An improved iterative stochastic multi-objective acceptability analysis method for robust alternative selection in new product development

Alternative selection in new product development (NPD) is a multi-criteria decision-making (MCDM) problem. It usually starts with incomplete, imprecise or even partially missing information. Currently, most existing methods in dealing with this problem cannot work well if required information is incomplete or missing. It is acknowledged that stochastic multi-objective acceptability analysis (SMAA) can be applied to address MCDM problem with incomplete preference information and uncertain criteria measurements. In SMAA, alternatives are evaluated based on SMAA measurements (acceptability index, central weight vector and confidence factor). The discriminability of SMAA for the optimum alternative heavily depends on differences of SMAA measurements among different alternatives. Usually, a large number of alternatives and high level of uncertainty are involved in alternative selection in NPD. In this situation, the differences among SMAA measurements are not obvious, and therefore SMAA cannot deal with such problem very well. To this end, this paper proposes an improved SMAA method called Iterative-SMAA (I-SMAA) for alternative selection in NPD. In the I-SMAA, an iterative multi-step decision-making process is suggested to improve differences of SMAA measurements among different alternatives, and thus assist decision makers (DMs) to positively discern from the most preferred alternative. To enhance the decision-making efficiency, sensitive criteria are acquired in each iteration by ranking sensitivity analysis. DMs are guided to provide partial preference information and give more accurate criteria measurements for sensitive criteria rather than all criteria. Eventually, to verify the proposed method, a numerical example of the existing literature is solved with the method, and the results are compared. And then, a practical example of a preparation equipment for coal samples is further employed to verify the practicability of the proposed I-SMAA.     

融入局部几何特征的流形谱聚类图像分割

为了改善谱聚类图像分割的精准性和时效性,文中提出融入局部几何特征的流形谱聚类图像分割算法.首先,考虑图像数据的流形结构,在数据点的K近邻域内执行局部PCA,得到数据间本征维数的关系.然后,引入流形学习中的局部线性重构技术,通过混合线性分析器得到数据间局部切空间的相似性,结合二者构造含有局部几何特征的相似性矩阵.再利用Nystr m技术逼近待分割图像的特征向量,对构造的k个主特征向量执行谱聚类.最后,在Berkeley数据集上的对比实验验证文中算法的准确性和时效性优势.     

Top-k-size keyword search on tree structured data

Keyword search is the most popular technique for querying large tree-structured datasets, often of unknown structure, in the web. Recent keyword search approaches return lowest common ancestors (LCAs) of the keyword matches ranked with respect to their relevance to the keyword query. A major challenge of a ranking approach is the efficiency of its algorithms as the number of keywords and the size and complexity of the data increase. To face this challenge most of the known approaches restrict their ranking to a subset of the LCAs (e.g., SLCAs, ELCAs), missing relevant results.In this work, we design novel top-k-size stack-based algorithms on tree-structured data. Our algorithms implement ranking semantics for keyword queries which is based on the concept of LCA size. Similar to metric selection in information retrieval, LCA size reflects the proximity of keyword matches in the data tree. This semantics does not rank a predefined subset of LCAs and through a layered presentation of results, it demonstrates improved effectiveness compared to previous relevant approaches. To address performance challenges our algorithms exploit a lattice of the partitions of the keyword set, which empowers a linear time performance. This result is obtained without the support of auxiliary precomputed data structures. An extensive experimental study on various and large datasets confirms the theoretical analysis. The results show that, in contrast to other approaches, our algorithms scale smoothly when the size of the dataset and the number of keywords increase.     

