Closed‐form Blending of Local Symmetries

We present a closed‐form solution for the symmetrization problem, solving for the optimal deformation that reconciles a set of local bilateral symmetries. Given as input a set of point‐pairs which should be symmetric, we first compute for each local neighborhood a transformation which would produce an approximate bilateral symmetry. We then solve for a single global symmetry which includes all of these local symmetries, while minimizing the deformation within each local neighborhood. Our main motivation is the symmetrization of digitized fossils, which are often deformed by a combination of compression and bending. In addition, we use the technique to symmetrize articulated models.     

Stabilization of a class of nonlinear uncertain ordinary differential equation by parabolic partial differential equation controller

This article is concerned with stabilization for a class of uncertain nonlinear ordinary differential equation (ODE) with dynamic controller governed by linear 1?d heat partial differential equation (PDE). The control input acts at the one boundary of the heat's controller domain and the second boundary injects a Dirichlet term in ODE plant. The main contribution of this article is the use of the recent infinite‐dimensional backstepping design for state feedback stabilization design of coupled PDE‐ODE systems, to stabilize exponentially the nonlinear uncertain systems, under the restrictions that (a) the right‐hand side of the ODE equation has the classical particular form: linear controllable part with an additive nonlinear uncertain function satisfying lower triangular linear growth condition, and (b) the length of the PDE domain has to be restricted. We solve the stabilization problem despite the fact that all known backstepping transformation in the literature cannot decouple the PDE and the ODE subsystems. Such difficulty is due to the presence of a nonlinear uncertain term in the ODE system. This is done by introducing a new globally exponentially stable target system for which the PDE and ODE subsystems are strongly coupled. Finally, an example is given to illustrate the design procedure of the proposed method.     

Global robust practical output regulation for nonlinear systems in output feedback form by output‐based event‐triggered control

In this paper, we study the event‐triggered global robust practical output regulation problem for a class of nonlinear systems in output feedback form with any relative degree. Our approach consists of the following three steps. First, we design an internal model and an observer to form the so‐called extended augmented system. Second, we convert the original problem into the event‐triggered global robust practical stabilization problem of the extended augmented system. Third, we design an output‐based event‐triggered control law and a Zeno‐free output‐based event‐triggered mechanism to solve the stabilization problem, which, in turn, leads to the solvability of the original problem. Finally, we apply our result to the controlled hyperchaotic Lorenz systems.     

Input-to-state stability for discrete-time time-varying systems with applications to robust stabilization of systems in power form

Input-to-state stability (ISS) of a parameterized family of discrete-time time-varying nonlinear systems is investigated. A converse Lyapunov theorem for such systems is developed. We consider parameterized families of discrete-time systems and concentrate on a semiglobal practical type of stability that naturally arises when an approximate discrete-time model is used to design a controller for a sampled-data system. An application of our main result to time-varying periodic systems is presented, and this is used to solve a robust stabilization problem, namely to design a control law for systems in power form yielding semiglobal practical ISS (SP-ISS).     

Design of multiobjective reconfigurable antenna array with discrete phase shifters using multiobjective differential evolution based on decomposition

The reconfigurable design problem is to find the element that will result in a sector pattern main beam with side lobes. The same excitation amplitudes applied to the array with zero phase should be in a high directivity, low‐side lobe pencil‐shaped main beam. This work presents a multiobjective approach to solve this problem. We consider two design objectives: the minimum value for the dual beam and the dynamic range ratio in qualify the entire array radiation pattern in order to achieve the optimal value between the antenna‐array elements. We use a recently developed and very competitive multiobjective evolutionary algorithm, called MOEA/D. This algorithm uses a decomposition approach to convert the problem of approximation of the Pareto Front into a number of single objective optimization problems. We illustrate that the best solutions obtained by the MOEA/D can outperform stat‐of‐art single objective algorithm: generalized generation‐gap model genetic algorithm (G3‐GA) and differential evolution algorithm (DE). In addition, we compare the results obtained by MOEA/D with those obtained by one of the most widely multiobjective algorithm called NSGA‐II and mutliobjective DE. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 22: 675–681, 2012.     

Learning Hierarchical Task Models from Input Traces

We describe HTN‐MAKER , an algorithm for learning hierarchical planning knowledge in the form of task‐reduction methods for hierarchical task networks (HTNs). HTN‐MAKER takes as input a set of planning states from a classical planning domain and plans that are applicable to those states, as well as a set of semantically annotated tasks to be accomplished. The algorithm analyzes this semantic information to determine which portion of the input plans accomplishes a particular task and constructs task‐reduction methods based on those analyses. We present theoretical results showing that HTN‐MAKER is sound and complete. Our experiments in five well‐known planning domains confirm the theoretical results and demonstrate convergence toward a set of HTN methods that can be used to solve any problem expressible as a classical planning problem in that domain, relative to a set of goal types for which tasks have been defined. In three of the five domains, HTN planning with the learned methods scales much better than a modern classical planner.     

