Waves Generated by a Moving Source in a Two-Layer Ocean of Finite Depth

The velocity potentials of a point source moving at a constant velocity in the upper layer of a two-layer fluid are obtained in a form amenable to numerical integration. Each fluid layer is of finite depth, and the density difference between the two layers is not necessarily small. The far-field asymptotic behavior of the surface waves and internal waves are also derived using the method of stationary phase. They show that the wave system at the free surface or at the interface each contains contributions from two different modes: a surface-wave mode and an internal-wave mode. When the density difference between the two layers is small or the depth of the upper layer is large, the surface-wave mode mainly affects the surface waves while the internal-wave mode mainly affects the internal waves. However, for large density difference, both modes contribute to the surface wave or internal wave system. For each mode, both divergent and transverse waves are present if the total depth Froude number is less than a certain critical Froude number which is mode-dependent. For depth Froude number greater than the critical Froude number, only divergent waves exist for that mode. This classification is similar to that of a uniform fluid of finite depth, where the critical Froude number is simply unity. The surface waves and internal waves are also calculated using the full expressions of the source potentials. They further confirm and illustrate the features observed in the asymptotic analysis.     

Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning

The development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the clonal selection and immune suppression theories, with a sorting scheme featuring uniform crossover, multi-point mutation, non-dominance and crowding distance sorting to attain the Pareto optimal front in an efficient manner. The proposed algorithm was verified with nine benchmarking functions on its global optimal search ability as well as compared with four optimization algorithms to assess its diversity and spread. Sensitivity analysis was also carried out to investigate the selection of key parameters of the algorithm. It is found that the developed immunity-based hybrid evolutionary algorithm provides a useful means for solving optimization problems and has successfully applied to the problem of global repositioning of containers, which is one of a constrained multi-objective optimization problem. The developed HAIS will assist shipping liners on timely decision making and planning of container repositioning operations in global container transportation business in an optimized and cost effective manner.     

A quantified sensitivity measure for multilayer perceptron to input perturbation

The sensitivity of a neural network's output to its input perturbation is an important issue with both theoretical and practical values. In this article, we propose an approach to quantify the sensitivity of the most popular and general feedforward network: multilayer perceptron (MLP). The sensitivity measure is defined as the mathematical expectation of output deviation due to expected input deviation with respect to overall input patterns in a continuous interval. Based on the structural characteristics of the MLP, a bottom-up approach is adopted. A single neuron is considered first, and algorithms with approximately derived analytical expressions that are functions of expected input deviation are given for the computation of its sensitivity. Then another algorithm is given to compute the sensitivity of the entire MLP network. Computer simulations are used to verify the derived theoretical formulas. The agreement between theoretical and experimental results is quite good. The sensitivity measure can be used to evaluate the MLP's performance.     

A multi-faceted and automatic knowledge elicitation system (MAKES) for managing unstructured information

Management of unstructured information, such as emails, is vital for supporting knowledge work in professional services. However, the conventional way for managing unstructured information is inadequate as the knowledge work and associated tasks are becoming more complex, are dynamically changing with time and involve multiple concepts. This paper attempts to address the inadequacy, deficiency and limitations of the methods presently used to elicit knowledge from masses of unstructured information. These methods rely heavily on manpower, are time consuming and costly. With the development of a multi-faceted and automatic knowledge elicitation system (MAKES) manpower, time and cost can be dramatically reduced. The MAKES integrates the processes of collecting data, classifying unstructured information, modelling knowledge flow and social network analysis, and makes all of these actions into a connected process to audit unstructured information automatically. This audit is based on specific search criteria, search keywords, and the user behaviours of the knowledge workers. The unstructured information is automatically organized, classified and presented in a multi-facet taxonomy map. New concepts and knowledge are uncovered, analyzed and updated continuously from the incoming unstructured information, using a purpose-built knowledge elicitation algorithm named self-associated concept mapping (SACM). The capability and advantages of the MAKES are demonstrated through a successful trial implementation and a verification test conducted in an electronics trading company. Encouraging results have been achieved and a number of potential advantages have been realized. The area of application in this first deployment is based on an email-intensive organization and the proposed study will contribute to the advancement of methods and tools for managing other kinds of unstructured information.     

