Rising Crack-Growth-Resistance (R-Curve) Behavior of Toughened Alumina and Silicon Nitride

R -curves for a sinter/HIPed SiC(whisker)-reinforced alumina and a sintered silicon nitride were assessed by direct measurements of lengths of cracks associated with Vickers indentation flaws. The fracture toughness measurements based on (a) initial (as-indented) crack lengths, (b) equilibrium growth of cracks during increasing far-field loading, and (c) crack lengths corresponding to unstable fracture showed definitive trends of R -curves for both materials. The fracture mechanics analyses employed an indenter-material constant that was independently estimated using a physical model for the residual driving force and a free surface correction factor that accounted for the effects of size and shape of the cracks on stress intensity. It is shown that R -curve estimations based on crack length measurements have the intrinsic advantage that crack length dependence of fracture toughness is not assumed a priori as is done in conventional analysis based on strength. The measured fracture toughness of SiC(whisker)-reinforced alumina was in agreement with the prediction of a toughening model based on crack bridging by partially debonded whiskers.     

Survey of denoising,segmentation and classification of magnetic resonance imaging for prostate cancer

Multimedia Tools and Applications - Prostate cancer (PCa) has become the second most dreadful cancer in men after lung cancer. Traditional approaches used for treatment of PCa were manual, time...     

Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

The human liver disorder is a genetic problem due to the habituality of alcohol or effect by the virus. It can lead to liver failure or liver cancer, if not been detected in initial stage. The aim of the proposed method is to detect the liver disorder in initial stage using liver function test dataset. The problem with many real-world datasets including liver disease diagnosis data is class imbalanced. The word imbalance refers to the conditions that the number of observations belongs to one class having more or less than the other class(es). Traditional K- Nearest Neighbor (KNN) or Fuzzy KNN classifier does not work well on the imbalanced dataset because they treat the neighbor equally. The weighted variant of Fuzzy KNN assign a large weight for the neighbor belongs to the minority class data and relatively small weight for the neighbor belongs to the majority class to resolve the issues with data imbalance. In this paper, Variable- Neighbor Weighted Fuzzy K Nearest Neighbor Approach (Variable-NWFKNN) is proposed, which is an improved variant of Fuzzy-NWKNN. The proposed Variable-NWFKNN method is implemented on three real-world imbalance liver function test datasets BUPA, ILPD from UCI and MPRLPD. The Variable-NWFKNN is compared with existing NWKNN and Fuzzy-NWKKNN methods and found accuracy 73.91% (BUPA Dataset), 77.59% (ILPD Dataset) and 87.01% (MPRLPD Dataset). Further, TL_RUS method is used for preprocessing and it improved the accuracy as 78.46% (BUPA Dataset), 78.46% (ILPD Dataset) and 95.79% (MPRLPD Dataset).

     

Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society

Innovations in Systems and Software Engineering - Recommender system is a computer-based intelligent technique which facilitates the customers to fulfill their purchase requirements. In addition to...     

ICCD: Interactive Continuous Collision Detection between Deformable Models Using Connectivity-Based Culling

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests. Second, we introduce the concept of “procedural representative triangles” to remove all redundant elementary tests between nonadjacent triangles. Finally, we exploit the mesh connectivity and introduce the concept of “orphan sets” to eliminate redundant elementary tests between adjacent triangle primitives. In practice, we can reduce the number of elementary tests by two orders of magnitude. These culling techniques have been combined with bounding volume hierarchies and can result in one order of magnitude performance improvement as compared to prior collision detection algorithms for deformable models. We highlight the performance of our algorithm on several benchmarks, including cloth simulations, N-body simulations, and breaking objects.     

Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.     

Study of DNA Origami Dimerization and Dimer Dissociation Dynamics and of the Factors that Limit Dimerization

Organizing DNA origami building blocks into higher order structures is essential for fabrication of large structurally and functionally diverse devices and molecular machines. Unfortunately, the yields of origami building block attachment reactions are typically not sufficient to allow programed assembly of DNA devices made from more than a few origami building blocks. To investigate possible reasons for these low yields, a detailed single‐molecule fluorescence study of the dynamics of rectangular origami dimerization and origami dimer dissociation reactions is conducted. Reactions kinetics and yields are investigated at different origami and ion concentrations, for different ion types, for different lengths of bridging strands, and for the “sticky end” and “weaving welding” attachment techniques. Dimerization yields are never higher than 86%, which is typical for such systems. Analysis of the dynamic data shows that the low yield cannot be explained by thermodynamic instability or structural imperfections of the origami constructs. Atomic force microscopy and gel electrophoresis evidence reveal self‐dimerization of the origami monomers, likely via blunt‐end interactions made possible by the presence of bridging strands. It is suggested that this mechanism is the major factor that inhibits correct dimerization and means to overcome it are discussed.     

基于SIMD指令的柔性物体并行碰撞检测

复杂场景中柔性物体间的碰撞检测依然难以满足交互设计的要求.为了提高处理速度,文中给出了一种充分利用现代CPU的并行处理能力的碰撞检测算法.算法基于两方面的并行处理:即基于SIMD指令的指令级并行处理和基于多线程的任务级并行处理.算法给出了一种针对SIMD指令特别优化的k-DOP模型--SIMD-DOP,从理论上分析了该包围盒的高效性,并与常规的16-DOP和24-DOP进行了运行效率对比.通过使用SIMD-DOP同时在多核间进行负载均衡,算法获得了优化的并行加速.文中算法已经在一台16核工作站上针对一组复杂测试场景进行了验证.     

Real-time path planning in dynamic virtual environments using multiagent navigation graphs

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for local dynamics computation of each agent by extending a social force model [Helbing05]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multi-agent planning in pursuit-evasion, terrain exploration and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.