Lightwave system modeling and optimization using Hermite-Gaussian functions

This paper studies a multi-tier cache-aided relaying network, where the destination <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D$ </tex-math></inline-formula> is randomly located in the network and it requests files from the source <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> through the help of cache-aided base station (BS) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> relays. In this system, the multi-tier architecture imposes a significant impact on the system collaborative caching and file delivery, which brings a big challenge to the system performance evaluation and optimization. To address this problem, we first evaluate the system performance by deriving analytical outage probability expression, through fully taking into account the random location of the destination and different file delivery modes related to the file caching status. We then perform the asymptotic analysis on the system outage probability when the signal-to-noise ratio (SNR) is high, to enclose some important and meaningful insights on the network. We further optimize the caching strategies among the relays and BS, to improve the network outage probability. Simulations are performed to show the effectiveness of the derived analytical and asymptotic outage probability for the proposed caching strategy. In particular, the proposed caching is superior to the conventional caching strategies such as the most popular content (MPC) and equal probability caching (EPC) strategies.

Simulation-based optimization of thermal systems

Evaluation of natural computation techniques in the modelling and optimization of a sequential injection flow system for colorimetric iron(III) determination

We investigate optimization of system performance in the below scenario: capturing and transmitting videos by single or multiple video sensors using distributed video coding (DVC) over ad hoc networks. There is an intrinsic contradiction in this scenario that could affect the system performance: the contradiction between the decoding quality and network lifetime. In this paper, we propose a joint optimization between the decoding quality and network lifetime using a quantitative metric of system performance, which is defined as the amount of collected visual information during the operational time of the video sensor. Based on the proposed metric, an optimal encoding rate is determined, which results in an optimal system performance. The simulation results show that the optimal encoding rate can be determined to achieve the optimal system performance.KeywordsDistributed video codingad hoc networksenergyoptimization

Computer-aided modeling and mathematical optimization of energy systems are essential for improving operational efficiency and achieving emission reductions, particularly for steam turbine systems with part-load-dependent efficiency characteristics. Mixed-Integer Linear Programming (MILP) is the state of the art, due to its short computational times and reliable convergence. However, its simplifications often reduce model accuracy. Mixed-Integer Nonlinear Programming (MINLP) offers high accuracy but faces long computational times and potential convergence issues. Recent advancements in Mixed-Integer Quadratically Constrained Programming (MIQCP) offer a promising approach for more accurate energy system modeling by enabling quadratic and bilinear representations while avoiding the full complexity of nonlinear programs. This study compares the optimization methods MILP, MINLP and MIQCP for the operational optimization of a steam turbine system. The parameterization of the models is based on hourly measurement data of two real-world steam turbines. Key evaluation criteria include accuracy, computational time, implementation complexity and the deviation in the calculated optimum. The results show that MIQCP improves accuracy compared with MILP while requiring lower computational time than MINLP. Overall, the results demonstrate that MIQCP provides a suitable compromise between model accuracy and computational efficiency for the operational optimization of steam turbine systems.

There is still lack of effective modelling, simulation and optimisation methods for complex electromechanical system. In this paper, the dynamics model of the PMSM-precision reducer system is established using Lagrange-Maxwell equation. The simulation model and optimisation model of this system are set up with Modelica language. The simulation and optimisation of the system are realised in Dymola and JModelica.org platforms respectively. Simulation results show that the dynamics model of the system is correct. Optimisation results show that the system can quickly respond to the given speed and loads. The proposed modelling, simulation and optimisation method based on Modelica may be commonly applied to other complex electromechanical systems.

It is important to design and operate energy conversion systems such as gas turbine cogeneration ones optimally from the thermoeconomic viewpoint. However, an energy conversion system has a complex network structure, and it takes much time to create its model for the thermoeconomic analysis and optimization. In this paper, a systems approach is presented for the performance analysis and optimization of mechanical systems with network structures, and it is applied to the thermoeconomic analysis and optimization of a gas turbine cogeneration unit. The system modeling for the performance analysis is conducted by a building block approach. Static and dynamic problems for the performance analysis are formulated as sets of nonlinear algebraic and differential algebraic equations, and are solved by the Newton-Raphson method and a hierarchical combination of the Runge-Kutta and Newton-Raphson methods, respectively. The performance optimization is conducted to determine design and operation conditions which optimize performance criteria. This problem is formulated as a nonlinear programming one and is solved by a global optimization method. In the application, the cycle analysis is conducted to determine mass flow rates, pressures, and temperatures, which is followed by the exergy and cost analyses to determine exergy flow rates and efficiencies, and capital costs, respectively. In addition, design and operation conditions are determined to maximize the exergy efficiency or minimize the annual total cost based on the results of the cycle, exergy, and cost analyses. Through a numerical study, it turns out that the proposed systems approach enables one to conduct the thermoeconomic analysis and optimization efficiently.

