of stress-sensitive reservoirs.

In recent years, polymer flooding has become a mature technology to improve the reservoir recovery rate and has been successfully implemented in many oil fields. Because of high operating costs of a polymer flooding project and the impact to the environment, the polymer injected per unit volume should be tracked for each polymer flooding pattern. Hence, the full-field reservoir simulations with fine-scale grids are needed to capture fine-scale phenomena and to optimize the process. To meet these objectives, a polymer module has been developed in our in-house parallel black oil simulator, which allows parallel simulations using clusters and supercomputers. With our parallel simulator, the elapsed time of full-field simulations with millions of grid blocks can be reduced from days to hours or minutes, and even for a much larger model with hundreds of millions ofgrid blocks, the simulations can be finished in practical time. In order to guarantee the computational efficiency and the parallel scalability, an inexact Newton method is applied and a new CPR (Constrained Pressure Residual)-type preconditioner is designed. In this paper, a SPE10-based polymer flooding case is tested, the performance of nonlinear and linear solvers are robust, and an encouraging parallel scalability is obtained. The second case is a full-field polymer flooding case, for which a coarse grid model anda fine grid model are both used. The results show the differences of the oil production rate and the water cut, which illustrates the accuracy of fine-grid simulations. For the fine grid model, up to 1024 CPU cores are employed, and an excellent speedup is achieved.

This poster presents a part-task haptic simulator for ophthalmic surgical training developed for the MicrovisTouch simulation platform. This ophthalmic surgical simulator provides both realistic 3D visualization and haptic feedback. Trainees are able to learn interacting with a physics-based dynamic virtual eye model and receiving tactile feedback by handling virtual instruments while simulating a series of surgical tasks and exercises, including micro-dexterity and corneal incision. A pilot study was conducted on the micro-dexterity to measure its effectiveness to evaluate trainees' skills performing precise instrument motion required for ophthalmic surgical procedures.

Superheating process in thermal power plant is a two-stage series system. In this paper, model predictive control strategy based on prediction error feedforward is proposed for superheating process. In this series system, the prediction error of the former unit is the estimation of unmeasured disturbance that will further affect the subsequent unit. Consequently, this prediction error can be constructed as an external measured feedforward variable for MPC design of the subsequent unit. The model identification method for this external feedforward variable is also presented. Simulation results indicate that the model prediction accuracy of steam temperature has been improved effectively. This method can enhance the control performance of disturbance rejection for the series system.

For low order process with large time delay, a kind of optimal PI controller tuning method is proposed based on generalized Hermite-Biehler theorem and Genetic Algorithm. Firstly, the calculation method of parameter stable region of PI controller that can stabilize a low order process with time delay is proposed by using the generalized Hermite-Biehler theorem. Then the optimum PI controller parameters are obtained within this region based on ITAE criterion and genetic algorithm. A PI controller tuning formula is finally obtained by nonlinear fitting of optimization results, which has the capability to cover the low order process with normalized time delays up to 100. Monte-Carlo stochastic experiment on robust performance indicates that the proposed PI controller tuning method has good performance robustness when parameter uncertainty occurs compared with other four PI tuning methods.

Rheumatoid arthritis (RA) is an autoimmune disease that results in a chronic, systemic inflammatory disorder that may affect many tissues and organs, but principally attacks flexible (synovial) joints. Traditional Chinese medicine (TCM) treats RA with herbal formulae according to different syndrome/pattern classification. One important syndrome/pattern in RA is Feng-Han-Shi-Bi. Targeting this syndrome, Juan-Bi-Tang is the basic herbal formula adopted in clinical practises with reliable therapeutic effect. However, the mechanism of Juan-Bi-Tang is still not clear. In this study, we explored the molecular mechanism of Juan-Bi-Tang with in silico analysis on its 11 herbal medicines' chemical compounds, target proteins, protein-protein interaction, and pathway. As a result, the molecular mechanism of Juan-Bi-Tang for Feng-Han-Shi-Bi syndrome in RA covers a variety of biological processes including immune system, signal transduction, metabolism, disease etc. Most of them are relevant to RA which indicate actual therapeutic effect of TCM clinical practises.

In order to meet the quality requirement of primary frequency regulation and AGC (Automatic Generation Control) for power plants in China Southern Power Grid “two rules”, the paper designs a AGC and primary frequency regulation monitoring and management system for power plant by analyzing the system requirements and combining with the actual situation of the power plant. Based on data acquisition and processing module and real-time database, the system has realized many functions such as automatic collecting, calculating and dealing with the data of the AGC and primary frequency regulation, giving a warning and analyzing the reason when the AGC and primary frequency regulation service are disqualification. After a period of trial running, it shows that the monitoring and management system can improve the quality of the AGC and primary frequency regulation services provided by the power plant. At the same time, the system can also provide guarantee for the safety, stability and economy of the generators.

In order to accelerate the mapping of Distributed Virtual Resource Block (DVRB) to Distributed Physical Resource Block (DPRB), a low computational complexity realization algorithm is proposed. It calculates the sorted index of PRBs directly, and the DPRB index can be got by cascading mapping module results of the plurality of VRBs. Its implementation is simple and easy to realize in the TMS320C64xDSP. The running results show that the proposed algorithm can save lots of simulation time. Meanwhile, the feasibility and effectiveness of this betterment method have been verified. The program has been applied to the development of TD-LTE RF conformance testing system.

