Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
TheTrainDesign Optimization Problem regardsmaking optimal decisions on the number and movement of locomotives and crews\nthrough a railway network, so as to satisfy requested pick-up and delivery of car blocks at stations. In amathematical programming\nformulation, the objective function to minimize is composed of the costs associated with the movement of locomotives and cars,\nthe loading/unloading operations, the number of locomotives, and the crews� return to their departure stations. The constraints\ninclude upper bounds for number of car blocks per locomotive, number of car block swaps, and number of locomotives passing\nthrough railroad segments.We propose here a heuristic method to solve this highly combinatorial problem in two steps. The first\none finds an initial, feasible solution by means of an ad hoc algorithm. The second step uses the simulated annealing concept to\nimprove the initial solution, followed by a procedure aiming to further reduce the number of needed locomotives. We show that\nour results are competitive with those found in the literature....
The harmonic and interharmonic analysis recommendations are contained in\nthe latest IEC standards on power quality. Measurement and analysis experiences\nhave shown that great difficulties arise in the interharmonic detection and measurement\nwith acceptable levels of accuracy. In order to improve the resolution\nof spectrum analysis, the traditional method (e.g. discrete Fourier transform)\nis to take more sampling cycles, e.g. 10 sampling cycles corresponding to\nthe spectrum interval of 5 Hz while the fundamental frequency is 50 Hz. However,\nthis method is not suitable to the interharmonic measurement, because\nthe frequencies of interharmonic components are non-integer multiples of the\nfundamental frequency, which makes the measurement additionally difficult.\nIn this paper, the tunable resolution multiple signal classification (TRMUSIC)\nalgorithm is presented, which the spectrum can be tuned to exhibit high resolution\nin targeted regions. Some simulation examples show that the resolution\nfor two adjacent frequency components is usually sufficient to measure interharmonics\nin power systems with acceptable computation time. The proposed\nmethod is also suited to analyze interharmonics when there exists an undesirable\nasynchronous deviation and additive white noise....
We propose a line-of-sight (LOS)/non-line-of-sight (NLOS) mixture source localization algorithms that utilize the\nweighted block Newton (WBN) and variable step size WBN (VSSWBN) method, in which the weighting matrix is\ndetermined in the form of the inverse of the squared error or as an exponential function with a negative exponent.\nThe proposed WBN and VSSWBN algorithms converge in two iterations; thus, the required number of extra samples in\nthe transient period is negligible. Also, we perform an analysis of the mean square error (MSE) of the weighted block\nNewton method. To verify the superiority of the proposed methods, the MSE performances are compared via\nextensive simulation....
Spectrum sensing is of great importance in the cognitive radio (CR) networks.\nCompared with individual spectrum sensing, cooperative spectrum sensing (CSS) has been shown to\ngreatly improve the accuracy of the detection. However, the existing CSS algorithms are sensitive\nto noise uncertainty and are inaccurate in low signal-to-noise ratio (SNR) detection. To address\nthis, we propose a double-threshold CSS algorithm based on Sevcik fractal dimension (SFD) in\nthis paper. The main idea of the presented scheme is to sense the presence of primary users in\nthe local spectrum sensing by analyzing different characteristics of the SFD between signals and\nnoise. Considering the stochastic fluctuation characteristic of the noise SFD in a certain range, we\nadopt the double-threshold method in the multi-cognitive user CSS so as to improve the detection\naccuracy, where thresholds are set according to the maximum and minimum values of the noise SFD.\nAfter obtaining the detection results, the cognitive user sends local detection results to the fusion\ncenter for reliability fusion. Simulation results demonstrate that the proposed method is insensitive\nto noise uncertainty. Simulations also show that the algorithm presented in this paper can achieve\nhigh detection performance at the low SNR region....
With the rapid development of long or extra-long highway tunnel, accurate and reliable methods and techniques to forecast\ntraffic flow for road tunnel are urgently needed to improve the ventilation efficiency and saving energy. This paper presents a new\nhybrid Gaussian process regression (GPR) optimized by particle swarm optimization (PSO) for coping with the forecasting of the\nuncertain, nonlinear, and complex traffic flow for road tunnel. In this proposed coupling approach, the PSO algorithm is employed\nto overcome the disadvantages of too strong dependence of optimization effect on initial value and easy falling into local optimum\nof the traditional conjugate gradient algorithm and accurately search the optimal hyperparameters of the GPR method, and the\nGPR model simulates the internal uncertainties and dynamic feature of tunnel traffic flow.The predicted results indicate that the\nproposed PSO-GPR algorithm with different kernel function is able to predict traffic flow for road tunnel with a higher degree of\naccuracy. The PSO-GPR-CK is effective in boosting the forecasting accuracy in comparison with the single kernel function and is\nworth promoting in the field of traffic flow forecasting for road tunnel to improve the ventilation efficiency....
Loading....