A hybrid constraint programming approach to a wood procurement problem with bucking decisions

In this article, we present a wood procurement problem that arises in Eastern Canada. We solve a multi-period wood supply planning problem, while taking into account bucking decisions. Furthermore, we present a new form of flexibility which allows the harvesting capacity to change from one time period to another. We study the impact of such flexibility upon the harvesting cost. We assess the performance of the problem by comparing it with a variant where the harvesting capacity is fixed during sites’ harvesting. To address this problem, we develop a hybrid approach based on both constraint and mathematical programming. In the first phase, we propose a constraint programming model dealing with forest sites harvesting and bucking problems. The result of this model is used as part of an initial solution for the whole problem formulated as a mixed integer model. We test the two versions of the problem on a set of different demand instances and we compare their results.     

Further remark on P systems with active membranes and two polarizations

P systems (or membrane systems) are a class of distributed parallel computing devices of a biochemical type. In this paper, some restrictions on the general form of the developing rules are considered, under which it is still possible to solve NP-complete problems. We present an algorithm for deterministically deciding SAT in linear time by P systems with active membranes using two polarizations and rules of restricted versions of types (a), (c), (e). The result obtained in this paper answered an open problem proposed by Alhazov and Freund in the aspect of computing efficiency.     

Two‐Finger Gestures for 6DOF Manipulation of 3D Objects

Multitouch input devices afford effective solutions for 6DOF (six Degrees of Freedom) manipulation of 3D objects. Mainly focusing on large‐size multitouch screens, existing solutions typically require at least three fingers and bimanual interaction for full 6DOF manipulation. However, single‐hand, two‐finger operations are preferred especially for portable multitouch devices (e.g., popular smartphones) to cause less hand occlusion and relieve the other hand for necessary tasks like holding the devices. Our key idea for full 6DOF control using only two contact fingers is to introduce two manipulation modes and two corresponding gestures by examining the moving characteristics of the two fingers, instead of the number of fingers or the directness of individual fingers as done in previous works. We solve the resulting binary classification problem using a learning‐based approach. Our pilot experiment shows that with only two contact fingers and typically unimanual interaction, our technique is comparable to or even better than the state‐of‐the‐art techniques.     

Origamizing Polyhedral Surfaces

This paper presents the first practical method for "origamizing” or obtaining the folding pattern that folds a single sheet of material into a given polyhedral surface without any cut. The basic idea is to tuck fold a planar paper to form a three-dimensional shape. The main contribution is to solve the inverse problem; the input is an arbitrary polyhedral surface and the output is the folding pattern. Our approach is to convert this problem into a problem of laying out the polygons of the surface on a planar paper by introducing the concept of tucking molecules. We investigate the equality and inequality conditions required for constructing a valid crease pattern. We propose an algorithm based on two-step mapping and edge splitting to solve these conditions. The two-step mapping precalculates linear equalities and separates them from other conditions. This allows an interactive manipulation of the crease pattern in the system implementation. We present the first system for designing three-dimensional origami, enabling a user can interactively design complex spatial origami models that have not been realizable thus far.     

An integer programming column generation principle for heuristic search methods

There is an increasing interest in integrating column generation and heuristic approaches to efficiently solve large‐scale discrete optimisation problems. We contribute in this direction. Based on the insights from Lagrangian duality theory, we present an auxiliary problem that can be used for finding near‐optimal solutions to a discrete column‐oriented model. The structure of this auxiliary problem makes it suitable for being addressed with a heuristic search method involving column generation. To this end, we suggest a large neighbourhood search strategy where the repair step is to solve a column generation type subproblem. The suggested search strategy and mathematical models involved need to be tailored to the problem structure. To illustrate important design options and computational behaviour, four applications are studied: bin packing, generalised assignment, a resource allocation problem and the fixed‐charge transportation problem.     

Minimum common string partition: on solving large‐scale problem instances

Minimum common string partition is an NP‐hard combinatorial optimization problem from the bioinformatics field. The current state‐of‐the‐art algorithm is a hybrid technique known as construct, merge, solve, and adapt (CMSA). This algorithm combines two main algorithmic components: generating solutions in a probabilistic way and solving reduced subinstances obtained from the tackled problem instances, if possible, to optimality. However, the CMSA algorithm was not intended for application to very large problem instances. Therefore, in this paper we present a technique that makes CMSA, and other available algorithms for this problem, applicable to problem instances that are about one order of magnitude larger than the largest problem instances considered so far. Moreover, a reduced variable neighborhood search (RVNS) for solving the tackled problem, based on integer programming, is introduced. The experimental results show that the modified CMSA algorithm is very strong for problem instances based on rather small alphabets. With growing alphabet size, it turns out that RVNS has a growing advantage over CMSA.     

