近距离下覆4#煤层综采工作面顶板管理经验

介绍了近距离深层综采工作面顶板和巷道顶板管理经验，及综采工作面在通过巷道冒项区域时所采取的办法和措施。     

Modern Teaching and Training in Metallurgical Engineering

This contribution deals with advanced educational technologies needed to equip customers from higher education institutions, research and industry with efficient tools supporting their work and operating new skills‐training methods. The challenges are reducing the training costs, improving quality and increasing the number of graduates in engineering departments. The concept of a Virtual Lab based on the combination of various teaching methods and tools is presented. Principles of mathematisation in metallurgical education and training are discussed. An example of online course designed in the form of Virtual Lab is demonstrated.     

Evaluation codes at singular points of algebraic differential equations

We use the special geometry of singular points of algebraic differential equations on the affine plane over finite fields to study the main features and parameters of error correcting codes giving by evaluating functions at sets of singular points. In particular, one gets new methods to construct codes with designed minimum distance. This work was partially supported by MCyT BFM2001-2251.     

Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition

We propose subspace distance measures to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. An intrapersonal subspace is referred to as specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies significantly from person to person, and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods if a small number of adaptation images are available.     

D-side: A facility and workforce planning group multi-criteria decision support system for Johnson Space Center

“To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers” is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility's alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility's replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach.     

Automatic processing of psychological distance: Evidence from a Stroop task.

A picture-word version of the Stroop task was used to test the automatic activation of psychological distance by words carrying various senses of psychological distance: temporal (tomorrow, in a year), social (friend, enemy), and hypotheticality (sure, maybe). The pictures implied depth, with the words appearing relatively close to or distant from the observer. The participants classified the spatial distance of words faster when the word's implicit psychological distance matched its spatial distance (e.g., a geographically close word was classified faster when it was "friend" than when it was "enemy"). The findings are consistent with the idea that psychological distance is accessed automatically, even when it is not directly related to people's current goals, and suggest that psychological distance is an important dimension of meaning, common to spatial distance, temporal distance, social distance, and hypotheticality. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

耦合模模型的峰峰值与有效值

介绍了峰峰值和有效值的偶合模方程,给出了峰峰值P矩阵和有效值P矩阵的表达式。利用峰峰值P矩阵和有效值P矩阵模拟了纵向耦合谐振结构以及阻抗元结构的声表面波滤波器的频响。对比两种P矩阵的模拟结果,说明了峰峰值耦合模方程和有效值耦合模方程分析频响的等效性。     

Effect of the particle size ratio on the conductivity of conductor-insulator powder composites: Numerical simulation

A theoretical method for determining the percolation limit and effective conductivity of composites based on polydisperse mixtures of insulator and conductor powders is proposed. The effect of the particle size ratio between the insulator and conductor on the percolation limit and effective conductivity is studied. Numerical simulation of the microstructure, effective properties, and percolation limit in composites is discussed. __________ Translated from Poroshkovaya Metallurgiya, Vol. 46, No. 1–2(453), pp. 31–40, 2007.     

On principal component analysis, cosine and Euclidean measures in information retrieval

Clustering groups document objects represented as vectors. An extensive vector space may cause obstacles to applying these methods. Therefore, the vector space was reduced with principal component analysis (PCA). The conventional cosine measure is not the only choice with PCA, which involves the mean-correction of data. Since mean-correction changes the location of the origin, the angles between the document vectors also change. To avoid this, we used a connection between the cosine measure and the Euclidean distance in association with PCA, and grounded searching on the latter. We applied the single and complete linkage and Ward clustering to Finnish documents utilizing their relevance assessment as a new feature. After the normalization of the data PCA was run and relevant documents were clustered.