Physical Sciences and Mathematics Commons^™

Benchmarks And Controls For Optimization With Quantum Annealing, Erica Kelley Grant

Doctoral Dissertations

Quantum annealing (QA) is a metaheuristic specialized for solving optimization problems which uses principles of adiabatic quantum computing, namely the adiabatic theorem. Some devices implement QA using quantum mechanical phenomena. These QA devices do not perfectly adhere to the adiabatic theorem because they are subject to thermal and magnetic noise. Thus, QA devices return statistical solutions with some probability of success where this probability is affected by the level of noise of the system. As these devices improve, it is believed that they will become less noisy and more accurate. However, some tuning strategies may further improve that probability of …

Micro Grid Control Optimization With Load And Solar Prediction, Shaju Saha

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Using renewable energy can save money and keep the environment cleaner. Installing a solar PV system is a one-time cost but it can generate energy for a lifetime. Solar PV does not generate carbon emissions while producing power. This thesis evaluates the value of being able to make accurate predictions in the use of solar energy. It uses predicted solar power and load for a system and a battery to store the energy for future use and calculates the operating cost or profit in several designed conditions. Various factors like a different place, tuning the capacity of sources, changing buy/sell …

Structured Data Mining Networks, Time Series, And Time Series Of Networks, Lin Zhang

Legacy Theses & Dissertations (2009 - 2024)

The rate at which data is generated in modern applications has created an unprecedented demand for novel methods to effectively and efficiently extract insightful patterns. Methods aware of known domain-specific structure in the data tend to be advantageous. In particular, a joint temporal and networked view of observations offers a holistic lens to many real-world systems. Example domains abound: activity of social network users, gene interactions over time, a temporal load of infrastructure networks, and others. Existing analysis and mining approaches for such data exhibit limited quality and scalability due to their sensitivity to noise, missing observations, and the need …

Simulation Research On Armored Equipment Maintenance Support Resource Optimization, Huiqi Zhang, Chunliang Chen, Junyan Liu, Liu Shuai, Yongqing Zhang

Journal of System Simulation

Abstract: Contraposing the problem of armored equipment maintenance resource computation and optimization, the armored equipment maintenance support process was analyzed. The armored equipment maintenance support process concept model and the mathematic model were constructed based on discrete system modeling theory; while the simulation model based on net discrete event was given in apply of Anylogic software. The armored equipment maintenance support task fulfilling degree, the resource utilization degree, and the mean maintenance time were got by simulation computation. As a result that maintenance support resource was evaluated based on index weight, and the optimized values of the maintenance support resource …

Quadratic Rational Trigonometric Spline Curves With Shape Controlling, Xinru Liu, Manman Wei, Shengjun Liu, Dangfu Yang

Journal of System Simulation

Abstract: A new quadratic rational trigonometric spline curve with a shape parameter was proposed. The value control and the inflection-point control of the interpolation scheme were discussed in theory. And the optimal methods for calculating the desired inflection-points was proposed, by using optimization theory. Numerical experiments show the interpolation spline and the optimization method can be used in modeling design.

Adaptive Quick Artificial Bee Colony Algorithm Based On Opposition Learning, Xiaojian Yang, Yiwei Dong

Journal of System Simulation

Abstract: On the basis of analyzing such shortcomings of the artificial bee colony algorithm (ABC) as slow convergence, low convergence precision and premature convergence, the opposition-learning adaptive quick artificial bee colony algorithm (OAQABC) was proposed. A new step size was proposed, which made the around food source parameter of quick artificial bee colony algorithm (QABC) adaptive, and combined the opposition-based learning to improve the employed bee phase. The experimental results show that OAQABC has better performance than basic ABC and QABC. Also the optimization performance of OAQABC is better than particle swarm optimization (PSO) algorithm and Cuckoo Search (CS) algorithm …

Affinity Propagation Based Improved Group Search Optimizer Clustering Algorithm, Zhang Kang, Xingsheng Gu

Journal of System Simulation

Abstract: The essence of clustering is an optimization problem. It can be solved by swarm intelligent algorithms which are the popular research area in recent years. A novel Group Search Optimizer (GSO) algorithm named Fast Global Group Search Optimizer (FGGSO) was proposed. FGGSO improved the individuals' updating strategies of GSO, adopting the campaign strategy, destruction-construction strategy and accelerating-jumping strategy. By this means, the proposed algorithm improved the global and local search capability of the original GSO. Furthermore, based on this FGGSO algorithm, a novel improved AP algorithm was proposed. On account of deficiency of AP clustering unable to deal with …

Least-Energy Maneuver Of Five-Link Manipulator Constrained Within Tunnel Space Using Direct Collocation, Xiuqiang Pan, Chengcai Mei, Junjie Chen

Journal of System Simulation

Abstract: Optimal control and designs least-energy maneuver control laws for a five-linked manipulator were applied in order to carry out designated tasks in a confined space. Lagrange-Euler equation described the relationships between the actuators and system dynamics. Euler-Lagrange formulation indicates how optimization can be achieved when optimum occurs. Direct collocation method was introduced in order to solve this highly nonlinear dynamic optimal control problem. Simulations were done to exploit how the manipulator reacted to the constraint. In this study, the diameter of the cylindrical space was shrunken each time by 0.1 meters. The value of the cost function and …

Optimization Scheme Of Average Time For Finding Idle Channel In Cognitive Radio System, Qiao Pei, Liyuan Xiao, Yanyan Han, Gao Ling

Journal of System Simulation

Abstract: In cognitive radio system, periodic spectrum sensing was taken by secondary users to prevent the interference to primary users. Supposed that there are many primary user channels, when the current primary user occupies channel, secondary users do spectrum handover. During spectrum handover, the time of finding an idle channel is a random variable. In order to speed up spectrum handover, the system used equal gain combining cooperative spectrum sensing to inspect an idle channel. This pattern optimized the sensing time of the single user channel, in order to get the best effect of the average time in finding idle …

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 …

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. …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …