Pattern retrieval of yarn-dyed plaid fabric based on modified interactive genetic algorithm

With the development of perceptual consumption, consumers sometimes cannot explicitly describe the purchase demands or only based on impression, like the color perception and aesthetic experience. Based on the consumer's expression, it is difficult to design a new fabric by repeated proofing to meet the consumer's demands. To retrieve the existing patterns incorporating human intuition and emotion, this study proposed a novel pattern retrieval method of yarn-dyed plaid fabric using modified interactive genetic algorithm. Each pattern was encoded based on the design elements and visual features were extracted to bridge the semantic gap between the designer and the consumer. Survival of the fittest and two special mutation operators, addition and deletion, were designed to increase the diversity of the generations. During the evolution, the generated patterns were replaced by the most similar patterns in the database based on visual features. Experimental results showed that the proposed scheme is feasible and effective to extract the consumer's preferences and retrieve satisfactory patterns, helping the factory obtain the process sheet to guide production and save labor and material resources.     

Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

改进蚁群算法结合粗糙集原理用于入侵检测

提出一种基于蚁群-粗糙集原理的入侵检测方法。首先通过粗糙集对不确性数据进行筛选;再利用改进蚁群算法对数据进行约简,减少了计算时间;然后再根据设定的阀值,用蚁群-粗糙集导出规则得到检测结果。实验结果表明,改进蚁群算法数据约简速度高于利用遗传算法以及蚁群算法,该方法对DoS和Probe攻击具有很高的检测率和较低的误检率,并且对U2R和R2L攻击也有较好的检测率。     

一种基于改进加权粗糙集的多模型软测量建模方法

根据多模型可以改善模型估计精度,提高泛化性的思想,提出一种基于改进加权粗糙集的多模型软测量建模方法。加权粗糙集可以有效地处理不平衡数据的分类问题,但是传统的样本权重选择方法缺乏整体考虑,容易引起分类器整体精度的下降。通过向加权粗糙集引入类别权重,得到了一种基于最小风险贝叶斯决策理论的加权粗糙集决策算法,并利用AdaBoostM2算法寻优样本权重及类别权重。通过上述方法构建的最小风险加权粗糙集分类器,有效地提高了分类精度,从而保证了各个子模型的可靠性。     

Computerized Color Distinguishing System for Color Printed Fabric by Using the Approach of Probabilistic Neural Network

This article proposes an innovative color printed fabric computer color distinguishing system whose main functions are to precisely distinguish the printed fabric pattern colors and match colors to improve the current time-consuming color distinguishing conducted by manpower. The RGB color mode is an industrial color standard, by which the change and overlapping of color channels of red, green, and blue represent types of colors. RGB stands for red, green, and blue, respectively. It is one of the most widely used color system and covers almost all of the colors sensible to human vision. Hence, this paper adopts the RGB color mode to present color printed fabric images. First, to reduce the color distinguishing computation, a genetic algorithm was applied in search of small images of the same color in the original color printed fabric. Then, color distinguishing computation was conducted by a probabilistic neural network (PNN), which has the advantage of a very fast learning speed. Finally, PANTONE® standard color tickets were applied in matching colors. The experimental results revealed that the PNN design can easily realize and achieve accurate, fast color classification. It is proved that this color distinguishing system can be practically applied in printed fabric color distinguishing and matching.     

Decentralized Fault Isolation by a Combination of Transfer Entropy and Classification Methods

Early fault detection and isolation in industrial systems is vitally necessary to prevent any potential product damage. The paper proposes a new decentralized multi-unit fault isolation methodology in which all the known process faults with similar time signatures are grouped into appropriate categories. An innovative genetic algorithm-based method is introduced to explore for optimum plant zones in a large-scale plant wide search to appropriately configure each architectural unit, having less reliance on excess process variables with redundant and uncorrelated diagnostic information. The methodology employs a set of Bayes and radial basis function neural network classifiers to properly isolate the most usual known faults. A new idea based on transfer entropy algorithm has been integrated in the decentralized configuration to be triggered for isolation of novel faults which have been left unrecognized by the set of maintained classifiers. Experimental results clearly demonstrate that the proposed methods are considerably superior to the conventional centralized methods.     

