Engineering Commons^™

An Empirical Study Of Semantic Similarity In Wordnet And Word2vec, Abram Handler

University of New Orleans Theses and Dissertations

This thesis performs an empirical analysis of Word2Vec by comparing its output to WordNet, a well-known, human-curated lexical database. It finds that Word2Vec tends to uncover more of certain types of semantic relations than others -- with Word2Vec returning more hypernyms, synonomyns and hyponyms than hyponyms or holonyms. It also shows the probability that neighbors separated by a given cosine distance in Word2Vec are semantically related in WordNet. This result both adds to our understanding of the still-unknown Word2Vec and helps to benchmark new semantic tools built from word vectors.

A Pareto-Frontier Analysis Of Performance Trends For Small Regional Coverage Leo Constellation Systems, Christopher Alan Hinds

Master's Theses

As satellites become smaller, cheaper, and quicker to manufacture, constellation systems will be an increasingly attractive means of meeting mission objectives. Optimizing satellite constellation geometries is therefore a topic of considerable interest. As constellation systems become more achievable, providing coverage to specific regions of the Earth will become more common place. Small countries or companies that are currently unable to afford large and expensive constellation systems will now, or in the near future, be able to afford their own constellation systems to meet their individual requirements for small coverage regions.

The focus of this thesis was to optimize constellation geometries …

Designing A Biomimetic Testing Platform For Actuators In A Series-Elastic Co-Contraction System, Ryan Tyler Schroeder

UNLV Theses, Dissertations, Professional Papers, and Capstones

Actuators determine the performance of robotic systems at the most intimate of levels. As a result, much work has been done to assess the performance of different actuator systems. However, biomimetics has not previously been utilized as a pretext for tuning a series elastic actuator system with the purpose of designing an empirical testing platform. Thus, an artificial muscle tendon system has been developed in order to assess the performance of two distinct actuator types: (1) direct current electromagnetic motors and (2) ultrasonic rotary piezoelectric motors. Because the design of the system takes advantage of biomimetic operating principles such as …

Second Order-Response Surface Model For The Automated Parameter Tuning Problem, Aldy Gunawan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Several automated parameter tuning procedures/configurators have been proposed in order to find the best parameter setting for a target algorithm. These configurators can generally be classified into model-free and model-based approaches. We introduce a recent approach which is based on the hybridization of both approaches. It combines the Design of Experiments (DOE) and Response Surface Methodology (RSM) with prevailing model-free techniques. DOE is mainly used for determining the importance of parameters. A First Order-RSM is initially employed to define the promising region for the important parameters. A Second Order-RSM is then built to approximate the center point as well as …

The Critical Role Of Cubesat Spacecraft In A Multi-Tier Mission For Mars Exploration, Jeremy Straub

Jeremy Straub

A multi-tier architecture is under development (with similar craft heterogeneity to Fink's work on ‘tier scalable’ missions) which will facilitate autonomous local control of multiple heterogeneous craft. This mission architecture has been developed with a Mars mission in mind and has included CubeSats in a variety of critical mission roles.

Two concepts will be presented: the addition of CubeSats to a larger-scale multi-tier mission, where the CubeSats serve a supporting role and a mission driven by CubeSat orbital capabilities. In the first, CubeSats are utilized to augment the area of spatial coverage that can be obtained and the temporal coverage …

Traccs: Trajectory-Aware Coordinated Urban Crowd-Sourcing, Cen Chen, Shih-Fen Cheng, Aldy Gunawan, Archan Misra, Koustuv Dasgupta, Deepthi Chander

Research Collection School Of Computing and Information Systems

We investigate the problem of large-scale mobile crowd-tasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker's current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time …

Machine Learning Nuclear Detonation Features, Daniel T. Schmitt, Gilbert L. Peterson

Faculty Publications

Nuclear explosion yield estimation equations based on a 3D model of the explosion volume will have a lower uncertainty than radius based estimation. To accurately collect data for a volume model of atmospheric explosions requires building a 3D representation from 2D images. The majority of 3D reconstruction algorithms use the SIFT (scale-invariant feature transform) feature detection algorithm which works best on feature-rich objects with continuous angular collections. These assumptions are different from the archive of nuclear explosions that have only 3 points of view. This paper reduces 300 dimensions derived from an image based on Fourier analysis and five edge …

Hippi Care Hospital: Towards Proactive Business Processes In Emergency Room Services, Kar Way Tan, Venky Shankaraman

