Two-tuple linguistic utility aggregation operator and its applications to group decision-making

This paper aims to develop an aggregation operator for two-tuple linguistic information based on utility function, which characterizes the influence of decision makers' psychological factors on the linguistic aggregation process. First, we propose a new two-tuple linguistic ordered utility aggregation (TOU) operator, and then, we investigate its properties that are suitable for any utility function. Subsequently, we derive a specific form of the TOU operator, which is called the two-tuple linguistic generalized ordered weighted utility averaging-hyperbolic absolute risk aversion (TOHU) operator, under the hyperbolic absolute risk aversion utility function. Then, we further investigate its families including a wide range of aggregation operators. To determine the weights of the TOHU operator, which take the form of two-tuple linguistic, we establish an optimization weighting model by combining the information of input arguments and subjective considerations of decision makers. Furthermore, we propose a two-tuple linguistic aggregation method to deal with the multiple attribute group decision-making (MAGDM) problem based on the TOHU operator. Finally, we provide an example to demonstrate the application of the TOHU operator to MAGDM.     

The Trace transform and its applications

The Trace transform proposed, a generalization of the Radon transform, consists of tracing an image with straight lines along which certain functionals of the image function are calculated. Different functionals that can be used may be invariant to different transformations of the image. The paper presents the properties the functionals must have in order to be useful in three different applications of the method: construction of invariant features to rotation, translation and scaling of the image, construction of sensitive features to the parameters of rotation, translation and scaling of the image, and construction of features that may correlate well with a certain phenomenon we wish to monitor     

The VISTA project and its applications

In Arizona, a window of opportunity exists for using planned infrastructure expenditures to construct intelligent lanes on Interstate Highway 10 between Phoenix and Tucson for deploying intelligent vehicles (IVs). In 1998, the University of Arizona formed the Vehicles with Intelligent Systems for Transport Automation research team, which the ADOT charged with the mission of investigating new and existing technologies and concepts that address those issues. The Arizona state legislature and ADOT funded the VISTA project initially. The paper discusses the vehicle and control system.     

Prototyping synchronous group applications

Cooperative team-based activities are changing the nature of work. Current frameworks provide the necessary base communication and coordination tools but require application designers to handle low-level details such as defining a communication protocol. They also provide limited support for prototyping such applications and experimenting with alternative designs. We introduce a sharing style called strong sharing. Its implementation in Object World insulates application designer from low-level communication details. CoSARA, a system built on Object World, lets application designers prototype synchronous group applications by graphically specifying the multiuser interactions. After we describe strong sharing, we describe Object World and how it facilitates building synchronous group applications. Then we describe the CoSARA design methodology for prototyping synchronous group applications and show how we used it to build a multiuser block diagram editor     

The generalized matrix product and its applications

In this paper we develop the notion of a generalized matrix product that includes in its formulation the common matrix and vector products of linear algebra. After defining the generalized matrix product and investigating some of its properties, we provide some specific examples of its use in image processing.     

A group theoretic construction of highly nonlinear substitution box and its applications in image encryption

Substitution box is the most significant component of block cipher. The property of nonlinearity is of great importance for the design of secure substitution boxes. Therefore, it is necessary to develop new methods for the production of substitution boxes with a high non-linearity score. In this paper, we have introduced a novel group theoretic method to construct a robust S-box with non-linearity score 113.75 greater than that of AES S-box. The performance of generated S-box is found to be excellent, when examined through various other well-known algebraic criteria such as strict avalanche criterion, bit independence criterion, differential uniformity and linear approximation probability. The suitability of proposed S-box is tested for image encryption applications through different statistical analyses. We got very encouraging outcomes from all these examinations which certify that the generated S-box meets all the criteria needed to be reliable for secure communication and image encryption.

