Physical Sciences and Mathematics Commons^™

A Cutting-Plane Method For Contiguity-Constrained Spatial Aggregation, Johannes Oehrlein, Jan-Henrik Haunert

Journal of Spatial Information Science

Aggregating areas into larger regions is a common problem in spatial planning, geographic information science, and cartography. The aim can be to group administrative areal units into electoral districts or sales territories, in which case the problem is known as districting. In other cases, area aggregation is seen as a generalization or visualization task, which aims to reveal spatial patterns in geographic data. Despite these different motivations, the heart of the problem is the same: given a planar partition, one wants to aggregate several elements of this partition to regions. These often must have or exceed a particular size, be …

How To Best Apply Neural Networks In Geosciences: Towards Optimal "Averaging" In Dropout Training, Afshin Gholamy, Justin Parra, Vladik Kreinovich, Olac Fuentes, Elizabeth Y. Anthony

Departmental Technical Reports (CS)

The main objectives of geosciences is to find the current state of the Earth -- i.e., solve the corresponding inverse problems -- and to use this knowledge for predicting the future events, such as earthquakes and volcanic eruptions. In both inverse and prediction problems, often, machine learning techniques are very efficient, and at present, the most efficient machine learning technique is deep neural training. To speed up this training, the current learning algorithms use dropout techniques: they train several sub-networks on different portions of data, and then "average" the results. A natural idea is to use arithmetic mean for this …

A Selective-Discrete Particle Swarm Optimization Algorithm For Solving A Class Of Orienteering Problems, Aldy Gunawan, Vincent F. Yu, Perwira Redi, Parida Jewpanya, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This study addresses a class of NP-hard problem called the Orienteering Problem (OP), which belongs to a well-known class of vehicle routing problems. In the OP, a set of nodes that associated with a location and a score is given. The time required to travel between each pair of nodes is known in advance. The total travel time is limited by a predetermined time budget. The objective is to select a subset of nodes to be visited that maximizes the total collected score within a path. The Team OP (TOP) is an extension of OP that incorporates multiple paths. Another …

Optimizing Barrier Removal To Restore Connectivity In Utah’S Weber Basin, Maggi Kraft

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

River barriers, such as dams, culverts and diversions are important for water conveyance, but disrupt river ecosystems and hydrologic processes. River barrier removal is increasingly used to restore and improve river habitat and connectivity. Most past barrier removal projects prioritized individual barriers using score-and-rank techniques, neglecting the spatial structure and cumulative change from multiple barrier removals. Similarly, most water demand models satisfy human water uses or, only prioritize aquatic habitat, failing to include both human and environmental water use benefits. In this study, a dual objective optimization model identified in-stream barriers that impede quality-weighted aquatic habitat connectivity for Bonneville cutthroat …

Efficient Gate System Operations For A Multipurpose Port Using Simulation Optimization, Ketki Kulkarni, Trong Khiem Tran, Hai Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Port capacity is determined by three major infrastructural resources namely, berths, yards and gates. Theadvertised capacity is constrained by the least of the capacities of the three resources. While a lot ofattention has been paid to optimizing berth and yard capacities, not much attention has been given toanalyzing the gate capacity. The gates are a key node between the land-side and sea-side operations in anocean-to-cities value chain. The gate system under consideration, located at an important port in an Asiancity, is a multi-class parallel queuing system with non-homogeneous Poisson arrivals. It is hard to obtaina closed form analytic approach for …

Optimization And Control Of Agent-Based Models In Biology: A Perspective, G. An, B. G. Fitzpatrick, S. Christley, P. Federico, A. Kanarek, R. Miller Neilan, M. Oremland, R. Salinas, R. Laubeanbacher, S. Lenhart

Ben G. Fitzpatrick

Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might …

Towards Food Service Sustainability In Suburban Environments By Optimally Locating Shared Anaerobic Digester Units, Rebecca Loraamm, Joni Downs, Robert Alonso Bair, Daniel Yeh

Suburban Sustainability

Anaerobic digestion is an effective method for reducing food waste at the consumer level. Drawbacks associated with this strategy include high construction costs for multiple digester units and limited public awareness of the method’s commercial potential. Given the large scale problem of food waste, an approach establishing community partnerships between local businesses and primary schools is offered to combat the problem of food waste. Optimizing the placement of shared digester units enabling utilization by multiple stakeholders is the suggested mitigation method. This research explores application of the p-median problem to determine the set of optimal sites for shared anaerobic digester …

