An efficient procedure for finding best compromise solutions to the multi-objective assignment problem

In this paper, we consider the problem of determining a best compromise solution for the multi-objective assignment problem. Such a solution minimizes a scalarizing function, such as the weighted Tchebychev norm or reference point achievement functions. To solve this problem, we resort to a ranking (or k-best) algorithm which enumerates feasible solutions according to an appropriate weighted sum until a condition, ensuring that an optimal solution has been found, is met. The ranking algorithm is based on a branch and bound scheme. We study how to implement efficiently this procedure by considering different algorithmic variants within the procedure: choice of the weighted sum, branching and bounding schemes. We present an experimental analysis that enables us to point out the best variants, and we provide experimental results showing the remarkable efficiency of the procedure, even for large size instances.     

Multiple targets enclosure by robotic swarm

Target enclosure by autonomous robots is useful for many practical applications, for example, surveillance of disaster sites. Scalability is important for autonomous robots because a larger group is more robust against breakdown, accidents, and failure. However, since the traditional models have discussed only the cases in which minimum number of robots enclose a single target, there has been no study on the utilization of the redundant number of robots. In this paper, to achieve a highly scalable target enclosure model about the number of target to enclose, we introduce swarm based task assignment capability to Takayama’s enclosure model. The original model discussed only single target environment but it is well suited for applying to the environments with multiple targets. We show the robots can enclose the targets without predefined position assignment by analytic discussion based on switched systems and a series of computer simulations. As a consequence of this property, the proposed robots can change their target according to the criterion about robot density while they enclose multiple targets.     

基于局部自适应先验MRF模型的快速BP图像分割算法

针对常规MRF分割模型不能有效描述图像的局部特征、常导致图像的过分割现象,提出了一种局部自适应先验的MRF模型。该模型利用图像的邻接区域信息建立了一种局部自适应特征MRF模型。基于提出的模型,建立了一种具有快速收敛策略的区域BP算法对MRF模型的区域消息进行传递,有效解决了区域BP算法的计算量大的问题。实验结果表明,与常规区域BP算法相比,提出的分割方法具有更快的分割速度和精度。     

一种检测单晶炉热场温度的新方法

为去除单晶炉热场温度测量信号中的低频干扰、提高温度信号的估计精度,本文提出了一种基于跨维模拟退火(trans dimensional simulated annealing,TDSA)的单晶炉热场温度检测方法.该方法首先将AIC(Akaike information criterion)信号个数判断准则纳入采样机制中以便对干扰个数采样估计,进而利用所设计的基于信号频谱和轮盘赌思想的Metropolis Hastings机制对相应个数的干扰频率进行采样,最后设计了混合均匀和高斯采样机制对单晶炉热场温度进行采样估计.仿真和单晶炉工程实验结果表明,该方法在干扰个数未知的情况下,能够有效地抑制低频干扰、准确检测单晶炉热场的温度.     

基于弹性网格混合特征的脱机手写维文识别

手写体维文识别技术的关键在于特征提取方法的选择。为探求一种可靠性高、可分性强的脱机手写维文特征提取方法,在分析现有方法的基础上,结合维吾尔文字词自身的特点,提出一种在局部特征上基于弹性网格区域笔划密度、方向分解特征,在全局特征上提取交叉点、环、弧形笔划、附加笔划、外围轮廓特征的混合特征提取方法。通过在IFN/ENIT标准数据库及自采样数据集的聚类分析实验,识别正确率分别达到85%、84.3%。结果表明,方法综合统计特征和结构特征提取的优点,具有较强的抗扰能力,可分性优于GSC法。     

Genome-wide genetic diversity may help identify fine-scale genetic structure among lake whitefish spawning groups in Lake Erie

