Design and analysis of a uranium hexafluoride actinide transmutation reactor

The long-term (> 1000 years) hazards of high-level wastes (HLW) can be reduced substantially by practising waste-actinide partitioning-transmutation (P-T). This paper investigates the waste-actinide transmutation performance of a uranium hexafluoride actinide transmutation reactor (UHATR). Using mostly present-day and near-term technology, a preliminary UHATR design is established. Because of the gaseous nature of the fuel, very high neutron fluxes are obtained. Compared with an LWR, the average blanket thermal flux of this UHATR is about 10–30 times higher, leading to a 15-fold improvement in the percentage of actinides fissioned per year of irradiation.     

Methoxymethylation of Alcohols Catalyzed by Sulfated Metal Oxides

More acidic sulfated metal oxides (SMO) such as SO₄ ^2-/ZrO₂ and SO₄ ^2-/TiO₂ catalyzed the methoxymethylation of alcohols with dimethoxymethane at ambient temperature to achieve a product yield of greater than 90% within a few hours. The reaction displayed a significant steric effect on the product yield with a preferential order of primary alcohol > secondary alcohol tertiary alcohol. The observed steric effect might result from the repulsion between the bulky alkyl group in the alcohol and the two methoxy groups in the protonated dimethoxymethane (oxonium ion).     

《家用和类似用途的过电流保护断路器》IEC60898—2国际电工标准续篇第二部分研究

文章对《家用和类似用途的过电流保护断路器》IEC60898－2国际标准的主要特点,特性和要求进行了研究。     

An interactive example-driven approach to graphics recognition in engineering drawings

An interactive example-driven approach to graphics recognition in engineering drawings is proposed. The scenario is that the user first interactively provides an example of a graphic object; the system instantly learns its graphical knowledge and uses the acquired knowledge to recognize the same type of graphic objects. The proposed approach represents the graphical knowledge of an object in terms of its structural components and their syntactical relationships. We summarize four types of geometric constraints for knowledge representation, based on which we develop an algorithm for knowledge acquisition. Another algorithm for graphics recognition using the acquired graphical knowledge is also proposed, which is actually a sequential examination of these constraints. In the algorithm, we first guess the next component’s attributes (e.g., size, position and orientation) by reasoning from an earlier found component and the constraint between them, and then search for this hypothetical component in the drawing. If all of the hypothetical components are found, a graphic object of this type is recognized. For improving the system’s recognition accuracy, we develop a user feedback scheme, which can update the graphical knowledge from both positive (missing) and negative (mis-recognized) examples provided by the user for subsequent recognition. Experiments have shown that our proposed approach is both efficient and effective for recognizing various types of graphic objects in engineering drawings. This paper is an extension of our papers published in ICDAR2003 and GREC2003.     

Statistical fuzzy interval neural networks for currency exchange rate time series prediction

In this paper, the statistical fuzzy interval neural network with statistical interval input and output values is proposed to perform statistical fuzzy knowledge discovery and the currency exchange rate prediction. Time series data sets are grouped into time series data granules with statistical intervals. The statistical interval data sets including week-based averages, maximum errors of estimate and standard deviations are used to train the fuzzy interval neural network to discover fuzzy IF-THEN rules. The output of the fuzzy interval neural network is an interval value with certain percent confidence. Simulations are completed in terms of the exchange rates between US Dollar and other three currencies (Japanese Yen, British Pound and Hong Kong Dollar). The simulation results show that the fuzzy interval neural network can provide more tolerant prediction results.     

Quantitative Comparison of Bilateral Teleoperation Systems Using μ-Synthesis

This paper presents a quantitative comparison framework for bilateral teleoperation systems (BTSs) that have different dynamic characteristics and sensory configurations for a given task-dependent performance objective (TDPO). mu-synthesis is used to develop the framework since it can efficiently treat systems containing uncertainties and disturbances. The framework consists of: 1) a feasibility test and 2) a comparison methodology using prioritized TDPOs. As the formulation used is based on mu-synthesis, the system, operator, and environment models are represented in the form of linear nominal models with frequency-dependent multiplicative uncertainties. This framework is applied to a BTS including an uncertain human operator and environment in a practical case study. The validity of the proposed quantitative framework is confirmed through experiments. The proposed framework can be used as a tool to design BTSs, especially when there are constraints in designing drive mechanisms and choosing sensory configurations.     

300 mm双面磨削硅片表面纹路模拟

分析了硅片双面磨削的运动轨迹,并给出了砂轮上P点相对于硅片的运动轨迹。还对砂轮运动轨迹进行了模拟。得出以下结论：砂轮上P点在硅片上的运动轨迹仅与它们的相对转速比I有关,而与两者的分别转动角速度值没有关系。硅片磨削的磨纹密度沿着硅片径向逐渐减小,硅片中心处磨纹最密集,磨纹密度最大,表面粗糙度最小,越靠近硅片的中心硅片的磨纹密度越大,表面粗糙度越小,表面质量越好;反之,越靠近硅片的边缘磨纹密度越小,表面粗糙度越大,表面质量越差。砂轮和硅片旋转方向相同时单颗磨粒的轨迹带有紫荆花形状,说明其磨削是不均匀的,磨削效果不好;而砂轮和硅片旋转方向相反时单颗磨粒的轨迹则不具有这种形状,磨削很均匀,磨削效果好。硅片磨纹密度是由砂轮和硅片的速比决定的,速比ωwωs的不可约分数m/n中n越大,硅片磨纹密度越密,表面粗糙度越小,磨削表面质量越好。     

DNF are teachable in the average case

We study the average number of well-chosen labeled examples that are required for a helpful teacher to uniquely specify a target function within a concept class. This “average teaching dimension” has been studied in learning theory and combinatorics and is an attractive alternative to the “worst-case” teaching dimension of Goldman and Kearns which is exponential for many interesting concept classes. Recently Balbach showed that the classes of 1-decision lists and 2-term DNF each have linear average teaching dimension. As our main result, we extend Balbach’s teaching result for 2-term DNF by showing that for any 1≤s≤2 ^Θ(n), the well-studied concept classes of at-most-s-term DNF and at-most-s-term monotone DNF each have average teaching dimension O(ns). The proofs use detailed analyses of the combinatorial structure of “most” DNF formulas and monotone DNF formulas. We also establish asymptotic separations between the worst-case and average teaching dimension for various other interesting Boolean concept classes such as juntas and sparse GF ₂ polynomials.     

Perceived cultural importance and actual self-importance of values in cultural identification.

Cross-cultural psychologists assume that core cultural values define to a large extent what a culture is. Typically, core values are identified through an actual self-importance approach, in which core values are those that members of the culture as a group strongly endorse. In this article, the authors propose a perceived cultural importance approach to identifying core values, in which core values are values that members of the culture as a group generally believe to be important in the culture. In 5 studies, the authors examine the utility of the perceived cultural importance approach. Results consistently showed that, compared with values of high actual self-importance, values of high perceived cultural importance play a more important role in cultural identification. These findings have important implications for conceptualizing and measuring cultures. (PsycINFO Database Record (c) 2011 APA, all rights reserved)