Framework to Model User Request Access Patterns in the World Wide Web

In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pages. Our framework model consists of three sub-models: one for user file access, one for web pages, and one for storage servers. Web pages are assumed to consist of different types and sizes of objects, which are characterized using several categories: articles, media, and mosaics. The model is implemented with a discrete event simulation and then used to investigate the performance of our system over a variety of parameters in our model. Our performance measure of choice is mean response time and by varying the composition of web pages through our categories, we find that our framework model is able to capture a wide range of conditions that serve as a basis for generating a variety of user request patterns. In addition, we are able to establish a set of parameters that can be used as base cases. One of the goals of this research is for the framework model to be general enough that the parameters can be varied such that it can serve as input for investigating other distributed applications that require the generation of user request access patterns.

Dynamic Signature Verification Using Pattern Recognition

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

Linking a farmer crop selection model(FCS) with an agronomic model(EPIC) to simulate cropping pattern in Northeast China

In this paper, authors established a farmer crop selection model（FCS） for the three provinces of Liaoning, Jilin and Heilongjiang of the Northeast China. With linking to the environmental policy integrated climate model（EPIC）, the simulated results of FCS model for maize, rice and soybean were spatialized with 1 km×1 km grids to obtain cropping pattern. The reference map of spatial distribution for the three staple crops acquired by remote sensing imageries was applied to validate the simulated cropping pattern. The results showed that（1） the total simulation accuracy for the study area was 78.62%, which proved simulation method was applicable and feasible;（2） simulation accuracy for Jilin Province was the highest among the three provinces with a rate of 82.45% since its simple cropping system and not complex topography;（3） simulation accuracy for maize was the best among the three staple crops with a ratio of 81.14% because the study area is very suitable for maize growth. We hope this study could provide the reference for cropping pattern forecasting and decision-making.

Derivation of Simplified Models of Plan View Pattern Control Function for Plate Mill

The plan view pattern control theoretical models were simplified. Under the condition of constant volume for the plan view pattern compensation, the relation between the thickness and the length can be simplified to the linearity in the segment for the plan view pattern control function. The compensation volume can be dispersed for easy calculation. By comparing the model calculation result with the actual result, it was concluded that the simplified model can be used for the online control process.

Evaluating the Formation Mechanisms of the Equatorial Pacific SST Warming Pattern in CMIP5 Models

Based on the historical and RCP8.5 runs of the multi-model ensemble of 32 models participating in CMIP5, the present study evaluates the formation mechanisms for the patterns of changes in equatorial Pacific SST under global warming. Two features with complex formation processes, the zonal E1 Nifio-like pattern and the meridional equatorial peak warm- ing （EPW）, are investigated. The climatological evaporation is the main contributor to the E1 Nifio-like pattern, while the ocean dynamical thermostat effect plays a comparable negative role. The cloud-shortwave-radiation-SST feedback and the weakened Walker circulation play a small positive role in the E1 Nifio-like pattern. The processes associated with ocean dynamics are confined to the equator. The climatological evaporation is also the dominant contributor to the EPW pattern, as suggested in previous studies. However, the effects of some processes are inconsistent with previous studies. For example, changes in the zonal heat advection due to the weakened Walker circulation have a remarkable positive contribution to the EPW pattern, and changes in the shortwave radiation play a negative role in the EPW pattern.

Modeling study on the flow patterns of gas–liquid flow for fast decarburization during the RH process

A water model and a high-speed video camera were utilized in the 300-t RH equipment to study the effect of steel flow patterns in a vacuum chamber on fast decarburization and a superior flow-pattern map was obtained during the practical RH process. There are three flow patterns with different bubbling characteristics and steel surface states in the vacuum chamber： boiling pattern（BP）, transition pattern（TP）, and wave pattern（WP）. The effect of the liquid-steel level and the residence time of the steel in the chamber on flow patterns and decarburization reaction were investigated, respectively. The liquid-steel level significantly affected the flow-pattern transition from BP to WP, and the residence time and reaction area were crucial to evaluate the whole decarburization process rather than the circulation flow rate and mixing time. A superior flow-pattern map during the practical RH process showed that the steel flow pattern changed from BP to TP quickly, and then remained as TP until the end of decarburization.

