Local Bifurcation Analysis of a Delayed Fractional-order Dynamic Model of Dual Congestion Control Algorithms

In this paper, we propose a delayed fractional-order congestion control model which is more accurate than the original integer-order model when depicting the dual congestion control algorithms. The presence of fractional orders requires the use of suitable criteria which usually make the analytical work so harder. Based on the stability theorems on delayed fractionalorder differential equations, we study the issue of the stability and bifurcations for such a model by choosing the communication delay as the bifurcation parameter. By analyzing the associated characteristic equation, some explicit conditions for the local stability of the equilibrium are given for the delayed fractionalorder model of congestion control algorithms. Moreover, the Hopf bifurcation conditions for general delayed fractional-order systems are proposed. The existence of Hopf bifurcations at the equilibrium is established. The critical values of the delay are identified, where the Hopf bifurcations occur and a family of oscillations bifurcate from the equilibrium. Same as the delay,the fractional order normally plays an important role in the dynamics of delayed fractional-order systems. It is found that the critical value of Hopf bifurcations is crucially dependent on the fractional order. Finally, numerical simulations are carried out to illustrate the main results.

Multiple model tracking algorithms based on neural network and multiple process noise soft switching

A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are running in parallel.The neural network algorithm is used to modify the adaptive noise filtering algorithm based on the mean value and variance of the＂current＂statistical model for maneuvering targets, and then the multiple model tracking algorithm of the multiple processing switch is used to improve the precision of tracking maneuvering targets.The modified algorithm is proved to be effective by simulation.

Mathematical Modeling Algorithms for Creating New Materials with Desired Properties Using Nano-Hierarchical Structures

In the enormous and still poorly mastered gap between the macro level, where well developed continuum theories of continuous media and engineering methods of calculation and design operate, and atomic, subordinate to the laws of quantum mechanics, there is an extensive meso-hierarchical level of the structure of matter. At this level unprecedented previously products and technologies can be artificially created. Nano technology is a qualitatively new strategy in technology: it creates objects in exactly the opposite way—large objects are created from small ones [1]. We have developed a new method for modeling acoustic monitoring of a layered-block elastic medium with several inclusions of various physical and mechanical hierarchical structures [2]. An iterative process is developed for solving the direct problem for the case of three hierarchical inclusions of l, m, s-th ranks based on the use of 2D integro-differential equations. The degree of hierarchy of inclusions is determined by the values of their ranks, which may be different, while the first rank is associated with the atomic structure, the following ranks are associated with increasing geometric sizes, which contain inclusions of lower ranks and sizes. Hierarchical inclusions are located in different layers one above the other: the upper one is abnormally plastic, the second is abnormally elastic and the third is abnormally dense. The degree of filling with inclusions of each rank for all three hierarchical inclusions is different. Modeling is carried out from smaller sizes to large inclusions;as a result, it becomes possible to determine the necessary parameters of the formed material from acoustic monitoring data.

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently,many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties.Although statistical analysis is a common method for developing regression models,but still selection of suitable transformation of the independent variables in a regression model is diffcult.In this paper,a genetic algorithm(GA)has been employed as a heuristic search method for selection of best transformation of the independent variables(some index properties of rocks)in regression models for prediction of uniaxial compressive strength(UCS)and modulus of elasticity(E).Firstly,multiple linear regression(MLR)analysis was performed on a data set to establish predictive models.Then,two GA models were developed in which root mean squared error(RMSE)was defned as ftness function.Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

MODELING, VALIDATION AND OPTIMAL DESIGN OF THE CLAMPING FORCE CONTROL VALVE USED IN CONTINUOUSLY VARIABLE TRANSMISSION

Associated dynamic performance of the clamping force control valve used in continuously variable transmission （CVT） is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm （GA） with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.

A Class of Generalized Approximate Inverse Solvers for Unsymmetric Linear Systems of Irregular Structure Based on Adaptive Algorithmic Modelling for Solving Complex Computational Problems in Three Space Dimensions

A class of general inverse matrix techniques based on adaptive algorithmic modelling methodologies is derived yielding iterative methods for solving unsymmetric linear systems of irregular structure arising in complex computational problems in three space dimensions. The proposed class of approximate inverse is chosen as the basis to yield systems on which classic and preconditioned iterative methods are explicitly applied. Optimized versions of the proposed approximate inverse are presented using special storage (k-sweep) techniques leading to economical forms of the approximate inverses. Application of the adaptive algorithmic methodologies on a characteristic nonlinear boundary value problem is discussed and numerical results are given.

