Engineering Commons^™

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 …

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 …

An Unmanned Aerial System For Prescribed Fires, Evan M. Beachly

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

Prescribed fires can lessen wildfire severity and control invasive species, but some terrains may be difficult, dangerous, or costly to burn with existing tools. This thesis presents the design of an unmanned aerial system that can ignite prescribed fires from the air, with less cost and risk than with aerial ignition from a manned aircraft. The prototype was evaluated in-lab and successfully used to ignite interior areas of two prescribed fires. Additionally, we introduce an approach that integrates a lightweight fire simulation to autonomously plan safe flight trajectories and suggest effective fire lines. Both components are unique in that they …

Policy Gradient With Value Function Approximation For Collective Multiagent Planning, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system. Given their computational complexity, recent research has focused on tractable yet practical subclasses of Dec-POMDPs. We address such a subclass called CDec-POMDP where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our main contribution is an actor-critic (AC) reinforcement learning method for optimizing CDec-POMDP policies. Vanilla AC has slow convergence for larger problems. To address this, we show how a particular decomposition of the approximate action-value function over agents leads to effective updates, and also derive a new way to …

Intent Detection Through Text Mining And Analysis, Samantha Akulick, El Sayed Mahmoud

Publications and Scholarship

The article is about the work investigated using n-grams, parts-Of-Speech and Support Vector machines for detecting the customer intents in the user generated contents. The work demonstrated a system of categorization of customer intents that is concise and useful for business purposes. We examined possible sources of text posts to be analyzed using three text mining algorithms. We presented the three algorithms and the results of testing them in detecting different six intents. This work established that intent detection can be performed on text posts with approximately 61% accuracy.

Formal Performance Guarantees For An Approach To Human In The Loop Robot Missions, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract— A key challenge in the automatic verification of robot mission software, especially critical mission software, is to be able to effectively model the performance of a human operator and factor that into the formal performance guarantees for the mission. We present a novel approach to modelling the skill level of the operator and integrating it into automatic verification using a linear Gaussians model parameterized by experimental calibration. Our approach allows us to model different skill levels directly in terms of the behavior of the lumped, robot plus operator, system.

Using MissionLab and VIPARS (a behavior-based robot mission verification …

Developing Grounded Goals Through Instant Replay Learning, Lisa Meeden, Douglas S. Blank

Computer Science Faculty Research and Scholarship

This paper describes and tests a developmental architecture that enables a robot to explore its world, to find and remember interesting states, to associate these states with grounded goal representations, and to generate action sequences so that it can re-visit these states of interest. The model is composed of feed-forward neural networks that learn to make predictions at two levels through a dual mechanism of motor babbling for discovering the interesting goal states and instant replay learning for developing the grounded goal representations. We compare the performance of the model with grounded goal representations versus random goal representations, and find …

Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang

Department of Computer Science Faculty Scholarship and Creative Works

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be …

Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Domains such as disaster rescue, security patrolling etc. often feature dynamic environments where allocations of tasks to agents become ineffective due to unforeseen conditions that may require agents to leave the team. Agents leave the team either due to arrival of high priority tasks (e.g., emergency, accident or violation) or due to some damage to the agent. Existing research in task allocation has only considered fixed number of agents and in some instances arrival of new agents on the team. However, there is little or no literature that considers situations where agents leave the team after task allocation. To that …

Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu

Research Collection School Of Computing and Information Systems

Organizing large scale projects (e.g., Conferences, IT Shows, F1 race) requires precise scheduling of multiple dependent tasks on common resources where multiple selfish entities are competing to execute the individual tasks. In this paper, we consider a well studied and rich scheduling model referred to as RCPSP (Resource Constrained Project Scheduling Problem). The key change to this model that we consider in this paper is the presence of selfish entities competing to perform individual tasks with the aim of maximizing their own utility. Due to the selfish entities in play, the goal of the scheduling problem is no longer only …

A Unified Framework For Vehicle Rerouting And Traffic Light Control To Reduce Traffic Congestion, Zhiguang Cao, Siwei Jiang, Jie Zhang, Hongliang Guo

Research Collection School Of Computing and Information Systems

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, …

Geometry-Based Mass Grading Of Mango Fruits Using Image Processing, M. A. Momin, Md Towfiqur Rahman, M. S. Sultana, C. Igathinathane, A. T. M. Ziauddin, T. E. Grift

Department of Biological Systems Engineering: Papers and Publications

Mango (Mangifera indica) is an important, and popular fruit in Bangladesh. However, the post-harvest processing of it is still mostly performed manually, a situation far from satisfactory, in terms of accuracy and throughput. To automate the grading of mangos (geometry and shape), we developed an image acquisition and processing system to extract projected area, perimeter, and roundness features. In this system, images were acquired using a XGA format color camera of 8-bit gray levels using fluorescent lighting. An image processing algorithm based on region based global thresholding color binarization, combined with median filter and morphological analysis was developed …

A Multi-Agent System For Coordinating Vessel Traffic, Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar

Research Collection School Of Computing and Information Systems

Environmental, regulatory and resource constraints affects the safety and efficiency of vessels navigating in and out of the ports. Movement of vessels under such constraints must be coordinated for improving safety and efficiency. Thus, we frame the vessel coordination problem as a multi-agent path-finding (MAPF) problem. We solve this MAPF problem using a Coordinated Path-Finding (CPF) algorithm. Based on the local search paradigm, the CPF algorithm improves on the aggregated path quality of the vessels iteratively. Outputs of the CPF algorithm are the coordinated trajectories. The Vessel Coordination Module (VCM) described here is the module encapsulating our MAPF-based approach for …

An Approach To Robust Homing With Stereovision, Fuqiang Fu, Damian Lyons

Faculty Publications

Visual Homing is a bioinspired approach to robot navigation which can be fast and uses few assumptions. However, visual homing in a cluttered and unstructured outdoor environment offers several challenges to homing methods that have been developed for primarily indoor environments. One issue is that any current image during homing may be tilted with respect to the home image. The second is that moving through a cluttered scene during homing may cause obstacles to interfere between the home scene and location and the current scene and location. In this paper, we introduce a robust method to improve a previous developed …

Directed Acyclic Graph Continuous Max-Flow Image Segmentation For Unconstrained Label Orderings, John Sh Baxter, Martin Rajchl, A. Jonathan Mcleod, Jing Yuan, Terry M. Peters

Robarts Imaging Publications

Label ordering, the specification of subset–superset relationships for segmentation labels, has been of increasing interest in image segmentation as they allow for complex regions to be represented as a collection of simple parts. Recent advances in continuous max-flow segmentation have widely expanded the possible label orderings from binary background/foreground problems to extendable frameworks in which the label ordering can be specified. This article presents Directed Acyclic Graph Max-Flow image segmentation which is flexible enough to incorporate any label ordering without constraints. This framework uses augmented Lagrangian multipliers and primal–dual optimization to develop a highly parallelized solver implemented using GPGPU. This …

An Efficient Approach To Model-Based Hierarchical Reinforcement Learning, Zhuoru Li, Akshay Narayan, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the stateof-the-art algorithms, and scales well in very large problems.

Decentralized Planning In Stochastic Environments With Submodular Rewards, Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

Decentralized Markov Decision Process (Dec-MDP) providesa rich framework to represent cooperative decentralizedand stochastic planning problems under transition uncertainty.However, solving a Dec-MDP to generate coordinatedyet decentralized policies is NEXP-Hard. Researchershave made significant progress in providing approximate approachesto improve scalability with respect to number ofagents. However, there has been little or no research devotedto finding guarantees on solution quality for approximateapproaches considering multiple (more than 2 agents)agents. We have a similar situation with respect to the competitivedecentralized planning problem and the StochasticGame (SG) model. To address this, we identify models in thecooperative and competitive case that rely on submodular rewards,where we show …

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 …

Deep Neural Networks With Confidence Sampling For Electrical Anomaly Detection, Norman L. Tasfi, Wilson A. Higashino, Katarina Grolinger, Miriam A. M. Capretz

Electrical and Computer Engineering Publications

The increase in electrical metering has created tremendous quantities of data and, as a result, possibilities for deep insights into energy usage, better energy management, and new ways of energy conservation. As buildings are responsible for a significant portion of overall energy consumption, conservation efforts targeting buildings can provide tremendous effect on energy savings. Building energy monitoring enables identification of anomalous or unexpected behaviors which, when corrected, can lead to energy savings. Although the available data is large, the limited availability of labels makes anomaly detection difficult. This research proposes a deep semi-supervised convolutional neural network with confidence sampling for …

Performance Verification For Robot Missions In Uncertain Environments, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract—Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means the state-space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian Network. Validation results …

Establishing A-Priori Performance Guarantees For Robot Missions That Include Localization Software, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

One approach to determining whether an automated system is performing correctly is to monitor its performance, signaling when the performance is not acceptable; another approach is to automatically analyze the possible behaviors of the system a-priori and determine performance guarantees. Thea authors have applied this second approach to automatically derive performance guarantees for behaviorbased, multi-robot critical mission software using an innovative approach to formal verification for robotic software. Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. Several …

Sensitivity Analysis Method To Address User Disparities In The Analytic Hierarchy Process, Marie Ivanco, Gene Hou, Jennifer Michaeli

Mechanical & Aerospace Engineering Faculty Publications

Decision makers often face complex problems, which can seldom be addressed well without the use of structured analytical models. Mathematical models have been developed to streamline and facilitate decision making activities, and among these, the Analytic Hierarchy Process (AHP) constitutes one of the most utilized multi-criteria decision analysis methods. While AHP has been thoroughly researched and applied, the method still shows limitations in terms of addressing user profile disparities. A novel sensitivity analysis method based on local partial derivatives is presented here to address these limitations. This new methodology informs AHP users of which pairwise comparisons most impact the derived …

An Ensemble Learning Framework For Anomaly Detection In Building Energy Consumption, Daniel B. Araya, Katarina Grolinger, Hany F. Elyamany, Miriam Am Capretz, Girma T. Bitsuamlak

Electrical and Computer Engineering Publications

During building operation, a significant amount of energy is wasted due to equipment and human-related faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework …

Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described …

Deep Models For Engagement Assessment With Scarce Label Information, Feng Li, Guangfan Zhang, Wei Wang, Roger Xu, Tom Schnell, Jonathan Wen, Frederic Mckenzie, Jiang Li

Electrical & Computer Engineering Faculty Publications

Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled …