Computational Resource Requirements Research Articles

Model Predictive Control Using Artificial Neural Network for Power Converters

There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this article, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. A power converter with a virtual MPC controller is first designed and operated under a circuit simulation or power hardware-in-the-loop simulation environment. An artificial neural network (ANN) is then trained offline with the input and output data of the virtual MPC controller. Next, an actual FPGA-based MPC controller is designed using the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the input elements. The basic concept, ANN structure, offline training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement (e.g., 2.11 times fewer slice LUTs and 2.06 times fewer DSPs) while offering a control performance same as the conventional MPC.

Mesoscale simulation of typhoon-generated storm surge: methodology and Shanghai case study

Abstract. The increasing vulnerability of coastal megacities to storm surge inundation means both infrastructure and populations are subject to significant threat. Planning for further urban development should include consideration of the changing circumstances in coastal cities to ensure a sustainable future. A sustainable urban plan relies on sound preparedness and prediction of future climate change and multiple natural hazards. In light of these needs for urban planning, this paper develops a general method to simulate typhoon-generated storm surge at the mesoscale (1–100 km in length). Mesoscale simulation provides a general approach with reasonable accuracy that could be implemented for planning purposes while having a relatively low computation resource requirement. The case study of Shanghai was chosen to implement this method. The mesoscale simulations of two historical typhoons not only provides realistic typhoon storm surge inundation results at the city level but is also suitable for implementing a large amount of simulations for future scenario studies. The method will be generally applicable to all coastal cities around the world to examine the effect of future climate change on typhoon-generated storm surge even when historical observation data are inadequate or not available.

Implementation of Hybridly Protected Quantum Gates

We explore the implementation of hybridly protected quantum operations combining the merits of holonomy, dynamical decoupling approach and dephasing-free feature based on a simple and experimentally achievable spin model. The implementation of the quantum operations can be achieved in different physical systems with controllable parameters. The protected quantum operations are hence controllable, well-suited for resolving various quantum computation tasks, such as executing quantum error-correction codes or quantum error mitigation. Our scheme is based on experimentally achievable Hamiltonian with reduced requirement of computational resources and thus, it brings us closer towards realizing protected quantum operations for resolving quantum computation tasks in near-term quantum devices.

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

Network function virtualization is a promising technology for providing personalized services via agile service function chains (SFCs). Flexible SFC orchestration and rational resource allocation are pivotal for improving the SFC's quality of service (QoS). However, the requirements for computational load and resources have frequently been changing. Consequently, static resource allocation can result in resource insufficiency when SFCs turn busy and resource waste due to resource overplus when SFCs are idle. Since a dynamic resource allocation is necessary, the existing dynamic resource allocation methods' responses have often been delayed. This paper proposes an SFC active reconfiguration mechanism (SARM) based on computational load and resource demand. The SARM predicts nodes' computation loads and SFCs' resource demands and uses these predictions to estimate future QoS and develop the SFC reconfiguration strategy. The SARM considers multiple factors and applies a heuristic algorithm to achieve the tradeoff between migration cost and QoS preservation. The experiments demonstrate that the SARM can effectively predict the nodes' load and the resource demand of SFCs. In addition, the SARM can successfully identify the SFCs to reconfigure and reduce the QoS maintenance costs. The simulation results indicate that the average delays of the SFCs can be reduced by at least 26%.

Evaluation of methods to distinguish trips from activities in walking and cycling GPS data

An abundance of GPS data on walking and cycling requires substantial data processing – a primary component of which is trip identification to distinguish between data recorded during travel versus activities. The objective of this paper is to systematically evaluate trip identification algorithms in the literature and provide recommendations to improve performance for walking and cycling trips. Fourteen algorithms are applied to 1685 GPS trajectories from Vancouver, Canada, and evaluated on the bases of their agreement, distinction of trip and activity data characteristics, processing time, and accuracy (based on a labeled subset). Error sources are identified in relationship to trajectory, network, and weather factors. Results indicate poor concordance and widely varying performance, with no algorithm best across all measures. Four high-performing algorithms are identified with at least 90% record-level accuracy; other considerations include accuracy of the inferred number and duration of trips, precision of identified trip end points, and computational resource requirements. Density is a key variable for trip identification in the best-performing algorithms. Proximity to tree canopy, buildings, bridges, and tunnels affects the accuracy of some algorithms more than others. Most algorithms err almost entirely with false trips or false activities, which is a bias of concern for analysis. The importance of trip identification decisions should motivate more thorough reporting to enhance reproducibility and reliability.

Analysis of shock wave-boundary layer interaction in a shock tube using higher order scheme

Over the last couple of decades, the shock-wave boundary-layer interaction has gained a lot of attention due to its practical importance in many engineering applications. It is a complex problem involving shock-wave bifurcation, boundary-layer separation, the interaction of contact discontinuity with the shock wave, the formation of shocklets, and the evolution of vortical structures having different length scales. It is difficult to capture experimentally the detailed flow field having the shocklets and vortices. Numerical solvers having negligible numerical dissipation are highly essential to predict these structures accurately. Over the years, many researchers have obtained a grid-converged solution for shock-wave boundary-layer interaction at Reynolds numbers of 200 and 1000. The shock-wave boundary-layer interaction at higher Reynolds numbers is not attempted due to the requirement of huge computational resources and challenges associated with convergence. In the investigation, the grid-converged solution is obtained for a Reynolds number of 2500 with a 13th order high-resolution hybrid scheme using 100 cores of a computational cluster equipped with 3.0 GHz Intel Xeon processors incorporating the MPI library for parallelization. The complex flow field is analysed in detail using wall density, density gradient, vorticity, pressure, and enstrophy plots after validating the solver with the benchmark wall pressure, density, and v-velocity around the primary vortex provided by Zhou et al. (2018), Phys. Fluids, 30, 016102 for a Reynolds number 1000. It is observed that the triple point height and number of vortices in both separated zone and at the shear layer increase with an increase in Reynolds number. The grid-converged data obtained from the present simulation can be used as benchmark data to validate different numerical schemes/solvers at higher Reynolds numbers.

Globally-optimal whole body motion planning under nonholonomic constraints using dynamic programming

Robotics is a key enabler for many applications in space including planetary exploration, On-Orbit Servicing (OOS), In-Space Manufacturing (ISM), Active Space Debris Removal (ADR) and deep space exploration. They are usually characterized by limited resources, which requires the robotic system to be controlled according to criteria of optimality. How to perform operations and plan the robot motion are then key factors to ensure feasibility and efficiency. This work analyzes and extends a recently proposed Dynamic Programming Optimization (DPO) algorithm for off-line motion planning in the context of planetary colonization, ranging from exploration to terrain preparation and construction. The algorithm is designed to be run on ground with the aim of (1) limiting spacecraft cost and (2) optimally plan complex tasks with medium to long term time horizons before or during mission. The analysis is performed on a robotic system including a six degree of freedom manipulator mounted on a nonholonomic base which is exercised on tasks that leave more than one degree of redundancy. The planning tasks are resolved without decomposition, allowing the algorithm to optimize the motion of the whole kinematic structure. Redundancy is exploited to optimize the cost function of interest, while considering any constraint the robotic asset and the surrounding environment are characterized by. In our use case, the optimization is performed on a cost function associated to energy consumption. A tradeoff analysis of the solution quality versus the computational resources requirements is provided. Finally, we highlight the advantages of the proposed algorithm with respect to a standard planning approach based on decomposition of the planning problem in two sub-problems: the first involving the planning of the mobile base according to some heuristics (e.g. reachability) and the second involving the planning of the manipulator.

Modeling of a turbine bladerow with stagger angle variation using the multi-fidelity influence superposition method

Manufacturing variabilities can significantly affect a turbine bladerow's performance. With the push for turbine's efficiency and reliability more than ever, the understanding of manufacturing variability impacts is sought after. It has been shown that a multi-passage simulation domain is required to capture the interaction among varied blades. This is a challenge for conventional single-passage methods, largely due to the requirement of prohibitive computational resources. The present work introduces an attempt to solve this challenge by combining the multi-fidelity method and the influence superposition approach. The proposed methodology combines the accuracy of a high-fidelity simulation and the speed of a low-fidelity simulation. Therefore, the multi-fidelity predictions tend to be both accurate and fast. The key enabler is that only a small set of configurations is needed to pre-compute the source term. Two representative geometries of a subsonic low-pressure turbine and a transonic high-pressure turbine have been chosen as the test cases. Each test case is subject to two further stagger angle variation patterns, namely alternating and sinusoidal pattern. The low-fidelity method has been shown to be inadequate for the transonic high-pressure turbine test case, owning to its incapability to capture correctly the shockwave formation and the shockwave/wake interaction. On the other hand, the proposed multi-fidelity method has been successful to match qualitatively and quantitatively compared to the direct high-fidelity solution. More interestingly, the multi-fidelity method has a reduced computational overhead of one order of magnitude compared to the direct high-fidelity simulation. With an ability to accurately and efficiently predict the manufacturing variability effects, this method provides a tool for engineers to explore and optimize their blading designs.

