基于遗传算法的车间设备布局优化设计

文章针对某厂曲轴生产车间设备布局问题采用遗传算法进行优化设计.首次提出了搬运矩概念,很好的解决了生产多种工件的车间设备布局优化问题.优化设计结果符合生产实际要求.     

Greedily computing associative aggregations on sliding windows

We present an algorithm for combining the elements of subsequences of a sequence with an associative operator. The subsequences are given by a sliding window of varying size. Our algorithm is greedy and computes the result with the minimal number of operator applications.     

Guided Volume Editing based on Histogram Dissimilarity

Segmentation of volumetric data is an important part of many analysis pipelines, but frequently requires manual inspection and correction. While plenty of volume editing techniques exist, it remains cumbersome and errorprone for the user to find and select appropriate regions for editing. We propose an approach to improve volume editing by detecting potential segmentation defects while considering the underlying structure of the object of interest. Our method is based on a novel histogram dissimilarity measure between individual regions, derived from structural information extracted from the initial segmentation. Based on this information, our interactive system guides the user towards potential defects, provides integrated tools for their inspection, and automatically generates suggestions for their resolution. We demonstrate that our approach can reduce interaction effort and supports the user in a comprehensive investigation for high‐quality segmentations.     

Computing Minimum Area Homologies

Calculating and categorizing the similarity of curves is a fundamental problem which has generated much recent interest. However, to date there are no implementations of these algorithms for curves on surfaces with provable guarantees on the quality of the measure. In this paper, we present a similarity measure for any two cycles that are homologous, where we calculate the minimum area of any homology (or connected bounding chain) between the two cycles. The minimum area homology exists for broader classes of cycles than previous measures which are based on homotopy. It is also much easier to compute than previously defined measures, yielding an efficient implementation that is based on linear algebra tools. We demonstrate our algorithm on a range of inputs, showing examples which highlight the feasibility of this similarity measure.     

基于混合遗传算法的去耦电容网络设计

结合基于目标阻抗的设计方法,将混合遗传算法应用于PDN(电源分配网络)的去耦电容网络设计,对所需去耦电容器的种类和数目进行优化计算,同时与标准遗传算法和传统设计方法进行对比,借助Cadence电源完整性仿真工具验证设计结果的有效性和合理性。避免了传统设计需要借助EDA工具进行反复修改和验证的繁琐过程,同时减少了设计冗余,节省了PCB布局布线空间,为高频电路的PDN设计提供参考。     

Process parameter optimization of lap joint fillet weld based on FEM–RSM–GA integration technique

This study introduces a welding process design tool to determine optimal arc welding process parameters based on Finite Element Method (FEM), Response Surface Method (RSM) and Genetic Algorithms (GA). Here, a sequentially integrated FEM–RSM–GA framework has been developed and implemented to reduce the weld induced distortion in the final welded structure. It efficiently incorporates finite element based numerical welding simulations to investigate the desired responses and the effect of design variables without expensive trial experiments. To demonstrate the effectiveness of the proposed methodology, a lap joint fillet weld specimen has been used in this paper. Four process parameters namely arc voltage, input current, welding speed and welding direction have been optimized to minimize the distortion of the structure. The optimization results revealed the effectiveness of the methodology for welding process design with reduced cost and time.     

线性时滞系统稳定的充分必要条件

提出一个新的线性时滞系统稳定的充分必要条件,与以前的结论不同的是,我们的结论的表达形式更容易数值计算。开发相应的二种收敛算法,解决系统时滞独立稳定判断和时滞相关稳定的时滞界估计。本文给出范数有界不确定性时,系统稳定的充分条件。文末给出的数值实例证明本文结果降低计算复杂度,改进了时滞估计保守性。     

Fuzzy decision fusion of complementary experts based on evolutionary cepstral coefficients for phoneme recognition

Optimal representation of acoustic features is an ongoing challenge in automatic speech recognition research. As an initial step toward this purpose, optimization of filterbanks for the cepstral coefficient using evolutionary optimization methods is proposed in some approaches. However, the large number of optimization parameters required by a filterbank makes it difficult to guarantee that an individual optimized filterbank can provide the best representation for phoneme classification. Moreover, in many cases, a number of potential solutions are obtained. Each solution presents discrimination between specific groups of phonemes. In other words, each filterbank has its own particular advantage. Therefore, the aggregation of the discriminative information provided by filterbanks is demanding challenging task. In this study, the optimization of a number of complementary filterbanks is considered to provide a different representation of speech signals for phoneme classification using the hidden Markov model (HMM). Fuzzy information fusion is used to aggregate the decisions provided by HMMs. Fuzzy theory can effectively handle the uncertainties of classifiers trained with different representations of speech data. In this study, the output of the HMM classifiers of each expert is fused using a fuzzy decision fusion scheme. The decision fusion employed a global and local confidence measurement to formulate the reliability of each classifier based on both the global and local context when making overall decisions. Experiments were conducted based on clean and noisy phonetic samples. The proposed method outperformed conventional Mel frequency cepstral coefficients under both conditions in terms of overall phoneme classification accuracy. The fuzzy fusion scheme was shown to be capable of the aggregation of complementary information provided by each filterbank.     

Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.     

Optimal design of adaptive type-2 neuro-fuzzy systems: A review

Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods are investigated: derivative-based (computational approaches), derivative-free (heuristic methods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.