Computer Sciences Commons^™

A Multi-Objective Approach To Tactical Maneuvering Within Real Time Strategy Games, Christopher D. Ball

Theses and Dissertations

The real time strategy (RTS) environment is a strong platform for simulating complex tactical problems. The overall research goal is to develop artificial intelligence (AI) RTS planning agents for military critical decision making education. These agents should have the ability to perform at an expert level as well as to assess a players critical decision-making ability or skill-level. The nature of the time sensitivity within the RTS environment creates very complex situations. Each situation must be analyzed and orders must be given to each tactical unit before the scenario on the battlefield changes and makes the decisions no longer relevant. …

Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Evolutionary Algorithm is a well-known meta-heuristics paradigm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for …

Geometric Aspects And Auxiliary Features To Top-K Processing [Advanced Seminar], Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision making software on PCs, PDAs and smart-phones. The objective of this seminar is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries that have a strong geometric nature. The seminar will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.

Robust Influence Maximization, Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

Influence Maximization is the problem of finding a fixed size set of nodes, which will maximize the expected number of influenced nodes in a social network. The number of influenced nodes is dependent on the influence strength of edges that can be very noisy. The noise in the influence strengths can be modeled using a random noise or adversarial noise model. It has been shown that all random processes that independently affect edges of the graph can be absorbed into the activation probabilities themselves and hence random noise can be captured within the independent cascade model. On the other hand, …

Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We study the problem of optimizing the trajectories of agents moving over a network given their preferences over which nodes to visit subject to operational constraints on the network. In our running example, a theme park manager optimizes which attractions to include in a day-pass to maximize the pass’s appeal to visitors while keeping operational costs within budget. The first challenge in this combinatorial optimization problem is that it involves quantities (expected visit frequencies of each attraction) that cannot be expressed analytically, for which we use the Sample Average Approximation. The second challenge is that while sampling is typically done …

Optimal Scheduling Of Pev Charging/Discharging In Microgrids With Combined Objectives, Chong Cao, Ming Cheng, Bo Chen

Michigan Tech Publications, Part 1

While renewable power generation and vehicle electrification are promising solutions to reduce greenhouse gas emissions, it faces great challenges to effectively integrate them in a power grid. The weather-dependent power generation of renewable energy sources, such as Photovoltaic (PV) arrays, could introduce significant intermittency to a power grid. Meanwhile, uncontrolled PEV charging may cause load surge in a power grid. This paper studies the optimization of PEV charging/discharging scheduling to reduce customer cost and improve grid performance. Optimization algorithms are developed for three cases: 1) minimize cost, 2) minimize power deviation from a pre-defined power profile, and 3) combine objective …

An Optimized Multiple Right-Hand Side Dslash Kernel For Intel Xeon Phi, Aaron Walden

Computer Science Theses & Dissertations

Lattice quantum chromodynamics (LQCD) stands unique as the only computationally tractable, non-perturbative, and model-independent quantum field theory of the strong nuclear force. The computational core of LQCD is the Wilson Dslash operator, a nearest neighbor stencil operator summing matrix-vector multiplications over lattice points, whose performance is bandwidth-bound on most architectures. Reportedly, up to 90\% of LQCD running time may be spent computing Dslash. In recent years, efforts have been made by researchers to optimize LQCD calculations for floating point coprocessor cards such as GPUs and Intel Xeon Phi Knights Corner (KNC), which boast powerful vector processing units. Most of these …

Enabling Optimizations Through Demodularization, Blake Dennis Johnson

Theses and Dissertations

Programmers want to write modular programs to increase maintainability and create abstractions, but modularity hampers optimizations, especially when modules are compiled separately or written in different languages. In languages with syntactic extension capabilities, each module in a program can be written in a separate language, and the module system must ensure that the modules interoperate correctly. In Racket, the module system ensures this by separating module code into phases for runtime and compile-time and allowing phased imports and exports inside modules. We present an algorithm, called demodularization, that combines all executable code from a phased modular program into a single …

Online Spatio-Temporal Matching In Stochastic And Dynamic Domains, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Spatio-temporal matching of services to customers online is a problem that arises on a large scale in many domains associated with shared transportation (ex: taxis, ride sharing, super shuttles, etc.) and delivery services (ex: food, equipment, clothing, home fuel, etc.). A key characteristic of these problems is that matching of services to customers in one round has a direct impact on the matching of services to customers in the next round. For instance, in the case of taxis, in the second round taxis can only pick up customers closer to the drop off point of the customer from the first …