Applying proxy-modeling tools to estimate the effective injection factor for an oil field

This work is devoted to the joint use of physically based proxy models of different levels of complexity to determine the effective injection ratio factor. Effective injection factor is a parameter that reflects the proportion of injected fluid that enters the reservoir and does useful work to maintain reservoir pressure and displace oil from the reservoir. The selection of the effective injection factor when using a full-scale hydrodynamic simulator is difficult due to the high requirements for computational and time resources. Therefore, to calculate this coefficient, it is justified to use the simplified proxy modeling approach, which allows to evaluate the desired parameter in an acceptable time frame. The task of finding the effective injection ratio is related to inverse problems. The following were used as proxy models: a material balance model for the field as a whole and for a system of hydrodynamically connected blocks, a capacitance-resistance model (CRM) and a two-dimensional filtration model. The order of the models corresponds to the hierarchical principle "from simple to complex". Based on the history of oil field development (production, fluid injection, reservoir and bottomhole pressure), the effective injection coefficients were obtained, they were compared, and the analysis of the results was carried out.

An Examination on Autoencoder Designs for Anomaly Detection in Video Surveillance

Current anomaly detection methods for video surveillance find anomalies effectively enough; however, it comes at a high computational cost and specific hardware resources demanding. In counterpart, other video analysis tasks such as video action recognition now employ techniques that reduce the need for higher computational cost. Some of those techniques can be helpful for video anomaly detection. Therefore, this paper explores the effectiveness of the potential concepts of distillation and joint spatiotemporal training, adapted to two novel convolutional autoencoder architectures for anomaly detection in video surveillance. Our experimental results show the feasibility of reducing the computational resources requirements with smaller architectures (only <inline-formula> <tex-math notation="LaTeX">$~6K$ </tex-math></inline-formula> trainable parameters), competing and outperforming current methods in challenging benchmarks.

Applying Grover's Algorithm to Hash Functions: A Software Perspective

Quantum software frameworks provide software engineers with the tools to study quantum algorithms as applied to practical problems. We implement classical hash functions MD5, SHA-1, SHA-2, and SHA-3 as quantum oracles to study the computational resource requirements of conducting a preimage attack with Grover's Algorithm. We introduce an improvement to the SHA-3 oracle that reduces the number of logical qubits required in the Keccak block permutation by 40%.

Method for Fast Map Construction Based on GPS Data and Compressed Grid Algorithm

Electronic maps play an important role in the field of urban traffic management, but the interface functions provided by map service agencies are limited, and commercial maps are generally expensive. Furthermore, the map generation algorithms based on the Global Positioning System (GPS) data can be very complex and take up a lot of storage space, which limits their application to specific practical problems, such as the real-time update of area maps, temporary road control, emergency route planning, and other scenarios. In order to solve this problem, an intuitive, extensible, and flexible method of constructing urban road maps is proposed. Using the Othello-coordinated method, the representation of the unit grid cell was redesigned. Through this method, the disadvantages of the raster map’s large storage space and computing resource requirements are compensated for during processing, improving the topological expression ability of the raster map and the speed with which the construction of the map is realized. The application potential of the proposed method is demonstrated by the evaluation of public transport service and road network resilience. In our experiments, the optimization efficiency of storage space was up to 99.914%, and the calculation accuracy of bus coverage was about 99.86%.

100+ FPS detector of personal protective equipment for worker safety: A deep learning approach for green edge computing.

