扬巴工程中承包商合同控制

阐述扬巴项目中合同控制的基本做法,比较国内工程与涉外工程项目中合同控制的差异,提出承包商在涉外工程项目中做好合同控制工作的建议。     

ESTIMATION OF EFFECTIVE SHEAR RATE FOR AERATED NON-NEWTONIAN LIQUIDS IN AIRLIFT BIOREACTOR”“

Effective shear rate is one of the indispensable parameters for the design of aerobic fermentors using a viscous non-Newtonian system. The estimation of effective shear rate in airlift loop bioreactors has been investigated with liquid circulation velocity. An empirical correlation of effective shear rate in airlift loop reactors is proposed.

γ_eπ= 3.26-3.51 ; 10²U_G + 1.48 10⁴U²_G

It is found that the effective shear rate is lower in airlift reactors than in bubble columns. This equation can be used for the cultivation of cells sensitive to shear stress.     

A survey of traffic-based routing metrics in family of expected transmission count for self-organizing networks

Self-Organizing Networks (SONs) are attractive for applications where flexibility, resilience, and a large network service area are required. In order to meet the requirements of those applications, the principal issue is to guarantee efficient routing in SONs. Hence, the design and selection of appropriate routing metrics is important. A mass of metrics have been proposed in the past several decades. Among all the metrics, Expected Transmission Count (ETX) has drawn the most attention. Since the proposal of ETX, many ETX-based or extended metrics have been proposed. ETX and its descendants compose the ETX family. This paper is an attempt to analyze, compare and summarize traffic-based routing metrics in the ETX family. Details of each routing metric are presented and analyzed. Some of our viewpoints on the principal for designing metrics in SONs are presented.     

国产核电厂蒸汽发生器下封头接管区模拟装置承受循环载荷能力的试验

国产核电厂蒸汽发生器下封头群孔接管区具有应力分布复杂、焊接施工条件差的特点。为考核其承受循环载荷的能力，设计制造了与实物相似的模拟试验装置，根据核电厂运行寿命期内的载荷特点，确定了模拟试验装置的试验载荷与试验循环数。循环加载试验的结果表明，蒸汽发生器下封头群孔接管区承受循环载荷的能力在寿命期内是符合要求的。     

Structure Aware Visual Cryptography

Visual cryptography is an encryption technique that hides a secret image by distributing it between some shared images made up of seemingly random black‐and‐white pixels. Extended visual cryptography (EVC) goes further in that the shared images instead represent meaningful binary pictures. The original approach to EVC suffered from low contrast, so later papers considered how to improve the visual quality of the results by enhancing contrast of the shared images. This work further improves the appearance of the shared images by preserving edge structures within them using a framework of dithering followed by a detail recovery operation. We are also careful to suppress noise in smooth areas.     

Pattern synthesis of time‐modulated conformal arrays by an improved differential evolution algorithm

In this article, an improved differential evolution algorithm (IDE) based on two different colonies is proposed and applied to time‐modulated conformal arrays syntheses. The whole population of IDE is divided into two parts. The one part searches the solution globally while the other searches the neighborhood of the solution provided by the previous one. Benchmark functions are provided to testify IDE. Furthermore, IDE is applied to synthetize sum‐difference patterns with a 1 × 16 elements time‐modulated circular array and low sidelobe level (SLL) patterns with an 8 × 12 elements time‐modulated cone array. After optimization, the sideband level (SBL) of the circular array at the first sideband frequency is ?1.00 dB. The SLL and SBL at the first sideband frequency of the cone array are lower than ?30.00 and ?20.00 dB, respectively. Experiment results verify the superior performance of IDE. Moreover, to accelerate the computation speed, graphics processing unit parallel computing technique is introduced into pattern synthesis and the acceleration ratios of more than 23 times can be achieved. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:697–705, 2014.     

Synthesis of antenna arrays with an efficient multiobjective differential evolution algorithm

An efficient multiobjective differential evolution algorithm for array synthesis is proposed in this article. By introducing novel strategies in the generalized differential evolution (GDE3), the proposed algorithm can generate a better converged and distributed Pareto front than the standard GDE3. Five typical benchmark functions are optimized by the proposed improved GDE3 (IGDE) to verify its effectiveness. Furthermore, the proposed algorithm is applied in two‐pattern synthesis problems, including an unequal spaced linear array and a conformal array. The superior performance of the proposed IGDE demonstrates that it is an efficient and competitive algorithm in the function optimization, pattern synthesis, and other electromagnetic problems. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:161–169, 2014.     

A Rough RBF Neural Network Based on Weighted Regularized Extreme Learning Machine

Adjusting parameters iteratively is a traditional way of training neural networks, and the Rough RBF Neural Networks (R-RBF-NN) follows the same idea. However, this idea has many disadvantages, for instance, the training accuracy and generalization accuracy etc. So how to change this condition is a hot topic in Academics. On the basis of Extreme Learning Machine (ELM), this paper proposes a Weighted Regularized Extreme Learning Machine (WRELM), taking into account both minimizing structured risk and weighted least-squares principle, to train R-RBF-NN. The traditional iterative training method is replaced by the minimal norm least-squares solution of general linear system. The method proposed in this paper, increasing controllability of the entire learning process and considering the structured risk and empirical risk, can improve the performance of learning and generalization. Experiments show that it can reach a very superior performance in both time and accuracy when WRELM trains the Rough RBF Neural Networks in pattern classification and function regression, especially in pattern classification, which can improve the generalization accuracy more than 3.36 % compared with ELM. Obviously, the performance of the method proposed in this paper is better than the traditional methods.     

Image super-resolution based on multi-space sparse representation

Sparse representation provides a new method of generating a super-resolution image from a single low resolution input image. An over-complete base for sparse representation is an essential part of such methods. However, discovering the over-complete base with efficient representation from a large amount of image patches is a difficult problem. In this paper, we propose a super-resolution construction based on multi-space sparse representation to efficiently solve the problem. In the representation, image patches are decomposed into a structure component and a texture component represented by the over-complete bases of their own spaces so that their high-level features can be captured by the bases. In the implementation, a prior knowledge about low resolution images generation is combined to the typical base construction for high construction quality. Experiment results demonstrate that the proposed method significantly improves the PSNR, SSIM and visual quality of reconstructed high-resolution image.     

Prediction-based realistic 3D model compression

The benefit of using the geometry image to represent an arbitrary 3D mesh is that the 3D mesh can be re-sampled as a completely regular structure and coded efficiently by common image compression methods. For geometry image-based 3D mesh compression, we need to code the normal-map images while coding geometry images to improve the subjective quality and realistic effects of the reconstructed model. In traditional methods, a geometry image and a normal-map image are coded independently. However a strong correlation exists between these two kinds of images, because both of them are generated from the same 3D mesh and share the same parameterization. In this paper we propose a predictive coding framework, in which the normal-map image is predicted based on the geometric correlation between them. Additionally we utilize the strong geometric correlation among three components of normal-map image to improve the predicting accuracy. The experimental results show the proposed coding framework improves the coding efficiency of normal-map image, meanwhile the realistic effect of a 3D mesh is significantly enhanced.