In Lake Erie, lake whitefish Coregonus clupeaformis supported lucrative fisheries before populations were decimated by overfishing and water quality degradation. In recent years, there has been a renewed interest in lake whitefish and management of the fishery they support. Lake whitefish spawn on several reefs throughout Lake Erie, but the relative recruitment dynamics and contributions of spawning groups to the fishery are not well understood. Modern high-throughput sequencing approaches offer new opportunities to census population diversity and to identify subtle differences among closely related populations. We used high-throughput sequencing data to evaluate the genetic structure and diversity of lake whitefish collected opportunistically across broad spatial scales in Lake Erie. Using RAD-capture (Rapture), we sequenced and genotyped individuals (N = 88) from the west, central, and east basin of Lake Erie at 120,268 single nucleotide polymorphisms (SNPs). Lake whitefish from Niagara and Crib Reefs (west basin) diverged from the three collections. Interestingly, these were the only lake whitefish collected during the act of spawning (late November), and all other fish were collected pre-spawn (August-early November). These results suggest that some lake whitefish spawning reefs may be reproductively isolated, though definition of these groups into stocks will require more intentional sampling during the act of spawning.     

Towards adaptive learning with improved convergence of deep belief networks on graphics processing units

In this paper we focus on two complementary approaches to significantly decrease pre-training time of a deep belief network (DBN). First, we propose an adaptive step size technique to enhance the convergence of the contrastive divergence (CD) algorithm, thereby reducing the number of epochs to train the restricted Boltzmann machine (RBM) that supports the DBN infrastructure. Second, we present a highly scalable graphics processing unit (GPU) parallel implementation of the CD-k algorithm, which boosts notably the training speed. Additionally, extensive experiments are conducted on the MNIST and the HHreco databases. The results suggest that the maximum useful depth of a DBN is related to the number and quality of the training samples. Moreover, it was found that the lower-level layer plays a fundamental role for building successful DBN models. Furthermore, the results contradict the pre-conceived idea that all the layers should be pre-trained. Finally, it is shown that by incorporating multiple back-propagation (MBP) layers, the DBNs generalization capability is remarkably improved.     

基于广义Hoek-Brown准则的节理岩体力学参数折剪计算

节理化岩体力学参数是矿山工程地质和稳定性分析的重要基础数据,一般在实验室通过单轴、三轴试验只能得到岩石的力学参数,无法得到岩体的力学参数。目前,利用岩石力学参数确定岩体力学参数的方法较多,但都过于烦琐或准确度不高,急需一种新的简易确定方法。因此,通过利用基于广义Hoek-Brown准则的地质强度指标（GSI法）,并结合相应的岩土分析软件,强度折剪岩石力学参数值,从而得到岩体的力学参数。通过将该方法应用于赤峰中色白音诺尔矿业有限公司岩体力学参数的计算中,证明采用该方法考虑岩体的结构面和完整性,可较为准确地反映节理化岩体的力学特性,且方便可行,具有工程实用价值。?     

The locomotive assignment problem: a survey on optimization models

The objective of the locomotive assignment problem (LAP) is to assign a fleet of locomotives to a set of trains while satisfying a rich set of operational and budget constraints and optimizing one or more crucial objectives. This paper presents a survey on optimization models for this class of scheduling problems. Depending on the context of application, the kind of model used to solve the LAP varies; accordingly, this influences the solution methodology and algorithms used. In this paper, we review and also suggest a classification of the various models.     

具有椭圆盘极点约束的线性系统滤波器设计

针对一类线性系统,研究其闭环系统极点具有椭圆盘约束的情况下滤波器设计问题。在极点约束的前提下,控制目标要求闭环系统渐近稳定,并且干扰抑制性能指标小于给定的上界。首先利用有界实引理给出椭圆盘极点约束前提下滤波器的存在条件;其次,利用线性矩阵不等式技术,将滤波器的存在性问题转化成一组线性矩阵不等式(LMI)的可行性问题,根据LMI的可行解,给出滤波器的设计方法,由该方法得到的滤波器满足给定的性能要求;最后,数值算例说明该方法的有效性和可行性。