Computer Sciences Commons^™

Robust Adaptive Rate Control Algorithm And Its Simulation For P2p-Tv Streaming Media Systems, Fengjie Yin, Yang Hui, Zhang Ying

Journal of System Simulation

Abstract: A control algorithm proposed to control the upstream rate based on sliding mode control in P2P-TV streaming media node. Through the rational design of sliding mode controller, the algorithm can adaptively control the numbers of signaling threads between neighbor pairs connected simultaneously and then control the upload rate. Further, it can achieve the fair use of resources. Due to the sliding mode control has strong robust with modeling uncertainties, time varying parameter fluctuations and external disturbances, it can avoid the system congestion under network condition change. Simulation results demonstrate that it can lead to the convergence of the …

Modeling And Simulation Of Health Degradation Trend For Wind Turbine Bearing, Xinghui Dong, Xiaoshuang Ma, Youxing Cheng, Wang Shuai

Journal of System Simulation

Abstract: By taking a wind turbine bearing as research object, the model of bearing temperature health’s degradation trend is established through using least squares surface fitting and the monitored parameters from Supervisory Control And Data Acquisition (SCADA). Bearings’ degradation trend with unsteady characteristics is decomposed by modified Ensemble Empirical Mode Decomposition(EEMD) to obtain several relatively steady components. Components are predicted respectively by time series neural network and the predicted results of all the components are added to obtain final prediction result. Comprehensive simulations and comparisons show that the proposed method can predict the health degradation trend of wind turbine bearings …

Soil Data Construction Method For Cross-Country Trafficability Analysis, Kunwei Li, You Xiong, Zhang Xin, Fen Tang

Journal of System Simulation

Abstract: Soil is one of the most important factors influencing the off-road maneuver of the army. The combination of soil and weather factors makes cross-country trafficability analysis extremely complicated. The soil data from unified soil classification system (USCS) are the basis for the analysis of soil trafficability. In this paper, the random forest method is used to predict the type of soil by using various attribute information. The method extracts sample data from existing USCS soil data to construct multiple random forest models, then analyses the accuracy of random forests and the importance of characteristic variables, and finally uses the …

Autonomous Watercraft Simulation And Programming, Nicholas J. Savino

Student Scholar Showcase

Automation of various modes of transportation is thought to make travel more safe and efficient. Over the past several decades, advances to semi-autonomous and autonomous vehicles have led to advanced autopilot systems on planes and boats, and an increasing popularity of self-driving cars. We predicted the motion of an autonomous vehicle using simulations in Python. The simulation models the motion of a small scale watercraft, which can then be built and programmed using an Arduino Microcontroller. We examined different control methods for a simulated rescue craft to reach a target. We also examined the effects of different factors, such as …

Improved Evolutionary Support Vector Machine Classifier For Coronary Artery Heart Disease Prediction Among Diabetic Patients, Narasimhan B, Malathi A Dr

Library Philosophy and Practice (e-journal)

Soft computing paves way many applications including medical informatics. Decision support system has gained a major attention that will aid medical practitioners to diagnose diseases. Diabetes mellitus is hereditary disease that might result in major heart disease. This research work aims to propose a soft computing mechanism named Improved Evolutionary Support Vector Machine classifier for CAHD risk prediction among diabetes patients. The attribute selection mechanism is attempted to build with the classifier in order to reduce the misclassification error rate of the conventional support vector machine classifier. Radial basis kernel function is employed in IESVM. IESVM classifier is evaluated through …

A Formal Approach To Circle Formation In Multi-Agent Systems, Rui Yang

Computer Science Graduate Research Workshop

No abstract provided.

Conflict Resolution Using Α-Shapes For Distributed Robotic Sampling Of Ambient Phenomena In Initially Unknown Environments, Brad Woosley

Computer Science Graduate Research Workshop

No abstract provided.

Processing Narratives By Means Of Action Languages, Craig Olson

Computer Science Graduate Research Workshop

No abstract provided.

Intelligent And Human-Aware Decision Making For Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots, Anoop Mishra

Computer Science Graduate Research Workshop

No abstract provided.

Machine Learning Techniques For Predicting Mobility-Related Perception Errors In Astronauts, Steven Belcher

Computer Science Graduate Research Workshop

No abstract provided.

A Technique For Improving Classification Accuracy Of Highly Imbalanced And Sparse Datasets, Sindhura Bonthu

Computer Science Graduate Research Workshop

No abstract provided.

