Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

General mapping of one-dimensional non-Hermitian mosaic models to non-mosaic counterparts:Mobility edges and Lyapunov exponents

We establish a general mapping from one-dimensional non-Hermitian mosaic models to their non-mosaic counterparts.This mapping can give rise to mobility edges and even Lyapunov exponents in the mosaic models if critical points of localization or Lyapunov exponents of localized states in the corresponding non-mosaic models have already been analytically solved.To demonstrate the validity of this mapping,we apply it to two non-Hermitian localization models:an Aubry-Andre-like model with nonreciprocal hopping and complex quasiperiodic potentials,and the Ganeshan-Pixley-Das Sarma model with nonreciprocal hopping.We successfully obtain the mobility edges and Lyapunov exponents in their mosaic models.This general mapping may catalyze further studies on mobility edges,Lyapunov exponents,and other significant quantities pertaining to localization in non-Hermitian mosaic models.

Modelling and mapping soil erosion potential in China

Soil erosion is an important environmental threat in China.However,quantitative estimates of soil erosion in China have rarely been reported in the literature.In this study,soil loss potential in China was estimated by integrating satellite images,field samples,and ground observations based on the Revised Universal Soil Loss Equation(RUSLE).The rainfall erosivity factor was estimated from merged rainfall data using Collocated CoKriging(ColCOK)and downscaled by geographically weighted regression(GWR).The Random Forest(RF)regression approach was used as a tool for understanding and predicting the relationship between the soil erodibility factor and a set of environment factors.Our results show that the average erosion rate in China is 1.44 t ha^(–1) yr^(–1).More than 60%of the territory in China is influenced by soil erosion limitedly,with an average potential erosion rate less than 0.1 t ha^(–1) yr^(–1).Other unused land and other forested woodlands showed the highest erosion risk.Our estimates are comparable to those of runoff plot studies.Our results provide a useful tool for soil loss assessments and ecological environment protections.

Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

The homotopic mapping method for sea-air oscillator model of interdecadal climate fluctuations

The E1 Nifio/La Nifia Southern Oscillation （ENSO） is an interannual phenomenon involved in the tropical Pacific ocean-atmosphere interactions. In this paper, a coupled system of sea-air oscillator model is studied. The aim is to create an asymptotic solving method of nonlinear equation for the ENSO model. And based on a class of oscillators of ENSO model, employing the method of homotopic mapping, the approximate solution of corresponding problem is studied. It is proven from the results that the homotopic method can be used for analysing the sea surface temperature anomaly in the equatorial eastern Pacific and the thermocline depth anomaly of the atmosphere-ocean oscillation for ENSO model.

XML-based integration data model and schema mappingin multidatabase systems

Multidatabase systems are designed to achieve schema integration and data interoperation among distributed and heterogeneous database systems. But data model heterogeneity and schema heterogeneity make this a challenging task. A multidatabase common data model is firstly introduced based on XML, named XML-based Integration Data Model (XIDM), which is suitable for integrating different types of schemas. Then an approach of schema mappings based on XIDM in multidatabase systems has been presented. The mappings include global mappings, dealing with horizontal and vertical partitioning between global schemas and export schemas, and local mappings, processing the transformation between export schemas and local schemas. Finally, the illustration and implementation of schema mappings in a multidatabase prototype - Panorama system are also discussed. The implementation results demonstrate that the XIDM is an efficient model for managing multiple heterogeneous data sources and the approaches of schema mapping based on XIDM behave very well when integrating relational, object-oriented database systems and other file systems.