Active hashing and its application to image and text retrieval

In recent years, hashing-based methods for large-scale similarity search have sparked considerable research interests in the data mining and machine learning communities. While unsupervised hashing-based methods have achieved promising successes for metric similarity, they cannot handle semantic similarity which is usually given in the form of labeled point pairs. To overcome this limitation, some attempts have recently been made on semi-supervised hashing which aims at learning hash functions from both metric and semantic similarity simultaneously. Existing semi-supervised hashing methods can be regarded as passive hashing since they assume that the labeled pairs are provided in advance. In this paper, we propose a novel framework, called active hashing, which can actively select the most informative labeled pairs for hash function learning. Specifically, it identifies the most informative points to label and constructs labeled pairs accordingly. Under this framework, we use data uncertainty as a measure of informativeness and develop a batch mode algorithm to speed up active selection. We empirically compare our method with a state-of-the-art passive hashing method on two benchmark data sets, showing that the proposed method can reduce labeling cost as well as overcome the limitations of passive hashing.     

Approximations and reducts with covering generalized rough sets

The covering generalized rough sets are an improvement of traditional rough set model to deal with more complex practical problems which the traditional one cannot handle. It is well known that any generalization of traditional rough set theory should first have practical applied background and two important theoretical issues must be addressed. The first one is to present reasonable definitions of set approximations, and the second one is to develop reasonable algorithms for attributes reduct. The existing covering generalized rough sets, however, mainly pay attention to constructing approximation operators. The ideas of constructing lower approximations are similar but the ideas of constructing upper approximations are different and they all seem to be unreasonable. Furthermore, less effort has been put on the discussion of the applied background and the attributes reduct of covering generalized rough sets. In this paper we concentrate our discussion on the above two issues. We first discuss the applied background of covering generalized rough sets by proposing three kinds of datasets which the traditional rough sets cannot handle and improve the definition of upper approximation for covering generalized rough sets to make it more reasonable than the existing ones. Then we study the attributes reduct with covering generalized rough sets and present an algorithm by using discernibility matrix to compute all the attributes reducts with covering generalized rough sets. With these discussions we can set up a basic foundation of the covering generalized rough set theory and broaden its applications.     

Mining Customer Value: From Association Rules to Direct Marketing

Direct marketing is a modern business activity with an aim to maximize the profit generated from marketing to a selected group of customers. A key to direct marketing is to select a subset of customers so as to maximize the profit return while minimizing the cost. Achieving this goal is difficult due to the extremely imbalanced data and the inverse correlation between the probability that a customer responds and the dollar amount generated by a response. We present a solution to this problem based on a creative use of association rules. Association rule mining searches for all rules above an interestingness threshold, as opposed to some rules in a heuristic-based search. Promising association rules are then selected based on the observed value of the customers they summarize. Selected association rules are used to build a model for predicting the value of a future customer. On the challenging KDD-CUP-98 dataset, this approach generates 41% more profit than the KDD-CUP winner and 35% more profit than the best result published thereafter, with 57.7% recall on responders and 78.0% recall on non-responders. The average profit per mail is 3.3 times that of the KDD-CUP winner.     

通过全局核降低高斯核的局部风险与基于遗传算法的两阶段模型选择

支持向量分类时,由于样本分布的不均匀性,单宽度的高斯核会在空间的稠密区域产生过学习现象,在稀疏区域产生欠学习现象,即存在局部风险.针对于此,构造了一个全局性次核来降低高斯核产生的局部风险.形成的混合核称为主次核.利用幂级数构造性地给出并证明了主次核的正定性条件,进一步提出了基于遗传算法的两阶段模型选择算法来优化主次核的参数.实验验证了主次核和模型选择法的优越性.     

Influence and adjustment goals: Sources of cultural differences in ideal affect.

Previous studies have found that in American culture high-arousal positive states (HAP) such as excitement are valued more and low-arousal positive states (LAP) such as calm are valued less than they are in Chinese culture. What specific factors account for these differences? The authors predicted that when people and cultures aimed to influence others (i.e., assert personal needs and change others' behaviors to meet those needs), they would value HAP more and LAP less than when they aimed to adjust to others (i.e., suppress personal needs and change their own behaviors to meet others' needs). They test these predictions in 1 survey and 3 experimental studies. The findings suggest that within and across American and Chinese contexts, differences in ideal affect are due to specific interpersonal goals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)