Abstract: In this paper, a novel technique for cost-efficient design optimization of microwave structures has been proposed. Our approach exploits an adaptive response scaling that ensures good alignment between an equivalent circuit (used as an underlying low-fidelity model) and an electromagnetic (EM) simulation model of the structure under design. As the adaptive scaling tracks the low-fidelity model changes both in terms of frequency and the response level, it exhibits better generalization capability than traditional (e.g., space mapping) surrogates. This translates into improved design reliability and reduced design cost. Our methodology is demonstrated using two examples of microstrip filters and compared to several variations of conventional space mapping.

Abstract: Re-designing circuits for various sets of performance specifications is an important problem in microwave and antenna engineering. Unfortunately, this is a difficult task that is normally realized as a separate design process, which is often as expensive (in computational terms) as obtaining the original design. In this work, we consider the application of inverse surrogate modeling for fast geometry scaling of microwave and antenna structures. Computational efficiency of the discussed procedure is ensured by representing the structure at the low-fidelity model level. The explicit relation between design specifications (here, operating frequency) of the structure and its geometry dimensions is determined based on a set of predetermined reference designs. Subsequently, the model is corrected to elevate the re-designed geometry to the high-fidelity electromagnetic (EM) model level. Our approach is demonstrated through a compact rat-race coupler and a patch antenna with enhanced bandwidth.

Abstract: Trawl-doors are a large part of the fluid flow resistance of trawlers fishing gear and has considerable effect on the fuel consumption. A key factor in reducing that consumption is by implementing computational models in the design process. This study presents a robust two dimensional computational fluid dynamics models that is able to capture the nonlinear flow past multi-element hydrofoils. Efficient optimization algorithms are applied to the design of trawl-doors using problem formulation that captures true characteristics of the design space where lift-to-drag ratio is maximized. Four design variables are used in the optimization process to control the fluid flow angle of attack, as well as position and orientation of a leading-edge slat. The optimization process involves both multi-point space mapping, and mixed modeling techniques that utilize space mapping to create a physics-based surrogate model. The results demonstrate that lift-to-drag maximization is more appropriate than lift-constraint drag minimization in this case and that local search using multi-point space mapping can yield satisfactory design at low computational cost. By using global search with mixed modeling a solution with higher quality is obtained, but at a higher computational cost than local search.

Abstract: A preference-based approach is proposed for Grid computing with regard to preferences given by various groups of virtual organization (VO) stakeholders (such as users, resource owners and administrators) to improve overall quality of service and resource load efficiency. Computational resources being competed for by local jobs (initiated by owners) and global (users') job flow complicate the problem of a required service quality level substantially. A specific cyclic job batch scheduling scheme is examined in the present work which enables to distribute and share resources considering all the VO stakeholders' preferences and find a balance between VO global preferences and those of its users. Two different general utility functions are introduced to represent users' preferences satisfaction.

Abstract: Parallelizing industrial simulation codes like the EUROPLEXUS software dedicated to the analysis of fast transient phenomena, is challenging. In this paper we focus on the efficient parallelization on shared memory node coupling. We propose to have each thread gather the data it needs for processing a given iteration range, before to actually advance the computation by one time step on this range. This lazy cache aware layout construction enables to keep the original data structure and leads to very localised code modifications. We show that this approach can improve execution by up to 40% when the task size is set to have the data fit in the L2 cache.

Abstract: In this paper, we discuss a simple and efficient technique for multi-objective design optimization of multi-parameter microwave and antenna structures. Our method exploits a stencil-based approach for identification of the Pareto front that does not rely on population-based metaheuristic algorithms, typically used for this purpose. The optimization procedure is realized in two steps. Initially, the initial Pareto-optimal set representing the best possible trade-offs between conflicting objectives is obtained using low-fidelity representation (coarsely-discretized EM model simulations) of the structure at hand. This is realized by sequential construction and relocation of small design space segments (patches) in order to create a path connecting the extreme Pareto front designs identified beforehand. In the second step, the Pareto set is refined to yield the optimal designs at the level of the high-fidelity electromagnetic (EM) model. The appropriate number of patches is determined automatically. The approach is validated by means of two multi-parameter design examples: a compact impedance transformer, and an ultra-wideband monopole antenna. Superiority of the patching method over the state-of-the-art multi-objective optimization techniques is demonstrated in terms of the computational cost of the design process.

Abstract: Supersonic vehicles are an important type of potential transports. Analysis of these vehicles requires the use of accurate models, which are also computationally expensive, to capture the highly nonlinear physics. This paper presents results of numerical investigations of using physics-based surrogate models to design supersonic airfoil shapes. Variable-fidelity models are generated using inviscid computational fluid dynamics simulations and analytical models. By using response correction techniques, in particular, the manifold mapping technique, fast surrogate models are constructed. The effectiveness of the approach is investigated using lift-constrained drag minimization problems of supersonic airfoil shapes. Compared with direct optimization, the results show that an order of magnitude speed up can be obtained. Furthermore, we investigate the effectiveness of the variable-fidelity technique in terms of speed and design quality using several combinations of medium-fidelity and low-fidelity models.

Abstract: Ultrasonic testing (UT) is used to detect internal flaws in materials or to characterize material properties. Computational simulations are an important part of the UT process. Fast models are essential for UT applications such as inverse design or model-assisted probability of detection. This paper presents applications of surrogate modeling techniques to create fast approximate models of UT simulator responses. In particular, we use data-driven surrogate modeling techniques (kriging interpolation), and physics-based surrogate modeling techniques (space mapping), as well a mixture of the two approaches. These techniques are demonstrated on metal components immersed in a water bath during the inspection process.

Abstract: A framework, PhaseLift, was recently proposed to solve the phase retrieval problem. In this framework, the problem is solved by optimizing a cost function over the set of complex Hermitian positive semidefinite matrices. This paper considers an approach based on an alternative cost function defined on a union of appropriate manifolds. It is related to the original cost function in a manner that preserves the ability to find a global minimizer and is significantly more efficient computationally. A rank-based optimality condition for stationary points is given and optimization algorithms based on state-of-the-art Riemannian optimization and dynamically reducing rank are proposed. Empirical evaluations are performed using the PhaseLift problem. The new approach is shown to be an effective method of phase retrieval with computational efficiency increased substantially compared to the algorithm used in original PhaseLift paper.

Abstract: The usage of heterogeneous multicore platforms is appealing for applications, e.g. hard real-time systems, due to the potential reduced energy consumption offered by such platforms. However, the power wall is still a barrier to improving the processor design process due to the power consumption of components. Hard real-time systems are part of life critical environments and reducing the energy consumption on such systems is an onerous and complex process. This paper reassesses the problem of finding assignments of hard real-time tasks among heterogeneous processors respecting time constraints and targeting low power consumption. We also propose models based on a well-established literature formulation of the Multilevel Generalized Assignment Problem (MGAP). We tackle the problem from the perspective of different models and their interplay on the search for optimal solutions. Experimentation show that using strict schedulability tests as restrictions of 0/1 integer linear programming results in faster solvers capable of finding optimum solutions with lower power consumption.

Abstract: Adjoints are an important computational tool for large-scale sensitivity evaluation, uncertainty quantification, and derivative-based optimization. An essential component of their performance is the storage/recomputation balance in which efficient adjoint checkpointing strategies play a key role. We introduce a novel asynchronous two-level adjoint checkpointing scheme for numerical time discretizations targeted at large-scale numerical simulations. The checkpointing scheme combines bandwidth-limited disk checkpointing and binomial memory checkpointing. Based on assumptions about the target petascale systems, which we later demonstrate to be realistic on the IBM Blue Gene/Q system Mira, we create a model of the predicted performance of the adjoint computation and validate it using the highly scalable Navier-Stokes spectral-element solver Nek5000 on small to moderate subsystems of the Mira supercomputer.

Abstract: The k-medoids methods for modeling clustered data have many desirable properties such as robustness to noise and the ability to use non-numerical values, however, they are typically not applied to large datasets due to their associated computational complexity. In this paper, we present AGORAS, a novel heuristic algorithm for the k-medoids problem where the algorithmic complexity is driven by, k, the number of clusters, rather than, n, the number of data points. Our algorithm attempts to isolate a sample from each individual cluster within a sequence of uniformly drawn samples taken from the complete data. As a result, computing the k-medoids solution using our method only involves solving k trivial sub-problems of centrality. This allows our algorithm to run in comparable time for arbitrarily large datasets with same underlying density distribution. We evaluate AGORAS experimentally against PAM and CLARANS -- two of the best-known existing algorithms for the k-medoids problem -- across a variety of published and synthetic datasets. We find that AGORAS outperforms PAM by up to four orders of magnitude for data sets with less than 10,000 points, and it outperforms CLARANS by two orders of magnitude on a dataset of just 64,000 points. Moreover, we find in some cases that AGORAS also outperforms in terms of cluster quality.

Abstract: The aim of this paper is to further develop the original method proposed by the authors in their previous publications and submitted as a patent to shape frequency response of a vibrating plate according to precisely dened demands. The method is based on modeling the plate together with additional masses and ribs, and applying a sophisticated optimization algorithm, which issues arrangement of the masses and ribs. It has a very high practical potential. It can be used to improve acoustic radiation of the plate for required frequencies or enhance acoustic isolation of noise barriers and device casings. It can be utilized for both passive and active control. For the latter case it allows at the same time to optimally arrange actuators and sensors. In the paper there are presented, compared and discussed simulation results of the method for a plate with dierent boundary conditions: simply supported, fully-clamped and elastically restrained against rotation (corresponding to a mounting in a real device casing). Proposed optimization criteria are followed from practical scenarios, where precise modication of a vibrating plate frequency response is desired. The application of the proposed method for active control is also shown. The important additional outcome of the paper are guidelines on de- signing device casings in terms of rigidity in order to obtain their required vibration and noise isolation features.

