Physical Sciences and Mathematics Commons^™

Big Data, Spatial Optimization, And Planning, Kai Cao, Wenwen Li, Richard Church

Research Collection School Of Computing and Information Systems

Spatial optimization represents a set of powerful spatial analysis techniques that can be used to identify optimal solution(s) and even generate a large number of competitive alternatives. The formulation of such problems involves maximizing or minimizing one or more objectives while satisfying a number of constraints. Solution techniques range from exact models solved with such approaches as linear programming and integer programming, or heuristic algorithms, i.e. Tabu Search, Simulated Annealing, and Genetic Algorithms. Spatial optimization techniques have been utilized in numerous planning applications, such as location-allocation modeling/site selection, land use planning, school districting, regionalization, routing, and urban design. These methods …

Parameters Optimization For Variable Speed And Pitch Controller Of Wind Turbine Based On Bladed, Gao Feng, Wang Wei, Xinmei Ling

Journal of System Simulation

Abstract: Due to nonlinearity and time-varying parameters of wind power system, its controller parameters are hard to be calculated and tuned during the process of design and optimization. The linear model which is suitable for parameters tuning was built through model linearization of Bladed and model reducing-order algorithm. The PI parameter was tuned with the IM-PSO (Immune Memory Particle Swarm Optimization). Moreover, the gain coefficient of optimal torque control and the gain divisor of adaptive PI pitch control conducted optimizing calculation based on the identification parameters of Bladed. A set of optimization method for variable speed and pitch controller …

Real-Time Control Method Of Hsss Based On Single Phrase Self Decoupling Strategy, Yingping Yi, Bogang Qu, Zhang Yang

Journal of System Simulation

Abstract: Zero Crossing Detection (ZCD) and phase locked loop are widely applied in the real-time control. Compared the performances of ZCD, SSRF SPLL and DDSRF SPLL,a method named as Single Phase Self Decoupling SPLL (SPSD SPLL) for HSSS(Hybrid Solid State Switch) is proposed and the mathematical method and control methods are given. The control parameters were obtained by analyzing the steady and dynamic performance of SPSD SPLL. By establishing the real-time controlling models based on the above strategies and HSSS model in the MATLAB/Simulink, the simulation and optimization analysis results verified that the SPSD SPLL was well performed …

Scheduling Problem Of Unidirectional Material Handling System With Short-Cut, Juntao Li, Kun Xia, Kise Hiroshi

Journal of System Simulation

Abstract: Unidirectional circulation-type material handling system on a single loop with a shortcut is a typical and basic unit in a flexible manufacturing system or logistics system. It is widely used in semiconductor wafer fabrication system. Superposition efficiency of the basic unit often determines the one of the whole system. Different scheduling rules impact the interference between AGVs and then have an important effect on the efficiency of the whole system. To decrease the interference and improve performance of the system, a mathematical model of this system was made, analyzing this system by simulation and comparing the merits and …

Study On Vacuum Dehydration Rate From Oil Based On T_S Fuzzy Identifying Model, Liu Ge, Bin Chen, Xianming Zhang

Journal of System Simulation

Abstract: The process of vacuum dehydration from oil is time-varying, nonlinear, and difficult to be specified with mathematical methods. Takagi-Sugeno (T_S) fuzzy model of vacuum dehydration rate of oil purifier is proposed, which a method of applying Fuzzy C-Means (FCM) clustering algorithm and using the least square method identifying the consequent parameters. The nonlinear mapping is set up from four influence factors (the initial water content, the vacuum pressure , the initial temperature and running time) to vacuum dehydration rate using the T_S fuzzy model. The simulation and experimental results show the T_S model reflects the laws of the influences …

Improved Particle Swarm Optimization Based On Lévy Flights, Rongyu Li, Wang Ying

Journal of System Simulation

Abstract: The particle swarm optimization (PSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. The Lévy particle swarm optimization (Lévy PSO) was proposed. In the particle position updating formula, Lévy PSO eliminated the impact of speed on the convergence rate, and used Levy flight to change the direction of particle positions movement to prevent particles getting into local optimum value, and then using greedy strategy to update the evaluation and choose the best solution to obtain the global optimum. The experimental results show that Lévy PSO can effectively …