Implementation Framework and Validation of Cluster-Nuclei Based Channel Model Using Environmental Mapping for 6G Communication Systems

With the research of the upcoming sixth generation(6 G) systems, new technologies will require wider bandwidth, larger scale antenna arrays and more diverse wireless communication scenarios on the future channel modeling. Considering channel model is prerequisite for system design and performance evaluation of 6 G technologies, we face a challenging task: how to accurately and efficiently model 6 G channel for various scenarios? This paper tries to answer it. Firstly, the features of cluster-nuclei(CN) and principle of cluster-nuclei based channel model(CNCM) are introduced. Then, a novel modeling framework is proposed to implement CNCM,which consists four steps: propagation environment reconstruction, cluster-nuclei identification, multipath parameters generation, and channel coefficients generation. Three-dimensional environment with material information is utilized to map CN with scatterers in the propagation pathway. CN are identified by geometrical and electric field calculation based on environmental mapping, and multipath components within CN are calculated by statistical characteristics of angle, power and delay domains. Finally, we present a three-level verification structure to investigate the accuracy and complexity of channel modeling comprehensively. Simulation results reveal that CNCM can perform higher accuracy than geometrybased stochastic model while lower complexity compared with ray-tracing model for practical propagation environment.

GIS and ANN model for landslide susceptibility mapping

Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage Une are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.

Mapping the potential distribution suitability of 16 tree species under climate change in northeastern China using Maxent modelling

Knowledge on the potential suitability of tree species to the site is very important for forest management planning.Natural forest distribution provides a good reference for afforestation and forest restoration.In this study,we developed species distribution model(SDM)for 16 major tree species with 2,825 permanent sample plots with natural origin from Chinese National Forest Inventory data collected in Jilin Province using the Maxent model.Three types of environmental factors including bioclimate,soil and topography with a total of 33 variables were tested as the input.The values of area under the curve(AUC,one of the receiver operating characteristics of the Maxent model)in the training and test datasets were between 0.784 and 0.968,indicating that the prediction results were quite reliable.The environmental factors affecting the distribution of species were ranked in terms of their importance to the species distribution.Generally,the climatic factors had the greatest contribution,which included mean diurnal range,annual mean temperature,temperature annual range,and iosthermality.But the main environmental factors varied with tree species.Distribution suitability maps under current(1950-2000)and future climate scenarios(CCSM4-RCP 2.6 and RCP 6.0 during 2050)were produced for 16 major tree species in Jilin Province using the model developed.The predicted current and future ranges of habitat suitability of the 16 tree species are likely to be positively and negatively affected by future climate.Seven tree species were found to benefit from future climate including B etula costata,Fraxinus mandshurica,Juglans mandshurica,Phellodendron amurense,Populus ussuriensis,Quercus mongolica and Ulmus pumila;five tree species will experience decline in their suitable habitat including B.platyphylla,Tilia mongolica,Picea asperata,Pinus sylvestris,Pinus koraiensis;and four(Salix koreensis,Abies fabri,Pinus densiflora and Larix olgensis)showed the inconsistency under RCP 2.6 and RCP 6.0 scenarios.The maps of the habitat suitability can be used as a basis for afforestation and forest restoration in northeastern China.The SDMs could be a potential tool for forest management planning.

MODELING METHOD FOR PRODUCT STRUCTURE MAPPING BASED ON REVERSE SOLVING OF LOCUS AND MOTION

Aiming at the problem of structure design in reverse-design of mechanism, a structure mapping method based on reverse solving of locus and motion （RSLM） is presented. The mechanism scheme meeting the requirements of geometric and structural features is obtained through RSLM. The element instance subsets related to component are established based on the element type mapping, pair structure type mapping and design knowledge mapping between components and elements layer by layer. The assembly position mapping of elements is established based on the topological structure information of mechanism scheme, and the product modeling of structure mapping is realized. The algorithm program and prototype system of product structure mapping based on RSLM are developed. Application samples show that the method implements the integration of scheme design, assembly design and structure design, and modeling for product structure mapping based on RSLM. The feasibility of assembly is analyzed in scheme design that contributes to reducing the design error, and raising the design efficiency and quality.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

