AUTOMATIC FINIT EELEMENT MODELING OF A WING

This paper is concerned about the automatic finite element modeling of a wing structure. The row and column method is used to identify the structure parts(ribs, spars, skins and pillars). A customization module of PCL(PATRAN Command Language under PATRAN 6.0) code from constructing airfoil curves to creating the entire wing FEM model is designed and developed. The geome tric, mesh density, material, load and boundary parameters can be easily and correctly input with the friendly interactive interface. A VFW614 wing is analyzed from creating airfoil curves to the show of stresses calculated by using NASTRAN 68 as an example. The results show that this customization module is very effective and efficient.

Automatic Evolutionary Modeling by the Lattice-Boltzmann Method

The Lattice-Boltzmann method is an effective tool for solving fluid mechanics problems, but there isn't still a good scheme to determinate some parameters in Boltzmann equations. In this paper, a technique using evolutionary algorithm to automatically model Boltzmann equations is introduced. Numerical simulation shows that the designed scheme is fast and efficient.

Backward automatic calibration for three-dimensional landslide models

Back-analysis is broadly used for approaching geotechnical problems when monitoring data are available and information about the soils properties is of poor quality.For landslide stability assessment back-analysis calibration is usually carried out by time consuming trial-and-error procedure.This paper presents a new automatic Decision Support System that supports the selection of the soil parameters for three-dimensional models of landslides based on monitoring data.The method considering a pool of possible solutions,generated through permutation of soil parameters,selects the best ten configurations that are more congruent with the measured displacements.This reduces the operator biases while on the other hand allows the operator to control each step of the computation.The final selection of the preferred solution among the ten best-fitting solutions is carried out by an operator.The operator control is necessary as he may include in the final decision process all the qualitative elements that cannot be included in a qualitative analysis but nevertheless characterize a landslide dynamic as a whole epistemological subject,for example on the base of geomorphological evidence.A landslide located in Northeast Italy has been selected as example for showing the system potentiality.The proposed method is straightforward,scalable and robust and could be useful for researchers and practitioners.

On the method of the automatic modeling in hydraulic pipe networks

In this paper the dynamic characteristics in pipes are analyzed with frequency method, and puts forward a simple and practical describing method. By establishing the model library beforehand, the modeling of the pipe net is completed automatically, and we can accurately calculate the impedance characteristics of the pipe network, achieve the reasonable configuration of the pipe network, so that to decrease the pressure pulsation.

A Method for Automatically Establishing a Mathematical Model of Kinematic Analysis to a Planar Mechanism with a Multiple Joint and Prismatic Pair

A method for automatically establishing a mathematical model of kinematic analysis to a planar mechanism with multiple joint and prismatic pair is presented. The breadth （ or depth ） first search spanning tree can be obtained based on an adjacency matrix of the mechanism. Then the kinematic chain （or mechanism）＇s basic loops can be obtained. On the basis of these basic loops, a mathematical model of kinematic analysis can be established and solved automatically. In the sense of a calculative mechanism, structural analysis of the kinematic chain relates to the kinematic analysis of a mechanism. Thus, an effective way is supplied to the given mechanism＇s kinematic analysis for automatic modeling and solving, and a method is supplied to the structural type to optimize kinematic synthesis.

A High Precision Comprehensive Evaluation Method for Flood Disaster Loss Based on Improved Genetic Programming

Precise comprehensive evaluation of flood disaster loss is significant for the prevention and mitigation of flood disasters. Here, one of the difficulties involved is how to establish a model capable of describing the complex relation between the input and output data of the system of flood disaster loss. Genetic programming (GP) solves problems by using ideas from genetic algorithm and generates computer programs automatically. In this study a new method named the evaluation of the grade of flood disaster loss (EGFD) on the basis of improved genetic programming (IGP) is presented (IGP-EGFD). The flood disaster area and the direct economic loss are taken as the evaluation indexes of flood disaster loss. Obviously that the larger the evaluation index value, the larger the corresponding value of the grade of flood disaster loss is. Consequently the IGP code is designed to make the value of the grade of flood disaster be an increasing function of the index value. The result of the application of the IGP-EGFD model to Henan Province shows that a good function expression can be obtained within a bigger searched function space; and the model is of high precision and considerable practical significance. Thus, IGP-EGFD can be widely used in automatic modeling and other evaluation systems.