Optimization And Control Of Production Of Graphene, Atharva Hans, Nimish M. Awalgaonkar, Majed Alrefae, Ilias Bilionis, Timothy S. Fisher

The Summer Undergraduate Research Fellowship (SURF) Symposium

Graphene is a 2-dimensional element of high practical importance. Despite its exceptional properties, graphene’s real applications in industrial or commercial products have been limited. There are many methods to produce graphene, but none has been successful in commercializing its production. Roll-to-roll plasma chemical vapor deposition (CVD) is used to manufacture graphene at large scale. In this research, we present a Bayesian linear regression model to predict the roll-to-roll plasma system’s electrode voltage and current; given a particular set of inputs. The inputs of the plasma system are power, pressure and concentration of gases; hydrogen, methane, oxygen, nitrogen and argon. This …

Development And Implementation Of An Optimization Model To Improve Airport Security., Kassandra Guajardo, Angela Waterworth, Robert Brigantic Ph.D.

STAR Program Research Presentations

What if airport security teams across the world could quantify and then minimize the amount of risk throughout areas of an airport? The Operations Research Team at the Pacific Northwest National Laboratory is developing and implementing an optimization model called ARAM (Airport Risk Analysis Model) for the Seattle-Tacoma International Airport. ARAM will provide a recommended optimal deployment of security assets to reduce risk in areas of an airport. The model is based on a risk equation that considers consequences, vulnerabilities, and threat magnitudes at airports. ARAM will also provide the estimated risk buy down percentage, which is how much risk …

Investigating Genetic Algorithm Optimization Techniques In Video Games, Nathan Ambuehl

Undergraduate Honors Theses

Immersion is essential for player experience in video games. Artificial Intelligence serves as an agent that can generate human-like responses and intelligence to reinforce a player’s immersion into their environment. The most common strategy involved in video game AI is using decision trees to guide chosen actions. However, decision trees result in repetitive and robotic actions that reflect an unrealistic interaction. This experiment applies a genetic algorithm that explores selection, crossover, and mutation functions for genetic algorithm implementation in an isolated Super Mario Bros. pathfinding environment. An optimized pathfinding AI can be created by combining an elitist selection strategy with …

Object Detection Meets Knowledge Graphs, Yuan Fang, Kingsley Kuan, Jie Lin, Cheston Tan, Vijay Chandrasekhar

Research Collection School Of Computing and Information Systems

Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve …

Evolutionary Game Theoretic Multi-Objective Optimization Algorithms And Their Applications, Yi Ren Cheng

Graduate Doctoral Dissertations

Multi-objective optimization problems require more than one objective functions to be optimized simultaneously. They are widely applied in many science fields, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conicting objectives. Most of the real world multi-objective optimization problems are NP-Hard problems. It may be too computationally costly to find an exact solution but sometimes a near optimal solution is sufficient. In these cases, Multi-Objective Evolutionary Algorithms (MOEAs) provide good approximate solutions to problems that cannot be solved easily using other techniques. However Evolutionary Algorithm is not …

Generalized Clusterwise Regression For Simultaneous Estimation Of Optimal Pavement Clusters And Performance Models, Mukesh Khadka

UNLV Theses, Dissertations, Professional Papers, and Capstones

The existing state-of-the-art approach of Clusterwise Regression (CR) to estimate pavement performance models (PPMs) pre-specifies explanatory variables without testing their significance; as an input, this approach requires the number of clusters for a given data set. Time-consuming ‘trial and error’ methods are required to determine the optimal number of clusters. A common objective function is the minimization of the total sum of squared errors (SSE). Given that SSE decreases monotonically as a function of the number of clusters, the optimal number of clusters with minimum SSE always is the total number of data points. Hence, the minimization of SSE is …

Decision-Making With Cross-Entropy For Self-Adaptation, Gabriel A. Moreno, Ofer Strichman, Sagar Chaki, Radislav Vaisman

Gabriel A. Moreno

Deterministic And Probabilistic Methods For Seismic Source Inversion, Juan Pablo Madrigal Cianci

Mathematics & Statistics ETDs

The national Earthquake Information Center (NEIC) reports an occurrence of about 13,000 earthquakes every year, spanning different values on the Richter scale from very mild (2) to "giant earthquakes'' (8 and above). Being able to study these earthquakes provides useful information for a wide range of applications in geophysics. In the present work we study the characteristics of an earthquake by performing seismic source inversion; a mathematical problem that, given some recorded data, produces a set of parameters that when used as input in a mathematical model for the earthquake generates synthetic data that closely resembles the measured data. There …

Artificial Immune Systems: Applications, Multi-Class Classification, Optimizations, And Analysis, Brian Haroldo Schmidt

Dissertations

The focus of this research is the application of the Artificial Immune System (AIS) paradigm to a new research area along with the modifications necessary to adapt it to a new problem. In the past 10 years, there has been much research into the use of various Machine Learning (ML) algorithms in Network Flow Traffic Classification. AIS algorithms have thus far not been applied to this problem. Because AIS algorithms have been used extensively for Network Intrusion Detection applications, which is a similar area of research, the motivation to extend them to the network flow classification problem is clear.

