An Information Theory for the Geotechnical Engineering Inverse Problem

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Research on the Integration Path of Postgraduate Professional Courses into the Construction of Curriculum Ideology and Politics

The curriculum ideology and politics play an important role in cultivating students’correct worldview,outlook on life,and values.In recent years,universities and colleges have begun to pay attention to the cultivation of postgraduates’moral education and the construction of postgraduates’curriculum ideology and politics.This paper takes the course“Theory of Software Engineering”of Anqing Normal University as an example,discusses the paths of integrating postgraduate professional courses into the construction of curriculum ideology and politics,and puts forward the paths of feeding back from scientific research,connecting with practice and summarizing and condensing,which are aimed at improving postgraduates’comprehensive quality and innovation ability,and have positive significance for cultivating high-level applied scientific and technological talents who are both moral and talented.

On engineering game theory with its application in power systems

Due to its capability of solving decision-making problems involving multiple entities and objectives, as well as complex action sequences, game theory has been a basic mathematical tool of economists, politicians, and sociologists for decades. It helps them understand how strategic interactions impact rational decisions of individual players in competitive and uncertain environment, if each player aims to get the best payoff. This situation is ubiquitous in engineering practices. This paper streamlines the foundations of engineering game theory, which uses concepts, theories and methodologies to guide the resolution of engineering design, operation, and control problems in a more canonical and systematic way. An overview of its application in smart grid technologies and power systems related topics is presented, and intriguing research directions are also envisioned.

Multiclassification algorithm and its realization based on least square support vector machine algorithm

As a new type of learning machine developed on the basis of statistics learning theory, support vector machine （SVM） plays an important role in knowledge discovering and knowledge updating by constructing non-linear optimal classifter. However, realizing SVM requires resolving quadratic programming under constraints of inequality, which results in calculation difficulty while learning samples gets larger. Besides, standard SVM is incapable of tackling multi-classification. To overcome the bottleneck of populating SVM, with training algorithm presented, the problem of quadratic programming is converted into that of resolving a linear system of equations composed of a group of equation constraints by adopting the least square SVM（LS-SVM） and introducing a modifying variable which can change inequality constraints into equation constraints, which simplifies the calculation. With regard to multi-classification, an LS-SVM applicable in multi-dassiftcation is deduced. Finally, efficiency of the algorithm is checked by using universal Circle in square and twospirals to measure the performance of the classifier.

REVERSE MODELING FOR CONIC BLENDING FEATURE

A novel method to extract conic blending feature in reverse engineering is presented. Different from the methods to recover constant and variable radius blends from unorganized points, it contains not only novel segmentation and feature recognition techniques, but also bias corrected technique to capture more reliable distribution of feature parameters along the spine curve. The segmentation depending on point classification separates the points in the conic blend region from the input point cloud. The available feature parameters of the cross-sectional curves are extracted with the processes of slicing point clouds with planes, conic curve fitting, and parameters estimation and compensation, The extracted parameters and its distribution laws are refined according to statistic theory such as regression analysis and hypothesis test. The proposed method can accurately capture the original design intentions and conveniently guide the reverse modeling process. Application examples are presented to verify the high precision and stability of the proposed method.

Paving the Way to Smart Micro Energy Grid:Concepts,Design Principles,and Engineering Practices

The interconnection between initially independent energy infrastructures offers additional system flexibility and efficiency.The integration at distribution level simplifies the implementation of the integrated energy system functionalities.This paper proposes concepts and design principles of a smart micro energy grid(MEG)for accommodating micro-grids,distributed poly-generation systems,energy storage facilities,and associated energy distribution infrastructures.The energy management system is responsible for the smart operation of the MEG while supporting multiple criteria,such as safety,economy,and environmental protection.To realize the vision of the smart MEG,an engineering game theory based energy management system with self-approaching-optimum capability is investigated.Based on the proposed concepts,design principles,and energy management system,this paper presents a prototype of China’s first conceptual solar-based smart MEG,established in Qinghai University.

INCIDENCE MATRIX APPROACH FOR CALCULATING READINESS LEVELS

Contemporary system maturity assessment approaches have failed to provide robust quantitative system evaluations resulting in increased program costs and developmental risks.Standard assessment metrics,such as Technology Readiness Levels（TRL）,do not sufficiently evaluate increasingly complex systems.The System Readiness Level（SRL）is a newly developed system development metric that is a mathematical function of TRL and Integration Readiness Level（IRL） values for the components and connections of a particular system.SRL acceptance has been hindered because of concerns over SRL mathematical operations that may lead to inaccurate system readiness assessments.These inaccurate system readiness assessments are called readiness reversals.A new SRL calculation method using incidence matrices is proposed to alleviate these mathematical concerns.The presence of SRL readiness reversal is modeled for four SRL calculation methods across several system configurations.Logistic regression analysis demonstrates that the proposed Incidence Matrix SRL（IMSRL）method has a decreased presence of readiness reversal than other approaches suggested in the literature.Viable SRL methods will foster greater SRL adoption by systems engineering professionals and will support system development risk reduction goals.