     

The theoretical analysis of GLRAM and its applications

Matrix-based methods such as two-dimensional principal component analysis (2DPCA) and generalized low rank approximations of matrices (GLRAM) have gained wide attention from researchers due to their computational efficiency. In this paper, we propose a non-iterative algorithm for GLRAM. Firstly, the optimal property of GLRAM is revealed, which is closely related to PCA. Moreover, it also shows that the reconstruction error of GLRAM is not smaller than that of PCA when considering the same dimensionality. Secondly, a non-iterative algorithm for GLRAM is derived. And the proposed method obtains smaller reconstruction error than 2DPCA or GLRAM. Finally, experimental results on face images and handwritten numeral characters show that the proposed method can achieve competitive results with some existing methods such as 2DPCA and PCA in terms of the classification performance or the reconstruction error.     

PARLOG and its applications

The key concepts of the parallel logic programming language PARLOG are introduced by comparing the language with Prolog. Some familiarity with Prolog and with the concepts of logic programming is assumed. Two major application areas of PARLOG, systems programming and object-oriented programming, are illustrated. Other applications are briefly surveyed     

The k-nearest neighbour-based GMDH prediction model and its applications

This paper centres on a new GMDH (group method of data handling) algorithm based on the k-nearest neighbour (k-NN) method. Instead of the transfer function that has been used in traditional GMDH, the k-NN kernel function is adopted in the proposed GMDH to characterise relationships between the input and output variables. The proposed method combines the advantages of the k-nearest neighbour (k-NN) algorithm and GMDH algorithm, and thus improves the predictive capability of the GMDH algorithm. It has been proved that when the bandwidth of the kernel is less than a certain constant C, the predictive capability of the new model is superior to that of the traditional one. As an illustration, it is shown that the new method can accurately forecast consumer price index (CPI).     

The reduction of binary fuzzy relations and its applications

This paper introduces the concept of T-reduction of binary fuzzy relations and establishes its properties. It presents an approach to providing the one-valued restoration of any boundary transitive and T-asymmetric binary fuzzy relation by its T-reduction. A theoretical basis for this approach is established, showing that T-reduction is the smallest fuzzy relation whose T-transitive closure coincides with the given binary fuzzy relation and where an (n × n) fuzzy relation on a finite universe with cardinality n can be represented by n − 1 values. This approach opens up an original way for constructing the membership functions of certain binary fuzzy relations. Important examples for the application of this concept are given.     

The parameterization reduction of soft sets and its applications

In this paper, we focus our discussion on the parameterization reduction of soft sets and its applications. First we point out that the results of soft set reductions offered in [1] are incorrect. We also observe that the algorithms used to first compute the reduct-soft-set and then to compute the choice value to select the optimal objects for the decision problems in [1] are not reasonable and we illustrate this with an example. Finally, we propose a reasonable definition of parameterization reduction of soft sets and compare it with the concept of attributes reduction in rough sets theory. By using this new definition of parameterization reduction, we improve the application of a soft set in a decision making problem found in [1].     

The multi-fuzzy N-soft set and its applications to decision-making

Neural Computing and Applications - The goal of this paper is to introduce a novel hybrid model called multi-fuzzy N-soft set, and to design an adjustable decision-making methodology for solving...     

Camera-sampling field and its applications

In this paper, we propose a novel vector field, called a camera-sampling field, to represent the sampling density distribution of a pinhole camera. We give the derivation and discuss some essential properties of the camera-sampling field, including flux, divergence, curl, gradient, level surface, and sampling patterns. This vector field reveals camera-sampling concisely and facilitates camera sampling analysis. The usage for this vector field in several computer graphics applications is introduced, such as determining the splat kernel for image-based rendering, texture filtering, mipmap level selection, level transition criteria for LOD, and LDI-construction.     

Graph-based induction and its applications

A machine learning technique called Graph-based induction (GBI) efficiently extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). In this paper, we introduce GBI for general graph structured data, which can handle directed/undirected, colored/uncolored graphs with/without (self) loop and with colored/uncolored links. We show that its time complexity is almost linear with the size of graph. We, further, show that GBI can effectively be applied to the extraction of typical patterns from DNA sequence data and organochlorine compound data from which are to be generated classification rules, and that GBI also works as a feature construction component for other machine learning tools.     

