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Articles 1 - 22 of 22
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Credit Assignment For Collective Multiagent Rl With Global Rewards, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment. We focus on a multiagent planning model subclass, relevant to urban settings, where agent interactions are dependent on their collective influence'' on each other, rather than their identities. Unlike previous work, we address a general setting where system reward is not decomposable among agents. We develop collective actor-critic RL approaches for this setting, and address the problem of multiagent credit assignment, and computing low variance policy gradient estimates that result in faster convergence to high quality solutions. We also develop difference …
Integrated Reward Scheme And Surge Pricing In A Ride-Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye
Integrated Reward Scheme And Surge Pricing In A Ride-Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye
Research Collection School Of Computing and Information Systems
Surge pricing is commonly used in on-demand ride-sourcing platforms (e.g., Uber, Lyft and Didi) to dynamically balance demand and supply. However, since the price for ride service cannot be unlimited, there is usually a reasonable or legitimate range of prices in practice. Such a constrained surge pricing strategy fails to balance demand and supply in certain cases, e.g., even adopting the maximum allowed price cannot reduce the demand to an affordable level during peak hours. In addition, the practice of surge pricing is controversial and has stimulated long debate regarding its pros and cons. To address the limitation of current …
Data Center Holistic Demand Response Algorithm To Smooth Microgrid Tie-Line Power Fluctuation, Ting Yang, Yingjie Zhao, Haibo Pen, Zhaoxia Wang
Data Center Holistic Demand Response Algorithm To Smooth Microgrid Tie-Line Power Fluctuation, Ting Yang, Yingjie Zhao, Haibo Pen, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal …
Cost Benefit Analysis Of Led Vs Florescent Lighting, Kurtis Clark, Phillip Humphrey
Cost Benefit Analysis Of Led Vs Florescent Lighting, Kurtis Clark, Phillip Humphrey
Student Research
Over the last few years, the state of Oklahoma has been looking at ways to reduce expenses to address concerns about a budget deficit. There have been efforts made to reduce expenses due to the use of energy. It has been said, when the lights are on, work is getting done. Running lights is therefore the cost of doing business. Our research examines the question, “is there a way to provide better lighting while operating at a lower cost.” This research examines the current lighting at Southwestern State University, primarily fluorescent lighting (FL), and a cost benefit analysis of switching …
A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi
A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi
FIU Electronic Theses and Dissertations
The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, …
Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li
Early Detection Of Disease Using Electronic Health Records And Fisher's Wishart Discriminant Analysis, Sijia Yang, Jian Bian, Zeyi Sun, Licheng Wang, Haojin Zhu, Haoyi Xiong, Yu Li
Engineering Management and Systems Engineering Faculty Research & Creative Works
Linear Discriminant Analysis (LDA) is a simple and effective technique for pattern classification, while it is also widely-used for early detection of diseases using Electronic Health Records (EHR) data. However, the performance of LDA for EHR data classification is frequently affected by two main factors: ill-posed estimation of LDA parameters (e.g., covariance matrix), and "linear inseparability" of the EHR data for classification. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fisher's Wishart Discriminant Analysis, which is developed as a faster and robust nonlinear classifier. Specifically, FWDA first surrogates the distribution of "potential" inverse …
Secondary Frequency Stochastic Optimal Control In Independent Microgrids With Virtual Synchronous Generator-Controlled Energy Storage Systems, Ting Yang, Yajian Zhang, Zhaoxia Wang, Haibo Pen
Secondary Frequency Stochastic Optimal Control In Independent Microgrids With Virtual Synchronous Generator-Controlled Energy Storage Systems, Ting Yang, Yajian Zhang, Zhaoxia Wang, Haibo Pen
Research Collection School Of Computing and Information Systems
With the increasing proportion of renewable energy in microgrids (MGs), its stochastic fluctuation of output power has posed challenges to system safety and operation, especially frequency stability. Virtual synchronous generator (VSG) technology, as one effectivemethod, was used to smoothen frequency fluctuation and improve the system's dynamic performance,which can simulate the inertia and damping of the traditional synchronous generator. This study outlines the integration of VSG-controlled energy storage systems (ESSs) and traditional synchronous generators so they jointly participate in secondary frequency regulation in an independent MG. Firstly, a new uncertain state-space model for secondary frequency control is established, considering the measurement …
Iterated Local Search Algorithm For The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau
Iterated Local Search Algorithm For The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper focuses on a recent variant of the Orienteering Problem (OP), namely the Capacitated Team Orienteering Problem (CTOP). In this problem, each node is associated with a demand that needs to be satisfied and a score that need to be collected. Given a set of homogeneous fleet of vehicles, the main objective is to find a path for each available vehicle in order to maximize the total score, without violating the capacity and time budget of each vehicle. We propose an Iterated Local Search algorithm that has been applied in solving various variants of the OP. We propose two …
Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson
Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson
Faculty Publications
In recent years, NoSQL database systems have become increasingly popular, especially for big data, commercial applications. These systems were designed to overcome the scaling and flexibility limitations plaguing traditional relational database management systems (RDBMSs). Given NoSQL database systems have been typically implemented in large-scale distributed environments serving large numbers of simultaneous users across potentially thousands of geographically separated devices, little consideration has been given to evaluating their value within single-box environments. It is postulated some of the inherent traits of each NoSQL database type may be useful, perhaps even preferable, regardless of scale. Thus, this paper proposes criteria conceived to …
Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Research Collection School Of Computing and Information Systems
Two fundamental challenges in local search based metaheuristics are how to determine parameter configurations and design the underlying Local Search (LS) procedure. In this paper, we propose a framework in order to handle both challenges, called ADaptive OPeraTor Ordering (ADOPT). In this paper, The ADOPT framework is applied to two metaheuristics, namely Iterated Local Search (ILS) and a hybridization of Simulated Annealing and ILS (SAILS) for solving two variants of the Orienteering Problem: the Team Dependent Orienteering Problem (TDOP) and the Team Orienteering Problem with Time Windows (TOPTW). This framework consists of two main processes. The Design of Experiment (DOE) …
Recent Trends In The Frequency And Duration Of Global Floods, Nasser Najibi, Naresh Devineni
Recent Trends In The Frequency And Duration Of Global Floods, Nasser Najibi, Naresh Devineni
Publications and Research
Frequency and duration of floods are analyzed using the global flood database of the Dartmouth Flood Observatory (DFO) to explore evidence of trends during 1985–2015 at global and latitudinal scales. Three classes of flood duration (i.e., short: 1–7, moderate: 8–20, and long: 21 days and above) are also considered for this analysis. The nonparametric Mann–Kendall trend analysis is used to evaluate three hypotheses addressing potential monotonic trends in the frequency of flood, moments of duration, and frequency of specific flood duration types. We also evaluated if trends could be related to large-scale atmospheric teleconnections using a generalized linear model framework. …
Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan
Instance-Specific Selection Of Aos Methods For Solving Combinatorial Optimisation Problems Via Neural Networks, Teck Hou (Deng Dehao) Teng, Hoong Chuin Lau, Aldy Gunawan
Research Collection School Of Computing and Information Systems
Solving combinatorial optimization problems using a fixed set of operators has been known to produce poor quality solutions. Thus, adaptive operator selection (AOS) methods have been proposed. But, despite such effort, challenges such as the choice of suitable AOS method and configuring it correctly for given specific problem instances remain. To overcome these challenges, this work proposes a novel approach known as I-AOS-DOE to perform Instance-specific selection of AOS methods prior to evolutionary search. Furthermore, to configure the AOS methods for the respective problem instances, we apply a Design of Experiment (DOE) technique to determine promising regions of parameter values …
Dispatch Guided Allocation Optimization For Effective Emergency Response, Supriyo Ghosh, Pradeep Varakantham