APPROACH ON INTELLIGENT OPTIMIZATION DESIGN BASED ON COMPOUND KNOWLEDGE

A concept of an intelligent optimal design approach is proposed, which isorganized by a kind of compound knowledge model. The compound knowledge consists of modularizedquantitative knowledge, inclusive experience knowledge and case-based sample knowledge. By usingthis compound knowledge model, the abundant quantity information of mathematical programming and thesymbolic knowledge of artificial intelligence can be united together in this model. The intelligentoptimal design model based on such a compound knowledge and the automatically generateddecomposition principles based on it are also presented. Practically, it is applied to theproduction planning, process schedule and optimization of production process of a refining &chemical work and a great profit is achieved. Specially, the methods and principles are adaptablenot only to continuous process industry, but also to discrete manufacturing one.

Mathematical model of electric hydraulic and powered support control system at a plough mining face

Given the actual working of a fully mechanized plough at a mining face, we have proposed a formula for running constraints between powered supports and a coal plough under assumed geological conditions of the coal face and, on this basis, established an automatic control model of powered supports for the coal plough face. We introduced the working principle of the powered support control system of the plough at the mining face. We established three advanced characteristics of this control system： response speed, reliability and easy maintenance of the system. As well, we briefly introduced, the principal function of primary and subordinate controllers and the realization of the communication system by a Single Bus. Ten controllers were constructed and tested in our laboratorium. The results show that the control model is practical and meets actual conditions. It provides a theoretical basis for designing a comouter control system for a oowered support system of a plough at a mining face.

Combined method for parallel manipulator configuration design

Configuration design is an essential, creative and decision-making step m parallel manipulator design process, in which modeling and assembly are iterative and trivial. Combined approach with automatic parametric modeling and automatic assembly is proposed for parallel manipulator configuration design. The design process and key techniques, such as configuration design, configuration verification, poses calculation of all parts in parallel manipulator, virtual assembly and etc., are discussed and demonstrated by an example. A software package is developed for parallel manipulator configuration design based on the proposed method with Visual C＋＋ and UG/OPEN on Unigraphics.

Attribution analysis and multi-scenario prediction of NDVI drivers in the Xilin Gol grassland,China

Grassland degradation is influenced by climate change and human activities,and has become a major obstacle for the development of arid and semi-arid areas,posing a series of environmental and socio-economic problems.An in-depth understanding of the inner relations among grassland vegetation dynamics,climate change,and human activities is therefore greatly significant for understanding the variation in regional environmental conditions and predicting future developmental trends.Based on MODIS(moderate resolution imaging spectroradiometer)NDVI(normalized difference vegetation index)data from 2000 to 2020,our objective is to investigate the spatiotemporal changes of NDVI in the Xilin Gol grassland,Inner Mongolia Autonomous Region,China.Combined with 12 natural factors and human activity factors in the same period,the dominant driving factors and their interactions were identified by using the geographic detector model,and multiple scenarios were also simulated to forecast the possible paths of future NDVI changes in this area.The results showed that:(1)in the past 21 a,vegetation cover in the Xilin Gol grassland exhibited an overall increasing trend,and the vegetation restoration(84.53%)area surpassed vegetation degradation area(7.43%);(2)precipitation,wind velocity,and livestock number were the dominant factors affecting NDVI(the explanatory power of these factors exceeded 0.4).The interaction between average annual wind velocity and average annual precipitation,and between average annual precipitation and livestock number greatly affected NDVI changes(the explanatory power of these factors exceeded 0.7).Moreover,the impact of climate change on NDVI was more significant than human activities;and(3)scenario analysis indicated that NDVI in the Xinlin Gol grassland increased under the scenarios of reduced wind velocity,increased precipitation,and ecological protection.In contrast,vegetation coverage restoration in this area was significantly reduced under the scenarios of unfavorable climate conditions and excessive human activities.This study provides a scientific basis for future vegetation restoration and management,ecological environmental construction,and sustainable natural resource utilization in this area.

Point-of-Interest Recommendation in LocationBased Social Networks with Personalized Geo-Social Influence

Point-of-interest(POI) recommendation is a popular topic on location-based social networks(LBSNs).Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance,leading to unsatisfactory recommendation results.In this paper,the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method,and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence.Moreover,a distributed learning algorithm is used to scale up our method to large-scale data sets.Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.

An Optimized Convolutional Neural Network Architecture Based on Evolutionary Ensemble Learning

Convolutional Neural Networks(CNNs)models succeed in vast domains.CNNs are available in a variety of topologies and sizes.The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture.Our proposed framework to automated design is aimed at resolving this problem.The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit.In comparison to the co-authored work,our proposed framework is concerned with creating lightweight architectures with a limited number of parameters while retaining a high degree of validity accuracy utilizing an ensemble learning technique.This architecture is intended to operate on low-resource machines,rendering it ideal for implementation in a number of environments.Four common benchmark image datasets are used to test the proposed framework,and it is compared to peer competitors’work utilizing a range of parameters,including accuracy,the number of model parameters used,the number of GPUs used,and the number of GPU days needed to complete the method.Our experimental findings demonstrated a significant advantage in terms of GPU days,accuracy,and the number of parameters in the discovered model.

Research of database-based modeling for mining management system

Put forward the method to construct the simulation model automatically with database-based automatic modeling(DBAM) for mining system. Designed the standard simulation model linked with some open cut Pautomobile dispatch system. Analyzed and finded out the law among them, and designed model maker to realize the automatic pro- gramming of the new model program.

Three-Dimensional Model Retrieval Using Dynamic Multi-Descriptor Fusion

In this paper, we propose a dynamic multi-descriptor fusion （DMDF） approach to improving the retrieval accuracy of 3-dimensional （3D） model retrieval systems. First, an independent retrieval list is generated by using each individual descriptor. Second, we propose an automatic relevant/irrelevant models selection （ARMS） approach to selecting the relevant and irrelevant 3D models automatically without any user interaction. A weighted distance, in which the weight associated with each individual descriptor is learnt by using the selected relevant and irrelevant models, is used to measure the similarity between two 3D models. Furthermore, a descriptor-dependent adaptive query point movement （AQPM） approach is employed to update every feature vector. This set of new feature vectors is used to index 3D models in the next search process. Four 3D model databases are used to compare the retrieval accuracy of our proposed DMDF approach with several descriptors as well as some well-known information fusion methods. Experimental results have shown that our proposed DMDF approach provides a promising retrieval result and always yields the best retrieval accuracy.

Automatic Algorithm Programming Model Based on the Improved Morgan's Refinement Calculus

The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development,including specification generation,program refinement,and formal verification.However,the existing model has two flaws:incompleteness of program refinement and inadequate automation of formal verification.This paper proposes an automatic algorithm programming model based on the improved Morgan’s refinement calculus.It extends the Morgan’s refinement calculus rules and designs the C++generation system for realizing the complete process of refinement.Meanwhile,the automation tools VCG(Verification Condition Generator)and Isabelle are used to improve the automation of formal verification.An example of a stock’s maximum income demonstrates the effectiveness of the proposed model.Furthermore,the proposed model has some relevance for automatic software generation.

Radar HRRP statistical recognition with temporal factor analysis by automatic Bayesian Ying-Yang harmony learning

Radar high-resolution range profiles(HRRPs)are typical high-dimensional and interdimension dependently distributed data,the statistical modeling of which is a challenging task for HRRP-based target recognition.Supposing that HRRP samples are independent and jointly Gaussian distributed,a recent work[Du L,Liu H W,Bao Z.IEEE Transactions on Signal Processing,2008,56(5):1931–1944]applied factor analysis(FA)to model HRRP data with a two-phase approach for model selection,which achieved satisfactory recognition performance.The theoretical analysis and experimental results reveal that there exists high temporal correlation among adjacent HRRPs.This paper is thus motivated to model the spatial and temporal structure of HRRP data simultaneously by employing temporal factor analysis(TFA)model.For a limited size of high-dimensional HRRP data,the two-phase approach for parameter learning and model selection suffers from intensive computation burden and deteriorated evaluation.To tackle these problems,this work adopts the Bayesian Ying-Yang(BYY)harmony learning that has automatic model selection ability during parameter learning.Experimental results show stepwise improved recognition and rejection performances from the twophase learning based FA,to the two-phase learning based TFA and to the BYY harmony learning based TFA with automatic model selection.In addition,adding many extra free parameters to the classic FA model and thus becoming even worse in identifiability,the model of a general linear dynamical system is even inferior to the classic FA model.