全局优化搜索新算法--列队竞争算法(Ⅰ)解非线性和混合整数非线性规划问题

提出了一种称为列队竞争算法(LCA)的群体搜索算法,该算法在进化过程中始终保持着独立并行进化的家族,通过家族内部的生存竞争和家族间的地位竞争这两种不同的竞争方式,使群体快速进化到最优或接近最优的区域.根据家族的目标函数值大小排列成一个列队,按列队中家族地位的不同分配不同的搜索空间,使局部搜索与全局搜索达到均衡,同时,应用逐步收缩搜索空间技术加速收敛速度.数值计算表明,列队竞争算法的搜索效率优于遗传算法和模拟退火法等算法.     

全局优化搜索新算法——列队竞争算法(Ⅰ) 解非线性和混合整数非线性规划问题

提出了一种称为列队竞争算法(LCA)的群体搜索算法,该算法在进化过程中始终保持着独立并行进化的家族,通过家族内部的生存竞争和家族间的地位竞争这两种不同的竞争方式,使群体快速进化到最优或接近最优的区域.根据家族的目标函数值大小排列成一个列队,按列队中家族地位的不同分配不同的搜索空间,使局部搜索与全局搜索达到均衡,同时,应用逐步收缩搜索空间技术加速收敛速度.数值计算表明,列队竞争算法的搜索效率优于遗传算法和模拟退火法等算法.     

基于小生境遗传算法的多峰函数全局优化研究

针对基本遗传算法在求解多峰函数时很难找到全部最优解的问题,研究了基于淘汰相似结构机制的小生境遗传算法。用该算法对两个典型多峰函数求解的测试结果表明,该算法较之基本遗传算法有更强的全局搜索能力和更快的收敛速度。     

应用遗传算法实现精馏分离序列优化综合

阐述了采用遗传算法求解具有组合爆炸特征的精馏分离序列综合问题。首先在数据结构上将精馏分离序列抽象为二叉树结构编码,然后应用图论方法建立起有效的交叉和变异算子,最后对10组分精馏分离序列综合问题实例进行求解。结果表明,遗传算法对最优解命中率达80％,平均空间搜索率仅为0．0318。因此遗传算法能够成功解算大规模精馏分离序列优化综合问题。     

Modified genetic algorithm with sampling techniques for chemical engineering optimization

In this work, we develop a new efficient technique to enhance the optimization ability, and to improve the convergence speed of genetic optimization algorithm. We investigate and introduce a number of sampling techniques to generate a good set of initial population that encourages the exploration through out the search space and hence achieves better discovery of possible global optimum in the solution space. The introduced sampling techniques include Latin hypercube sampling (LHS), Faure sequence sampling (FSS), and Hammersley sequence sampling (HSS). The performances of the proposed algorithms and a conventional genetic algorithm using uniformly random population are compared, both in terms of solution quality and speed of convergence. A number of test problems and a case study, optimization of multi-effect distillation, demonstrate the feasibility and effectiveness of the proposed techniques. With the same parameters, our technique provides a better solution and converge to the global optimum faster than the traditional genetic algorithm.     

基于新型蚁群算法优化的重油热裂解模型

针对重油热裂解模型的参数估计问题呈高维、高度非线性的特征,提出一种基于新型蚁群算法优化的重油热裂解模型.通过新型蚁群算法优化确定模型参数,获得具有良好预测精度的模型.新型蚁群算法通过将解空间划分成若干子域,并引入遗传操作,实现连续优化问题的寻优.仿真结果表明它具有良好的性能,且优于传统的遗传算法.     

Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy al constraints while meeting demand requirement of packed products from various product fam-ilies. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore, we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromo-somes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to de-termine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for com-parison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, al heuristics show the capability to solve large instances within reason-able computational time. In al problem instances, genetic algorithm averagely outperforms ant colony optimiza-tion and Tabu search with slightly longer computational time.     

Fabric defect detection based on golden image subtraction

To realise the universality and practicality of fabric defect detection in the textile industry, this paper proposes two approaches based on the Gabor filter and the golden image subtraction method. A method known as Gabor preprocessed golden image subtraction is first introduced, which filters a test fabric image by the real component of the Gabor filter with a 1 Hz centre frequency and a 90° angle. Golden image subtraction performs subtractions between the golden template and the filtered image to obtain a resultant image, and the segmentation threshold is determined by the direct threshold. The second method is Gabor preprocessed golden image subtraction based on a genetic algorithm, which can automatically select the parameter groups of the Gabor filter via the genetic algorithm. In addition, the paper also presents an extensive comparison between the proposed methods and wavelet preprocessed golden image subtraction. Meanwhile, the performances of the aforementioned three methods are tested in a real machine vision detection system to meet the actual demands of the textile industry. It can be concluded that Gabor preprocessed golden image subtraction provides the best detection results. The overall detection success rate is 95.62%, with 80 defect-free images and 80 defective images for fabric textures of common types.     