Adaptive Loss-Aware Quantization For Multi-Bit Networks, Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele

Research Collection School Of Computing and Information Systems

We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms. We propose Adaptive Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to achieve an average bitwidth below one-bit without notable loss in inference accuracy. Unlike previous MBN quantization solutions that train a quantizer by minimizing the error to reconstruct full precision weights, ALQ directly minimizes the quantizationinduced error on the loss function involving neither gradient approximation nor …

Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. …

Data Assimilation For Conductance-Based Neuronal Models, Matthew Moye

Dissertations

This dissertation illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. Throughout this work, twin experiments, where the data is synthetically generated from output of the model, are used to validate use of these techniques for conductance-based models observing only the voltage trace. In Chapter 1, these techniques are described in detail and the …

Sustainability And Optimization Of Rangeland Uses: Issues Of Perspective And Scale, T. L. Thurow

IGC Proceedings (1993-2023)

No abstract provided.

Calibration Optimization Of A Stream Temperature Model Applied To The Nooksack River, Ian Edgar

Scholars Week

The River Basin Model (RBM) is used to assess how stream temperatures will change in the Nooksack River due to warming climates by tracking heat exchanges along stream segments. Before modeling forecasted climate scenarios, I first calibrated the model to observed historical stream temperatures. The calibration of the RBM to a stream network involves the adjustment of eleven different variables until the simulated temperatures match observed historical stream temperatures. Because the manual process of calibrating the model is extremely time consuming, I developed a Python script to converge on the optimal variables required for the RBM calibration. The script adjusts …

Target Control Of Networked Systems, Isaac S. Klickstein

Mechanical Engineering ETDs

The control of complex networks is an emerging field yet it has already garnered interest from across the scientific disciplines, from robotics to sociology. It has quickly been noticed that many of the classical techniques from controls engineering, while applicable, are not as illuminating as they were for single systems of relatively small dimension. Instead, properties borrowed from graph theory provide equivalent but more practical conditions to guarantee controllability, reachability, observability, and other typical properties of interest to the controls engineer when dealing with large networked systems. This manuscript covers three topics investigated in detail by the author: (i) the …

Characterizing Uncertainty In Correlated Response Variables For Pareto Front Optimization, Peter A. Calhoun

Theses and Dissertations

Current research provides a method to incorporate uncertainty into Pareto front optimization by simulating additional response surface model parameters according to a Multivariate Normal Distribution (MVN). This research shows that analogous to the univariate case, the MVN understates uncertainty, leading to overconfident conclusions when variance is not known and there are few observations (less than 25-30 per response). This research builds upon current methods using simulated response surface model parameters that are distributed according to an Multivariate t-Distribution (MVT), which can be shown to produce a more accurate inference when variance is not known. The MVT better addresses uncertainty in …

Optimizing The Environmental And Economic Sustainability Of Contingency Base Infrastructure, Jamie E. Filer

Theses and Dissertations

Contingency bases are often located in remote areas with limited access to established infrastructure grids. This isolation leads to standalone systems comprised of inefficient, resource-dependent infrastructure, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. Planners can mitigate these negative impacts by selecting sustainable technologies. However, such alternatives often come at a higher procurement cost and mobilization requirement. Accordingly, this study aims to develop and implement a novel infrastructure sustainability assessment model capable of optimizing the tradeoffs between environmental and economic performance of infrastructure alternatives.

