三峡水库减淤增容调度方式研究——多汛限水位调度方案

本文建议在汛期中小流量时（Q<35000m3/s），将坝前水位维持在148～151m；出现汛情且流量更较大后，将坝前水位降低到143m；入库流量大于35000m3/s且短期预报将出现大于十年一遇洪水时，预泄洪水到135m.按这一调度，汛期约80％时间可以维持在较高水位，一般洪水期。汛限水位143m不影响坝区通航，135m水位迎洪可大量增加防洪库客。到100年后可减淤30亿m3，增加防洪库容约40亿m3.变动回水区减淤40%，优化了坝区水沙搭配，可改善通航条件。降低库区洪水位，缓解防洪与移民的矛盾。可对发电带来较大好处：提高发电效益，减少粗沙过机。初期水库排沙比大于原方案，可减轻下游冲刷。同时，可减小三峡汛初泄水与鄱阳湖防洪的矛盾。     

试论降低成本的设计技术

一个新产品的功能和成本水平基本上已在产品设计阶段就确定了。研究降低成本的设计技术就是为了给设计师提供一套行之有效的降低和优化成本的辅助设计工具。降低成本设计的技术内涵是与设计过程并行的成本估算、知识基系统的建立和质量及成本的综合优化。论述了各国对此项技术研究应用的状况、技术分析、发展特点和研究前沿的项目及内容 ,如对此技术研究应用的迫切性、对降低成本的最佳时机和产品的全成本概念等的探讨和认识 ;对成本扩展到产品寿命全周期、对目标成本控制法和成本优化设计等的研究和应用。     

合成氨扩能改造中锅炉存在的问题及采取的措施

针对天脊集团年产30万t合成氨扩能改造,给2台225t/h高压锅炉带来的除氧器热量过剩、锅炉给水温度升高、主蒸汽温度降低等一系列问题,分析了产生问题的原因,从节约能源、提高锅炉效率考虑,提出了相应的解决问题的办法:利用热量分流、改造省煤器管、增加二级过热器受热面积。经过3年多的生产实践证明,取得了良好的效果。     

A new iterative order reduction (IOR) method for eigensolutions of large structures

Order reduction is a computationally efficient method to estimate some lowest eigenvalues and the corresponding eigenvectors of large structural systems by reducing the order of the original model to a smaller one. But its accuracy is limited to a small range of frequencies that depends on the selection of the retained degrees of freedom. This paper proposes a new iterative order reduction (IOR) technique to obtain accurately the eigensolutions of large structural systems. The technique retains all the inertia terms associated with the removed degrees of freedom. This hence leads to the reduced mass matrix being in an iterated form and the reduced stiffness matrix constant. From these mass and stiffness matrices, the eigensolutions of the reduced system can be obtained iteratively. On convergence the reduced system reproduces the eigensolutions of the original structure. A proof of the convergence property is also presented. Applications of the method to a practical GARTEUR structure as well as a plate have demonstrated that the proposed method is comparable to the commonly used Subspace Iteration method in terms of numerical accuracy. Moreover, it has been found that the proposed method is computationally more efficient than the Subspace Iteration method. Copyright © 2003 John Wiley & Sons, Ltd.     

Elimination of organic matter present in wastewaters from the cork industry by membrane filtration

BACKGROUND: The first stage of the cork industrial process generates great volumes of wastewater with moderate to high organic pollutant content that must be purified using different procedures, such as filtration by membranes. RESULTS: The tangential filtration of these wastewaters was studied using two different laboratory equipments. In the first one, three ultrafiltration (UF) membranes were tested, with molecular weight cut‐off (MWCO) 100 kDa and 30 kDa, and two operating modes were used: total recycling of permeate and retentate streams, and in continuous mode, without recycling both streams. In the total recycling UF experiments, the influence of the operating variables on the permeate flux was first established. The effectiveness of the different membranes was determined by evaluating the rejection coefficients for several parameters that measure the global pollutant content of the effluent. The values found for these rejection coefficients were in the following order: ellagic acid and color > absorbance at 254 nm > tannic content > COD (chemical oxygen demand). In the continuous mode experiments, the fouling mechanism for each membrane was established by fitting the experimental data to various filtration fouling models given in the literature. The operating mode in the second equipment was batch concentration, and additional experiments were carried out with an UF membrane (2 kDa), and with a NF membrane (with MWCO in the range 150–300 Da). CONCLUSIONS: The three operating modes tested provided different rejection levels of organic matter; among them, the most effective procedure tested was batch concentration mode using a NF membrane. Copyright © 2007 Society of Chemical Industry     

Solving the small sample size problem in face recognition using generalized discriminant analysis

The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.     

High training set size reduction by space partitioning and prototype abstraction

Instance-based learning methods like the nearest neighbour classifier generally suffer from the indiscriminate storage of all training instances, resulting in large memory requirements and slow execution speed. In this paper, new training set size reduction methods based on prototype generation and space partitioning are proposed. Experimental results show that the new algorithms achieve a very high reduction rate with still an important classification accuracy.     

钛精矿回转窑直接还原过程结圈可能性的探讨

通过煤种，入窑球冷态强度和还原过程强度，以及钛精矿与煤灰混合物的软化温度等分析探讨了钛精矿预热球团回转窑直接还原产生结圈的可能性，获得了可以避免结圈的结论。     

A Theoretical Framework for Core Material Properties Identification in Cellular Solids using Novelty Detection

Abstract:  This paper presents a novelty detection-based technique to identify core material properties of honeycombs and cellular structures. A numerical model (finite element) representing full scale and/or reduced size of the cellular solid is used to generate transmissibilities between topological homologous points at cells in different locations. In order to make the procedure robust against noise, these transmissibilities are artificially corrupted. This step is representative of a series of experimentally obtained measurements which automatically return information relating to the size and shape of data noise along with the mean measurement. The next stage, both in this paper and in the proposal for the experimental approach, is to generate several further sets of transmissibilities from the finite element model; the only difference being that the core material properties are altered from the original values. A novelty detection framework is then adopted to find a similarity measure between each of these 'test' transmissibilities and the original set thereby identifying the material properties. Although this work is concerned with identifying only one material property, the methodology extends to identifying several properties.     

Product Evaluation Knowledge Acquisition Based on Rough Sets

1 Introduction Acquiring knowledge is constantly encountered in the mechanical product evaluation process, such as during setting up evaluation index system and determining evaluation knowledge. The most useful expression form of knowledge is production rule whenever concerning the field knowledge or decision knowledge. How to effectively acquire and express the knowledge becomes the issue that the evaluation work must solve at first. One of an important application of the rough set theory is …