Multiagent Based Algorithmic Approach For Fast Response In Railway Disaster Handling, Poulami Dalapati, Arambam James Singh, Animesh Dutta

Research Collection School Of Computing and Information Systems

Disaster management in railway network is an important issue. It requires to minimize negative impact and also fast, efficient recovery from the disturbances. The main challenge here is that, the effect of inconvenience spreads out very fast in time and space. It takes noticeable amount of time to get back everything in the previous situation. This paper proposes a multi agent based algorithmic approach for disaster handling in Railway Network. This takes care of fast response to get total number of affected trains in a fast and efficient manner. We propose few algorithms to handle this situation and simulate it …

Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar

Research Collection School Of Computing and Information Systems

We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …

Optimizing Vehicle Usage Using Csp, Sat And Max-Sat, Raheem T. Al Rammahi

Electronic Theses and Dissertations

Most of the companies in Iraq spend significant amounts of time and money when transferring employees between home and work. In this thesis, we model the problem of the Dhi Qar Oil company (DQOC) transportations using three modeling languages from AI: Constraint Programing (CP), Boolean Satisfiability (SAT), and Maximum Satisfiability (MAX-SAT). We then use solvers to find optimal solutions to this problem.

We show which of these solvers is more efficient when finding optimal solutions. For this purpose, we create a test suite of 360 problems to test these solvers. All solvers are applied to these problems and the final …

Optimization Of Pid Parameters Using Bbbc For A Multiarea Agc Scheme In A Deregulated Power System, Nagendra Kumar, Vishal Kumar, Barjeev Tyagi

Turkish Journal of Electrical Engineering and Computer Sciences

Automatic generation control (AGC) is an important service used for the secure and reliable operation of a power system. In this paper, a suitable AGC scheme for a deregulated environment has been studied for various load perturbation scenarios. A proportional integral derivative (PID) controller has been utilized to control the real power output of the generators. The optimal parameters of the PID controller have been determined using the big bang big crunch (BBBC) algorithm, (BBBC-PID). The performance of the BBBC-PID controller has been checked on three different sized multiarea power systems. The results obtained with the applied algorithm have also …

A New Deployment Method For Electric Vehicle Charging Infrastructure, Bünyami̇n Yağciteki̇n, Mehmet Uzunoğlu, Ari̇f Karakaş

Turkish Journal of Electrical Engineering and Computer Sciences

The fast depletion of fossil fuels, climate change, and global warming have become major worldwide problems and alternatives for conventional transportation have been actively researched in the last decade. Compared to available conventional vehicles, electric vehicles have a leading position due to their environmentally friendly transportation. Recent electric vehicle penetration brings the necessity of a high number of charging stations, which are considered to be established in community areas such as shopping centers, hospitals, commercial areas, university campuses, residential areas, and streets. Deployment planning of charging stations is very important for driver expectations and social and economic impacts of electric …

Energy-Efficient And Environmentally Friendly Power Dispatch By Trigeneration With Renewable Energy And Energy Storage, Faisal Pazheri, Mohd Fauzi Othman, Nazar H. Malik, Abdulrehman Ali Al-Arainy

Turkish Journal of Electrical Engineering and Computer Sciences

The importance of energy-efficient power generation that effectively utilizes the available fossil fuels is increasing due to the gradual decline in fossil fuel reserves. Furthermore, concern about global warming has led to rapid growth in renewable energy and energy storage technologies. This trend is encouraging power utilities to effectively exploit the use of renewable energy resources (RESs) and energy storage facilities (ESFs) for power supply systems. In this regards, trigeneration in the presence of RESs and ESFs can play an important role for efficient and clean power dispatch. This paper discusses the optimum power dispatch using a hybrid power plant …

The New Issues In Classification Problems, Md Mahmudul Hasan

Open Access Theses & Dissertations

The data involved with science and engineering getting bigger everyday. To study and organize a big amount of data is difficult without classification. In machine learning, classification is the problem of identifying a given data from a set of categories. There are several classification technique people using to classify a given data. In our work we present a sparse representation technique to perform classification. The popularity of this technique motivates us to use on our collected samples. To find a sparse representation, we used an $l_1$-minimization algorithm which is a convex relaxation algorithm proven very efficient by researchers. The purpose …

Mechanisms For Improving Information Quality In Smartphone Crowdsensing Systems, Francesco Restuccia

Doctoral Dissertations

"Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs.

Existing smartphone sensing systems depend completely on the participants' willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the …

An Efficient Method For Optimizing Segmentation Parameters, Jacob D' Avy, Wei-Wen Hsu, Chung-Hao Chen, Andreas F. Koschan, Mongi Abidi

Electrical & Computer Engineering Faculty Publications

Segmenting an image into meaningful regions is an important step in many computer vision applications such as facial recognition, target tracking and medical image analysis. Because image segmentation is an ill-posed problem, parameters are needed to constrain the solution to one that is suitable for a given application. For a user, setting parameter values is often unintuitive. We present a method for automating segmentation parameter selection using an efficient search method to optimize a segmentation objective function. Efficiency is improved by utilizing prior knowledge about the relationship between a segmentation parameter and the objective function terms. An adaptive sampling of …

Modeling And Performance Optimization Of Photovoltaic And Thermal Collector Hybrid System, Di̇lşad Engi̇n, Meti̇n Çolak

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, the hybrid photovoltaic/thermal collector (PV/T) system's electrical and thermal efficiency was examined by stating a mathematical model and developing a prototype of the system. To enhance the electrical efficiency, the cell temperature of the PV module was decreased with cooling and the heated fluid could be used for low heating applications. For this purpose, a PV/T system was modeled and constructed using a thermal collector placed beneath the photovoltaic panel where the excess heat and solar radiation through the transparent PV module was the input of the thermal collector. A transparent solar module was used in order …

Online Arima Algorithms For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun

Research Collection School Of Computing and Information Systems

Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In this paper, we propose online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency. The idea of our ARIMA method is to …

Optimization Design Of A Doubly Salient 8/6 Srm Based On Three Computational Intelligence Methods, Fares Rebahi, Amar Bentounsi, Houssem Rafik El-Hana Bouchekara, Redjem Rebbah

Turkish Journal of Electrical Engineering and Computer Sciences

The aim of this paper is to optimize an 8/6 doubly salient switched reluctance machine using three computational intelligence methods, which include particle swarm optimization, a genetic algorithm, and differential evolution. Three cases are investigated where different parameters are considered like the stator pole arc, rotor pole arc, and ratios, which define the stator yoke and rotor thickness. The objective functions considered are the average torque and the torque-to-weight functions. The simulations are carried out using MATLAB and FEMM software. The optimal results found are compared with the initial design, and it is shown that high improvements are achieved.

A Problem Approximation Surrogate Model (Pasm) For Fitness Approximation In Optimizing The Quantization Table For The Jpeg Baseline Algorithm, Vinoth Kumar Balasubramanian, Karpagam Manavalan

Turkish Journal of Electrical Engineering and Computer Sciences

The quantization table in the baseline Joint Photographic Experts Group (JPEG) algorithm plays an important role in compression/quality trade-off. Hence the detection of the optimal quantization table is viewed as an optimization problem. The genetic algorithm (GA) is an attractive optimization tool by many researchers for this application due to its ability in dealing with complex problems. In spite of its advantages, the GA requires more computation time to achieve an optimal solution if it has an expensive fitness evaluation. This paper proposes a problem approximation surrogate model (PASM) for fitness approximation to assist the GA in optimizing the quantization …

A Gis-Based Novel Active Monitoring System For Fiber Networks, Özer Koray Akdemi̇r, Taner Dursun, Sadik Arslan, Recep Benzer, Muhammet Ali̇ Akcayol

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a geographic information system (GIS)-based novel fiber network monitoring system has been developed to improve the operation and maintenance of fiber optic networks (FONs). The main aim of the developed system is to provide the required capabilities for both detailed digital modeling and central monitoring of FONs. The system can perform scheduled measurements and dispatch alarms if any fault or degradation is detected in the concerned FON. It also indicates the alarm locations on a map as a considerable contribution to decreasing mean time to repair. The developed monitoring system has more security features than others.

