We study a generalization of classical combinatorial graph spanners to the spectral setting. Given a set of vectorswhere for two matrices A, B ∈ R d×d we writeWe show that any set V has an Õ(k)-spectral spanner of size Õ(k) and this bound is almost optimal in the worst case.We use spectral spanners to study composable core-sets for spectral problems. We show that for many objective functions one can use a spectral spanner, independent of the underlying function, as a core-set and obtain almost optimal composable core-sets. For example, for the k-determinant maximization problem, we obtain an Õ(k) k -composable core-set, and we show that this is almost optimal in the worst case.Our algorithm is a spectral analogue of the classical greedy algorithm for finding (combinatorial) spanners in graphs. We expect that our spanners find many other applications in distributed or parallel models of computation. Our proof is spectral. As a side result of our techniques, we show that the rank of diagonally dominant lower-triangular matrices are robust under "small perturbations" which could be of independent interests.
We prove that the permanent of nonnegative matrices can be deterministically approximated within a factor of √ 2 n in polynomial time, improving upon the previous deterministic approximations. We show this by proving that the Bethe approximation of the permanent, a quantity computable in polynomial time, is at least as large as the permanent divided by √ 2 n .This resolves a conjecture of Gurvits [Gur11]. Our bound is tight, and when combined with previously known inequalities lower bounding the permanent, fully resolves the quality of Bethe approximation for permanent. 1 We remark that for any matrix A, |per(A)| ≤ A n .
This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine-bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.
Maximum power point tracking (MPPT) is a technique that grid tie inverters, solar battery chargers and similar devices use to get the maximum possible power from one or more solar panels. In this paper the simulation result of the P&O algorithm and the conventional maximum power point tracking technique compared with Intelligent Control (fuzzy logic controller-FLC). In fact the target of this manuscript is study the effect of the environmental conditions like variation of solar intensity and temperature on output power of photovoltaic module. Solarex MSX-83 PV module and MATLAB/SIMULINK software are used for simulation. In this paper, the construction of stand-alone photovoltaic system consists of PV module, boost converter, maximum power point tracker and Pulse Width Modulator.
Abstract-The computational intelligence such as artificial neural network (ANN) and fuzzy inference system (FIS) is a strong tool for prediction and simulation in engineering applications. In this paper, radial basis function (RBF) network and adaptive neuro-fuzzy inference system (ANFIS) are used for prediction of IC50 (the 50% inhibitory concentration) values evaluated by the MTT assay in human cancer cell lines. For developing of the proposed models, the input parameters are the concentration of the drug and the types of cell lines and the output is IC50 values in the A549, H157, H460 and H1975 cell lines. The predicted IC50 values using the proposed RBF and ANFIS models are compared with the experimental data. The obtained results show that both RBF and ANFIS models have achieved good agreement with the experimental data. Therefore, the proposed RBF and ANFIS models are useful, reliable, fast and cheap tools to predict the IC50 values determined by the MTT assay in human cancer cell lines.
In recent years, the growth of urbanization has increased the impermeable levels and has caused an increase in the volume of floods and peak flood discharges. Many types of research have been done in the field of implementing environmentally friendly methods to make the urban environment more natural and at the same time be effective in controlling urban floods. The use of Low Impact Development (LID) methods is one such study. But choosing the best designs from among these strategies has always been a vital problem for urban water system designers. One of the goals researchers in the field of urban hydrology is to find methods to determine the required volume reduction. In the present study, the hydraulic behavior of the surface water collection system in the Golestan town of Semnan has been simulated using Storm Water Management Model (SWMM). In the following, the performance of implementing different proposed designs of three types of LID_ namely rain barrel (RB), infiltration trench (IT), and permeable pavement (PP) _ was investigated. These plans include seven general scenarios, each with ten different LID combinations. These plans include seven general scenarios, each with ten different LID combinations. The results of hydraulic studies indicate the effectiveness of the PP-RB scenario with an average reduction of 90% of peak discharge and an average reduction of 80% of total flood volume. Also, the weakest performance is related to the IT scenario with an average reduction of 60% of peak discharge and 47% of total flow volume. In this regard, the considering the importance of estimation of flood reduction, present paper introduces a novel approach for urban flood mitigation estimation. In this method, intelligent algorithms are used to perform flood calculation operations taking into account the percentage of LIDs (Low-Impact Developments) proposed. In this research, SVM (support vector machines), LSSVM (Least square support vector machines) and LSSVM-GOA (Least square support vector machines-grasshopper optimization algorithm) algorithms have been used. The allocated percentage area of different combinations of the used LIDs, and the reduced peak flow coefficient in each combination were considered as input data; and the reduced flood volume corresponding to each LID combination was used as the output data. This research has been conducted in Golestan town of Semnan city in Iran. The results obtained in this study indicate the success of these algorithms in predicting reduced flood volume. In the test period, the value of R2 index for LSSVM-GOA model (0.9896) compared to LSSVM (0.9266) and SVM (0.8990) intelligent models, indicates the high accuracy of this model in this period. Also, the values of R2 index for the other two algorithms indicate the adequacy of these algorithms in predicting the amount of reduced flood. Also in the training course, LSSVM - GOA model showed that with values of 0.0101 and 0.0185 for MAE and RMSE indices, respectively, has a higher predictive power. The values of these indices are 0.0268 and 0.0361 for LSSVM model and 0.0318 and 0.0434 for SVM model, respectively. According to the results, the use of intelligent algorithms can be introduced as an accurate tool in estimating and predicting reduced flood volume in urban basins.
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