The development of sensor nodes in wireless sensor network is usually not regular. It makes sense when the sensed information of sensor must contain the location of nodes. Therefore, the positioning of the sensor nodes becomes an important issue. This paper uses RSSI value of reference nodes to implement positioning system in an indoor parking lot. It overcomes the effects of RSSI drift lead to poor positioning accuracy. The experimental development of the CC2430/CC2431 wireless positioning system is used in the parking lot. The experimental results show that the method of the proposed algorithm can effectively reduce the positioning error caused due to the RSSI drift effect.

In TD-LTE (Time Division-Long Term Evolution) system, Control Channel Element (CCE), which transmits control information, is the resource element mapping unit of Physical Downlink Control Channel (PDCCH). It is used by uplink and downlink schedulers. Rational utilization of CCE resources and choose appropriate aggregation of one or several consecutive CCEs to transmit Downlink Control Information (DCI) have significant impact on success ratio of User Equipment (UE) blind detection and system performance. In this paper, one modified method which is based on reported Channel Quality Indicator (CQI) by UE to make selection of CCE aggregation is proposed. Its implementation complexity is very low. The simulation results show the modified method improves the success ratio of UE blind detection and the CCE resources utilization. At the same time, the feasibility and effectiveness of this modified method have been verified.

Statistical performance monitoring aims at improving process operation by distinguishing abnormal process conditions from common cause variations. However, for some complicated cases in industrial process, because of the nonlinear correlations between process variables, conventional linear statistical methods often have poor ability for monitoring these processes. In this paper, a novel nonlinear on-line performance monitoring and fault identification method based on kernel discriminant analysis (kernel GDA) was proposed. The basic idea of this method is to first map data in the original space into high-dimensional feature space via nonlinear kernel function and then extracts the optimal Fisher feature vector and discriminant vector to perform process monitoring. If faults occurred, it uses the similar degree between the present discriminant vector and the optimal vector of fault in historical dataset to diagnosis. The proposed method can effectively capture nonlinear relationship in process variables. It was evaluated by the application to the CSTR process and its effectiveness was demonstrated.

Tank situation display system is a basic component of the tank integrated simulator. This paper describes the design process of virtual instrument for tank integrated simulator based on LabWindows. The hardware architecture and diagrams of system are introduced first. The software module architecture and design development process are given later. The research of several key issues is focused on, such as signal collection, communication in TCP model, control of operation load system as well as parameters tests in automatic condition. And finally practical interface images of tank virtual instruments which give a powerful technical support for detection and maintenance are achieved so that met the demands for training purpose.

Architecture technology has become the important method in the top-level design of weapons and equipment system. Based on the research of existing architecture framework and supporting technologies, DoD has developed the comprehensive framework for architecture and the development planning for related fields, and energetically promotes the development and application of the architecture. The latest research result is "Department of Defense Architecture Framework Version 2.0". The development of Department of Defense Architecture Framework (DoDAF) and main versions are described. Otherwise, the prime changes and advantages of "DoDAF2.0" are analyzed. The PLA should use the experience of DoD for reference, and actively conduct research on the architecture framework and the related supporting technologies suitable for PLA.

Nowadays, Distributed Control Systems (DCS) are playing increasingly important roles in industrial process. Accordingly, the performance evaluation of DCS is attracting more and more attention. This paper focuses on the DCS real-time testing project in term of application. The test technique for the real-time performance of analog and digital signals, and the test technique for processing period of controllers and that for function test of SOE are individually proposed. Moreover, the test result of DCS in term of real-time performance will be presented. The proposed real-time test technique is especially useful for the site performance evaluation of DCS in industrial field.

Waveform inversion of crosshole data based on acoustic wave equation is investigated in this paper. The inversion is set as an optimization problem with the Lagrange multiplier function. The Tikhonov regularization is adopted in objective function. The gradient of the objective function is generated by solving the adjoint equation of the forward problem with the finite difference method. In order to prevent the quasi-Newton method to trap in local optimal value, a suitable initial model is required. For comparison, the traveltime inversion or tomography is implemented which provide smooth features of the velocity model. Numerical computations are given which show the good ability of the new velocity inversion method.

This paper focuses on that the surface wave on antennas has great effects on its performance in Dedicated Short Range Communication (DSRC) system. A slot coupled right-hand circularly polarized (RHCP) patch antenna operated at 5.8GHz is presented, on which the coupling slots optimized to not only remarkably reduce the surface wave but also reduce the radiation on back. The simulated and measured return losses show that the proposed antenna has the bandwidth of about 2GHz.

A systematic method to test human skin based on the high performance microconverter ADuC841 is presented in this paper. The system includes the design of the sensors with the function of skin information collection, the realization of liquid crystal display (LCD) and the function of print. This paper focuses on the hardware circuit design, it also introduces the relevant software design. Because of its characteristic of low cost, flexible function and conveniently to carry, it has the widespread application prospect.

In this paper, we proposed a Structured Multi-Agent (SMA) model for Massive Multi-player Online Game based on the peer to peer overlay (P2P MMOG). This model first defined some important theories relevant to P2P MMOG. Then, three core algorithms including node joining algorithm, neighbor discovery algorithm and node cross domain algorithm were implemented. In order to ensure the consistency of the states of resources, we required that event should be handled by the agent who had the resource processing license. Through analyzing the response speed, scalability, load and robustness of SMA, fine performance is indicated.

The USB interface has the advantages of plug and play, convenient installation, high-speed transmission and so on. An information monitoring device between the On Board Unit (OBU) and the Rode Side Unit (RSU) used in the Dedicated Short Range Communication (DSRC) in the Electronic Toll Collection (ETC) will be realized in this design. This article focuses on the design of the hardware circuit and the firmware for the USB interface chip, and the realization of the computer applications based on the USB interface will also be introduced briefly.