Research Collection School Of Computing and Information Systems

It was 2.35 am on a Saturday morning. Wiki Lim, process specialist from the Process Innovation Centre (PIC) of Hippi Care Hospital (HCH), desperately doodling on her notepad for ideas to improve service delivery at HCH’s Emergency Department (ED). HCH has committed to the public that its ED would meet the service quality criterion of serving 90% of A3 and A4 patients, non-emergency patients with moderate to mild symptoms, within 90 minutes of their arrival at the ED. The ED was not able to meet this performance goal and Dr. Edward Kim, the head of the ED at HCH, had …

Multi-Agent Orienteering Problem With Time-Dependent Capacity Constraints, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we formulate and study the Multi-agent Orienteering Problem with Time-dependent Capacity Constraints (MOPTCC). MOPTCC is similar to the classical orienteering problem at single-agent level: given a limited time budget, an agent travels around the network and collects rewards by visiting different nodes, with the objective of maximizing the sum of his collected rewards. The most important feature we introduce in MOPTCC is the inclusion of multiple competing agents. All agents in MOPTCC are assumed to be self-interested, and they interact with each other when arrive at certain nodes simultaneously. As all nodes are capacitated, if a particular …

Automated Image Interpretation For Science Autonomy In Robotic Planetary Exploration, Raymond Francis

Electronic Thesis and Dissertation Repository

Advances in the capabilities of robotic planetary exploration missions have increased the wealth of scientific data they produce, presenting challenges for mission science and operations imposed by the limits of interplanetary radio communications. These data budget pressures can be relieved by increased robotic autonomy, both for onboard operations tasks and for decision- making in response to science data.

This thesis presents new techniques in automated image interpretation for natural scenes of relevance to planetary science and exploration, and elaborates autonomy scenarios under which they could be used to extend the reach and performance of exploration missions on planetary surfaces.

Two …

Improved Microrobotic Control Through Image Processing And Automated Hardware Interfacing, Archit R. Aggarwal, Wuming Jing, David J. Cappelleri

The Summer Undergraduate Research Fellowship (SURF) Symposium

Untethered submilliliter-sized robots (microrobots) are showing potential use in different industrial, manufacturing and medical applications. A particular type of these microrobots, magnetic robots, have shown improved performance in power and control capabilities compared to the other thermal and electrostatic based robots. However, the magnetic robot designs have not been assessed in a robust manner to understand the degree of control in different environments and their application feasibility. This research project seeks to develop a custom control software interface to provide a holistic tool for researchers to evaluate the microrobotic performance through advance control features. The software deliverable involved two main …

3d Robotic Sensing Of People: Human Perception, Representation And Activity Recognition, Hao Zhang

Doctoral Dissertations

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives.

As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical …

A Comparative Study Of Underwater Robot Path Planning Algorithms For Adaptive Sampling In A Network Of Sensors, Sreeja Banerjee

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex large-scale ecosystems. We introduce a method of underwater monitoring using semi-mobile underwater sensor networks and mobile underwater robots in this thesis. The underwater robots can move freely in all dimension while the sensor nodes are anchored to the bottom of the water column and can move only up and down along the depth of the water column. We develop three different algorithms to optimize the path of the underwater robot and the positions of the sensors to improve the overall quality of sensing of an area of …

Hybrid Metaheuristics For Solving The Quadratic Assignment Problem And The Generalized Quadratic Assignment Problem, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper presents a hybrid metaheuristic for solving the Quadratic Assignment Problem (QAP). The proposed algorithm involves using the Greedy Randomized Adaptive Search Procedure (GRASP) to construct an initial solution, and then using a hybrid Simulated Annealing and Tabu Search (SA-TS) algorithm to further improve the solution. Experimental results show that the hybrid metaheuristic is able to obtain good quality solutions for QAPLIB test problems within reasonable computation time. The proposed algorithm is extended to solve the Generalized Quadratic Assignment Problem (GQAP), with an emphasis on modelling and solving a practical problem, namely an examination timetabling problem. We found that …

A Mathematical Model And Metaheuristics For Time Dependent Orienteering Problem, Aldy Gunawan, Zhi Yuan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper presents a generalization of the Orienteering Problem, the Time-Dependent Orienteering Problem (TDOP) which is based on the real-life application of providing automatic tour guidance to a large leisure facility such as a theme park. In this problem, the travel time between two nodes depends on the time when the trip starts. We formulate the problem as an integer linear programming (ILP) model. We then develop various heuristics in a step by step fashion: greedy construction, local search and variable neighborhood descent, and two versions of iterated local search. The proposed metaheuristics were tested on modified benchmark instances, randomly …

