Location allocation modeling for healthcare facility planning in Malaysia

Malaysia has seen tremendous growth in the standard of living and household per capita income. The demand for a more systematic and efficient planning has become increasingly more important, one of the keys to achieving a high standard in healthcare. In this paper, a Maximal Covering Location Problem (MCLP) is used to study the healthcare facilities of one of the districts in Malaysia. We address the limited capacity of the facilities and the problem is formulated as Capacitated MCLP (CMCLP). We propose a new solution approach based on genetic algorithm to examine the percentage of coverage of the existing facilities within the allowable distance specified/targeted by Malaysian government. The algorithm was shown to generate good results when compared to results obtained using CPLEX version 12.2 on a medium size problem consisting of 179 nodes network. The algorithm was extended to solve larger network consisting of 809 nodes where CPLEX failed to produce non-trivial solutions. We show that the proposed solution approach produces significant results in determining good locations for the facility such that the population coverage is maximized.     

Power Law Velocity Profile in Fully Developed Turbulent Pipe and Channel Flows

The power law velocity profile has been analyzed in terms of the envelope of the friction factor which gives the friction factor log law. The power law index α and prefactor C are shown as the function of the friction Reynolds number, as well as the function of the alternate variable the nondimensional friction velocity. The fully developed turbulent superpipe flow data of McKeon et al. and fully developed channel flow data of Zanoun et al. have been analyzed and the power law index α and prefactor C data have been estimated, first as a function of the friction Reynolds number and second as function of the nondimensional friction velocity. Based on analysis, several correlations have been proposed that have been supported by the data.     

Feature Selection and Extraction Methods for Power Systems Transient Stability Assessment Employing Computational Intelligence Techniques

One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia’s power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

Speeding up dynamic time warping distance for sparse time series data

Dynamic time warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, in its original formulation DTW is extremely inefficient in comparing long sparse time series, containing mostly zeros and some unevenly spaced nonzero observations. Original DTW distance does not take advantage of this sparsity, leading to redundant calculations and a prohibitively large computational cost for long time series. We derive a new time warping similarity measure (AWarp) for sparse time series that works on the run-length encoded representation of sparse time series. The complexity of AWarp is quadratic on the number of observations as opposed to the range of time of the time series. Therefore, AWarp can be several orders of magnitude faster than DTW on sparse time series. AWarp is exact for binary-valued time series and a close approximation of the original DTW distance for any-valued series. We discuss useful variants of AWarp: bounded (both upper and lower), constrained, and multidimensional. We show applications of AWarp to three data mining tasks including clustering, classification, and outlier detection, which are otherwise not feasible using classic DTW, while producing equivalent results. Potential areas of application include bot detection, human activity classification, search trend analysis, seismic analysis, and unusual review pattern mining.     

On a new class of analytic functions associated with conic domain

The aim of this paper is to generalize the conic domain defined by Kanas and Wisniowska, and define the class of functions which map the open unit disk E onto this generalized conic domain. A brief comparison between these conic domains is the main motivation of this paper. A correction is made in selecting the range interval of order of conic domain.     

The categories L-\mathbf{Top}_{0} and L-\mathbf{Sob} as epireflective hulls

We show that the category \(L\) - \(\mathbf{Top}_{0}\) of \(T_{0}\) - \(L\) -topological spaces is the epireflective hull of Sierpinski \(L\) -topological space in the category \(L\) - \(\mathbf{Top}\) of \(L\) -topological spaces and the category \(L\) - \(\mathbf{Sob}\) of sober \(L\) -topological spaces is the epireflective hull of Sierpinski \(L\) -topological space in the category \(L\) - \(\mathbf{Top}_{0}\) .     

Reanalysis procedure for large structural systems

An efficient procedure is presented for repetitive analysis of structures, with large numbers of degrees of freedom and design variables, as they are progressively modified during the automated optimum design process. The three key elements of the procedure are: (a) lumping of the large number of design variables into a single tracing parameter; (b) operator splitting or additive decomposition of the different arrays in the governing finite element equations of the modified structure into the corresponding arrays of the original structure plus correction terms; and (c) application of a reduction method through the successive use of the finite element method and the classical Bubnov-Galerkin technique. The reanalysis procedure is applied to the linear static and free vibration problems of framed structures. Changes in both the sizing and shape (configuration) design variables are considered. For static problems the similarities between the proposed procedure and the preconditioned conjugate gradient technique are identified and are exploited to provide a physical meaning for the preconditioned residual vectors. The effectiveness of the proposed procedure is demonstrated by means of numerical examples.