In industrial production, personal protective equipment (PPE) protects workers from accidental injuries. However, wearing PPE is not strictly enforced among workers due to all kinds of reasons. To enhance the monitoring of workers and thus avoid safety accidents, it is essential to design an automatic detection method for PPE. In this paper, we constructed a dataset called FZU-PPE for our study, which contains four types of PPE (helmet, safety vest, mask, and gloves). To reduce the model size and resource consumption, we propose a lightweight object detection method based on deep learning for superfast detection of whether workers are wearing PPE or not. We use two lightweight methods to optimize the network structure of the object detection algorithm to reduce the computational effort and parameters of the detection model by 32% and 25%, respectively, with minimal accuracy loss. We propose a channel pruning algorithm based on the BN layer scaling factor γ to further reduce the size of the detection model. Experiments show that the automatic detection of PPE using our lightweight object detection method takes only 9.5 ms to detect a single video frame and achieves a detection speed of 105 FPS. Our detection model has a minimum size of 1.82 MB and a model size compression rate of 86.7%, which can meet the strict requirements of memory occupation and computational resources for embedded and mobile devices. Our approach is a superfast detection method for green edge computing.

Fusing CNNs and statistical indicators to improve image classification

Convolutional Neural Networks have dominated the field of computer vision for the last ten years, exhibiting extremely powerful feature extraction capabilities and outstanding classification performance. The main strategy to prolong this trend in the state-of-the-art literature relies on further upscaling networks in size. However, costs increase rapidly while performance improvements may be marginal. Our main hypothesis is that adding additional sources of information can help to increase performance and that this approach is more cost-effective than building bigger networks, which involve higher training time, larger parametrisation space and higher computational resources requirements. In this paper, an ensemble method for accurate image classification is proposed, fusing automatically detected features through a Convolutional Neural Network and a set of manually defined statistical indicators. Through a combination of the predictions of a CNN and a secondary classifier trained on statistical features, a better classification performance can be achieved cheaply. We test five different CNN architectures and multiple learning algorithms in a diverse number of datasets to validate our proposal. According to the results, the inclusion of additional indicators and an ensemble classification approach help to increase the performance in all datasets. Both code and datasets are publicly available via GitHub at: https://github.com/jahuerta92/cnn-prob-ensemble.

HUNTER: AI based holistic resource management for sustainable cloud computing

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.

Flow simulation in air intake system of gas turbine

THE PURPOSE. To study the issues of air intake system’s performance as the part of the gas turbines. To estimate the possibility of modeling different performance factors of air intake systems with numerical simulation methods. To develop the recommendations of setting up the grid and the numerical models for researches in air intake system’s performance and assessing the technical condition of elements of it. METHODS. The main method, which was used during the whole study, is computational fluid dynamics with usage of CAE-systems.RESULTS. During the study the recommendations for setting up the numerical model were developed. Such factors as grid model parameters, roughness scale, pressure drop in elements of air intake system and some more were investigated. The method for heat exchanger’s performance simulation were created for modeling the air temperature raising. CONCLUSION. The air intake system’s performance analysis becomes one of the actual topics for research because of the high demands of gas turbines to air, which is used in its annulus. The main part of these researches is in analysis of dangerous regimes of work (e.g. the icing process of annulus elements) or in assessing technical condition of air intake systems and its influence to the gas turbine as a whole. The developed method of numerical simulation allows to get the adequate results with low requirements for computational resources. Also this method allows to model the heat exchanger performance and study its defects’ influence to the performance of air intake system as a whole.

Search for efficient formulations for Hamiltonian simulation of non-Abelian lattice gauge theories

Hamiltonian formulation of lattice gauge theories (LGTs) is the most natural framework for the purpose of quantum simulation, an area of research that is growing with advances in quantum-computing algorithms and hardware. It, therefore, remains an important task to identify the most accurate, while computationally economic, Hamiltonian formulation(s) in such theories, considering the necessary truncation imposed on the Hilbert space of gauge bosons with any finite computing resources. This paper is a first step toward addressing this question in the case of non-Abelian LGTs, which further require the imposition of non-Abelian Gauss's laws on the Hilbert space, introducing additional computational complexity. Focusing on the case of SU(2) LGT in 1+1 D coupled to matter, a number of different formulations of the original Kogut-Susskind framework are analyzed with regard to the dependence of the dimension of the physical Hilbert space on boundary conditions, system's size, and the cutoff on the excitations of gauge bosons. The impact of such dependencies on the accuracy of the spectrum and dynamics is examined, and the (classical) computational-resource requirements given these considerations are studied. Besides the well-known angular-momentum formulation of the theory, the cases of purely fermionic and purely bosonic formulations (with open boundary conditions), and the Loop-String-Hadron formulation are analyzed, along with a brief discussion of a Quantum Link Model of the same theory. Clear advantages are found in working with the Loop-String-Hadron framework which implements non-Abelian Gauss's laws a priori using a complete set of gauge-invariant operators. Although small lattices are studied in the numerical analysis of this work, and only the simplest algorithms are considered, a range of conclusions will be applicable to larger systems and potentially to higher dimensions.