An Adaptive Lasso Grey Model for Regional FDI Statistics Prediction

To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.Next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.

Collusion detector based on G-N algorithm for trust model

In the open network environment, malicious attacks to the trust model have become increasingly serious. Compared with single node attacks, collusion attacks do more harm to the trust model. To solve this problem, a collusion detector based on the GN algorithm for the trust evaluation model is proposed in the open Internet environment. By analyzing the behavioral characteristics of collusion groups, the concept of flatting is defined and the G-N community mining algorithm is used to divide suspicious communities. On this basis, a collusion community detector method is proposed based on the breaking strength of suspicious communities. Simulation results show that the model has high recognition accuracy in identifying collusion nodes, so as to effectively defend against malicious attacks of collusion nodes.

Auxiliary Model Based Multi-innovation Stochastic Gradient Identification Methods for Hammerstein Output-Error System

Special input signals identification method based on the auxiliary model based multi-innovation stochastic gradient algorithm for Hammerstein output-error system was proposed.The special input signals were used to realize the identification and separation of the Hammerstein model.As a result,the identification of the dynamic linear part can be separated from the static nonlinear elements without any redundant adjustable parameters.The auxiliary model based multi-innovation stochastic gradient algorithm was applied to identifying the serial link parameters of the Hammerstein model.The auxiliary model based multi-innovation stochastic gradient algorithm can avoid the influence of noise and improve the identification accuracy by changing the innovation length.The simulation results show the efficiency of the proposed method.

Dislocation parameters of Gonghe earthquake jointly inferred by using genetic algorithms and least squares method

The Second Crustal Deformation Monitoring Center, China Seismological Bureau, has detected a marked uplift associated with the Gonghe Ms=7.0 earthquake on April 26, 1990, Qinghai Province. From the observed vertical deformations and using a rectangular uniform slip model in a homogeneous elastic half space, we first employ genetic algorithms (GA) to infer the approximate global optimal solution, and further use least squares method to get more accurate global optimal solution by taking the approximate solution of GA as the initial parameters of least squares. The inversion results show that the causative fault of Gonghe Ms=7.0 earthquake is a right-lateral reverse fault with strike NW60°, dip SW and dip angle 37°, the coseismic fracture length, width and slip are 37 km, 6 km and 2.7 m respectively. Combination of GA and least squares algorithms is an effective joint inversion method, which could not only escape from local optimum of least squares, but also solve the slow convergence problem of GA after reaching adjacency of global optimal solution.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

A New Three-Dimensional(3D)Printing Prepress Algorithm for Simulation of Planned Surgery for Congenital Heart Disease

Background:Three-dimensional printing technology may become a key factor in transforming clinical practice and in significant improvement of treatment outcomes.The introduction of this technique into pediatric cardiac surgery will allow us to study features of the anatomy and spatial relations of a defect and to simulate the optimal surgical repair on a printed model in every individual case.Methods:We performed the prospective cohort study which included 29 children with congenital heart defects.The hearts and the great vessels were modeled and printed out.Measurements of the same cardiac areas were taken in the same planes and points at multislice computed tomography images(group 1)and on printed 3D models of the hearts(group 2).Pre-printing treatment of the multislice computed tomography data and 3D model preparation were performed according to a newly developed algorithm.Results:The measurements taken on the 3D-printed cardiac models and the tomographic images did not differ significantly,which allowed us to conclude that the models were highly accurate and informative.The new algorithm greatly simplifies and speeds up the preparation of a 3D model for printing,while maintaining high accuracy and level of detail.Conclusions:The 3D-printed models provide an accurate preoperative assessment of the anatomy of a defect in each case.The new algorithm has several important advantages over other available programs.They enable the development of customized preliminary plans for surgical repair of each specific complex congenital heart disease,predict possible issues,determine the optimal surgical tactics,and significantly improve surgical outcomes.