Catgame: A Tool For Problem Solving In Complex Dynamic Systems Using Game Theoretic Knowledge Distribution In Cultural Algorithms, And Its Application (Catneuro) To The Deep Learning Of Game Controller, Faisal Waris

Wayne State University Dissertations

Cultural Algorithms (CA) are knowledge-intensive, population-based stochastic optimization methods that are modeled after human cultures and are suited to solving problems in complex environments. The CA Belief Space stores knowledge harvested from prior generations and re-distributes it to future generations via a knowledge distribution (KD) mechanism. Each of the population individuals is then guided through the search space via the associated knowledge. Previously, CA implementations have used only competitive KD mechanisms that have performed well for problems embedded in static environments. Relatively recently, CA research has evolved to encompass dynamic problem environments. Given increasing environmental complexity, a natural question arises …

Optimizing Cluster Sets For The Scan Statistic Using Local Search, James Shulgan

Graduate Research Theses & Dissertations

In recent years, scattering sensors to produce wireless sensor networks (WSN) has been proposed for detecting localized events in large areas. Because sensor measurements are noisy, the WSN needs to use statistical methods such as the scan statistic. The scan statistic groups measurements into various clusters, computes a cluster statistic for each cluster, and decides that an event has happened if any of the statistics exceeds a threshold. Previous researchers have investigated the performance of the scan statistic to detect events; however, little attention was given to the optimization of which clusters the scan statistic should use. Using the scan …

Optimal Design Of A Flux Reversal Permanent Magnet Machine As A Wind Turbinegenerator, Majid Ghasemian, Farzad Tahami, Zahra Nasiri-Gheidari

Turkish Journal of Electrical Engineering and Computer Sciences

Flux reversal permanent magnet generators are well suited for use as wind turbine generators owing to their high torque generation ability and magnetic gear. However, they suffer from poor voltage regulation due to their high winding inductance. In this paper, a design optimization method is proposed for flux reversal generators in wind turbine applications. The proposed method includes a new multiobjective function. Cost, volume of the generator, and mass of the permanent magnet are considered in it independently and simultaneously. Besides the new objective function, the main superiority of this paper compared with published papers is considering winding inductance in …

Computational Model For Neural Architecture Search, Ram Deepak Gottapu

Doctoral Dissertations

"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.

The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …

A Novel Penalty-Based Wrapper Objective Function For Feature Selection In Big Data Using Cooperative Co-Evolution, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2014 to 2021

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even …

Adaptive Modified Artificial Bee Colony Algorithms (Amabc) For Optimization Ofcomplex Systems, Rabi̇a Korkmaz Tan, Şebnem Bora

Turkish Journal of Electrical Engineering and Computer Sciences

Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in …

Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that …

Testing And Improving An Optimization-Based Digital Colorblindness Corrective Filter, Zachary Kenneth Mcintyre

Senior Projects Fall 2020

Computers often communicate essential information via color which is lost to colorblind users. In order to address this information loss, designers and computer scientists have created a variety of different correction methods to improve computer accessibility. One such method was created by Luke Jefferson and Richard Harvey in their 2006 paper, “Accommodating Color Blind Computer Users” which consists of a difference histogram, differences of key colors, optimization and interpolation to adjust images for specific types of congenital colorblindness. I have recreated their algorithm as well as their original test images. I then conducted extensive tests on challenging images to examine …

Co-Optimization Of A Robot's Body And Brain Via Evolution And Reinforcement Learning, Jack Felag

Graduate College Dissertations and Theses

Agents are often trained to perform a task via optimization algorithms. One class of algorithms used is evolution, which is ``survival of the fitness'' used to pick the best agents for the objective, and slowly changing the best over time to find a good solution. Evolution, or evolutionary algorithms, have been commonly used to automatically select for a better body of the agent, which can outperform hand-designed models. Another class of algorithms used is reinforcement learning. Through this strategy, agents learn from prior experiences in order to maximize some reward. Generally, this reward is how close the objective is to …

Optimization Of Real-World Outdoor Campaign Allocations, Fatmanur Akdoğan Uzun, Doğan Altan, Ercan Peker, Mahmut Altuğ Üstün, Sanem Sariel

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we investigate the outdoor campaign allocation problem (OCAP), which asks for the distribution of campaign items to billboards considering a number of constraints. In particular, for a metropolitan city with a large number of billboards, the problem becomes challenging. We propose a genetic algorithm-based method to allocate campaign items effectively, and we compare our results with those of nonlinear integer programming and greedy approaches. Real-world data sets are collected with the given constraints of the price class ratios of billboards located in İstanbul and the budgets of the given campaigns. The methods are evaluated in terms of …