Dispatch Guided Allocation Optimization For Effective Emergency Response, Supriyo Ghosh, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Effective emergency (medical, fire or criminal) response iscrucial for improving safety and security in urban environments. Recent research in improving effectiveness of emergency management systems (EMSs) has utilized data-drivenoptimization models for efficient allocation of emergency response vehicles (ERVs) to base locations. However, thesedata-driven optimization models either ignore the dispatchstrategy of ERVs (typically the nearest available ERV is dispatched to serve an incident) or employ myopic approaches(e.g., greedy approach based on marginal gain). This resultsin allocations that are not synchronised with the real evolution dynamics on the ground or can be improved significantly.To bridge this gap, we make the following contributions: …
Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein
Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Observing that many real-world sequential decision problems are not purely cooperative or purely competitive, we propose a new model—cooperative-competitive process (CCP)—that can simultaneously encapsulate both cooperation and competition.First, we discuss how the CCP model bridges the gap between cooperative and competitive models. Next, we investigate a specific class of group-dominant CCPs, in which agents cooperate to achieve a common goal as their primary objective, while also pursuing individual goals as a secondary objective. We provide an approximate solution for this class of problems that leverages stochastic finite-state controllers.The model is grounded in two multi-robot meeting and box pushing domains that …
Risk-Sensitive Stochastic Orienteering Problems For Trip Optimization In Urban Environments, Pradeep Varakantham, Akshat Kumar, Hoong Chuin Lau, William Yeoh
Risk-Sensitive Stochastic Orienteering Problems For Trip Optimization In Urban Environments, Pradeep Varakantham, Akshat Kumar, Hoong Chuin Lau, William Yeoh
Research Collection School Of Computing and Information Systems
Orienteering Problems (OPs) are used to model many routing and trip planning problems. OPs are a variantof the well-known traveling salesman problem where the goal is to compute the highest reward path thatincludes a subset of vertices and has an overall travel time less than a specified deadline. However, the applicabilityof OPs is limited due to the assumption of deterministic and static travel times. To that end, Campbellet al. extended OPs to Stochastic OPs (SOPs) to represent uncertain travel times (Campbell et al. 2011). Inthis article, we make the following key contributions: (1) We extend SOPs to Dynamic SOPs (DSOPs), …
Resource-Constrained Scheduling For Maritime Traffic Management, Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau
Resource-Constrained Scheduling For Maritime Traffic Management, Lucas Agussurja, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We address the problem of mitigating congestion and preventing hotspots in busy water areas such as Singapore Straits and port waters. Increasing maritime traffic coupled with narrow waterways makes vessel schedule coordination for just-in-time arrival critical for navigational safety. Our contributions are: 1) We formulate the maritime traffic management problem based on the real case study of Singapore waters; 2) We model the problem as a variant of the resource-constrained project scheduling problem (RCPSP), and formulate mixed-integer and constraint programming (MIP/CP) formulations; 3) To improve the scalability, we develop a combinatorial Benders (CB) approach that is significantly more effective than …
Scalable Urban Mobile Crowdsourcing: Handling Uncertainty In Worker Movement, Shih-Fen Cheng, Cen Chen, Thivya Kandappu, Hoong Chuin Lau, Archan Misra, Nikita Jaiman, Randy Tandriansyah Daratan, Desmond Koh
Scalable Urban Mobile Crowdsourcing: Handling Uncertainty In Worker Movement, Shih-Fen Cheng, Cen Chen, Thivya Kandappu, Hoong Chuin Lau, Archan Misra, Nikita Jaiman, Randy Tandriansyah Daratan, Desmond Koh
Research Collection School Of Computing and Information Systems
In this article, we investigate effective ways of utilizing crowdworkers in providing various urban services. The task recommendation platform that we design can match tasks to crowdworkers based on workers’ historical trajectories and time budget limits, thus making recommendations personal and efficient. One major challenge we manage to address is the handling of crowdworker’s trajectory uncertainties. In this article, we explicitly allow multiple routine routes to be probabilistically associated with each worker. We formulate this problem as an integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures …
Communication Based Control For Dc Microgrids, Mahmoud S. Saleh, Yusef Esa, Ahmed Mohamed
Communication Based Control For Dc Microgrids, Mahmoud S. Saleh, Yusef Esa, Ahmed Mohamed
Publications and Research
Centralized communication-based control is one of the main methods that can be implemented to achieve autonomous advanced energy management capabilities in DC microgrids. However, its major limitation is the fact that communication bandwidth and computation resources are limited in practical applications. This can be often improved by avoiding redundant communications and complex computations. In this paper, an autonomous communication-based hybrid state/event driven control scheme is proposed. This control scheme is hierarchical and heuristic, such that on the primary control level, it encompasses state-driven local controllers, and on the secondary control level, an event-driven MG centralized controller (MGCC) is used. This …
Magic Triangle – Human, Exoskeleton, And Collaborative Robot Scenario, R. A. Goehlich, M. H. Rutsch, I. Krohne
Magic Triangle – Human, Exoskeleton, And Collaborative Robot Scenario, R. A. Goehlich, M. H. Rutsch, I. Krohne
Publications
The incidence of musculoskeletal disorders in workplaces with difficult ergonomic conditions is increasing. Today, there is a growing market for technical support systems that avoid repetitive strain on the musculoskeletal system. We have been observing two (parallel) lines of development: on the one hand, the development of exoskeletons supporting shop floor operators and, on the other hand, the development of collaborative robots for the creation of hybrid teams. The focus of our research is the combined application of exoskeletons AND collaborative robots for shop floor operators in the aerospace industry. Our approach is to analyze various scenarios to understand which …
A Proposed Taxonomy For The Systems Statistical Engineering Body Of Knowledge, Teddy Steven Cotter
A Proposed Taxonomy For The Systems Statistical Engineering Body Of Knowledge, Teddy Steven Cotter
Engineering Management & Systems Engineering Faculty Publications
In the ASEM-IAC 2012, Cotter (2012) identified the gaps in knowledge that statistical engineering needs to address, explored additional gaps in knowledge not addressed in the prior works, and set forth a working definition of and body of knowledge for statistical engineering. In the ASEM-IAC 2015, Cotter (2015) proposed a systemic causal Bayesian hierarchical model that addressed the knowledge gap needed to integrate deterministic mathematical engineering causal models within a stochastic framework. Missing, however, is the framework for specifying the hierarchical qualitative systems structures necessary and sufficient for specifying systemic causal Bayesian hierarchical models. In the ASEM-IAC 2016, Cotter (2016) …
An Attribute Agreement Method For Hfacs Inter-Rater Reliability Assessment, Teddy Steven Cotter, Veysel Yesilbas
An Attribute Agreement Method For Hfacs Inter-Rater Reliability Assessment, Teddy Steven Cotter, Veysel Yesilbas
Engineering Management & Systems Engineering Faculty Publications
Inter-rater reliability can be regarded as the degree of agreement among raters on a given item or a circumstance. Multiple approaches have been taken to estimate and improve inter-rater reliability of the United States Department of Defense Human Factors Analysis and Classification System used by trained accident investigators. In this study, three trained instructor pilots used the DoD-HFACS to classify 347 U.S. Air Force Accident Investigation Board (AIB) Class-A reports between the years of 2000 and 2013. The overall method consisted of four steps: (1) train on HFACS definitions, (2) verify rating reliability, (3) rate HFACS reports, and (4) random …
Wavelet Anova Bisection Method For Identifying Simulation Model Bias, Andrew D. Atkinson, Raymond R. Hill, Joseph J. Pignatiello Jr., G. Geoffrey Vining, Edward D. White, Eric Chicken
Wavelet Anova Bisection Method For Identifying Simulation Model Bias, Andrew D. Atkinson, Raymond R. Hill, Joseph J. Pignatiello Jr., G. Geoffrey Vining, Edward D. White, Eric Chicken
Faculty Publications
High-resolution computer models can simulate complex systems and processes in order to evaluate a solution quickly and inexpensively. Many simulation models produce dynamic functional output, such as a set of time-series data generated during a process. These computer models require verification and validation (V&V) to assess the correctness of these simulations. In particular, the model validation effort evaluates if the model is an appropriate representation of the real-world system that it is meant to simulate. However, when assessing a model capable of generating functional output, it is useful to learn more than simply whether the model is valid or invalid. …