Performance Evaluation of Parallel Structure from Motion (SfM) Processing with Public Cloud Computing and an On-Premise Cluster System for UAS Images in Agriculture

Thanks to sensor developments, unmanned aircraft system (UAS) are the most promising modern technologies used to collect imagery datasets that can be utilized to develop agricultural applications in these days. UAS imagery datasets can grow exponentially due to the ultrafine spatial and high temporal resolution capabilities of UAS and sensors. One of the main obstacles to processing UAS data is the intensive computational resource requirements. The structure from motion (SfM) is the most popular algorithm to generate 3D point clouds, orthomosaic images, and digital elevation models (DEMs) in agricultural applications. Recently, the SfM algorithm has been implemented in parallel computing to process big UAS data faster for certain applications. This study evaluated the performance of parallel SfM processing on public cloud computing and on-premise cluster systems. The UAS datasets collected over cropping fields were used for performance evaluation. We used multiple computing nodes and centralized network storage with different network environments for the SfM workflow. In single-node processing, an instance with the most computing power in the cloud computing system performed approximately 20 and 35 percent faster than in the most powerful machine in the on-premises cluster. The parallel processing results showed that the cloud-based system performed better in speed-up and efficiency metrics for scalability, although the absolute processing time was faster in the on-premise cluster. The experimental results also showed that the public cloud computing system could be a good alternative computing environment in UAS data processing for agricultural applications.

Polarization twist reflector antenna — A systematic design approach

In this paper, we present an easy and systematic design approach to realize a polarization twist reflector antenna. Detail description has been provided on how to design a transreflector antenna and its constituents including the transreflector, the polarization twist-reflector, and the feed horn. Simple relations for calculation of reflection coefficients of the trans-reflector and the twist- reflector with plane wave incidence have been considered. Simple transmission line model based analyses have been presented. As it does not involve any integral equation or summation of series of some functions, the final results can be obtained within few seconds with the minimum computational resource requirement. Conductive gratings have been conceived for both trans-reflector and twist-reflector. Their performances have been optimized individually in terms of their grating parameters. A linearly polarized pyramidal horn located at the prime focus of the transreflector has been used for the excitation. A prototype of the complete antenna has been designed at Ka-band, fabricated and experimentally measured. 19% impedance bandwidth with average gain of ≈32.3 dBi has been demonstrated.

Demonstration of a three-dimensional dynamically adaptive atmospheric dynamic framework for the simulation of mountain waves

In this paper, Fluidity-Atmosphere, representative of a three-dimensional (3D) non-hydrostatic Galerkin compressible atmospheric dynamic framework, is generated to resolve large-scale and small-scale phenomena simultaneously. This achievement is facilitated by the use of non-hydrostatic equations and the adoption of a flexible 3D dynamically adaptive mesh where the mesh is denser in areas with higher gradients of variable solutions and relatively sparser in the rest of the domain while maintaining promising accuracy and reducing computational resource requirements. The dynamic core is formulated based on anisotropic tetrahedral meshes in both the horizontal and vertical directions. The performance of the adaptive mesh techniques in Fluidity-Atmosphere is evaluated by simulating the formation and propagation of a non-hydrostatic mountain wave. The 2D anisotropic adaptive mesh shows that the numerical solution is in good agreement with the analytic solution. The variation in the horizontal and vertical resolutions has a strong impact on the smoothness of the results and maintains convergence even at high resolutions. When the simulation is extended to 3D, Fluidity-Atmosphere shows stable and symmetric results in the benchmark test cases. The flows over a bell-shaped mountain are resolved quite smoothly. For steep mountains, Fluidity-Atmosphere performs very well, which shows the potential of using 3D adaptive meshes in atmospheric modeling. Finally, as an alternative cut-cell mesh in Fluidity-Atmosphere, the anisotropic adaptive mesh coupled with the Galerkin method provides an alternative accurate representation of terrain-induced flow.