Optimal Power Flow With Svc Devices By Using The Artificial Bee Colony Algorithm, Kadi̇r Abaci, Volkan Yamaçli, Ali̇ Akdağli

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper a simple and efficient heuristic search method based on the artificial bee colony (ABC) algorithm is presented and used for the optimal power flow (OPF) problem in power systems with static VAR compensator (SVC) devices. The total generation cost of a power system with SVC devices (which improve the voltage stability at load buses) is optimally minimized with the use of ABC. The ABC, which is based on the foraging behavior of honey bees searching for the best food source, is a recently proposed optimization algorithm. The performance of the presented ABC algorithm was tested and verified …

Optimization With Genetic Algorithm Of Temperature-Dependent Fiber Length Of L-Band Edfa Gain, Murat Yücel, Damt Adnan Mustafa Mustafa

Turkish Journal of Electrical Engineering and Computer Sciences

Erbium-doped fiber amplifiers (EDFAs) have great importance in long-distance communication. It is required to have equal gain for all signals that are transferred and to avoid loss in the receiver of long-distance communication systems. However, temperature dependence changes the output spectrum of the designed gain-flattening systems. In this study, each erbium-doped fiber (EDF) length of a two-stage L-band EDFA has been optimized using a genetic algorithm method; because of the temperature dependence of EDFAs, there is no general rule. Thus, a simple, fast, dynamic, and highly accurate model has been developed and obtained for different EDF lengths that will fix …

Gravitational Search Algorithm For Determining Controller Parameters In An Automatic Voltage Regulator System, Serhat Duman, Nuran Yörükeren, İsmai̇l Hakki Altaş

Turkish Journal of Electrical Engineering and Computer Sciences

proportional-integral-derivate (PID) controller for an automatic voltage regulator (AVR) system using a heuristic gravitational search algorithm (GSA) based on mass interactions and Newton's law of gravity. The determination of optimal controller parameters is considered an optimization problem in which different performance indexes and a performance criterion in the time domain have been used as objective functions to test the performance and effectiveness of the GSA. In the determining process of the parameters, the designed PID controller with the proposed approach is simulated under different conditions and the performance of the controller is compared with those reported in the literature. From …

Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu

Research Collection School Of Computing and Information Systems

As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing …

Design And Verification Environment For High-Performance Video-Based Embedded Systems, Michael Mefenza Nentedem

Graduate Theses and Dissertations

In this dissertation, a method and a tool to enable design and verification of computation demanding embedded vision-based systems is presented. Starting with an executable specification in OpenCV, we provide subsequent refinements and verification down to a system-on-chip prototype into an FPGA-Based smart camera. At each level of abstraction, properties of image processing applications are used along with structure composition to provide a generic architecture that can be automatically verified and mapped to the lower abstraction level. The result is a framework that encapsulates the computer vision library OpenCV at the highest level, integrates Accelera's System-C/TLM with UVM and QEMU-OS …

Effective Auto Encoder For Unsupervised Sparse Representation, Faria Mahnaz

Wayne State University Theses

High dimensionality and the sheer size of unlabeled data available today demand

new development in unsupervised learning of sparse representation. Despite of recent

advances in representation learning, most of the current methods are limited when

dealing with large scale unlabeled data. In this study, we propose a new unsupervised

method that is able to learn sparse representation from unlabeled data efficiently. We

derive a closed-form solution based on the sequential minimal optimization (SMO)

for training an auto encoder-decoder module, which efficiently extracts sparse and

compact features from any data set with various size. The inference process in the

proposed learning …

The Impact Of Increased Optimization Problem Dimensionality On Cultural Algorithm Performance, Yang Yang

Wayne State University Theses

ABSTRACT

The Impact of Increased Optimization Problem Dimensionality on

Cultural Algorithm Performance

by

Yang Yang

August 2015

Advisor: Dr. Robert Reynolds

Major: Computer Science

Degree: Master of Science

In this thesis, we investigate the performance of Cultural Algorithms when dealing with the increasing dimensionality of optimization problems. The research is based on previous cultural algorithm approaches with the Cultural Algorithms Toolkit, CAT 2.0, which supports a variety of co-evolutionary features at both the knowledge and population levels. In this project, the system was applied to the solution of 60 randomly generated problems that ranged from 2-dimensional to 5-dimensional problem spaces. …