Learning Gaussian mixture with automatic model selection:A comparative study on three Bayesian related approaches

Three Bayesian related approaches,namely,variational Bayesian(VB),minimum message length(MML)and Bayesian Ying-Yang(BYY)harmony learning,have been applied to automatically determining an appropriate number of components during learning Gaussian mixture model(GMM).This paper aims to provide a comparative investigation on these approaches with not only a Jeffreys prior but also a conjugate Dirichlet-Normal-Wishart(DNW)prior on GMM.In addition to adopting the existing algorithms either directly or with some modifications,the algorithm for VB with Jeffreys prior and the algorithm for BYY with DNW prior are developed in this paper to fill the missing gap.The performances of automatic model selection are evaluated through extensive experiments,with several empirical findings:1)Considering priors merely on the mixing weights,each of three approaches makes biased mistakes,while considering priors on all the parameters of GMM makes each approach reduce its bias and also improve its performance.2)As Jeffreys prior is replaced by the DNW prior,all the three approaches improve their performances.Moreover,Jeffreys prior makes MML slightly better than VB,while the DNW prior makes VB better than MML.3)As the hyperparameters of DNW prior are further optimized by each of its own learning principle,BYY improves its performances while VB and MML deteriorate their performances when there are too many free hyper-parameters.Actually,VB and MML lack a good guide for optimizing the hyper-parameters of DNW prior.4)BYY considerably outperforms both VB and MML for any type of priors and whether hyper-parameters are optimized.Being different from VB and MML that rely on appropriate priors to perform model selection,BYY does not highly depend on the type of priors.It has model selection ability even without priors and performs already very well with Jeffreys prior,and incrementally improves as Jeffreys prior is replaced by the DNW prior.Finally,all algorithms are applied on the Berkeley segmentation database of real world images.Again,BYY considerably outperforms both VB and MML,especially in detecting the objects of interest from a confusing background.

On essential topics of BYY harmony learning： Current status, challenging issues, and gene analysis applications

As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.

A Probabilistic Liquefaction Hazard Assessment for Urban Regions Based on Dynamics Analysis Considering Soil Uncertainties

Earthquake induced liquefaction is one of the main geo-disasters threating urban regions, which not only causes direct damages to buildings, but also delays both real-time disaster relief actions and reconstruction activities. It is thus important to assess liquefaction hazard of urban regions effectively and efficiently for disaster prevention and mitigation. Conventional assessment approaches rely on engineering indices such as the factor of safety(FS) against liquefaction, which cannot take into account directly the uncertainties of soils. In contrast, a physics simulation-based approach, by solving soil dynamics problems coupled with excess pore water pressure(EPWP) it is possible to model the uncertainties directly via Monte Carlo simulations. In this study, we demonstrate the capability of such an approach for assessing an urban region with over 10 000 sites. The permeability parameters are assumed to follow a base-10-lognormal distribution among 100 model analyses for each site. A dynamic simulation is conducted for each model analysis to obtain the EPWP results. Based on over 1 million EPWP analysis models, we obtained a probabilistic liquefaction assessment. Empowered by high performance computing, we present for the first time a probabilistic liquefaction hazard assessment for urban regions based on dynamics analysis, which consider soil uncertainties.

Codimensional matrix pairing perspective of BYY harmony learning:hierarchy of bilinear systems,joint decomposition of data-covariance,and applications of network biology

One paper in a preceding issue of this journal has introduced the Bayesian Ying-Yang(BYY)harmony learning from a perspective of problem solving,parameter learning,and model selection.In a complementary role,the paper provides further insights from another perspective that a co-dimensional matrix pair(shortly co-dim matrix pair)forms a building unit and a hierarchy of such building units sets up the BYY system.The BYY harmony learning is re-examined via exploring the nature of a co-dim matrix pair,which leads to improved learning performance with refined model selection criteria and a modified mechanism that coordinates automatic model selection and sparse learning.Besides updating typical algorithms of factor analysis(FA),binary FA(BFA),binary matrix factorization(BMF),and nonnegative matrix factorization(NMF)to share such a mechanism,we are also led to(a)a new parametrization that embeds a de-noise nature to Gaussian mixture and local FA(LFA);(b)an alternative formulation of graph Laplacian based linear manifold learning;(c)a codecomposition of data and covariance for learning regularization and data integration;and(d)a co-dim matrix pair based generalization of temporal FA and state space model.Moreover,with help of a co-dim matrix pair in Hadamard product,we are led to a semi-supervised formation for regression analysis and a semi-blind learning formation for temporal FA and state space model.Furthermore,we address that these advances provide with new tools for network biology studies,including learning transcriptional regulatory,Protein-Protein Interaction network alignment,and network integration.