Deep Learning Enables Robust Assessment And Selection Of Human Blastocysts After In Vitro Fertilization, Pegah Khosravi, Ehsan Kazemi, Qiansheng Zhan, Jonas E. Malmsten, Marco Toschi, Pantelis Zisimopoulos, Alexandros Sigaras, Stuart Lavery, Lee A. D. Cooper, Cristina Hickman, Marcos Meseguer, Zev Rosenwaks, Olivier Elemento, Nikica Zaninovic, Iman Hajirasouliha

Publications and Research

Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We …

Quantifying Iron Overload Using Mri, Active Contours, And Convolutional Neural Networks, Andrea Sajewski, Stacey Levine

Undergraduate Research and Scholarship Symposium

Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, …

Artificial Intelligence, Real Concerns…And Cash, Singapore Management University

Perspectives@SMU

Regulating development of self-aware robots is crucial. Data privacy is key to user-app power dynamic

Development And Initial Evaluation Of A Reinforced Cue Detection Model To Assess Situation Awareness In Commercial Aircraft Cockpits, Aysen K. Taylor

Engineering Management & Systems Engineering Theses & Dissertations

Commercial transport aircraft of today vary greatly from early aircraft with regards to how the aircraft are controlled and the feedback provided from the machine to the human operator. Over time, as avionics systems became more automated, pilots had less direct control over their aircraft. Much research exists in the literature about automation issues, and several major accidents over the last twenty years spurred interest about how to maintain the benefits of automation while improving the overall human-machine interaction as the pilot is considered the last line of defense.

An important reason for maintaining or even improving overall pilot situation …

Computational Contributions To The Automation Of Agriculture, Micah Nagel

Senior Honors Theses

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of …

Integrating Mathematics And Educational Robotics: Simple Motion Planning, Ronald I. Greenberg, George K. Thiruvathukal, Sara T. Greenberg

Computer Science: Faculty Publications and Other Works

This paper shows how students can be guided to integrate elementary mathematical analyses with motion planning for typical educational robots. Rather than using calculus as in comprehensive works on motion planning, we show students can achieve interesting results using just simple linear regression tools and trigonometric analyses. Experiments with one robotics platform show that use of these tools can lead to passable navigation through dead reckoning even if students have limited experience with use of sensors, programming, and mathematics.

Variable Speed Limit Control At Sag Curves Through Connected Vehicles: Implications Of Alternative Communications And Sensing Technologies, Reza Vatani Nezafat

Civil & Environmental Engineering Theses & Dissertations

Connected vehicles (CVs) will enable new applications to improve traffic flow. This study’s focus is to investigate how potential implementation of variable speed limit (VSL) through different types of communication and sensing technologies on CVs may improve traffic flow at a sag curve. At sag curves, the gradient changes from negative to positive values which causes a reduction in the roadway capacity and congestion. A VSL algorithm is developed and implemented in a simulation environment for controlling the inflow of vehicles to a sag curve on a freeway to minimize delays and increase throughput. Both vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) …

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 OP (CTOP) which arises in the logistics industry. 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 objective is to find a path for each vehicle in order to maximize the total collected score, without violating the capacity and time budget. We propose an Iterated Local Search (ILS) algorithm for solving the CTOP. Two strategies, either accepting a new solution as …

Route Planning For A Fleet Of Electric Vehicles With Waiting Times At Charging Stations, Baoxiang Li, Shashi Shekhar Jha, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Electric Vehicles (EVs) are the next wave of technology in the transportation industry. EVs are increasingly becoming common for personal transport and pushing the boundaries to become the mainstream mode of transportation. Use of such EVs in logistic fleets for delivering customer goods is not far from becoming reality. However, managing such fleet of EVs bring new challenges in terms of battery capacities and charging infrastructure for efficient route planning. Researchers have addressed such issues considering different aspects of the EVs such as linear battery charging/discharging rate, fixed travel times, etc. In this paper, we address the issue of waiting …

Dependable Machine Intelligence At The Tactical Edge, Archan Misra, Kasthuri Jayarajah, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Randy Tandriansyah Daratan, Shuochao Yao, Tarek Abdelzaher

Research Collection School Of Computing and Information Systems

The paper describes a vision for dependable application of machine learning-based inferencing on resource-constrained edge devices. The high computational overhead of sophisticated deep learning learning techniques imposes a prohibitive overhead, both in terms of energy consumption and sustainable processing throughput, on such resource-constrained edge devices (e.g., audio or video sensors). To overcome these limitations, we propose a ``cognitive edge" paradigm, whereby (a) an edge device first autonomously uses statistical analysis to identify potential collaborative IoT nodes, and (b) the IoT nodes then perform real-time sharing of various intermediate state to improve their individual execution of machine intelligence tasks. We provide …

