基于关闭煤矿沉陷区—地下硐室群的抽水蓄能电站构建与利用

关闭煤矿具有建造高水头抽水蓄能电站的天然拓扑结构,利用关闭煤矿建设抽水蓄能电站是储能方式的新探索,也是关闭煤矿资源利用的新方式.以常规抽水蓄能电站主要工程结构为蓝本,结合淮南矿区关闭的潘一矿工程地质条件,提出了淮南矿区沉陷区—地下硐室群抽水蓄能电站的构建模型.该模型主要由关闭煤矿采煤沉陷区(上水库)、地下硐室群(下水库...     

棒材高效低碳生产技术与集成化应用

为了减少钢铁生产流程的能耗,降低废气排放,实现低碳生产,河钢集团用冶金流程工程学理论指导开发了高效炼钢、高效连铸技术并与棒材轧钢工序集成化应用,实现了棒材生产流程的低碳化和绿色化.讨论了提高转炉冶炼效率、提高连铸机拉速、钢坯定重、提高钢坯热送温度,实现转炉、连铸、轧钢高效生产与快速连接的关键技术.实践表明,通过优化钢铁...     

AN ALTERNATIVE PERSPECTIVE ON FUZZY SET-BASED APPROXIMATE REASONING

Abstract

On the subject of fuzzy set-based approximate reasoning, this paper attempts to present a perspective that is both rigorously stated and different from the standard approach as stated by Mamdani and many others. Functionally speaking, this alternative perspective produces results essentially the same as the standard one, and in fact we intend to prove that subject to certain assumptions the results will be exactly die same. We feel however that the alternative perspective offers advantages in that it makes a more appropriate and more understandable use of such concepts as fuzzy relations, the extension principle, and fuzzy composition. If indeed it can lead to a clearer and more widespread understanding of the approximate reasoning formalism then this could conceivably spark broader interest in fuzzy control and other applications.     

An optimal regularization method for space-fractional backward diffusion problem

In this paper, a space-fractional backward diffusion problem (SFBDP) in a strip is considered. By the Fourier transform, we proposed an optimal modified method to solve this problem in the presence of noisy data. The convergence estimates for the approximate solutions with the regularization parameter selected by an a priori and an a posteriori strategy are provided, respectively. Numerical experiments show that the proposed methods are effective and stable.     

Experimental evaluation of the impact of b-learning methodologies on engineering students in Spain

The aim of this article is to highlight the importance of an active learning methodology in engineering degrees in Spain. We present the outcomes of an experimental study carried out during the academic years 2007/2008 and 2008/2009 with engineering students at the University of Salamanca (Spain). In the present research, as we have done in previous ones, we have selected a subject which is common to the four degrees under consideration: Computer Science. This study explores in greater depth the validity of experimental designs coming from educational research and the impact of innovative teaching methodologies. The hypothesis that impulses this research is formulated to ascertain that the learning level and the satisfaction of students will be higher after the implementation of new teaching methodologies (based on constructive learning, collaborative work, and blended learning resources), than in more traditional teaching contexts. The obtained results partially confirm this hypothesis. The ultimate purpose of this work is that of providing evidence that contributes to the improvement of education and teaching methods for a better performance of students in engineering.     

GaiaDoc： A Tool Focused on Business Requirements for Code Documentation with a CMMI Compliant and RUP Based on Requirement Flow

Aiming to fill a gap in traditional methods of source code documentation, which focus mainly on the API （application programming interface） documentation for other programmers, this article presents a new approach for business requirements, mapping them through a set of annotations. These annotations, in turn, are interpreted by the GaiaDoc tool, which is specified in this paper and is able to generate documentation in form of use case specifications in a language and format easily understandable by the project stakeholders. Along with the GaiaDoc tool proposal, a RUP （rational unified process） based requirements flow is developed to fit with its needs and it is validated through CMMI （capability maturity model integration） requirement process areas and a case study of the proposed methodology＇s application is presented before the final considerations.     

Feature engineering for semantic place prediction

We present in this paper our winning solution to Dedicated Task 1 in Nokia Mobile Data Challenge (MDC). MDC Task 1 is to infer the semantic category of a place based on the smartphone sensing data obtained at that place. We approach this task in a standard supervised learning setting: we extract discriminative features from the sensor data and use state-of-the-art classifiers (SVM, Logistic Regression and Decision Tree Family) to build classification models. We have found that feature engineering, or in other words, constructing features using human heuristics, is very effective for this task. In particular, we have proposed a novel feature engineering technique, Conditional Feature (CF), a general framework for domain-specific feature construction. In total, we have generated 2,796,200 features and in our final five submissions we use feature selection to select 100 to 2000 features. One of our key findings is that features conditioned on fine-granularity time intervals, e.g. every 30 min, are most effective. Our best 10-fold CV accuracy on training set is 75.1% by Gradient Boosted Trees, and the second best accuracy is 74.6% by L1-regularized Logistic Regression. Besides the good performance, we also report briefly our experience of using F# language for large-scale (～70 GB raw text data) conditional feature construction.     

Concept maps as the first step in an ontology construction method

A method is proposed to be used as the first step in the ontology acquisition process. This method is based on the use of concept maps as a means of expression for the expert, followed by an application that assists the expert in detailing the structure of the knowledge represented in the map. This application analyses the concept map, taking into account the map topology and key words used by the expert. From this analysis a series of questions are presented to the expert that, when answered, reduce the map ambiguity and identify some common patterns in ontological representations, such as generalizations and mereologic relations. This information can be used by the knowledge engineer during further knowledge acquisition sessions or to direct the expert to a further improvement of the map. The method was tested by a group of volunteers, most of them engineers working at the aerospace sector, and the results suggest that both the use of concept mapping as well as the refining step are acceptable from the point of view of the end user, supporting the claim that this method is viable as an option to reduce some of the difficulties in large scale ontology construction.     

新工科环境下“EDA技术”课程改革的探索

高校新工科人才培养的要求是培养一大批多样化、创新型卓越工程科技人才。针对培养要求切实做出积极的改革,首先重构EDA技术课程的教学时间,由线上和线下两部分组成。线上课要求完成课程视频学习、单元测验、讨论和期末测试;线下课堂要求完成见面课项目设计、实验课和期末考试。通过线上线下课堂教学的结合,加强过程性考核手段,完善学生学习评价机制。     

用改进的遗传算法实现架构恢复

高层架构恢复对软件维护和软件进化至关重要.把实现架构恢复的聚类问题看作优化问题,通过对常规遗传算法中初始群体产生策略、选择操作方法、交叉概率和变异概率的自适应性等重要参数和关键环节的改进,设计并实现了混合遗传聚类算法(hybrid genetic clustering algorithm,简称HGCA).同时也对该算法的有效性和综合性能进行了实验分析,结果表明,该算法对初始群体的产生有较好的约束作用.与传统遗传算法相比,它的群体性能和收敛性能都较优,且收敛精度高.同时,基于MoJo度量模型的相似性度量值充分说明了HGCA算法对架构恢复的正确性和有效性.