Research on a Fog Computing Architecture and BP Algorithm Application for Medical Big Data

Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.

Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

This research recognizes the limitation and challenges of adaptingand applying Process Mining as a powerful tool and technique in theHypothetical Software Architecture (SA) Evaluation Framework with thefeatures and factors of lightweightness. Process mining deals with the largescalecomplexity of security and performance analysis, which are the goalsof SA evaluation frameworks. As a result of these conjectures, all ProcessMining researches in the realm of SA are thoroughly reviewed, and ninechallenges for Process Mining Adaption are recognized. Process mining isembedded in the framework and to boost the quality of the SA model forfurther analysis, the framework nominates architectural discovery algorithmsFlower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductiveand compares them vs. twelve quality criteria. Finally, the framework’s testingon three case studies approves the feasibility of applying process mining toarchitectural evaluation. The extraction of the SA model is also done by thebest model discovery algorithm, which is selected by intensive benchmarkingin this research. This research presents case studies of SA in service-oriented,Pipe and Filter, and component-based styles, modeled and simulated byHierarchical Colored Petri Net techniques based on the cases’ documentation.Processminingwithin this framework dealswith the system’s log files obtainedfrom SA simulation. Applying process mining is challenging, especially for aSA evaluation framework, as it has not been done yet. The research recognizesthe problems of process mining adaption to a hypothetical lightweightSA evaluation framework and addresses these problems during the solutiondevelopment.

Design and development of safety evaluation system of buildings on a seismic field based on the network platform

This paper describes a set of on-site earthquake safety evaluation systems for buildings, which were developed based on a network platform. The system embedded into the quantitative research results which were completed in accordance with the provisions from Post-earthquake Field Works, Part 2： Safety Assessment of Buildings, GB18208.2 -2001, and was further developed into an easy-to-use software platform. The system is aimed at allowing engineering professionals, civil engineeing technicists or earthquake-affected victims on site to assess damaged buildings through a network after earthquakes. The authors studied the function structure, process design of the safety evaluation module, and hierarchical analysis algorithm module of the system in depth, and developed the general architecture design, development technology and database design of the system. Technologies such as hierarchical architecture design and Java EE were used in the system development, and MySQL5 was adopted in the database development. The result is a complete evaluation process of information collection, safety evaluation, and output of damage and safety degrees, as well as query and statistical analysis of identified buildings. The system can play a positive role in sharing expert post-earthquake experience and promoting safety evaluation of buildings on a seismic field.

Adaptive learning algorithm based on mixture Gaussian background

The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are inferred based on the maximum likelihood rule. Secondly, the forgetting factor and learning rate factor are redefined, and their still more general formulations are obtained by analyzing their practical functions. Lastly, the convergence of the proposed algorithm is proved to enable the estimation converge to a local maximum of the data likelihood function according to the stochastic approximation theory. The experiments show that the proposed learning algorithm excels the formers both in converging rate and accuracy.

Supply-based optimal scheduling of oil product pipelines

Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model was established for a single-source multi-distribution oil pro- duct pipeline, and scheduling plans were made based on supply. In the model, time node constraints, oil offloading plan constraints, and migration of batch constraints were taken into consideration. The minimum deviation between the demanded oil volumes and the actual offloading volumes was chosen as the objective function, and a linear programming model was established on the basis of known time nodes＇ sequence. The ant colony optimization algo- rithm and simplex method were used to solve the model. The model was applied to a real pipeline and it performed well.

Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

The leaf area index(LAI)is an important agroecological physiological parameter affecting vegetation growth.To apply the genetic algorithms neural network model(GANNM)to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar(GF-3 SAR)images and GaoFen-1 Wide Field of View(GF-1 WFV)images,the Xiangfu District in the east of Kaifeng City,Henan Province,was selected as the testing region.Winter wheat LAI data from five growth stages were combined,and optical and microwave polarization decomposition vegetation index models were used.The backscattering coefficient was extracted by modified water cloud model(MWCM),and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI.The root mean square error(RMSE)and determination coefficient(R2)were used as evaluation indicators of the model.The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model;the R2 was higher than 0.8,and RMSE was lower than 0.3,indicating that the model could accurately invert the growth status of winter wheat in five growth stages.