A Data-Driven Approach For Modeling Agents, Hamdi Kavak

Computational Modeling & Simulation Engineering Theses & Dissertations

Agents are commonly created on a set of simple rules driven by theories, hypotheses, and assumptions. Such modeling premise has limited use of real-world data and is challenged when modeling real-world systems due to the lack of empirical grounding. Simultaneously, the last decade has witnessed the production and availability of large-scale data from various sensors that carry behavioral signals. These data sources have the potential to change the way we create agent-based models; from simple rules to driven by data. Despite this opportunity, the literature has neglected to offer a modeling approach to generate granular agent behaviors from data, creating …

Localizing Little Landmarks With Transfer Learning, Sharad Kumar

Dissertations and Theses

Locating a small object in an image -- like a mouse on a computer desk or the door handle of a car -- is an important computer vision problem to solve because in many real life situations a small object may be the first thing that gets operated upon in the image scene. While a significant amount of artificial intelligence and machine learning research has focused on localizing prominent objects in an image, the area of small object detection has remained less explored. In my research I explore the possibility of using context information to localize small objects in an …

Robotic Motion Generation By Using Spatial-Temporal Patterns From Human Demonstrations, Yongqiang Huang

USF Tampa Graduate Theses and Dissertations

Robots excel in manufacturing facilities because the tasks are repetitive and do not change. However, when the tasks change, which happens in almost all tasks that humans perform daily, such as cutting, pouring, and grasping, etc., robots perform much worse. We aim at teaching robots to perform tasks that are subject to change using demonstrations collected from humans, a problem referred to as learning from demonstration (LfD).

LfD consists of two parts: the data of human demonstrations, and the algorithm that extracts knowledge from the data to perform the same motions. Similarly, this thesis is divided into two parts. The …

Imitating Human Responses Via A Dual-Process Model Approach, Matthew A. Grimm

Theses and Dissertations

Human-autonomous system teaming is becoming more prevalent in the Air Force and in society. Often, the concept of a shared mental model is discussed as a means to enhance collaborative work arrangements between a human and an autonomous system. The idea being that when the models are aligned, the team is more productive due to an increase in trust, predictability, and apparent understanding. This research presents the Dual-Process Model using multivariate normal probability density functions (DPM-MN), which is a cognitive architecture algorithm based on the psychological dual-process theory. The dual-process theory proposes a bipartite decision-making process in people. It labels …

Neural Machine Translation, Quinn M. Lanners, Thomas Laurent

Honors Thesis

Neural Machine Translation is the primary algorithm used in industry to perform machine translation. This state-of-the-art algorithm is an application of deep learning in which massive datasets of translated sentences are used to train a model capable of translating between any two languages. The architecture behind neural machine translation is composed of two recurrent neural networks used together in tandem to create an Encoder Decoder structure. Attention mechanisms have recently been developed to further increase the accuracy of these models. In this senior thesis, the various parts of Neural Machine Translation are explored towards the eventual creation of a tutorial …

Conversational Agent: Developing A Model For Intelligent Agents With Transient Emotional States, Angie Dowdell

Theses and Dissertations

The demonstration of aggressive behaviors in the workplace can usually lead to negative professional consequences. However, combat-related professions may involve life and death decision making on an almost daily basis. This is one occupational area in which major duties may invoke or even require aggression. Today’s Army leaders face a different set of challenges than those faced by leaders of the past. Leaders of today must be trained to be both assertive and aggressive at the appropriate times while also displaying empathy. While this is not a new task for an Army leader, it is one that is approached differently …

Automation Tax Vs Robot-Tax, Vincent Ooi

Research Collection Yong Pung How School Of Law

The positive impact of developments in technology on the economy has historically outweighed the disruptive impact on employment. Society has benefited from the efficiency gains derived from the application of technology in production, while workers displaced by these technologies have largely been successfully retrained and employed in other jobs. However, the pace of development of the “Fourth Industrial Revolution” now presents a risk of mass displacement of human labour, particularly in tasks that are repetitive and menial. The “Fourth Industrial Revolution” is characterised by significant progress in a closely-linked cluster of areas such as robot dexterity, machine learning, processing power, …

Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority …

A Study Of Face Embedding In Face Recognition, Khanh Duc Le

Master's Theses

Face Recognition has been a long-standing topic in computer vision and pattern recognition field because of its wide and important applications in our daily lives such as surveillance system, access control, and so on. The current modern face recognition model, which keeps only a couple of images per person in the database, can now recognize a face with high accuracy. Moreover, the model does not need to be retrained every time a new person is added to the database.

By using the face dataset from Digital Democracy, the thesis will explore the capability of this model by comparing it with …