Diversity-Oriented Bi-Objective Hyper-Heuristics For Patrol Scheduling, Mustafa Misir, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The patrol scheduling problem is concerned with assigning security teams to different stations for distinct time intervals while respecting a limited number of contractual constraints. The objective is to minimise the total distance travelled while maximising the coverage of the stations with respect to their security requirement levels. This paper introduces a hyper-heuristic strategy focusing on generating diverse solutions for a bi-objective patrol scheduling problem. While a variety of hyper-heuristics have been applied to a large suite of problem domains usually in the form of single-objective optimisation, we suggest an alternative approach for solving the patrol scheduling problem with two …

An Empirical Study Of Off-Line Configuration And On-Line Adaptation In Operator Selection, Zhi Yuan, Stephanus Daniel Handoko, Duc Thien Nguyen, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Automating the process of finding good parameter settings is important in the design of high-performing algorithms. These automatic processes can generally be categorized into off-line and on-line methods. Off-line configuration consists in learning and selecting the best setting in a training phase, and usually fixes it while solving an instance. On-line adaptation methods on the contrary vary the parameter setting adaptively during each algorithm run. In this work, we provide an empirical study of both approaches on the operator selection problem, explore the possibility of varying parameter value by a non-adaptive distribution tuned off-line, and incorporate the off-line with on-line …

Streets: Game-Theoretic Traffic Patrolling With Exploration And Exploitation, Matthew Brown, Sandhya Saisubramanian, Pradeep Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

To dissuade reckless driving and mitigate accidents, cities deploy resources to patrol roads. In this paper, we present STREETS, an application developed for the city of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counterterrorism settings. STREETS moves beyond counterterrorism and represents the first use of Stackelberg games for traffic patrolling, in the process providing a novel algorithm for solving such games that addresses three major challenges in modeling and scale-up. First, there exists a high degree of unpredictability in travel times …

Decentralized Multi-Agent Reinforcement Learning In Average-Reward Dynamic Dcops, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Shlomo Zilberstein, Chongjie Zhang

Research Collection School Of Computing and Information Systems

Researchers have introduced the Dynamic Distributed Constraint Optimization Problem (Dynamic DCOP) formulation to model dynamically changing multi-agent coordination problems, where a dynamic DCOP is a sequence of (static canonical) DCOPs, each partially different from the DCOP preceding it. Existing work typically assumes that the problem in each time step is decoupled from the problems in other time steps, which might not hold in some applications. Therefore, in this paper, we make the following contributions: (i) We introduce a new model, called Markovian Dynamic DCOPs (MD-DCOPs), where the DCOP in the next time step is a function of the value assignments …

Creating Autonomous Adaptive Agents In A Real-Time First-Person Shooter Computer Game, Di Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Games are good test-beds to evaluate AI methodologies. In recent years, there has been a vast amount of research dealing with real-time computer games other than the traditional board games or card games. This paper illustrates how we create agents by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first-person shooter computer game called Unreal Tournament. Rewards used for learning are either obtained from the game environment or estimated using the temporal difference learning scheme. In this way, the agents are able to acquire proper strategies and discover the effectiveness of different weapons without …

Decentralized Stochastic Planning With Anonymity In Interactions, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

In this paper, we solve cooperative decentralized stochastic planning problems, where the interactions between agents (specified using transition and reward functions) are dependent on the number of agents (and not on the identity of the individual agents) involved in the interaction. A collision of robots in a narrow corridor, defender teams coordinating patrol activities to secure a target, etc. are examples of such anonymous interactions. Formally, we consider problems that are a subset of the well known Decentralized MDP (DEC-MDP) model, where the anonymity in interactions is specified within the joint reward and transition functions. In this paper, not only …

Reinforcement Learning For Adaptive Operator Selection In Memetic Search Applied To Quadratic Assignment Problem, Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.