Volume splitting and its applications

Splitting a volumetric object is a useful operation in volume graphics and its applications, but is not widely supported by existing systems for volume-based modeling and rendering. In this paper, we present an investigation into two main algorithmic approaches, namely, explicit and implicit splitting, for modeling and rendering splitting actions. We consider a generalized notion based on scalar fields, which encompasses discrete specifications (e.g., volume data sets) as well as procedural specifications (e.g., hypertextures) of volumetric objects. We examine the correctness, effectiveness, efficiency, and deficiencies of each approach in specifying and controlling a spatial and temporal specification of splitting. We propose methods for implementing these approaches and for overcoming their deficiencies. We present a modeling tool for creating specifications of splitting functions, and describe the use of volume scene graphs for facilitating direct rendering of volume splitting. We demonstrate the use of these approaches with examples of volume visualization, medical illustration, volume animation, and special effects     

Zoom tracking and its applications

We present a new active vision technique called zoom tracking. Zoom tracking is the continuous adjustment of a camera's focal length in order to keep a constant-sized image of an object moving along the camera's optical axis. Two methods for performing zoom tracking are presented: a closed-loop visual feedback algorithm based on optical flow, and use of depth information obtained from an autofocus camera's range sensor. We explore two uses of zoom tracking: recovery of depth information and improving the performance of scale-variant algorithms. We show that the image stability provided by zoom tracking improves the performance of algorithms that are scale variant, such as correlation-based trackers. While zoom tracking cannot totally compensate for an object's motion, due to the effect of perspective distortion, an analysis of this distortion provides a quantitative estimate of the performance of zoom tracking. Zoom tracking can be used to reconstruct a depth map of the tracked object. We show that under normal circumstances this reconstruction is much more accurate than depth from zooming, and works over a greater range than depth from axial motion while providing, in the worst case, only slightly less accurate results. Finally, we show how zoom tracking can also be used in time-to-contact calculations. Received: 15 February 2000 / Accepted: 19 June 2000     

人工情感及其应用

自然系统中,情感影响人的行为.目前,人工情感在拟人agent的研究得到了越来越多的重视.为此,本文从工程角度综述了人工情感研究的基本问题,简要介绍了几类情感模型,并着重讨论了基于人工情感的agent体系结构的典型应用.人工情感的研究不仅体现在情感辨识、情感表达等人机交互方面,同时情感的作用机理也影响到智能agent的控制体系结构和算法设计.基于人工情感的体系结构具有混合分层的特点,强调情感和其他过程的联系以增强agent在与动态环境交互中的自适应能力.在这个结构中人工情感的核心作用主要体现在两个方面:首先,人工情感是联系agent的内部状态和外部环境的基础,情感状态影响到agent信息处理的整个过程,包括紧急情况下的快速响应和规划任务时的复杂推理.其次,人工情感作为内部驱动机制对学习过程十分重要,人工情感作为强化刺激促使agent创建更复杂的行为功能.在设计中引入人工情感,对agent实现智能化和个性化至关重要.     

拓扑指数及其应用

拓扑指数是从化合物的结构图衍生出来的一种数学不变量,由于计算简便,取值客观,近年来在各领域的应用中取得了丰硕的成果,备受研究者关注。本文主要介绍几类有代表性的拓扑指数及其在定量结构-性质/活性关系(QSPR/QSAR)研究中的应用,讨论了它们的优缺点,提出了拓扑指数的发展趋势。     

二阶控制论及其应用

本文综述二阶控制论学派的缘起和成就,包括它的创导人冯·福尔斯特和他的BCL实验室,以及它的主要论点和与前期控制论的差异.介绍了二阶控制论在对话和理解协议方面、以及管理和经济系统方面的应用.最后本文作者对学派的工作进行了分析和评论.