Abstract: This paper presents a hybrid algorithm employed to reduce the weight of an elec-tric motor, designed for electric vehicle (EV) propulsion. The approach uses a hybridization between Cuckoo Search and CMAES to generate an initial popula-tion. Then, the population is transferred to a new procedure which adaptively switches between two search strategies, i.e. one for exploration and one for ex-ploitation. Besides the electric motor optimization, the proposed algorithm per-formance is also evaluated using the 15 functions of the CEC 2015 competition benchmark. The results reveal that the proposed approach can show a very com-petitive performance when compared with different state-of-the-art algorithms.

Abstract: Nowadays, mobile cloud applications are growing progressively. It supports both compute and data intensive application integrates with the mobile device. Many attempts have been proposed to boost the application performance and to reduce the energy power of the mobile device by offloading computational components to the cloud server. However, current research only focused on mobile run time offloading cost. On the other hand, the cloud platform offloading cost has been ignored by the literature research. In this paper, we are studying the response time minimization problem of a real time applications (i.e., augmented reality, 3D gaming, mathematical tools) by considering offloading cost from both ends such as mobile run time and cloud run time with delay constraints (transmission cost and process cost). To minimize the total offloading cost, we have developed novel hybrid run time platform framework which will improve the offloading performance. However, the real time applications must be executed within a given deadline. To solve the above problem we have proposed the Latency Aware Task Assignment (LATA) algorithm which always executes real time application within a given deadline. Performance evaluation shows our proposed framework and algorithm are improving the response time and offloading cost as compared to baseline and conventional method.

Abstract: This work presents computations of electric current distributions inside an industrial submerged arc furnace. A 3D model has been developed in ANSYS Fluent that solves Maxwell’s equations based on scalar and vector potentials approach that are treated as transport equations. In this paper, the approach is described in detail and numerical simulations are performed on an industrial three-phase sub-merged arc furnace. The current distributions within electrodes due to skin and proximity effects are presented. The results show that the proposed method adequately models these phenomena

Abstract: Deep Convolutional Neural Network (CNN), which is one of the prominent deep learning methods, has shown a remarkable success in a variety of computer vision tasks, especially image classification. However, tuning CNN hyper-parameters requires expert knowledge and a large amount of manual effort of trial and error. In this work, we present the use of CNN on classifying good quality images versus bad quality images without understanding the image content. The well known data-sets were used for performance evaluation. More importantly we propose a hyper-heuristics approach on CNN for tuning its hyper-parameters. The proposed method encompasses of a high level strategy and various low level heuristics. The high level strategy utilises search performance to determine how to apply low level heuristics to automatically find an optimal set of CNN hyper-parameters. Our experiments show the effectiveness of this hyper-heuristic approach which can achieve high accuracy even when the training size is significantly reduced and conventional CNNs can no longer perform well. In short the proposed hyper-heuristic method does enhance CNN deep learning.

Abstract: Shared bikes are wildly welcomed and becoming increasing popular in the world, as a result, quite a few bike sharing systems have been conducted to provide services for the bicycle users. However, the current bike sharing systems are not fexible and humanly enough for the public bicycle users because of the fixed stations and not well considered for the users. In this paper, an agent-based distributed approach for bike sharing systems is proposed, this approach aims at helping users with obtaining a needed shared bike successfully and effciently. We pay more attention on user's preference to improve the satifised degree of the target shared bikes, meanwhile, agent trust and the probabilities are considered to improve the eciency and the success rate. To the end, a practical example simulation and results analysis are given to show its efficiency.

Abstract: The prize-collecting Steiner tree problem (PCSTP) is one of the important topics in computational science and operations research. The vertex-swap operation, which involves removal and addition of a pair of vertices based on a given minimum spanning tree (MST), has been proven very effective for some particular PCSTP instances with uniform edge costs. This paper extends the vertex-swap operator to make it applicable for solving more general PCSTP instances with varied edge costs. Furthermore, we adopt multiple dynamic data structures (such as ST trees and logarithmic-time heaps), which guarantee that the total time complexity for evaluating all the O(n2) possible vertex-swap moves is bounded by O(n) · O(m·log n), where n and m denote the number of ver- tices and edges respectively (if we choose to run Kruskal’s algorithm with a Fibonacci heap from scratch after swapping any pair of vertices, the total time complexity would reach O(n2)·O(m+n·logn)). We also prove that after applying the vertex-swap operation, the resulting solutions are necessarily MSTs (unless infeasible).

Abstract: CSS-Sprite is a technique of regrouping small images of a web page, called tiles, into images called sprites in order to reduce network transfer time. CSS-sprite packing problem is considered as an optimization problem. We approach it as a probabilistic non-oriented two-dimensional bin packing problem (2PBPP|R). Our main contribution is to allow tiles rotation while packing them in sprites. An experimental study evaluated our solution, which outperforms current solutions.