Feedback Control For Multi-Modal Optimization Using Genetic Algorithms, Jun Shi, Ole J. Mengshoel, Dipan K. Pal

Ole J Mengshoel

Direct Neighbor Search, Jilian Zhang, Kyriakos Mouratidis, Hwee Hwa Pang

Kyriakos MOURATIDIS

In this paper we study a novel query type, called direct neighbor query. Two objects in a dataset are direct neighbors (DNs) if a window selection may exclusively retrieve these two objects. Given a source object, a DN search computes all of its direct neighbors in the dataset. The DNs define a new type of affinity that differs from existing formulations (e.g., nearest neighbors, nearest surrounders, reverse nearest neighbors, etc.) and finds application in domains where user interests are expressed in the form of windows, i.e., multi-attribute range selections. Drawing on key properties of the DN relationship, we develop an …

Budgeted Personalized Incentive Approaches For Smoothing Congestion In Resource Networks, Pradeep Varakantham, Na Fu, William Yeoh, Shih-Fen Cheng, Hoong Chuin Lau

Shih-Fen CHENG

Congestion occurs when there is competition for resources by sel sh agents. In this paper, we are concerned with smoothing out congestion in a network of resources by using personalized well-timed in- centives that are subject to budget constraints. To that end, we provide: (i) a mathematical formulation that computes equilibrium for the re- source sharing congestion game with incentives and budget constraints; (ii) an integrated approach that scales to larger problems by exploiting the factored network structure and approximating the attained equilib- rium; (iii) an iterative best response algorithm for solving the uncon- strained version (no budget) of the …

Multi-Agent Orienteering Problem With Time-Dependent Capacity Constraints, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau

Shih-Fen CHENG

The Orienteering Problem (OP), as originally defined by Tsiligirides, is the problem of cross-countr sport in which participants get rewards from visiting a predefined set of checkpoints. As Orienteering Problem can be used to describe a wide variety of real-world problems like route planning for facility inspection, patrolling of strategic location, and reward-weighted traveling salesman problem, it has attracted continuous interests from researchers and a large number of variants and corresponding algorithms for solving them have been introduced.

A Multi-Objective Memetic Algorithm For Vehicle Resource Allocation In Sustainable Transportation Planning, Hoong Chuin Lau, Lucas Agussurja, Shih-Fen Cheng, Pang Jin Tan

Shih-Fen CHENG

Sustainable supply chain management has been an increasingly important topic of research in recent years. At the strategic level, there are computational models which study supply and distribution networks with environmental considerations. At the operational level, there are, for example, routing and scheduling models which are constrained by carbon emissions. Our paper explores work in tactical planning with regards to vehicle resource allocation from distribution centers to customer locations in a multi-echelon logistics network. We formulate the bi-objective optimization problem exactly and design a memetic algorithm to efficiently derive an approximate Pareto front. We illustrate the applicability of our approach …

An Agent-Based Simulation Approach To Experience Management In Theme Parks, Shih-Fen Cheng, Larry Junjie Lin, Jiali Du, Hoong Chuin Lau, Pradeep Reddy Varakantham

Shih-Fen CHENG

In this paper, we illustrate how massive agent-based simulation can be used to investigate an exciting new application domain of experience management in theme parks, which covers topics like congestion control, incentive design, and revenue management. Since all visitors are heterogeneous and self-interested, we argue that a high-quality agent-based simulation is necessary for studying various problems related to experience management. As in most agent-base simulations, a sound understanding of micro-level behaviors is essential to construct high-quality models. To achieve this, we designed and conducted a first-of-its-kind real-world experiment that helps us understand how typical visitors behave in a theme-park environment. …

Interacting Knapsack Problem In Designing Resource Bundles, Truong Huy D. Nguyen, Pradeep Reddy Varakantham, Hoong Chuin Lau, Shih-Fen Cheng

Shih-Fen CHENG

In many real-life businesses, the service provider/seller keeps a log of the visitors’ behavior as a way to assess the efficiency of the current business/operation model and find room for improvement. For example, by tracking when visitors entering attractions in a theme park, theme park owners can detect when and where congestion may occur, thus having contingency plans to reroute the visitors accordingly. Similarly, a Cable TV service provider can track channel switching events at each household to identify uninteresting channels. Subsequently, the repertoire of channels up for subscription can evolve over time to better serve the entertainment demand of …

Opportunistic Service Differentiation And Cloud Resource Management In Support Of Enhanced Vehicular Applications, Mohammad Ali Salahuddin

Dissertations

An integral part of Intelligent Transportation Systems (ITS) are Vehicular Ad hoc Networks (VANETs), which consist of vehicles with on-board units (OBUs) and fixed road-side units (RSUs). Wireless Access in Vehicular Environment (WAVE) offers QoS via service differentiation by using application defined priorities. However, WAVE has unbounded delay and is oblivious to network load and severity of vehicles with respect to their environment. Our context severity metric innovatively enhances WAVE to be sensitive to vehicle and environment interactions. Our novel Opportunistic Service Differentiation (OSD) technique, dynamically readjusts the WAVE packet priorities to improve utilization of lower latency queues, prioritizing packets …