Turing pattern selection in a reaction-diffusion epidemic model

We present Turing pattern selection in a reaction-diffusion epidemic model under zero-flux boundary conditions. The value of this study is twofold. First, it establishes the amplitude equations for the excited modes, which determines the stability of amplitudes towards uniform and inhomogeneous perturbations. Second, it illustrates all five categories of Turing patterns close to the onset of Turing bifurcation via numerical simulations which indicates that the model dynamics exhibits complex pattern replication： on increasing the control parameter v, the sequence ＂H0 hexagons → H0-hexagon-stripe mixtures →stripes → Hπ-hexagon-stripe mixtures → Hπ hexagons＂ is observed. This may enrich the pattern dynamics in a diffusive epidemic model.

Technical Note on a Track-pattern-based Model for Predicting Seasonal Tropical Cyclone Activity over the Western North Pacific

Recently, the National Typhoon Center （NTC） at the Korea Meteorological Administration launched a track-pattern-based model that predicts the horizontal distribution of tropical cyclone （TC） track density from June to October. This model is the first approach to target seasonal TC track clusters covering the entire western North Pacific （WNP） basin, and may represent a milestone for seasonal TC forecasting, using a simple statistical method that can be applied at weather operation centers. In this note, we describe the procedure of the track-pattern-based model with brief technical background to provide practical information on the use and operation of the model. The model comprises three major steps. First, long-term data of WNP TC tracks reveal seven climatological track clusters. Second, the TC counts for each cluster are predicted using a hybrid statistical-dynamical method, using the seasonal prediction of large-scale environments. Third, the final forecast map of track density is constructed by merging the spatial probabilities of the seven clusters and applying necessary bias corrections. Although the model is developed to issue the seasonal forecast in mid-May, it can be applied to alternative dates and target seasons following the procedure described in this note. Work continues on establishing an automatic system for this model at the NTC.

Spatial Pattern Induced by Cross-Diffusion in a Chemostat Model with Maintenance Energy

A chemostat model with maintenance energy and crossdiffusion is considered,and the formation of patterns is caused by the cross-diffusion. First, through linear stability analysis, the necessary conditions for the formation of the spatial patterns are given. Then numerical simulations by changing the values of crossdiffusions in the unstable domain are performed. The results showthat the cross-diffusion coefficient plays an important role in the formation of the pattern, and the different values of the crossdiffusion coefficients may lead to different types of pattern formation.

Transformation from computation independent model to platform independent model with pattern

Model driven architecture(MDA) is an evolutionary step in software development.Model transformation forms a key part of MDA.The transformation from computation independent model(CIM) to platform independent model(PIM) is the first step of the transformation.This paper proposes an approach for this transformation with pattern.In this approach, we take advantage of"reuse"from various standpoints.Feature model is used to describe the requirement of the application.This can help us bring"reuse"into effect at requirement level.Moreover we use pattern to transform CIM to PIM.This can help us bring"reuse"into effect at development level.Meanwhile, pattern was divided into four hierarchies.Different hierarchies of pattern are used to help us utilize reuse at different phase of development.From another standpoint, feature model describes the problem of a domain while pattern describe the problem across domains.This can help us reuse the element in and across domains.Finally, the detailed process of the transformation is given.

Sandbox Modeling of the Fault-increment Pattern in Extensional Basins

Three series of sandbox modeling experiments were performed to study the fault-increment pattern in extensional basins. Experimental results showed that the tectonic action mode of boundaries and the shape of major boundary faults control the formation and evolution of faults in extensional basins. In the process of extensional deformation, the increase in the number and length of faults was episodic, and every ＇episode＇ experienced three periods, strain-accumulation period, quick fault-increment period and strain-adjustment period. The more complex the shape of the boundary fault, the higher the strain increment each ＇episode＇ experienced. Different extensional modes resulted in different fault-increment patterns. The horizontal detachment extensional mode has the ＇linear＇ style of fault-increment pattern, while the extensional mode controlled by a listric fault has the ＇stepwise＇ style of fault-increment pattern, and the extensional mode controlled by a ramp-flat boundary fault has the ＇stepwise-linear＇ style of fault-increment pattern. These fault-increment patterns given above could provide a theoretical method of fault interpretation and fracture prediction in extensional basins.

Determinants of Response Pattern of Cochlear Nucleus Neuron:a Study Based on the Model

Objective: Neurons in the cochlear nucleus show different response patterns to the short tone bursts. Because of the limitations of animal experiments, it is hard to explore the principle. Therefore, using a model to simulate CN neurons will be a feasible way. Methods: Based on the initial model mentioned in the previous study, we proposed an improved CN model in MATLAB R2012b. Results: By modifying the parameters of the model we found the interchanges among "primary-like", "chopper",and "onset" response patterns. Furthermore, we simulated the "pauser" response pattern by adding an extra input in our model. Conclusion: The results indicate that the synaptic integrations and the input modes can give rise to different characteristics of CN neurons, which eventually determine the response patterns of CN neurons.

Using autologistic spatial models to simulate the distribution of land-use patterns in Zhangjiajie, Hunan Province

Nowadays, spatial simulation on land use patterns is one of the key contents of LUCC. Modeling is an important tool for simulating land use patterns due to its ability to integrate measurements of changes in land cover and the associated drivers. The conventional regression model can only analyze the correlation between land use types and driving factors but cannot depict the spatial autocorrelation characteristics. Land uses in Yongding County, which is located in the typical karst mountain areas in northwestern Hunan province, were investigated by means of modeling the spatial autocorrelation of land use types with the purpose of deriving better spatial land use patterns on the basis of terrain characteristics and infrastructural conditions. Through incorporating components describing the spatial autocorrelation into a conventional logistic model, we constructed a regression model （Autologistic model）, and used this model to simulate and analyze the spatial land use patterns in Yongding County. According to the comparison with the conventional logistic model without considering the spatial autocorrelation, this model showed better goodness and higher accuracy of fitting. The distribution of arable land, wood land, built-up land and unused land yielded areas under the ROC curves （AUC） was improved to 0.893, 0.940, 0.907 and 0.863 respectively with the autologistic model. It is argued that the improved model based on autologistic method was reasonable to a certain extent. Meanwhile, these analysis results could provide valuable information for modeling future land use change scenarios with actual conditions of local and regional land use, and the probability maps of land use types obtained from this study could also support government decision-making on land use management for Yongding County and other similar areas.

Studies on Models,Patterns and Requirements of Digestible Amino Acids for Layers by Nitrogen Metabolism

The nitrogen (N) metabolic experiments were made to estimate separately amino acid requirements of 4348 weeks old layers for maintenance, for protein accretion to estabolish models to estimate digestible amino acid requirements. The regression relationship of nitrogen retention vs amino acid intake was estimated for each amino acid by giving, at rate of N intake of 091, 052, 015 and 0007gkg-1 body-weight (W075) per d, the semi-synthetic diets was made specially deficient in one amino acid. From the regression coefficients, it was calculated that, for the accretion of 1 g protein, the dietary digestible amino acid requirements were (mg) Thr 631, Val 1004, Met 399, Ile 886, Leu 1143, Phe 632, Lys 870, His 205, Arg 879, Trp 214, Met+Cys 776, and Phe+Tyr 1143. Daily amino acid requirements for N equilibrium were estimated to be (mgkg-1W075 per day) Thr 506, Val 747, Met 303, ILe 667 Leu 814, Phe 448, Lys 605 His 147, Arg 739 ,Trp 173, Met+Cys 586, and Phe+Tyr 839 The dietary degestible amino acid patterns for protein accretion and N equilibrium were also proposed. The models of estimating digestible amino acid requirements for the different productions were developed.