基于改进差别矩阵属性约简的聚合釜粗糙集-神经网络故障诊断

引言 聚氯乙烯树脂(PVC)是重要的有机合成材料,其产品具有良好的物理性能和化学性能,广泛应用于工业、建筑、农业、电力、公用事业等领域.聚合釜则是聚氯乙烯生产装置的关键设备,聚合釜能否稳定运行直接关系到整个聚氯乙烯生产装置的运行状况.     

A novel search framework for multi‐stage process scheduling with tight due dates

This article improves the original genetic algorithm developed by He and Hui (Chem Eng Sci. 2007; 62:1504–1527) and proposes a novel global search framework (GSF) for the large‐size multi‐stage process scheduling problems. This work first constructs a comprehensive set of position selection rules according to the impact factors analysis presented by He and Hui (in this publication in 2007), and then selects suitable rules for schedule synthesis. In coping with infeasibility emerging during the search, a penalty function is adopted to force the algorithm to approach the feasible solutions. The large‐size problems with tight due dates are challenging to the current solution techniques. Inspired by the gradient used in numerical analysis, we treat the deviation existing among the computational tests of the algorithm as evolutionary gradient. Based on this concept, a GSF is laid out to fully utilize the search ability of the current algorithm. Numerical experiments indicate that the proposed search framework solves such problems with satisfactory solutions. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

一类协同分布式实时电站仿真系统报警机制研究

为了满足用户对仿真系统提出的新需求,结合传统的实时电站仿真系统,提出了协同分布式实时电站仿真系统的概念,并给出了其特征、结构与应用实例。结合报警机制的数学模型,针对协同分布式系统列出了报警机制的结构图、时空图,并针对计算单元端与报警结果获取端的程序模型进行了详细描述。     

免疫文化算法及其在乙烯裂解炉故障诊断中的应用

引言迄今为止,科研人员已经根据生物进化的机理提出很多用以解决复杂优化问题的方法,如遗传算法、蚁群优化算法、粒子群优化算法等。然而这些传统的进化算法只提供有限的或者隐性关于种群个体经验的知识表示和保存机制,这就让研究人员开始寻找一种利用显性机制来获取并保存种群进化求解知识和经验。在人类学的角度上,文化通常被看成是保存信     

Multi-objective steady state optimization of biochemical reaction networks using a constrained genetic algorithm

A hybrid genetic algorithm-based method to solve constrained multi-objective optimization problems is proposed. Considering operation around a steady state of a dynamical system, the task of the algorithm consists on finding a set of optimal, but constrained solutions. The method is exemplified on a (bio)chemical reaction network in Saccharomyces cerevisiae. In the steady state the model reduces to a system of non-linear equations which must be solved by a search method. This iterative search was integrated into a genetic algorithm in order to look up for optimal steady states. The basic idea is to use individuals of the genetic algorithm as starting points for the search algorithm. The optimization goal was to simultaneously maximize ethanol production and reduce metabolic burden. Two alternative kinetic approaches are compared to Michaelis Menten-type kinetics: a S-System and a generalized mass action model, both based on Power-Law kinetics.     

设计演化算法实现精馏分离序列优化综合

采用演化算法求解具有组合爆炸特征的精馏分离序列优化综合问题。由于精馏分离序列与二叉树之问具有同构性．在数据结构上精馏分离序列可以抽象为二又树,直接采用二叉树结构编码方案可以同时表达个体的基因型和表现形。借鉴生物界父本可以经过有性繁殖或无性繁殖得到子代的现象。对遗传算子进行设计。应冒图论方法建立有效的二叉树结构演化重组机制,从而形成基于双亲或单亲父本的交叉算子。实例表明：双亲遗传操作其空间搜索率较低,而单亲遗传操作其最优解命中率较高。因此。演化算法能够成功解算大规模精馏分离序列优化综合问题。