矿床开采设计方案评价与排序的DEA模型

矿床开采设计方案的综合评价与排序是一典型的多目标决策问题,传统的决策方法多由人为主观地给出各评价指标（目标）的权系数计算出综合排序指标,难以保证评价系统的客观性。运用DEA方法的C^2R基本模型,客观地判断设计方案的相对有效性,较好的体现了采矿方案设计的基本理念。同时构造评价系统的相对最差方案,计算评价系统中各方案的效率指数,可有效的对各设计方案进行排序分析;对于非DEA有效的设计方案,通过其在DEA相对有效面上的“投影”,可以直接判定其存在的不足和设计改进方案。该模型实际应用简单方便,决策信息充分,对多设计方案的评价具有较强的针对性和可操作性。     

Using Landscape Characteristics for Targeting Habitat Conservation and Restoration: A case study of ancient semi-natural woodland in the Chiltern Hills Area of Outstanding Natural Beauty,UK

Ancient semi-natural deciduous woodland is a rare and threatened habitat cited by the European Union's Habitats Directive (92/43/EEC). Current protection of this endangered habitat in the UK takes the form of site-specific reserve designations but, if the habitat is to be conserved and enhanced, a landscape-scale selective targeting system is required. In the absence of suitable species data, physical attributes of habitat patches are used as surrogate indicators of biodiversity. Using a land-use database held on a geographical information system, values of four abiotic criteria (patch area, patch shape, nearest-neighbour distance and surrounding land use) of 48 ancient semi-natural woodland patches in part of the Chiltern Hills Area of Outstanding Natural Beauty were calculated. In order to validate the findings, three lists of floral and faunal species, indicative of ancient semi-natural woodland, were compiled, and the patches ranked according to their alpha diversity using each list. Spearman's rank correlation coefficients were calculated for patch alpha diversity and patch area (0.322 to 0.482), patch shape (0.275 to 0.465) and nearest-neighbour distance ( - 0.092 to - 0.223). Multiple regression indicated that between 15 and 22% of the variation in alpha patch diversity was explained by the abiotic factors. It is suggested that spatial targeting at the landscape scale is a valuable approach to habitat conservation.     

Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine

Fault diagnosis for wind turbine transmission systems is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine transmission systems. In this paper, a novel fault diagnosis method based on manifold learning and Shannon wavelet support vector machine is proposed for wind turbine transmission systems. Firstly, mixed-domain features are extracted to construct a high-dimensional feature set characterizing the properties of non-stationary vibration signals from wind turbine transmission systems. Moreover, an effective manifold learning algorithm with non-linear dimensionality reduction capability, orthogonal neighborhood preserving embedding (ONPE), is applied to compress the high-dimensional feature set into low-dimensional eigenvectors. Finally, the low-dimensional eigenvectors are inputted into a Shannon wavelet support vector machine (SWSVM) to recognize faults. The performance of the proposed method was proved by successful fault diagnosis application in a wind turbine's gearbox. The application results indicated that the proposed method improved the accuracy of fault diagnosis.     

Progressively Censored Samples in Life Testing

In life and dosage-response studies, progressively censored samples arise when at various stages of an experiment, some though not all of the surviving sample specimens are eliminated from further observation. The sample specimens remaining after each stage of censoring are continued under observation until failure or until a subsequent stage of censoring. In this paper maximum likelihood estimators of the distribution parameters are derived for the normal, and for the exponential distribution when samples are progressively censored.     

Negative statements considered useful

Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialog. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statistical inference method for compiling and ranking negative statements, based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research.     

A distance-type-insensitive clustering approach

In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attribute/feature ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the ranks of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternative approaches. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles.     

Optimization of Mixing and Mass Transfer in In-line Multi-Jet Ozone Contactors Using Computational Fluid Dynamics

Typical ozone mixing and mass transfer calculations are lumped approaches based on ideal operating conditions and can misrepresent behavior in real-life installations. This article models the effect of local hydrodynamics and mixing on the overall mass transfer of ozone into water with the aid of multiphase computational fluid dynamics (CFD). CFD models were validated with measured data from a pipeline ozone contactor installation which was optimized for more rapid, uniform mixing and mass transfer. Results emphasize the sensitivity of mixing quality to nozzle placement, size, orientation and spacing relative to main pipeline diameter and flows.