HYPERSTATIC STRUCTURE MAPPING MODEL BUILDING AND OPTIMIZING DESIGN

Hyperstatic structure plane model being built by structural mechanics is studied. Space model precisely reflected in real stress of the structure is built by finite element method （FEM） analysis commerce software. Mapping model of complex structure system is set up, with convenient calculation just as in plane model and comprehensive information as in space model. Plane model and space model are calculated under the same working condition. Plane model modular construction inner force is considered as input data; Space model modular construction inner force is considered as output data. Thus specimen is built on input data and output dam. Character and affiliation are extracted through training specimen, with the employment of nonlinear mapping capability of the artificial neural network. Mapping model with interpolation and extrpolation is gained, laying the foundation for optimum design. The steel structure of high-layer parking system （SSHLPS） is calculated as an instance. A three-layer back-propagation （BP） net including one hidden layer is constructed with nine input nodes and eight output nodes for a five-layer SSHLPS. The three-layer structure optimization result through the mapping model interpolation contrasts with integrity re-analysis, and seven layers structure through the mapping model extrpulation contrasts with integrity re-analysis. Any layer SSHLPS among 1-8 can be calculated with much accuracy. Amount of calculation can also be reduced if it is appfied into the same topological structure, with reduced distortion and assured precision.

Extended Generalized Riccati Equation Mapping for Thermal Traveling-Wave Distribution in Biological Tissues through a Bio-Heat Transfer Model with Linear/Quadratic Temperature-Dependent Blood Perfusion

Analytical thermal traveling-wave distribution in biological tissues through a bio-heat transfer (BHT) model with linear/quadratic temperature-dependent blood perfusion is discussed in this paper. Using the extended generalized Riccati equation mapping method, we find analytical traveling wave solutions of the considered BHT equation. All the travelling wave solutions obtained have been used to explicitly investigate the effect of linear and quadratic coefficients of temperature dependence on temperature distribution in tissues. We found that the parameter of the nonlinear superposition formula for Riccati can be used to control the temperature of living tissues. Our results prove that the extended generalized Riccati equation mapping method is a powerful tool for investigating thermal traveling-wave distribution in biological tissues.

Landslide susceptibility mapping using Genetic Algorithm for the Rule Set Production(GARP) model

Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors(LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index(TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production(GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve(AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps(LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages.

Establishment and application of service mapping model for proactive remanufacturing impeller

Since the complex impeller structure and the difficult remanufacturing process may easily cause advance remanufacturing or excessive use,an optimized design method of impeller and service mapping model was presented for its proactive remanufacturing with setting up to explore the best remanufacturing time point in this work.Considering a certain model of long distance pipeline compressor impeller with the Basquin equation and the design method of impeller,the mathematical relationship between the changes of structure and life of the impeller was established.And the service mapping model between the structure and life was set up and simulated by ANSYS software.Thus,the service mapping model was applied to feedback the original design for proactive remanufacturing.In this work,the best proactive remanufacturing time point of impeller was analyzed with the service mapping model,and the structural parameter values could be optimized at this time point.Meanwhile,in the results of this simulation,it proves that the impeller under this optimization performance could satisfy the impeller operating demands.Therefore,comparing with the traditional optimization design method,the remanufacturing optimized design based on the service mapping model is feasible in proactive remanufacturing for sustainable development.

NORMALIZED DIFFERENCE SNOW INDEX SIMULATION FOR SNOW-COVER MAPPING IN FOREST BY GEOSAIL MODEL

The snow-cover mapping in forest area is always one of the difficult points for optical satellite remote sensing. To investigate reflectance variability and to improve the mapping of snow in forest area, GeoSail model was used to simulate the reflectance of a snow-covered forest. Using this model, the effects of varying canopy density, solar illumination and view geometry on the performance of the MODIS (Moderate-resolution Imaging Spectroradiometer) snow-cover mapping algorithm were investigated. The relationship between NDSI (Normalized Difference Snow Index), NDVI (Normalized Difference Vegetation Index) and snow fraction was discussed in detail. Results indicated that the weak performance would be achieved if fixed criteria were used for different regions especially in the complicated land cover components. Finally, some suggestions to MODIS SNOWMAP algorithm were put forward to improve snow mapping precision in forest area based on the simulation, for example, new criteria should be used in coniferous forest, that is, NDSI greater than 0.3 and NDVI greater than zero. Otherwise, a threshold on view zenith angle may be used in the criteria such as 45°.