Abstract: Partial Update LMS (PU LMS) algorithms started to play an important role in adaptive processing of sound. Due to reduction of computational power demands, these algorithms allow to use longer adaptive filters, and therefore achieve better results in adaptive filtering applications, e.g., system identification, adaptive line enhancement (ALE), and active noise control (ANC). There are two main groups of PU algorithms: data-independent algorithms and data-dependent algorithms. While application of an algorithm belonging to the first group almost always results in a degradation of performance, application of data-dependent PU algorithms may even result in an increase of the performance, compared with full parameters update. However, the latter group of algorithms requires sorting. A number of updated parameters is the factor that allows to decide how much of performance should be sacrificed to obtain computational power savings. In the extreme case only one filter tap out of possilbly hundreds is updated during each sampling period. The goal of this paper is to show extensive simulations prooving that careful selection of this one tap results in a useful and well performing algorithm, even in the demanding application of active noise control. As a final step, the simulations are confirmed in laboratory experiments

Abstract: This paper provides a comprehensive and rigorous study of a novel collision resolution algorithm for wireless sensor networks: 2CS-WSN. It is specifically designed to be used during the contention phase of IEEE 802.15.4. This algorithm has been modelled in terms of discrete time Markov chains (DTMCs) and, using the probabilistic symbolic model checker PRISM, correctness properties and different operation modes of the algorithm have been studied. Moreover, different model abstractions have been used in order to identify any inconsistencies or ambiguities, and to prove interesting properties for non-trivial, practical and relevant scenarios. Finally, since the biggest source of energy waste is the collision, this paper conducts a wide study of energy saving in this algorithm.

Abstract: In recent years, improvised explosive devices has been an aspect of crusades by terrorist or movements around the world. The blast wave propagation of an explosive detonation can cause disastrous damage on the buildings, vehicles and also injuries to vehicle occupants. Full scale blast tests are expensive and time consuming but by using computational based numerical simulations can virtually predict these wave propagations and minimize the need of experimental testing. Computational fluid dynamics (CFD) is a common tool to do an analysis of free-field blast wave and against structure. This paper presents two different blast analyses; free field air blast and blast loading towards a structure using ANSYS FLUENT software. A high explosive of 1 kg blast peak overpressure data from an experiment has been patched at the specific domain of the symmetry plane. The computed results were found to be in agreement with theoretical and additional experimental data. The verified free field air blast model was expanded to study the blast loading response towards a structure. It was found that developed CFD can be further used to predict the blast wave propagation subjected to the vehicle structures or buildings.

Abstract: We address a vehicle routing problem with time windows (VRPTW) that also contains vehicle with preventive maintenance constraints (VRPTW-PM) and propose a MIP mathematical formulation as well as a general variable neighborhood search metaheuristic (VNS) to solve with large instance the problematic situation. First we create a initial solution using Solomon heuristic then we minimize the number of used routes, and then the total travelled distance by all vehicles is minimized. Computational results show the efficiency of the proposed approach.

Abstract: Explosion from an anti-tank mines or improvised explosive devices are recognized as one of the lethal threat towards occupants inside an armoured vehicle. The detonation of these threats creates high intensity blast waves that transmitted to the occupant through vehicle structures and seats. Minimizing the occupant’s casualty can be achieved by properly dissipating the shock waves exerted to the vehicle. It is important to distinguish the contributing factors that affect the behavior of the blast wave so that proper reduction on the shock waves can be achieved. In this paper, three factors such as occupant seating height, charge weight placement and the Hopkinson-Cranz blast scaling were studied using numerical simulations. Design of experiment (DOE) was utilized to determine the ranks and interaction between each factor from the most influential on the results to the least effecting towards the results. From the results it was found that the seating position plays a significant role in reduction the shock response towards the finite element dummy model.

Abstract: Due to limitation of mobile device in terms of battery life and processing power, Mobile Cloud Computing (MCC) has become an attractive choice to leverage this shortcoming as the mobile computation could be offloaded to the cloud, which is so-called \emph{mobile computation offloading}. Existing research on mobile computation offloading considers offloading a mobile computation to a single cloud. However, in the real world a computation service could be provided by multiple clouds and each computation service may have different performance and different prices. Thus, a new and interesting research problem in mobile computation offloading is how to select a computation service for each of the computation tasks of a mobile computation such that the computation time of the mobile computation, the energy consumption of the mobile device and the cost of using the computation services are minimized. This is so called multi-site computation offloading in mobile cloud computing. In this paper we formulate the multi-site computation offloading problem, propose a heuristic algorithm for the multi-site computation offloading problem and evaluate the heuristic algorithm.