Multi-objective design optimization of battery thermal management system for electric vehicles

This study aimed to investigate a method for empirically evaluating 3D imaging task performance of breast tomosynthesis imaging systems. A simulation and experimental approach was used to develop a robust method for performance assessment. To identify a method for experimentally assessing the 3D modulation transfer function (MTF), a breast tomosysnthesis system was first simulated using cascaded system analysis to model the signal and noise characteristics of the projections. A range of spheres with varying contrast and size were reconstructed using filtered back projection from which the 3D MTF was evaluated. Results revealed that smaller spheres result in lower artifacts in the measured MTF, where a sphere of 0.5 mm was found ideal for experimental purposes. A clinical tomosynthesis unit was used as a platform for quantifying the effect of acquisition and processing parameters (e.g., angular extent and sampling, dose, and voxel size) on breast imaging performance. The 3D noise-power spectrum (NPS) was measured using a uniform phantom and 3D MTF was measured using 0.5 mm ruby spheres. These metrics were combined with a mathematical description of imaging task to generate a figure of merit called the detectability index for system evaluation and optimization. Clinically relevant imaging tasks were considered, such as the detection and localization of a spherical mass. The detectability index was found to provide a useful metric that accounts for the complex 3D imaging characteristics of breast tomosynthesis. Results highlighted the dependence of optimal technique on the imaging task. They further provided initial validation of an empirically assessed figure of merit for clinical performance assessment and optimization of breast tomosynthesis systems.

The 8th International Workshop on Performance Modeling, Evaluation, and Optimization of Ubiquitous Computing and Networked Systems (PMEO-UCNS'09)

Salvatore Dipietro is a final-year PhD candidate in Computing at Imperial College London. His current research focus is on performance modelling and optimization of NoSQL database systems. His work is supported by HiPEDS centre for doctoral training, funded by EPSRC. Before the PhD, Salvatore completed his MRes in Advance computing at Imperial College London (2015) and his MSc in computer security and forensics at Bedfordshire University (2013). Earlier he gained his undergraduate degree in computing engineering at Politecnico di Torino (2012). Besides, he also has extensive professional work experience as Cloud and DevOps engineer. His interest lies in performance and optimization, capacity planning, distributed applications and network security.

Consideration is given to the design and status of the Integrated Modeling of Optical Systems (IMOS) tool and to critical design issues. A multidisciplinary spacecraft design and analysis tool with support for structural dynamics, controls, thermal analysis, and optics, IMOS provides rapid and accurate end-to-end performance analysis, simulations, and optimization of advanced space-based optical systems. The requirements for IMOS-supported numerical arrays, user defined data structures, and a hierarchical data base are outlined, and initial experience with the tool is summarized. A simulation of a flexible telescope illustrates the integrated nature of the tools.

Bi-directional inductive power transfer (BD-IPT) systems are suitable for applications such as wireless integration of Electric Vehicle (EV) with the utility grid. This paper presents a comprehensive study of the most widely used BD-IPT systems in EV applications - a series tuned inductor-capacitor (LC) type BD-IPT system. The system parameters used in the paper have been set according to SAE J2954 guidelines for wireless electrical vehicle charging. Mathematical and simulation models of the system have been developed to investigate the behavior and sensitivity of the system to variations in circuit parameters. The validity of the mathematical models is verified and the efficiency of each system is investigated using simulated results based on a 3.7 kW simulated systems. Both frequency and relative phase modulation between converters are being considered for performance optimization of the systems. Nearly a 4% improvement in efficiency can be observed with these optimum modulations values.

In positron emission tomography (PET), 3D iterative image reconstruction methods have a huge computational burden. In this paper, we developed a list-mode image reconstruction method using graphics processing units (GPUs). Efficiency of acceleration for GPU implementation largely depends on the method chosen, where a reduced number of conditional statements and a reduced memory size are required. On the other hand, accurate system models are required to improve the quality of reconstructed images. Various accurate system models for conventional CPU implementation have been proposed, but these models basically require many conditional statements and huge memory size. Therefore, we developed a new system model which matches GPU implementation better. In this model, the detector response functions, which vary depending on each line of response (LOR), are pre-computed in CPUs and modeled by sixth-order polynomial functions in order to reduce the memory size occupied in GPUs. Each element of a system matrix is obtained on-the-fly in GPUs by calculating the distance between an LOR and a voxel. Therefore the developed system model enables efficient GPU implementation of the accurate system modeling with a reduced number of conditional statements and a reduced memory size. We applied the developed method to a small OpenPET prototype, in which 4-layered depth-of-interaction (DOI) detectors were used. For image reconstruction, we used the dynamic row-action maximum likelihood algorithm (DRAMA). Compared with a conventional model for GPU implementation, in which DRFs are given as a Gaussian function of fixed width, we saw no remarkable difference for DOI data, but for non-DOI data the proposed model outperformed the conventional at the peripheral region of the field-of-view. The proposed model had almost the same calculation time as the conventional model did. For further acceleration, we tried parallel GPU implementation, and we obtained 3.8-fold acceleration by using 4 GPUs.

Data-driven optimization methods play a major role in industrial system performance optimization. As the requirements for performance optimization efficiency become more and more stringent, the efficiency of data-driven methods becomes the key to their application. The efficiency of the data-driven methods mainly depends on their algorithm parameter settings. However, the current parameters settings scheme optimization process is usually conducted in trial-and-error mode, which depends on the experience of engineers or researchers, and is cumbersome and time-consuming. To address this problem, this paper proposes a closed-loop datadriven optimization method, which can guide the improvement of the next optimization process through the morphological characteristics of the current optimization process. In this method, the Visual Geometry Group(VGG) neural network is used to obtain the morphological characteristics of the current optimization process to guide the data-driven algorithm based on Simultaneous Perturbation Stochastic Approximation (SPSA) to improve the current optimization process, realize the auxiliary optimization of control parameters, and show the role of using multidimensional data to assist the traditional control method to achieve rapid convergence and achieve high efficiency and economy. Simulation experiments were conducted on the performance optimization of the steam generator level control system. The simulation results showed that this method is feasible and could be assisted in optimizing the control parameters of the data-driven optimization methods.