Abstract: In this work, we introduce slot selection and co-allocation algorithms for parallel jobs in distributed computing with non-dedicated and heterogeneous resources (clusters, CPU nodes equipped with multi-core processors, networks etc.). A single slot is a time span that can be assigned to a task, which is a part of a parallel job. The job launch requires a co-allocation of a specified number of slots starting and finishing synchronously. The challenge is that slots associated with different heterogeneous resources of distributed computing environments may have arbitrary start and finish points, different pricing policies. Some existing algorithms assign a job to the first set of slots matching the resource request without any optimization (the first fit type), while other algorithms are based on an exhaustive search. In this paper, algorithms for effective slot selection are studied and compared with known approaches. The novelty of the proposed approach is in a general algorithm selecting a set of slots efficient according to the specified criterion.

Abstract: In this paper, an explicit size reduction of a compact rat-race coupler implemented in a microstrip technology is considered. The coupler circuit features a simple to-pology with a densely arranged layout that exploits a combination of high- and low-impedance transmission line sections. All relevant dimensions of the struc-ture are simultaneously optimized in order to explicitly reduce the coupler size while maintaining equal power split at the operating frequency of 1 GHz and suf-ficient bandwidth for return loss and isolation characteristics. Acceptable levels of electrical performance are ensured by using a penalty function approach. Two de-signs with footprints of 350 mm2 and 360 mm2 have been designed and experi-mentally validated. The latter structure is characterized by 27% bandwidth. For the sake of computational efficiency, surrogate-based optimization principles are utilized. In particular, we employ an iterative construction and re-optimization of the surrogate model involving a suitably corrected low-fidelity representation of the coupler structure. This permits rapid optimization at the cost corresponding to a handful of evaluations of the high-fidelity coupler model.

Abstract: Probability of detection (POD) is used for reliability analysis of nondestructive testing systems. POD is determined by experiments, but it can be enhanced by information through physics-based simulation models and model-assisted probability of detection (MAPOD) methods. Due to time-consuming evaluations of the physics-based models and a large random input parameter space, MAPOD analysis can be impractical to complete in a timely manner. In this paper, we use stochastic polynomial chaos expansions (PCE) in place of the true model to accelerate the MAPOD analysis. In particular, we use state-of-the-art least-angle regression method and hyperbolic sparse technique to construct the PCE. The proposed method is demonstrated on a spherically-void-defect benchmark problem developed by the World Federal Nondestructive Evaluation Center. In this work, the benchmark problem is setup with two random input parameters. The results show that accurate MAPOD analysis obtained with the proposed approach. Moreover, the proposed framework requires around 100 samples for the convergence on the statistical moments, whereas direct Monte Carlo sampling (MCS) with the true model needs over 10,000 samples, and MCS with the deterministic Kriging model does not converge due to its inability to accurately represent the true model.

Abstract: We present an efficient way to solve the Bethe-Salpeter equation (BSE), which is developed to model collective excitation of electron-hole pairs in molecules and solids. The BSE is an eigenvalue problem. The Bethe--Salpeter Hamiltonian matrix to be diagonalized requires at least $O(N_e^5)$ operations with a large pre-constant to construct, where $N_e$ is proportional to the number of electrons in the system, in a conventional approach. This can be extremely costly for large systems. Our approach is based on using the interpolative separable density fitting (ISDF) technique to construct low-rank approximations to the bare and screened exchange operators associated with the BSE Hamiltonian. This approach allows us to reduce the complexity of the Hamiltonian construction to $O(N_e^3)$ with a much smaller pre-constant. We implement this ISDF method for the BSE calculations under the Tamm-Dancoff approximation (TDA) in the BerkeleyGW software package. We show that the ISDF based BSE calculations in molecules and solids can produce accurate exciton energies and optical absorption spectra with significantly reduced computational cost.

Abstract: The paper presents a computational framework for assessing quantitatively the detection capability of structural health monitoring (SHM) systems for flat plates. The detection capability is quantified using the probability of detection (POD) metric, developed within the area of nondestructive testing, which accounts for the variability of the uncertain system parameters and describes the detection accuracy using confidence bounds. SHM provides the capability of continuously monitoring the structural integrity using multiple sensors placed sensibly on the structure. It is important that the SHM can reliably and accurately detect damage when it occurs. The proposed computational framework models the structural behavior of flat plate using a spring-mass system with a lumped mass at each sensor location. The quantity of interest is the degree of damage of the plate, which is defined in this work as the difference in the strain field of a damaged plate with respect to the strain field of the healthy plate. The computational framework determines the POD based on the degree of damage of the plate for a given loading condition. The proposed approach is demonstrated on a numerical example of a flat plate with two sides fixed and a load acting normal to the surface. The POD is estimated for two uncertain parameters, the plate thickness and the modulus of elasticity of the material, and a damage located in one spot of the plate. The results show that the POD is close to zero for small loads, but increases quickly with increasing loads.