遗传距离聚类法和模型聚类法在地方鸡种群体遗传结构分析中的比较

利用16个微卫星标记,计算品种间DA遗传距离和基因流(Nm),并基于DA遗传距离运用NJ算法和基于Structure程序构建2类聚类图,比较分析10个地方鸡品种群体间的遗传结构和亲缘关系。结果表明:基于DA遗传距离运用NJ算法构建的聚类图将10个地方鸡品种总体上分为2大类:轻体型的鸡种(包括茶花鸡、藏鸡、仙居鸡、固始鸡和白耳鸡)和重体型的鸡种(包括狼山鸡、大骨鸡、北京油鸡、鹿苑鸡和萧山鸡);在轻体型的类别中,茶花鸡和藏鸡、仙居鸡和固始鸡聚为一类;在重体型的类别中,鹿苑鸡和萧山鸡聚为一类,狼山鸡、大骨鸡和北京油鸡则表现为独立分支,聚类结果与品种间基因流动相一致。Structure程序在预先未标识个体品种来源的条件下,准确推断出10个地方鸡种群体所属类别,构建的聚类图与遗传距离聚类结果基本一致。Structure程序还根据个体基因组分数,指出了10个地方鸡种群体中迁移个体和具有复杂遗传基础个体的分布,这是遗传距离聚类法所不能揭示的。

运用模型分类法分析9个地方鸡品种间遗传结构和亲缘关系

以16个中度或高度多态性微卫星标记,运用基于模型分类法的Structure程序推断9个地方鸡品种群体遗传结构,构建聚类图分析相互间亲缘关系,并鉴定群体中迁移个体和具有复杂遗传基础个体的分布。结果表明:Structure程序在预先未标明品种起源的条件下准确推断出9个地方鸡种群体所属类别,揭示按照体型外貌、地理分布等为依据划分的9个地方鸡品种类型,也真实反映了其在遗传上的差异。Structure聚类图在K=2时将9个地方鸡种分为轻体型的鸡种(包括茶花鸡、藏鸡、仙居鸡、固始鸡和白耳鸡)和重体型的鸡种(包括大骨鸡、北京油鸡、鹿苑鸡和萧山鸡)两个类别;K=4、5、6时北京油鸡、固始鸡、大骨鸡分离出来形成独立类群;仙居鸡与白耳鸡在K=6、萧山鸡与鹿苑鸡在K=7、藏鸡与茶花鸡在K=8时仍聚在同一类别中,这与9个地方鸡种的地理分布及形成历史相吻合。9个地方鸡品种群体中均有具有复杂遗传基础的个体分布,迁移个体只分布在藏鸡、萧山鸡和鹿苑鸡群体中。

智慧建造绿色施工技术应用效益评价模型体系

智慧建造在施工阶段的应用为绿色施工技术的发展提供了理论以及技术支持,尤其是BIM技术在建筑业中的应用,为绿色建造技术的发展增添了动力。本文基于施工阶段的实践,总结了智慧建造技术在绿色施工中的创新性应用。从节能、节材、环保等方面对施工阶段不同施工过程的主要应用点进行分析、评价,以及在绿色施工过程中的应用情况,定量化的进行经济效益评价。通过建立评价模型体系,辅助项目策划以及决策,推广应用成熟的施工经验,促进智慧建造绿色施工技术的应用。

超效率DEA模型在人口发展评价中的应用

本文阐述了数据包络分析的基本原理与方法，用超效率DEA模型与聚类分析对江苏省人口发展效率进行动态评价，并提出相应的政策建议。

Brain MRI Segmentation Using KFCM and Chan-Vese Model

To extract region of interests （ROI） in brain magnetic resonance imaging （MRI） with more than two objects and improve the segmentation accuracy, a hybrid model of a kemel-based fuzzy c-means （KFCM） clustering algorithm and Chan-Vese （CV） model for brain MRI segmentation is proposed. The approach consists of two succes- sive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clus- tering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means （FCM） clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.

Recognition of Finger Motion with sEMG and Gyrosensor Signals

A novel method to infer the finger flexing motions of various arm postures is proposed. From the gyroscope signal, the authors recognized forearm posture using K-means clustering method. Then finger motion inferred. For finger motion inference, Gaussian model of information entropy and maximum likelibood method was utilized. Experimentally it is obtained that the average recognition rate with the forearm posture inference is much higher than those without the inference by 30.7%.

Bag-of-visual-words model for artificial pornographic images recognition

It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.

Research on Parallel K-Medoids algorithm based on MapReduce

In order to solve the bottleneck problem of the traditional K-Medoids clustering algorithm facing to deal with massive data information at the time of memory capacity and processing speed of CPU, the paper proposed a parallel algorithm MapReduce programming model based on the research of K-Medoids algorithm. This algorithm increase the computation granularity and reduces the communication cost ratio based on the MapReduce model. The experimental results show that the improved parallel algorithm compared with other algorithms, speedup and operation efficiency is greatly enhanced.

Hierarchical method of task assignment for multiple cooperating UAV teams

The problem of task assignment for multiple cooperating unmanned aerial vehicle(UAV) teams is considered. Multiple UAVs forming several small teams are needed to perform attack tasks on a set of predetermined ground targets. A hierarchical task assignment method is presented to address the problem. It breaks the original problem down to three levels of sub-problems: target clustering, cluster allocation and target assignment. The first two sub-problems are centrally solved by using clustering algorithms and integer linear programming, respectively, and the third sub-problem is solved in a distributed and parallel manner, using a mixed integer linear programming model and an improved ant colony algorithm. The proposed hierarchical method can reduce the computational complexity of the task assignment problem considerably, especially when the number of tasks or the number of UAVs is large. Experimental results show that this method is feasible and more efficient than non-hierarchical methods.