A New Computer Aided Volleyball Training System Based on Pattern Recognition Model

This paper proposes the novel computer aided volleyball training system based on pattern recognition model. In general the quality of volleyball training at the same time, we should strengthen coordination, mobile power, quality training, strengthen mobile exercises, moving fast and flexible to overcome the sudden fall of soft volleyball on the defensive and pilling adverse, such practice can be used for mobile.Strengthen the strength training, enhance the strength of the upper and lower limbs, improve the team＇ s bounce and batting speed is conducive to compete for the advantage of the Internet, better undermine the defense. With the help of the proposed system, the training process will be more effective, and the performance will be verified in the later discussions.

New conditions for pattern solutions of a Brusselator model

This paper is devoted to establishing a critical value of the concentration of one intermediary reactant which determines whether pattern solutions of a class of Brusselator models exist or not.We introduce a new method to compute the degree index of the related linear operator so that the obtained sufficient conditions are easier to verify than those in the known references.The proofs mainly rely on Leray-Schauder degree theory,implicit function theorem and analytical techniques.

Three-dimensional Simulation and Pattern Making of Collar Using Geometric Model

An algorithm is presented for computationally simulating collars and drafting patterns. The collar shape was modeled by three-dimensional Bezier patch. Changing the position of consol points of the patch can interactively control the collar shape. Using triangular surface developing method, patterns of various styles of stand collar and separating collar were drafted. As the subsystem of 3D apparel design system, an interactive collar design system is constructed. To inspect the practical usage, we reproduced the collars using these patterns drafted by the interactive collar design system.Comparing with simulated collars, the system demonstrated well and we found it is more reliable and accurate than the method of handwork.

Various patterns in dielectric barrier glow discharges simulated by a dynamic model

In this paper, various patterns of dielectric barrier glow discharge simulated by a phenomenological dynamic model are reported.The model is constructed based on the basic dynamic process of dielectric barrier glow discharge and involves the voltage-transfer characteristic as well as the lateral inhibition effect.In simulations, different driving voltage profiles are applied to achieve one or two pulsed discharges in each half-period and the corresponding pattern evolution is investigated.The final stable patterns driven by a rectangular wave voltage organize simply as stationary striations or hexagonal lattices.The patterns driven by a multi-step wave appear to be much more complicated, with complementary striations,concentric rings and square superlattices observed.The evolutions of these patterns from the initial uniform state are described and it is found that the spreading of the inhibition effect plays a key role in these evolutions.The numerical simulations in the present work are in excellent accordance with previous experiments and fluid modeling.This dynamic model proves to be a convenient and promising approach to reproducing different pattern structures and pattern evolutions in dielectric barrier glow discharge systems.

A CASCADED MODEL OF NEURAL NETWORK FOR PATTERN RECOGNITION

A cascaded model of neural network and its learning algorithm suitable for opticalimplementation are proposed.Computer simulations have shown that this model may successfullybe applied to an error-tolerance pattern recognitions of multiple 3-D targets with arbitrary spatialorientations.

Integrating ecosystem services evaluation and landscape pattern analysis into urban planning based on scenario prediction and regression model

Urban planning has become a widely concern for minimizing the negative effects of urban expansion on terrestrial ecosystems. We developed an interdisciplinary modeling framework to evaluate the effectiveness and shortcomings of urban expansion management strategies. A three-step method was applied to Yinchuan Plain in the northwestern of China, including(1)analyzing the relationship between landscape pattern and ecosystem service values through mathematical statistics;(2) predicting landscape pattern and ecosystem services change under different scenarios based on cellular automaton model(SLEUTH-3r model); and(3) designing and validating optimized scenario through integrating historical analysis experiments and future multi-comparison suggestions. Results have suggested that landscape composition and configuration can significantly affect regional ecosystem service values, especially the connectivity and shape of landscape. Compact urban growth policy and medium environment protection policy are the appropriate setting for urban expansion plan. Optimization validation of the combined designed scenario implied the reliability of this method. Our results highlighted the significance of integrating application of landscape pattern analysis, ecosystem service value evaluation,model simulation and multi-scenario prediction in urban planning.