Mesoporous Starch Aerogels Production As Drug Delivery Matrices: Synthesis Optimization, Ibuprofen Loading, And Release Property, Akbar Mohammadi, Jafar Sadegh Moghaddas

Turkish Journal of Chemistry

he aim of this work was to prepare biodegradable starch aerogels as drug carriers. The effective parameters in the synthesis and the optimal values of these parameters were determined using Minitab experimental design software. Ibuprofen was selected as a model drug for the dissolution study and loaded into optimized aerogel during the last solvent exchange step. The Fourier Transform Infrared Spectroscopy (FTIR) analysis showed that ibuprofen has been successfully loaded into the aerogel matrix without any effect on the aerogel nature. The drug loading was calculated to be 29%. The isotherm of ibuprofen adsorption into aerogels matrices followed from the …

Hybrid Electric Vehicle Energy Management Strategy With Consideration Of Battery Aging, Bin Zhou

Dissertations, Master's Theses and Master's Reports

The equivalent consumption minimization strategy (ECMS) is a well-known energy management strategy for Hybrid Electric Vehicles (HEV). ECMS is very computationally efficient since it yields an instantaneous optimal control. ECMS has been shown to minimize fuel consumption under certain conditions. But, minimizing the fuel consumption often leads to excessive battery damage. The objective of this dissertation is to develop a real-time implementable optimal energy management strategy which improves both the fuel economy and battery aging for Hybrid Electric Vehicles by using ECMS. This work introduces a new optimal control problem where the cost function includes terms for both fuel consumption …

Optimization Of Handicap Ramp, Tyler Schilling

Undergraduate Journal of Mathematical Modeling: One + Two

The objective of this project is to minimize the cost of building a handicap ramp. This is done by introducing an equation that represents the total cost of the construction, including labor and materials. Variables are then defined in terms of block length l, allowing for an equation with one variable to be graphed and derived. This equation then undergoes the first derivative test to find a value of l that would create a minimum output for cost. This value is then compared to the physical constraints of the project allowing for a realistic minimum cost to be found. …

Accurate Indoor Positioning With Ultra-Wide Band Sensors, Taner Arsan

Turkish Journal of Electrical Engineering and Computer Sciences

Ultra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m × 5.41 m …

Optimizing Pollution Routing Problem, Shivika Dewan

All Master's Theses

Pollution is a major environmental issue around the world. Despite the growing use and impact of commercial vehicles, recent research has been conducted with minimizing pollution as the primary objective to be reduced. The objective of this project is to implement different optimization algorithms to solve this problem. A basic model is created using the Vehicle Routing Problem (VRP) which is further extended to the Pollution Routing Problem (PRP). The basic model is updated using a Monte Carlo Algorithm (MCA). The data set contains 180 data files with a combination of 10, 15, 20, 25, 50, 75, 100, 150, and …

Combined Analytic Hierarchy Process And Binary Particle Swarm Optimization Formultiobjective Plug-In Electric Vehicles Charging Coordination With Time-Of-Usetariff, Junaid Bin Fakhrul Islam, Mir Toufikur Rahman, Hazlie Mokhlis, Mohamadariff Othman, Tengku Fiaz Tengku Mohmed Noor Izam, Hasmaini Mohamad

Turkish Journal of Electrical Engineering and Computer Sciences

Plug-in electric vehicles (PEVs) are gaining popularity as an alternative vehicle in the past few years. The charging activities of PEVs impose extra electrical load on residential distribution system as well as increasing operational cost. There are multiple conflicting requirements and constraints during the charging activities. Therefore, this paper presents multiobjective PEV charging coordination based on weighted sum technique to provide simultaneous benefits to the power utilities and PEV users. The optimization problem of the proposed coordination is solved using binary particle swam optimization. The objectives of the coordination are to (i) minimize daily power loss, (ii) maximize power delivery …