This …

Inference In Networking Systems With Designed Measurements, Chang Liu

Doctoral Dissertations

Networking systems consist of network infrastructures and the end-hosts have been essential in supporting our daily communication, delivering huge amount of content and large number of services, and providing large scale distributed computing. To monitor and optimize the performance of such networking systems, or to provide flexible functionalities for the applications running on top of them, it is important to know the internal metrics of the networking systems such as link loss rates or path delays. The internal metrics are often not directly available due to the scale and complexity of the networking systems. This motivates the techniques of inference …

Dynamic Repositioning To Reduce Lost Demand In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing …

An Adaptive Total Variation Algorithm For Computing The Balanced Cut Of A Graph, Xavier Bresson, Thomas Laurent, David Uminsky, James H. Von Brecht

Thomas Laurent

We propose an adaptive version of the total variation algorithm proposed in [3] for computing the balanced cut of a graph. The algorithm from [3] used a sequence of inner total variation minimizations to guarantee descent of the balanced cut energy as well as convergence of the algorithm. In practice the total variation minimization step is never solved exactly. Instead, an accuracy parameter is specified and the total variation minimization terminates once this level of accuracy is reached. The choice of this parameter can vastly impact both the computational time of the overall algorithm as well as the accuracy of …

Convergence Of A Steepest Descent Algorithm For Ratio Cut Clustering, Xavier Bresson, Thomas Laurent, David Uminsky, James H. Von Brecht

Thomas Laurent

Unsupervised clustering of scattered, noisy and high-dimensional data points is an important and difficult problem. Tight continuous relaxations of balanced cut problems have recently been shown to provide excellent clustering results. In this paper, we present an explicit-implicit gradient flow scheme for the relaxed ratio cut problem, and prove that the algorithm converges to a critical point of the energy. We also show the efficiency of the proposed algorithm on the two moons dataset.

Renewable Energy Systems Optimization Using Monte Carlo Simulation And Evolutionary Algorithms, Nicolas Lopez

Open Access Theses & Dissertations

This Dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid

microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a …

Gica: Imperialist Competitive Algorithm With Globalization Mechanism For Optimization Problems, Yousef Abdi, Mahmoud Lak, Yousef Seyfari

Turkish Journal of Electrical Engineering and Computer Sciences

The imperialist competitive algorithm (ICA) is a recent global search strategy developed based on human social evolutionary phenomena in the real world. However, the ICA has the drawback of trapping in local optimum solutions when used for high-dimensional or complex multimodal functions. In order to deal with this situation, in this paper an improved ICA, named GICA, is proposed that can enhance ICA performance by using a new assimilation method and establishing a relationship between countries inspired by the globalization concept in the real world. The proposed algorithm is evaluated using a set of well-known benchmark functions for global optimization. …

A Novel Wound Field Flux Switching Machine With Salient Pole Rotor And Nonoverlapping Windings, Faisal Khan, Erwan Sulaiman, Md Zarafi Ahmad

Turkish Journal of Electrical Engineering and Computer Sciences

A flux switching machine (FSM) with a segmented rotor and nonoverlapping windings is an attractive alternative for driving high torque density applications. However, a rotor with segments makes the motor less robust as well as difficult to be assembled, while a FSM with salient rotor and overlapping windings inherits high copper losses and less efficiency due to high coil volume. In this paper, a novel structure of FSM with nonoverlapping windings and salient rotor is proposed, making it different from the 10Slot-8Pole segmental and salient rotor FSM. Several design parameters defined in the rotor, armature coil, and field excitation coil …

An Improved Clonal Selection Algorithm Using A Tournament Selection Operator And Its Application To Microstrip Coupler Design, Ezgi̇ Deni̇z Ülker

