Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
Many industrial processes are inherently distributed in space and time and are called spatially distributed dynamical systems\n(SDDSs). Sensor placement affects capturing the spatial distribution and then becomes crucial issue to model or control an SDDS.\nIn this study, a new data-driven based sensor placement method is developed. SVR algorithm is innovatively used to extract the\ncharacteristics of spatial distribution froma spatiotemporal data set.Thesupport vectors learned by SVR represent the crucial spatial\ndata structure in the spatiotemporal data set, which can be employed to determine optimal sensor location and sensor number. A\nsystematic sensor placement design scheme in three steps (data collection, SVR learning, and sensor locating) is developed for an\neasy implementation. Finally, effectiveness of the proposed sensor placement scheme is validated on two spatiotemporal 3D fuzzy\ncontrolled spatially distributed systems....
In this paper, we propose two soft computing localization techniques for wireless sensor\nnetworks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural\nNetwork (ANN), focus on a range-based localization method which relies on the measurement of the\nreceived signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout\nthe track cycling field. The soft computing techniques aim to estimate the distance between bicycles\nmoving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS\nwas considered, whereas in the second approach the ANN was hybridized individually with three\noptimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm\n(GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN\noutperforms the other methods adopted in this paper in terms of accuracy localization and distance\nestimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of\n0.02 m and 0.2 m for outdoor and indoor velodromes, respectively....
In order to discover the structure of local community more effectively, this paper puts forward a new local community detection\nalgorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration\nability of a single nodemust be less thanmultiple nodes, so the beginning of the community extension of the algorithm in this paper\nis no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively\ndensely connected with each other.The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the\nlocal community extended from the minimal cluster. Experimental results show that the quality of the local community detected\nby our algorithmismuch better than other algorithms no matter in real networks or in simulated networks....
The study aims to assess and quantify the discriminate parameters of\nbalance among patients affected by vestibular dysfunction. Several data were obtained\nusing the Satel force plate. A total of 14 patients participated in the study. The postural\nstrategies were studied from the trajectory of the Center Of Pressure (COP), in standing\nposition and under the cognitive ââ?¬Å?open eyes (Cognitive-Task-Free - CTF)ââ?¬â?¢Ã¢â?¬â?¢ and ââ?¬Å?open eyes\nwith a cognitive task (Cognitive-Task-Active -CTA)ââ?¬â?¢Ã¢â?¬â?¢. Experimental data were used for\ntraining of the intelligent soft computing scheme Support Vector Regression (SVR). In the\npresent study, the polynomial and Radial Basis Functions (RBF) were applied as the SVR\nkernel function to predict the two cognitive positions. Performance of the proposed\nestimators was confirmed through the simulation results. According to our findings, a\ngreater improvement in accuracy estimation can be achieved through the SVR with radial\nbasis function compared to SVR with polynomial basis function. The SVR coefficient of\ndetermination (R2) with radial basis function was found to be equal to 0.8613, while R2 with\nthe polynomial basis function was equal to 0.6437....
In this article a variable order variable step size technique in backwards difference form is used to solve nonlinear Riccati\ndifferential equations directly. The method proposed requires calculating the integration coefficients only once at\nthe beginning, in contrast to current divided difference methods which calculate integration coefficients at every step\nchange. Numerical results will show that the variable order variable step size technique reduces computational cost in\nterms of total steps without effecting accuracy....
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