A multistage system of microbial fed-batch fermentation and its parameter identification

In fed-batch fermentation of glycerol bio-dissimilation to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae, glycerol and alkali are fed into the reactor to provide nutrient and maintain a suitable environment for cells growth. Taking the feeding process as a time-continuous process, we present a nonlinear multistage dynamical system to formulate the fed-batch fermentation. In view of the big errors between experimental data and computational values, a parameter identification model is established. Finally, an improved Nelder–Mead simplex search method is developed to seek the unknown parameters in the model. Numerical simulations show that the multistage dynamical system can describe the fed-batch process better, compared with the previous results.     

Multilevel fuzzy relational systems: structure and identification

 Existing fuzzy relational equations (FRE) typically possess an evident single-level structure, where no consequence part of the rule being modeled, is used as a fact to another rule. Corresponding to multistage fuzzy reasoning, a natural extension of traditional fuzzy relational systems (FRS) is to introduce some intermediate levels of processing governed by enhanced FRE's so that the structure resulted becomes multilevel or multistage. Three basic multilevel FRS structures, namely, incremental, aggregated, and cascaded, are considered in this paper and they correspond to different reasoning mechanisms being frequently used by human beings in daily life. While the research works on multilevel FRS are sparse and our ability to solve a system of multilevel FRE's in a purely analytical manner is very limited, we address the identification problem from an optimization approach and introduce three fuzzy neural models. The proposed models consist of single-level FRS modules that are arranged in different hierarchical manners. Each module can be realized by Lin and Lee's fuzzy neural model for implementing the Mamdani fuzzy inference. We have particularly addressed the problem of how to distribute the input variables to different (levels of) relational modules for the incremental and aggregated models. In addition, the new models can learn a complete multistage fuzzy rule set from stipulated data pairs using structural and parameter learning. The effectiveness of the multilevel models has been demonstrated through various benchmarking problems. It can be generally concluded that the new models are distinctive in learning, generalization, and robustness.     

多级互连网络互连函数的矩阵理论

多级互连网网络是大规模并行处理系统和大型ＡＴＭ交换机采用的主要互连结构。     

低信噪比条件下的高分辨DOA估计算法

提出一种基于多级维纳滤波器（MSWF）的信号波达方向（DOA）估计算法。通过测试信号子空间的估计值与噪声子空间的正交性实现DOA粗估计,通过测试MSWF分解的互相关函数实现信号DOA的精估计。仿真实验表明,在低信噪比条件下,该算法比已有的子空间类算法有更好的分辨率和误差性能。     

Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm

Multi-criteria human resource allocation involves deciding how to divide human resource of limited availability among multiple demands in a way that optimizes current objectives. In this paper, we focus on multi-criteria human resource allocation for solving multistage combinatorial optimization problem. Hence we tackle this problem via a multistage decision-making model. A multistage decision-making model is similar to a complex problem solving, in which a suitable sequence of decisions is to be found. The task can be interpreted as a series of interactions between a decision maker and an outside world, at each stage of which some decisions are available and their immediate effect can be easily computed. Eventually, goals would be reached due to the found of optimized variables. In order to obtain a set of Pareto solutions efficiently, we propose a multiobjective hybrid genetic algorithm (mohGA) approach based on the multistage decision-making model for solving combinatorial optimization problems. According to the proposed method, we apply the mohGA to seek feasible solutions for all stages. The effectiveness of the proposed algorithm was validated by its application to an illustrative example dealing with multiobjective resource allocation problem.     

Performance estimation of banyan semi layer networks with drop resolution mechanism

Multistage interconnection networks (MINs) are a basic class of switch-based network architectures, which are used for constructing scalable parallel computers or for connecting networks. Semi-layer MINs are a special case of MINs. A performance evaluation of semi-layer MINs (using simulation models) is presented in this paper. The configurations of the under study networks apply a conflict drop resolution mechanism. The proposed architecture's performance is studied under uniform traffic conditions and various offered loads, buffer-lengths and MIN sizes. In this paper, the improvements on semi-layer MIN performance, in terms of throughput and latency, are demonstrated quantitatively. These performance measures can be valuable tools for designers of parallel multiprocessor systems and networks, in order to minimize the overall deployment costs and help deliver efficient systems.     

基于延拓法的电力系统稳定模型中二维参数局部分岔边界的计算

该文提出表示微分．代数模型中的奇异性、鞍结点和霍普夫分岔的代数方程以便应用延拓法来求解获得二维参数的分岔边界。该方程保留了电力系统稳定微分．代数模型的形式不变，也未涉及到矩阵求逆或行列式值的计算，同时该方程也具有直接法计算分岔时速度快的优点。其缺点是方程的维数增加了。应用所提方法计算了一简单电压稳定和一多机电力系统稳定模型中的二维参数局部分岔边界，并和实域仿真进行比较，结果表明该方法是准确可行的。     

Performance evaluation of the augmented data vortex switch fabric: An all-optical packet switched interconnection network

Modern high-performance computing systems require networks with high capacity, extremely high throughput and low latency in order to pass messages between thousands of processors and memory elements. Optical Interconnection Networks (OIN) offer a potentially viable solution to this requirement. An all-optical packet switched interconnection network called a Data Vortex (DV) switch has already been proposed by Yang et al. for the purpose of large scale photonic interconnections. For any interconnection network, fault tolerance and reliability are crucial issues, evaluation of which lacked attention for the case of the DV switch. In our earlier work we therefore presented the results for fault tolerance and reliability analysis of the primary DV switch. We also proposed a new Augmented Data Vortex (ADV) switch fabric, to improve the fault tolerance of the primary DV switch. The performance as regards fault tolerance of the ADV switch was computed and detailed results were obtained. In this paper, performance of ADV is investigated with reference to parameters such as latency and injection ratio (throughput) by means of numerical simulations. A uniform random traffic model has been used for the performance evaluation. The results obtained are compared with the results reported for the DV switch. The results show that the ADV switch with enhanced fault tolerance also improves the performance regarding latency. For same switch sizes (i.e. the same number of angles A, and height H) the injection ratios (throughput) for the DV and the ADV switches are comparable. Hence it can serve as a suitable candidate for high performance computing.     

Global optimization of linear hybrid systems with explicit transitions

The global optimization of hybrid systems described by linear time-varying ordinary differential equations is examined. A method to construct convex relaxations of general, nonlinear Bolza-type objective functions or constraints subject to an embedded hybrid system with explicit transitions is presented. The optimization problem can be solved using gradient-based algorithms in a branch and bound framework that is shown to be infinitely convergent when the implied state bounds are employed.     

基于传递效率的多级丝杠结构参数优化

多级丝杠电动缸相比单级丝杠电动缸结构更为复杂,传动机构更多,其效率损失环节相对增加。多级丝杠机构作为多级电动缸的核心传动机构,其传动效率直接影响着电动缸的总体效率。通过对多级丝杠结构分析,对多级丝杠效率进行了建模分析,推导了多级丝杠效率公式。分析了多级丝杠传动效率与各级丝杠公称直径及导程之间的变化规律。结合电动缸结构的特殊要求,以电动缸效率最优为目标,通过加权函数非线性约束最值优化计算,确定最佳的结构参数组合,并分析在最优效率下载荷变化对各级结构参数的影响。研究结果为多级电动缸的设计提供了理论参考。