带有力觉和触觉临场感的灵巧手主从系统的设计

针对遥控作业中控制者操作时缺乏力觉和触觉临场感等问题，介绍了设计的带有力觉和触觉临场感主、从灵巧手系统，讨论了在从机械手上触觉和力觉的感知以及在主机械手上触觉和力觉的再现等问题，提出了利用模糊控制实现触觉再现以及改进的力反馈－位置型结构来实现力觉再现的新方法，最后进行了实验验证．     

Fast in‐memory XPath search using compressed indexes

Extensible Markup Language (XML) documents consist of text data plus structured data (markup). XPath allows to query both text and structure. Evaluating such hybrid queries is challenging. We present a system for in‐memory evaluation of XPath search queries, that is, queries with text and structure predicates, yet without advanced features such as backward axes, arithmetics, and joins. We show that for this query fragment, which contains Forward Core XPath, our system, dubbed Succinct XML Self‐Index (‘SXSI’), outperforms existing systems by 1–3 orders of magnitude. SXSI is based on state‐of‐the‐art indexes for text and structure data. It combines two novelties. On one hand, it represents the XML data in a compact indexed form, which allows it to handle larger collections in main memory while supporting powerful search and navigation operations over the text and the structure. On the other hand, it features an execution engine that uses tree automata and cleverly chooses evaluation orders that leverage the speeds of the respective indexes. SXSI is modular and allows seamless replacement of its indexes. This is demonstrated through experiments with (1) a text index specialized for search of bio sequences, and (2) a word‐based text index specialized for natural language search. Copyright © 2013 John Wiley & Sons, Ltd.     

Specifying and enforcing the principle of least privilege in role‐based access control

The principle of least privilege in role‐based access control is an important area of research. There are two crucial issues related to it: the specification and the enforcement. We believe that the existing least privilege specification schemes are not comprehensive enough and few of the enforcement methods are likely to scale well. In this paper, we formally define the basic principle of least privilege problem and present different variations, called the delta‐approx principle of least privilege problem and the minimizing‐approx principle of least privilege problem. Since there may be more than one result to enforce the same principle of least privilege, we introduce the notation about weights of permissions and roles to optimize the results. Then we prove that all least privilege problems are NP‐complete. As an important contribution of the paper, we show that the principle of least privilege problem can be reduced to minimal cost set covering (MCSC) problem. We can borrow the existing solutions of MCSC to solve the principle of least privilege problems. Finally, different algorithms are designed to solve the proposed least privilege problems. Experiments on performance study prove the superiority of our algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

Managing the consensus in group decision making in an unbalanced fuzzy linguistic context with incomplete information

To solve group decision-making problems we have to take in account different aspects. On the one hand, depending on the problem, we can deal with different types of information. In this way, most group decision-making problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts’ opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e., using term sets which are not uniformly and symmetrically distributed. On the other hand, there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem and, as a result, they may present incomplete information. The aim of this paper is to present a consensus model to help experts in all phases of the consensus reaching process in group decision-making problems in an unbalanced fuzzy linguistic context with incomplete information. As part of this consensus model, we propose an iterative procedure using consistency measures to estimate the incomplete information. In addition, the consistency measures are used together with consensus measures to guided the consensus model. The main novelty of this consensus model is that it supports the management of incomplete unbalanced fuzzy linguistic information and it allows to achieve consistent solutions with a great level of agreement.     

一种求解多车辆合乘匹配问题的适应性算法

车辆合乘匹配问题是研究如何通过优化车辆路线及车辆一乘客匹配来搭乘尽量多的乘客的问题。目前国内 外的研究多存在模型单一、脱离实际、算法效率不高等问题。针对该问题,提出一种基于吸引粒子群算法的问题求解 方法。通过吸引粒子群算法进行多车辆问题向单车辆问题的转化,形成车辆同乘客之间的初次匹配。根据初次匹配 结果利用先验聚类的思想将初次匹配结果进行排序,寻找较优需求序列排序方式。最后,通过相应的匹配再优化策略 将需求序列进行再优化。对比实验表明,基于吸引粒子群算法的问题求解方式能以较高的搭乘成功率以及较低的花 费完成车辆合乘匹配问题。     

A New Intuitionistic Fuzzy Linguistic Hybrid Aggregation Operator and Its Application for Linguistic Group Decision Making

The linguistic aggregation operator is an important decision‐making model that is proving effective for dealing with the input data that takes the form of uncertain information. In this paper, considering the principal component of the intuitionistic fuzzy linguistic variables, we develop a new intuitionistic fuzzy linguistic hybrid aggregation (NIFLHA) operator to solve group decision‐making problems under the situation with intuitionistic fuzzy linguistic information. Then, we study some of its main properties by utilizing some operational laws of intuitionistic fuzzy linguistic variables and the different families of the NIFLHA operator. Moreover, the multiperson NIFLHA (MP‐NIFLHA) operator is introduced to evaluate the opinions of experts. Finally, an illustrative example about a multiperson decision‐making problem is developed to reveal the applicability and the availability of the raised operator.     

The hysteretic Hopfield neural network

A new neuron activation function based on a property found in physical systems-hysteresis-is proposed. We incorporate this neuron activation in a fully connected dynamical system to form the hysteretic Hopfield neural network (HHNN). We then present an analog implementation of this architecture and its associated dynamical equation and energy function. We proceed to prove Lyapunov stability for this new model, and then solve a combinatorial optimization problem (i.e., the N-queen problem) using this network. We demonstrate the advantages of hysteresis by showing increased frequency of convergence to a solution, when the parameters associated with the activation function are varied.