Indirect augmented reality

Developing augmented reality (AR) applications for mobile devices and outdoor environments has historically required a number of technical trade-offs related to tracking. One approach is to rely on computer vision which provides very accurate tracking, but can be brittle, and limits the generality of the application. Another approach is to rely on sensor-based tracking which enables widespread use, but at the cost of generally poor tracking performance. In this paper we present and evaluate a new approach, which we call Indirect AR, that enables perfect alignment of virtual content in a much greater number of application scenarios.To achieve this improved performance we replace the live camera view used in video see through AR with a previously captured panoramic image. By doing this we improve the perceived quality of the tracking while still maintaining a similar overall experience. There are some limitations of this technique, however, related to the use of panoramas. We evaluate these boundaries conditions on both a performance and experiential basis through two user studies. The result of these studies indicates that users preferred Indirect AR over traditional AR in most conditions, and when conditions do degrade to the point the experience changes, Indirect AR can still be a very useful tool in many outdoor application scenarios.     

A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets

Almost all subspace clustering algorithms proposed so far are designed for numeric datasets. In this paper, we present a k-means type clustering algorithm that finds clusters in data subspaces in mixed numeric and categorical datasets. In this method, we compute attributes contribution to different clusters. We propose a new cost function for a k-means type algorithm. One of the advantages of this algorithm is its complexity which is linear with respect to the number of the data points. This algorithm is also useful in describing the cluster formation in terms of attributes contribution to different clusters. The algorithm is tested on various synthetic and real datasets to show its effectiveness. The clustering results are explained by using attributes weights in the clusters. The clustering results are also compared with published results.     

FNN上的竞争学习及混合学习方法

本文根据（１）所提出的ＦＮＮ结构,首先讨论它的学习方法,字除了可以应用到ＦＮＮ上的输入均值和输出权重的调整外,还可以用于实现网络连线的裁剪。然后对混合学习竞争学习与反向传播学习结合使用）算法的实现问题加以探讨。     

A ranking selection-based particle swarm optimizer for engineering design optimization problems

Particle swarm optimization (PSO) algorithms have been proposed to solve optimization problems in engineering design, which are usually constrained (possibly highly constrained) and may require the use of mixed variables such as continuous, integer, and discrete variables. In this paper, a new algorithm called the ranking selection-based PSO (RSPSO) is developed. In RSPSO, the objective function and constraints are handled separately. For discrete variables, they are partitioned into ordinary discrete and categorical ones, and the latter is managed and searched directly without the concept of velocity in the standard PSO. In addition, a new ranking selection scheme is incorporated into PSO to elaborately control the search behavior of a swarm in different search phases and on categorical variables. RSPSO is relatively simple and easy to implement. Experiments on five engineering problems and a benchmark function with equality constraints were conducted. The results indicate that RSPSO is an effective and widely applicable optimizer for optimization problems in engineering design in comparison with the state-of-the-art algorithms in the area.     

Simplifying the weft hierarchy

We give simple, self-contained proofs of the basic hardness results for the classes W[t]$W[t]$ of the weft hierarchy. We extend these proofs to higher levels of the hierarchy and illuminate the distinctions among its classes. The anti-monotone collapse at W[1,s]$W[1,s]$ and the normalization of weft-t$t$ formulas arise as by-products of the proofs.     

IDL与VisualC／C＋＋语言进行混合编程的方法研究

介绍将VisualC／C＋＋和IDL语言的诸多特点相互结合、相互补充以进行应用程序开发的技术,并用简单、代表性的实例程序进行验证。实践证明,在共享目标库机制上实现IDL与VisualC/C＋＋的混合编程技术,可以实现两种语言的资源和功能共享,达到优势互补。     

Optimal cyclic single crane scheduling for two parallel train oilcan repairing lines

Crane is widely used to move a heavy object from one place to another not only in manufacturing industry but also service industry. As an important resource in the train oilcan repairing, crane scheduling affects directly the productivity of the systems. In this paper, we study cyclic single crane scheduling problem with two parallel train oilcan repairing lines, where jobs are loaded into the line at one end and unloaded at the other end. The processing time at each workstation must be within a given range. There is no buffer between these stations. A crane is used to move jobs between the workstations in two parallel lines. The objective is to schedule the moves to minimize the production cycle. We proposed a time way diagram for two parallel lines and developed a mixed integer linear programming model. Then we extended the model to the scheduling problem with multi-station to eliminate the bottleneck in lines. Examples are given to demonstrate the effectiveness of the model.     

Alternative mixed integer linear programming models for identifying the most efficient decision making unit in data envelopment analysis

A mixed integer linear model for selecting the best decision making unit (DMU) in data envelopment analysis (DEA) has recently been proposed by Foroughi [Foroughi, A. A. (2011a). A new mixed integer linear model for selecting the best decision making units in data envelopment analysis. Computers and Industrial Engineering, 60(4), 550–554], which involves many unnecessary constraints and requires specifying an assurance region (AR) for input weights and output weights, respectively. Its selection of the best DMU is easy to be affected by outliers and may sometimes be incorrect. To avoid these drawbacks, this paper proposes three alternative mixed integer linear programming (MILP) models for identifying the most efficient DMU under different returns to scales, which contain only essential constraints and decision variables and are much simpler and more succinct than Foroughi’s. The proposed alternative MILP models can make full use of input and output information without the need of specifying any assurance regions for input and output weights to avoid zero weights, can make correct selections without being affected by outliers, and are of significant importance to the decision makers whose concerns are not DMU ranking, but the correct selection of the most efficient DMU. The potential applications of the proposed alternative MILP models and their effectiveness are illustrated with four numerical examples.     

Efficient solution of the 3G network planning problem

The 3G universal mobile telecommunications system (UMTS) planning problem is combinatorially explosive and difficult to solve optimally, though solution methods exist for its three main subproblems (cell, access network, and core network planning). We previously formulated the entire problem as a single integrated mixed-integer linear program (MIP) and showed that only small instances of this global planning problem can be solved to optimality by a commercial MIP solver within a reasonable amount of time ( St-Hilaire, Chamberland, & Pierre, 2006). Heuristic methods are needed for larger instances. This paper provides the first complete formulation for the heuristic sequential method ( St-Hilaire, Chamberland, & Pierre, 2005) that re-partitions the global formulation into the three conventional subproblems and solves them in sequence using a MIP solver. This greatly improves the solution time, but at the expense of solution quality. We also develop a new hybrid heuristic that uses the results of the sequential method to generate constraints that provide tighter bounds for the global planning problem with the goal of reaching the provable optimum solution much more quickly. We empirically evaluate the speed and solution accuracy of four solution methods: (i) the direct MIP solution of the global planning problem, (ii) a local search heuristic applied to the global planning problem, (iii) the sequential method and (iv) the new hybrid method. The results show that the sequential method provides very good results in a fraction of the time needed for the direct MIP solution of the global problem, and that optimum results can be provided by the hybrid heuristic in greatly reduced time.     

基于混合加密算法与FUK技术的SMIS数据安全设计与实现

以大型销售MIS的数据安全问题为例,提出以终端ID号、钥匙盘ID号和终端用户样本指纹绑定为一体的身份认证方案。该方案采用软硬件协同工作方式,克服了传统身份认证模式或安全性能低,或运算成本高等缺陷,提高了认证效率,并保证了合法用户操作的唯一性和认证过程的不可抵赖性。提出的基于ECC与一维混沌加密算法优点相结合的混合加密方案,使基于互联网传送的数据安全性有了实质性提高。基于分治叠加法的大整数计算成果运用,克服了计算机精度域的限制,使密钥空间从理论上趋向于无穷大。试验结果表明,基于混合加密算法的数据安全方案具有良好的抗穷举攻击和抗差分攻击能力。该项研究为解决各类MIS的数据安全问题提供了重要策略,意义重大。