Turkish Journal of Electrical Engineering and Computer Sciences

The clonal selection algorithm (CLONALG) is a nature-inspired metaheuristic algorithm that has been applied to various complex optimization problems from different fields of study. Tournament selection (TS) is a selection operator that is mainly used in genetic algorithms. In this paper, a novel improved clonal selection algorithm by using the TS operator (ICSAT) is introduced. To observe the improvement, ICSAT was first tested on selected benchmark functions and then to validate its efficiency ICSAT was applied to a microstrip coupler design problem. Although showing some disadvantages that generally exist in all modified algorithms, it is observed that ICSAT has a …

A Novel Generation And Capacitor Integration Technique For Today's Distribution Systems, Okan Özgönenel, Serap Karagöl

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, the problem of optimally placing shunt capacitors and generators in radial distribution systems is handled and a new calculation technique based on wavelet neural network (WNN), which is computationally effective compared to well-known techniques, is proposed. The objectives for the proposed method are simply selected as the minimum cost of peak power and losses and maximum voltage stability. The suggested optimization technique is tested on various IEEE radial buses and then compared to the well-known methods in the literature, i.e. golden section search, grid search, and Acharya's heuristic method. The proposed and conventional methods are applied to …

Reactive Power Optimization In A Power System Network Through Metaheuristic Algorithms, Chandragupta Mauryan Kuppamuthu Sivalingam, Subramanian Ramachandran, Purrnimaa Shiva Sakthi Rajamani

Turkish Journal of Electrical Engineering and Computer Sciences

Reactive power optimization (RPO) in a power system is a rudimentary necessity for the reduction of the loss of power. For the requirement of a unity power factor in the RPO system, the reduction of the system losses is ensured. The pivotal requirements of a power system are inclusive of a perfect compensation technique and methodology for stable reactive power compensation. The proposed concept in this paper utilizes the different reactive power optimization algorithms and performs a comparison. The process is accomplished by the use of IEEE 6-bus, 14-bus, and 30-bus systems to test the optimization technique. The conclusive information …

Comparing Of Phase Shifting Method And One-Dimensional Continuous Wavelet Transform Method For Reconstruction Using Phase-Only Information, Gülhan Ustabaş Kaya, Zehra Saraç

Turkish Journal of Electrical Engineering and Computer Sciences

The one-dimensional continuous wavelet transform method has some advantages in hologram reconstruction, when used in the only phase, compared with the phase shifting method. This paper aims to discuss these advantages. One advantage is related to image quality. Another advantage is less power spent and saving time, because the one-dimensional continuous wavelet transform method uses only one hologram and the phase shifting method uses four holograms for recording and reconstruction processes. One final advantage is that the one-dimensional continuous wavelet transform method can also be used in real-time applications. Within the context of the ongoing optimization studies, this study will …

Nursing Approaches For Use And Sustainability Of Barcode Medication Administration Technology, Jackson Ngigi Njeru

Walden Dissertations and Doctoral Studies

Approximately 43.4% of medication errors occur at the time of administration despite the use of bar code medication administration (BCMA) System. This trend has prompted a national effort to mitigate this problem in the United States. Implementing BCMA in health care settings is one of those efforts. Studies focusing on the approaches employed by nurses when using this system are scant. The purpose of this qualitative case study was to investigate strategies nurses and their leaders use to ensure BCMA is implemented, maximized, and sustained. The technology acceptance model was used to guide the study. The 2 research questions addressed …

Deployment, Coverage And Network Optimization In Wireless Video Sensor Networks For 3d Indoor Monitoring, Tisha Lafaye Brown

Electronic Theses and Dissertations

As a result of extensive research over the past decade or so, wireless sensor networks (wsns) have evolved into a well established technology for industry, environmental and medical applications. However, traditional wsns employ such sensors as thermal or photo light resistors that are often modeled with simple omni-directional sensing ranges, which focus only on scalar data within the sensing environment. In contrast, the sensing range of a wireless video sensor is directional and capable of providing more detailed video information about the sensing field. Additionally, with the introduction of modern features in non-fixed focus cameras such as the pan, tilt …

Optimization Of A Fuel Cell, Eduardo Gines

Undergraduate Journal of Mathematical Modeling: One + Two

Fuel cells are devices that generate energy from a chemical reaction that takes place inside the cell. The main parts of these devices are two electrodes and an electrolyte solution. The project consists of determining the area of the electrodes for the fuel cell at which the cell produces its maximum amount of power. This was done with the performance curve of the fuel cell which was in terms of voltage vs current density. The performance curve was turned into terms of power density vs current density, and through this curve the maximum power was determined by identifying the maximum …