基于多策略的维吾尔文网页识别方法

经过对大量维吾尔文网站的调查与分析,该文从多语种混合网页中针对维吾尔文网页识别进行了研究,这对维吾尔语信息处理工作起着关键作用。首先该文探讨了维吾尔文不规范网页的字符编码转换规则及原理,以此对不规范维吾尔文字符进行了相应的处理,之后介绍了基于修改的N-Gram方法和基于维吾尔语常用词特征向量的两种方法,其中后者融合了维吾尔文常用候选词语料库及向量空间模型(Vector Space Model)。使用三种不同类型的维吾尔文网页文本作为本研究的数据集,在此基础上验证了该文提出的网页识别方法,以及采用不同的方法进行了网页识别的实验。实验结果表明,基于N-Gram的方法对正文较长的新闻或论坛网页的识别性能最佳,反而基于常用词特征向量的方法对短文本的网页识别性能优越N-Gram。所提方法对维吾尔文网页识别的整体性能达到90%以上,并验证了这两种方法的有效性。     

Rainfall-Runoff Model Considering Microtopography Simulated in a Laboratory Erosion Flume

A comprehensive process-based rainfall-runoff model for simulating overland flow generated in rills and on interrill areas of a hillslope is evaluated using a laboratory experimental data set. For laboratory experiments, a rainfall simulator has been constructed together with a 6.50 m × 1.36 m erosion flume that can be given adjustable slopes changing between 5 % and 20 % in both longitudinal and lateral directions. The model is calibrated and validated using experimental data of simulated rainfall intensities between 45 and 105 mm/h. Results show that the model is capable of simulating the flow coming from the rill and interrill areas. It is found that most of the flow occurs in the form of rill flow. The hillslope-scale model can be used for better prediction of overland flow at the watershed-scale; it can also be used as a building block for an associated erosion and sediment transport model.     

Semantic web-based social network access control

The existence of online social networks that include person specific information creates interesting opportunities for various applications ranging from marketing to community organization. On the other hand, security and privacy concerns need to be addressed for creating such applications. Improving social network access control systems appears as the first step toward addressing the existing security and privacy concerns related to online social networks. To address some of the current limitations, we have created an experimental social network using synthetic data which we then use to test the efficacy of the semantic reasoning based approaches we have previously suggested.     

A General BRDF Representation Based on Tensor Decomposition

Generating photo‐realistic images through Monte Carlo rendering requires efficient representation of light–surface interaction and techniques for importance sampling. Various models with good representation abilities have been developed but only a few of them have their importance sampling procedure. In this paper, we propose a method which provides a good bidirectional reflectance distribution function (BRDF) representation and efficient importance sampling procedure. Our method is based on representing BRDF as a function of tensor products. Four‐dimensional measured BRDF tensor data are factorized using Tucker decomposition. A large data set is used for comparing the proposed BRDF model with a number of well‐known BRDF models. It is shown that the underlying model provides good approximation to BRDFs.     

Convex cone-based partial order for multiple criteria alternatives

In this paper, we consider the problem of finding a preference-based strict partial order for a finite set of multiple criteria alternatives. We develop an approach based on information provided by the decision maker in the form of pairwise comparisons. We assume that the decision maker's value function is not explicitly known, but it has a quasi-concave form. Based on this assumption, we construct convex cones providing additional preference information to partially order the set of alternatives. We also extend the information obtained from the quasi-concavity of the value function to derive heuristic information that enriches the strict partial order. This approach can as such be used to partially rank multiple criteria alternatives and as a supplementary method to incorporate preference information in, e.g. Data Envelopment Analysis and Evolutionary Multi-Objective Optimization.     

A new algorithm for initial cluster centers in k-means algorithm

Clustering is one of the widely used knowledge discovery techniques to reveal structures in a dataset that can be extremely useful to the analyst. In iterative clustering algorithms the procedure adopted for choosing initial cluster centers is extremely important as it has a direct impact on the formation of final clusters. Since clusters are separated groups in a feature space, it is desirable to select initial centers which are well separated. In this paper, we have proposed an algorithm to compute initial cluster centers for k-means algorithm. The algorithm is applied to several different datasets in different dimension for illustrative purposes. It is observed that the newly proposed algorithm has good performance to obtain the initial cluster centers for the k-means algorithm.     

A multi-dimensional shooting algorithm for the two-facility location–allocation problem with dense demand

We develop an efficient allocation-based solution framework for a class of two-facility location–allocation problems with dense demand data. By formulating the problem as a multi-dimensional boundary value problem, we show that previous results for the discrete demand case can be extended to problems with highly dense demand data. Further, this approach can be generalized to non-convex allocation decisions. This formulation is illustrated for the Euclidean metric case by representing the affine bisector with two points. A specialized multi-dimensional shooting algorithm is presented and illustrated on an example. Comparisons with two alternative methods through a computational study confirm the efficiency of the proposed methodology.     

Speeding Up Cycle Based Logic Simulation Using Graphics Processing Units

Verification has grown to dominate the cost of electronic system design, consuming about 60% of design effort. Among several verification techniques, logic simulation remains the major verification technique. Speeding up logic simulation results in great savings and shorter time-to-market. We parallelize logic simulation using Graphics Processing Units (GPUs). In the past, GPUs were special-purpose application accelerators, suitable only for conventional graphics applications. The new generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. We develop a parallel cycle-based logic simulation algorithm that uses And Inverter Graphs (AIGs) as design representations. AIGs have proven to be an effective representation for various design automation applications, and we obtain similar benefits for speeding up logic simulation. We develop two clustering algorithms that partition the gates in the designs into independent blocks. Our algorithms exploit the massively parallel GPU architecture featuring thousands of concurrent threads, fast memory, and memory coalescing for optimizations. We demonstrate up-to 5x and 21x speedups on several benchmarks using our simulation system with the first and second clustering algorithms, respectively. Our work ultimately results in significant reduction in the overall design cycle.     

Condition monitoring of speed controlled induction motors using wavelet packets and discriminant analysis

This work presents an intelligent method for the condition monitoring of induction motors supplied with adjustable speed drives (ASD). Most of the previous work in this area concentrated on the fault detection and classification of induction motors supplied directly from an a.c. line. However, ASD driven induction motors are widely used in industrial processes and therefore obtaining an intelligent tool for the condition monitoring of these motors is necessary in terms of preventive maintenance and reducing down time due to motor faults. Here 3-phase supply side current of the ASD driving an induction motor is used to extract statistical features of wavelet packet decomposition coefficients within a frequency range of interest. This way, the information regarding the output frequency of the ASD and hence the motor speed is not required. Six identical three-phase induction motors were used for the experimental verification of the proposed method. One healthy machine was used as a reference, while other five with various synthetic faults were used for condition detection and classification. Extracted features obtained from decomposition coefficients of different wavelet filter types for all motors were employed in three different and popular classifiers. The proposed method and the performance of the features used for fault detection and classification are examined at various motor loads and speed levels, and it is shown that a successful condition monitoring system for induction motors supplied with ASDs is developed. The effect of selected filter type in wavelet decomposition to the condition monitoring process is analyzed and presented.     

Hard boron rich boron nitride nanoglasses

Boron‐rich amorphous boron nitride (B_xN_1?x, 0.55 ≤ x ≤ 0.95) alloys are generated by means of ab initio molecular dynamics simulations and their local structure, mechanical properties and electronic structure are exposed. BN:B phase separations are perceived in all amorphous networks, suggesting that these materials can serve as nanoglass ceramics. The sp² hybridization is the main building unit in the BN‐rich regions for low boron concentrations, and the models carry locally the signature of the two‐dimensional hexagonal BN structure. The amorphous states having both sp² and sp³ hybridizations form for boron contents between 70% and 80%. At higher boron concentrations, sp³ hybridization with a fraction of ~90%‐98% is detected as seen in the cubic or wurtize BN crystals. In the boron rich regions, the ideal and defective pentagonal pyramids emerge at 60% boron content, and the first complete B₁₂ molecule develops at 70% boron concentration. In addition to the B₁₂ icosahedron, the formation of a cage‐like B₁₆ molecule is, for the first time, discovered in some amorphous alloys. The isolated B₁₆ molecule is, however, found to be unstable. The Vickers harness calculations reveal that some of these amorphous alloys can serve as hard materials. When their electron properties are considered, all amorphous materials are predicted to be semiconducting.