Mixed films of TiO(2)-SiO(2) deposited by double electron-beam coevaporation

We used double electron-beam coevaporation to fabricate TiO(2)-SiO(2) mixed films. The deposition process included oxygen partial pressure, substrate temperature, and deposition rate, all of which were real-time computer controlled. The optical properties of the mixed films varied from pure SiO(2) to pure TiO(2) as the composition of the films varied accordingly. X-ray diffraction showed that the mixed films all have amorphous structure with a SiO(2) content of as low as 11%. Atomic force microscopy showed that the mixed film has a smoother surface than pure TiO(2) film because of its amorphous structure.

Linear and Bruggeman's effective medium approximation models fit the experimental data better than other models.

     

Global exponential robust stability of reaction–diffusion interval neural networks with continuously distributed delays

This paper considers the existence of the equilibrium point and its global exponential robust stability for reaction-diffusion interval neural networks with variable coefficients and distributed delays by means of the topological degree theory and Lyapunov-functional method. The sufficient conditions on global exponential robust stability established in this paper are easily verifiable. An example is presented to demonstrate the effectiveness and efficiency of our results.     

Filtering Data Streams for Entity-Based Continuous Queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?”). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based.” In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly.     

Ant colony optimization based sensor deployment protocol for wireless sensor networks

Sensor deployment is one of the most important issues in wireless sensor networks, because an efficient deployment scheme can reduce the deployment cost and enhance the detection capability of the wireless sensor networks. In addition, it can enhance the quality of monitoring in wireless sensor networks by increasing the coverage area. Ant colony optimization (ACO) algorithm provides a natural and intrinsic way of exploration of search space for multiple knapsack problem (MKP). In this work, we consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. We model the deployment problem as the multiple knapsack problem. Based on ACO algorithm, we proposed a deployment scheme to prolong the network lifetime, while ensuring complete coverage of the service region. The simulations show that our algorithm can prolong the lifetime of the network.     

An integrated fuzzy TOPSIS and MCGP approach to supplier selection in supply chain management

Supplier selection is an important issue in supply chain management. In recent years, determining the best supplier in the supply chain has become a key strategic consideration. However, these decisions usually involve several objectives or criteria, and it is often necessary to compromise among possibly conflicting factors. Thus, the multiple criteria decision making (MCDM) becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, this study proposes integrated fuzzy techniques for order preference by similarity to ideal solution (TOPSIS) and multi-choice goal programming (MCGP) approach to solve the supplier selection problem. The advantage of this method is that it allows decision makers to set multiple aspiration levels for supplier selection problems. The integrated model is illustrated by an example in a watch firm.     

A novel intrusion detection system based on hierarchical clustering and support vector machines

This study proposed an SVM-based intrusion detection system, which combines a hierarchical clustering algorithm, a simple feature selection procedure, and the SVM technique. The hierarchical clustering algorithm provided the SVM with fewer, abstracted, and higher-qualified training instances that are derived from the KDD Cup 1999 training set. It was able to greatly shorten the training time, but also improve the performance of resultant SVM. The simple feature selection procedure was applied to eliminate unimportant features from the training set so the obtained SVM model could classify the network traffic data more accurately. The famous KDD Cup 1999 dataset was used to evaluate the proposed system. Compared with other intrusion detection systems that are based on the same dataset, this system showed better performance in the detection of DoS and Probe attacks, and the beset performance in overall accuracy.     

On NFAs where all states are final,initial, or both

We examine questions involving nondeterministic finite automata where all states are final, initial, or both initial and final. First, we prove hardness results for the nonuniversality and inequivalence problems for these NFAs. Next, we characterize the languages accepted. Finally, we discuss some state complexity problems involving such automata.     

Matrix games with interval data

The conventional game theory is concerned with how rational individuals make decisions when they are faced with known payoffs. In the real world, sometimes the payoffs are not known and have to be estimated, and sometimes the payoffs are only approximately known. This paper develops a solution method for the two-person zero-sum game where the payoffs are imprecise and are represented by interval data. Since the payoffs are imprecise, the value of the game should be imprecise as well. A pair of two-level mathematical programs is formulated to obtain the upper bound and lower bound of the value of the game. Based on the duality theorem and by applying a variable substitution technique, the pair of two-level mathematical programs is transformed to a pair of ordinary one-level linear programs. Solving the pair of linear programs produces the interval of the value of the game. It is shown that the two players in the game have the same upper bound and lower bound for the value of the imprecise game. An example illustrates the whole idea and sheds some light on imprecise game.     

An index selection method without repeated optimizer estimations

The index selection problem (ISP) concerns the selection of an appropriate index set to minimize the total cost for a given workload containing read and update queries. Since the ISP has been proven to be an NP-hard problem, most studies focus on heuristic algorithms to obtain approximate solutions. However, even approximate algorithms still consume a large amount of computing time and disk space because these systems must record all query statements and frequently request from the database optimizers the cost estimation of each query in each considered index. This study proposes a novel algorithm without repeated optimizer estimations. When a query is delivered to a database system, the optimizer evaluates the costs of various query plans and chooses an access path for the query. The information from the evaluation stage is aggregated and recorded with limited space. The proposed algorithm can recommend indexes according to the readily available information without querying the optimizer again. The proposed algorithm was tested in a PostgreSQL database system using TPC-H data. Experimental results show the effectiveness of the proposed approach.     

Impacts of geographical knowledge, spatial ability and environmental cognition on image searches supported by GIS software

Google Earth search function was used to study the impacts of small-scale spatial ability, large-scale environmental cognition, and geographical knowledge on new technology usage. The participants were 153 junior high students from central Taiwan. Geography grades served as indicators of prior knowledge, mental rotation and abstract reasoning skills as indicators of spatial ability, and sketch maps of school neighborhoods as indicators of environmental cognition (including landmark representation, intersection representation, and frame of reference). Lastly, the authors announced the landmarks searching worksheet and asked the participants to accomplish 16 familiar and unfamiliar landmark searching tasks using Google Earth with keyword search function disabled. The result showed the strongest predictor of landmark searching performance is ‘frame of reference’ in environmental cognition, followed by ‘mental rotation’ of spatial ability, ‘landmark representation’ of environmental cognition, and geographical knowledge. Google Earth landmark searches require complex cognitive processing; therefore, our conclusion is that GIS-supported image search activities give students good practice of active knowledge construction.