Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 6 Articles
The K-harmonic means clustering algorithm (KHM) is a new clustering method used to\ngroup data such that the sum of the harmonic averages of the distances between each entity\nand all cluster centroids is minimized. Because it is less sensitive to initialization than Kmeans\n(KM), many researchers have recently been attracted to studying KHM. In this study,\nthe proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO)\nand integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of\nthe utility of the proposed iSSO-KHM, we present extensive computational results on eight\nbenchmark problems. From the computational results, the comparison appears to support\nthe superiority of the proposed iSSO-KHM over previously developed algorithms for all\nexperiments in the literature....
We consider a new type of hybrid neuro-fuzzy system based on\nfuzzy and neural computing in hierarchical sequential structure, the total\neffect exceeds the effect of each component separately. The proposed\nsystem can be applied to multi-criteria analysis, automatic classification on\nsigns and obtain evidence-based estimates of the efficiency of scientific\nand technical solutions and technologies, engineering and robotics. An\nexample of a neuro-fuzzy system measuring the intensity of the emotions\nof a robot, with the extraction of diagnostic decision rules \"If ... then\"....
Human action recognition based on 3D skeleton has become an active research field in recent years with the recently developed\ncommodity depth sensors. Most published methods analyze an entire 3D depth data, construct mid-level part representations, or\nuse trajectory descriptor of spatial-temporal interest point for recognizing human activities. Unlike previous work, a novel and\nsimple action representation is proposed in this paper which models the action as a sequence of inconsecutive and discriminative\nskeleton poses, named as key skeleton poses.The pairwise relative positions of skeleton joints are used as feature of the skeleton poses\nwhich are mined with the aid of the latent support vector machine (latent SVM). The advantage of our method is resisting against\nintraclass variation such as noise and large nonlinear temporal deformation of human action.We evaluate the proposed approach\non three benchmark action datasets captured by Kinect devices: MSR Action 3D dataset, UT Kinect Action dataset, and Florence\n3D Action dataset. The detailed experimental results demonstrate that the proposed approach achieves superior performance to\nthe state-of-the-art skeleton-based action recognition methods....
Recently the hardware performance of mobile devices have been extremely increased\nand advanced mobile devices provide multi-cores and high clock speed. In addition,\nmobile devices have advantages in mobility and portability compared with PC and\nConsole, so many games and simulation programs have been developed under mobile\nenvironments. Physically-based simulation is a one of the key issues for deformable\nobject modeling which is widely used to represent the realistic expression of 3D soft\nobjects with tetrahedrons for game and 3D simulation. However, it requires high\ncomputation power to plausibly and realistically represent the physical behaviors and\ninteractions of deformable objects. In this paper, we implemented parallel cloth and\nmass-spring simulation using graphics processing unit (GPU) with OpenCL and multithreaded\ncentral processing unit (CPU) on a mobile device. We applied CPU and GPU\nparallel computing technique into spring force computation and integration methods\nsuch as Euler, Midpoint, 4th-order Runge-Kutta to optimize the computational burden\nof dynamic simulation. The integration methods compute the next step of positions\nand velocities in each node. In this paper, we tested the performance analysis for\nthe spring force calculation and integration method process using CPU only, multithreaded\nCPU, and GPU on mobile device respectively. Our experimental results\nconcluded that the calculation using proposed multi-threaded CPU and GPU multithreaded\nCPU are much faster than using just the CPU only....
The growing demand in the field of security led to the development of interesting approaches in face classification. These works\nare interested since their beginning in extracting the invariant features of the face to build a single model easily identifiable by\nclassification algorithms. Our goal in this article is to develop more efficient practical methods for face detection. We present a\nnew fast and accurate approach based on local binary patterns (LBP) for the extraction of the features that is combined with the\nnew classifier Neighboring Support Vector Classifier (NSVC) for classification.The experimental results on different natural images\nshow that the proposed method can get very good results at a very short detection time. The best precision obtained by LBP-NSVC\nexceeds 99%....
The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular\nfor its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but\nseveral modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has\nbeen mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial\ndata analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which\nmeet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE)\napplying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of\nthe MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered\nto get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator\nutilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial\ndatasets and optimization problems.The experiments show that the MDDE is an efficient and fast method for discrete optimizations\nin the multidimensional point clouds....
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