Block and parallel modelling of broad domain nonlinear continuous mapping based on NN

The necessity of the use of the block and parallel modeling of the nonlinear continuous mappings with NN is firstly expounded quantitatively. Then, a practical approach for the block and parallel modeling of the nonlinear continuous mappings with NN is proposed. Finally, an example indicating that the method raised in this paper can be realized by suitable existed software is given. The results of the experiment of the model discussed on the 3-D Mexican straw hat indicate that the block and parallel modeling based on NN is more precise and faster in computation than the direct ones and it is obviously a concrete example and the development of the large-scale general model established by Tu Xuyan.

Feature Modeling and Variability Modeling Syntactic Notation Comparison and Mapping

Feature Model (FM) became an important role in Software Product Line Engineering (SPLE) field. Many approaches have been introduced since the original FM came up with Feature Oriented Domain Analysis (FODA) introduced by Kang in 1990. The main purpose of FM is used for commonality and variability analysis in domain engineering, to optimize the reusable aspect of software features or components. Cardinality-based Feature Model (CBFM) is one extension of original FM, which integrates several notations of other extensions. In CBFM, feature model defined as hierarchy of feature, with each of feature has a cardinality. The other notation to express variability within SPLE is Orthogonal Variability Model (OVM). At the other hand, OMG as standard organization makes an effort to build standard generic language to express the commonality and variability in SPL field, by initiate Common Variability Language (CVL). This paper reports the comparison and mapping of FODA, CBFM and OVM to CVL where need to be explored first to define meta model mapping of these several approaches. Furthermore, the comparison and mapping of those approaches are discussed in term of R3ST (read as “REST”) software feature model as the case study.

Bridging mapping and simulation modelling in the ecosystem service assessments of boreal forests:effects of bioenergy production on carbon dynamics

Background:Increasing the use of forest harvest residues for bioenergy production reduces greenhouse emissions from the use of fossil fuels.However,it may also reduce carbon stocks and habitats for deadwood dependent species.Consequently,simple tools for assessing the trade-offs of alternative management practices on forest dynamics and their services to people are needed.The objectives of this study were to combine mapping and simulation modelling to investigate the effects of forest management on ecosystem services related to carbon cycle in the case of bioenergy production;and to evaluate the suitability of this approach for assessing ecosystem services at the landscape level.Stand level simulations of forest growth and carbon budget were combined with extensive multi-source forest inventory data across a southern boreal landscape in Finland.Stochastic changes in the stand age class distribution over the study region were simulated to mimic variation in management regimes.Results:The mapping framework produced reasonable estimates of the effects of forest management on a set of key ecosystem service indicators:the annual carbon stocks and fluxes of forest biomass and soil,timber and energy-wood production and the coarse woody litter production over a simulation period 2012–2100.Regular harvesting,affecting the stand age class distribution,was a key driver of the carbon stock changes at a landscape level.Extracting forest harvest residues in the final felling caused carbon loss from litter and soil,particularly with combined aboveground residue and stump harvesting.It also reduced the annual coarse woody litter production,demonstrating negative impacts on deadwood abundance and,consequently,forest biodiversity.Conclusions:The refined mapping framework was suitable for assessing ecosystem services at the landscape level.The procedure contributes to bridging the gap between ecosystem service mapping and detailed simulation modelling in boreal forests.It allows for visualizing ecosystem services as fine resolution maps to support sustainable land use planning.In the future,more detailed models and a wider variety of ecosystem service indicators could be added to develop the method.

Stochastic Binary Neural Networks for Qualitatively Robust Predictive Model Mapping

We consider qualitatively robust predictive mappings of stochastic environmental models, where protection against outlier data is incorporated. We utilize digital representations of the models and deploy stochastic binary neural networks that are pre-trained to produce such mappings. The pre-training is implemented by a back propagating supervised learning algorithm which converges almost surely to the probabilities induced by the environment, under general ergodicity conditions.