Genetically modified starter and protective cultures

Modern approaches towards starter and protective culture improvement rely on advances in molecular biology. For most microorganisms used for food production, gene technological methods have been well developed. By recombinant DNA technology, ‘tailor-made’ starter and protective cultures may be constructed so as to combine technically desirable features. A single strain which normally would fail to accomplish a given ‘wtask’ may now be improved so as to meet a set of requirements necessary for a specific production or preservation process (e.g. wholesomeness, no off-flavour production, overproduction of bacteriocins or particular enzymes). In addition, undesirable properties (e.g. mycotoxin or antibiotic production by cheese moulds) may be eliminated by techniques such as ‘gene disruption’.     

Machine assignment and part-families formation using group technology

The conventional way of solving the group technology (GT) problem is to start from an assignment of parts to machines and try to find a partitioning of machine cells and part families. The similarity between parts is measured based on commonality of the machines assigned to them. However, parts are assigned to machines based on their operation requirements and the operation capabilities of machines. Similarity between parts should be based on their required operations. In this paper, the authors attempt to solve or facilitate solving the GT problem at the assignment level. An algorithm for assigning parts to machines is provided which utilizes the types of operations required by parts and applies GT principles in producing the assignment. This leads to better partitioning of machine cells and part-families. Furthermore, operation sequences required by parts in determining the similarity between parts have been considered. An algorithm to form part-families based on the operation sequence similarity coefficient has been developed. The resulting families are then used by the assignment algorithm to produce machine assignments to part-families. The use of the algorithm is demonstrated by examples.     

Adaptive solutions to the mutual exclusion problem

Summary Algorithms for mutual exclusion that adapt to the current degree of contention are developed. Afilter and a leader election algorithm form the basic building blocks. The algorithms achieve system response times that are independent of the total number of processes and governed instead by the current degree of contention. The final algorithm achieves a constant amortized system response time. Manhoi Choy was born in 1967 in Hong Kong. He received his B.Sc. in Electrical and Electronic Engineerings from the University of Hong Kong in 1989, and his M.Sc. in Computer Science from the University of California at Santa Barbara in 1991. Currently, he is working on his Ph.D. in Computer Science at the University of California at Santa Barbara. His research interests are in the areas of parallel and distributed systems, and distributed algorithms. Ambuj K. Singh is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara. He received a Ph.D. in Computer Science from the University of Texas at Austin in 1989, an M.S. in Computer Science from Iowa State University in 1984, and a B.Tech. from the Indian Institute of Technology at Kharagpur in 1982. His research interests are in the areas of adaptive resource allocation, concurrent program development, and distributed shared memory.A preliminary version of the paper appeared in the 12th Annual ACM Symposium on Principles of Distributed ComputingWork supported in part by NSF grants CCR-9008628 and CCR-9223094     

A novel dynamic survivable routing in WDM optical networks with/without sparse wavelength conversion

In this paper, we study the dynamic survivable routing problem, both in optical networks without wavelength conversion and in optical networks with sparse wavelength conversion, and propose a novel hybrid algorithm for it based on the combination of mobile agents technique and genetic algorithms (GA). By keeping a suitable number of mobile agents in the network to cooperatively explore the network states and continuously report cycles (that are formed by two disjoint-link routes) into the routing tables, our new hybrid algorithm can promptly determine the first population of cycles for a new request based on the routing table of its source node, without the time consuming process associated with current GA-based lightpath protection schemes. We further improve the performance of our algorithm by introducing a more advanced fitness function that is suitable for both the above networks. Extensive simulation studies on the ns-2 network simulator show that our hybrid algorithm achieves a significantly lower blocking probability than the conventional survivable routing algorithms for all the cases we studied.     

从数据结构角度设计导游系统

从数据结构角度为旅游胜地设计导游系统。向游人提供景点的信息查询服务,根据指定的景点提供相关的景点信息。任意给定起点和终点,查询两点之间的最短路径。     

Simple algorithms for searching a polygon with flashlights

The k-searcher is a mobile guard whose visibility is limited to k rays emanating from her position, where the direction of each ray can be changed continuously with bounded angular rotation speed. Given a polygonal region P, is it possible for the k-searcher to eventually see a mobile intruder that is arbitrarily faster than the searcher within P? We present O(n²)-time algorithms for constructing a search schedule of the 1-searcher and the 2-searcher, respectively. Our framework for the 1-searcher can be viewed as a modification of that of LaValle et al. [Proc. 16th ACM Symp. on Computational Geometry, 2000, pp. 260-269] and is naturally extended for the 2-searcher.     

Interweaving genetic programming and genetic algorithm for structural and parametric optimization in adaptive platform product customization

An adaptive product platform offers high customizability for generating feasible product variants for customer requirements. Customization takes place not only to product platform structure but also to its relevant parameters. Structural and parametric optimization processes are interwoven with each other to achieve the total optimality. This paper presents an evolutionary method dealing with interwoven structural and parametric optimization of adaptive platform product customization. The method combines genetic programming and genetic algorithm for handling structural and parametric optimization, respectively. Efficient genetic representation and operation schemes are carefully adapted. While designing these schemes, features specific to structural and parameter customization are considered for the simplification of platform product management. The experimental results show that the performance of the proposed algorithm outperforms that of the tandem evolutionary algorithm in which a genetic algorithm for parametric optimization is totally nested in a genetic programming for structural optimization.     

激光切割工艺参数优化数据库系统

用Visual Basic 6.0和用SQL server 7.0设计了激光切割工艺参数数据查询系统.能实现对常用材料的激光切割工艺参数进行查询.同时,提供了方便的数据库维护方法,提出用户基于Web技术的数据库更新方法.     

A genetic‐based neuro‐fuzzy controller for blind equalization of time‐varying channels

This paper presents a neuro‐fuzzy network (NFN) where all its parameters can be tuned simultaneously using genetic algorithms (GAs). The approach combines the merits of fuzzy logic theory, neural networks and GAs. The proposed NFN does not require a priori knowledge about the system and eliminates the need for complicated design steps such as manual tuning of input–output membership functions, and selection of fuzzy rule base. Although, only conventional GAs have been used, convergence results are very encouraging. A well‐known numerical example derived from literature is used to evaluate and compare the performance of the network with other equalizing approaches. Simulation results show that the proposed neuro‐fuzzy controller, all parameters of which have been tuned simultaneously using GAs, offers advantages over existing equalizers and has improved performance. From the perspective of application and implementation, this paper is very interesting as it provides a new method for performing blind equalization. The main contribution of this paper is the use of learning algorithms to train a feed‐forward neural network for M‐ary QAM and PSK signals. This paper also provides a platform for researchers of the area for further development. Copyright © 2008 John Wiley & Sons, Ltd.     

组织协同进化分类算法用于雷达目标一维像识别

针对雷达目标一维像识别问题，提出了一种基于组织协同进化分类算法的识别方法．该方法与现有进化分类方法的不同之处在于它的进化操作直接作用于样本而不是规则，采用了一种自下而上的搜索机制，即先使若干样本的集合得到进化，再从进化结果中提取规则．这样有利于避免在进化过程中产生无意义的规则．该方法不需要进行特征提取；对于高维数据，不需要预先进行降维处理；没有复杂的运算，训练和识别的速度都很快．对3种飞机微波暗室实测数据的识别实验表明，该方法性能稳定，优于基于支撑矢量机与子波核函数的方法，识别率均达到了96％以上．实验中还对算法的抗噪能力进行了测试，获得了良好的效果．