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Artificial intelligence

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Full-Text Articles in Engineering

Optimizing The Performance Of Complex Engineering Systems Aided By Artificial Neural Networks, Khalil Qatu Jan 2019

Optimizing The Performance Of Complex Engineering Systems Aided By Artificial Neural Networks, Khalil Qatu

Electronic Theses and Dissertations

In the first problem Polyetherimide graphene nanoplatelets papers (PEIGNP) were tested with different graphene loadings varying from 0-97 weight percent (WT%). The resulting stress-strain curves were utilized to develop two ANN models. Stress-controlled and strain-controlled models. Both models shoan excellent correlation to the experimental. Several Mechanical properties were calculated from the predicted stress-strain curves namely; toughness maximum strength maximum strain and maximum tangent modulus. Both models captured the same overall behavior of the PEIGNP composite. However the strain-controlled model was found to predict lower stress than the stress-controlled model. Finally a Graphical User Interface (GUI) was developed to aid in …

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Artificial Intelligence In Wet Weather Infrastructure, Matt Hammerstein Jan 2019

Artificial Intelligence In Wet Weather Infrastructure, Matt Hammerstein

Williams Honors College, Honors Research Projects

Effective management of runoff from rain and snowmelt is critical as increased water flows can negatively affect efficiency and reliability at treatment facilities, as well as potentially damage property or the natural environment. Implementation of artificial intelligence for real-time decision making and support in wet weather infrastructure is a recent technological development; as such, a problem has emerged: experience and knowledge of best practices for successful implementation is limited. Artificial intelligence is being employed to inform operational decisions that are intended to improve the efficiency and reliability of physical wet weather infrastructure. The goal of municipalities and utilities in utilizing …

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Artificial Intelligence In Wastewater Treatment Facilities: Implementing Practical New Technologies For The End User, Jordan Spano Jan 2019

Artificial Intelligence In Wastewater Treatment Facilities: Implementing Practical New Technologies For The End User, Jordan Spano

Williams Honors College, Honors Research Projects

Design phase operator input can prove useful; however, it is not essential in every application, as Akron’s treatment plant implemented AI to increase treatment capacity without design-phase operator input. They implemented a system flight simulator, a few hours training, and have communicated with the designer to make system tweaks as needed. In larger applications, the owner may benefit greatly by incorporating design input from operational staff. Those representing a municipality as a project owner for a treatment plant upgrade should always maintain an active role in the design of smart water infrastructure. They must keep the operators in mind when …

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A Practitioner Survey Exploring The Value Of Forensic Tools, Ai, Filtering, & Safer Presentation For Investigating Child Sexual Abuse Material, Laura Sanchez, Cinthya Grajeda, Ibrahim Baggili, Cory Hall Jan 2019

A Practitioner Survey Exploring The Value Of Forensic Tools, Ai, Filtering, & Safer Presentation For Investigating Child Sexual Abuse Material, Laura Sanchez, Cinthya Grajeda, Ibrahim Baggili, Cory Hall

Electrical & Computer Engineering and Computer Science Faculty Publications

For those investigating cases of Child Sexual Abuse Material (CSAM), there is the potential harm of experiencing trauma after illicit content exposure over a period of time. Research has shown that those working on such cases can experience psychological distress. As a result, there has been a greater effort to create and implement technologies that reduce exposure to CSAM. However, not much work has explored gathering insight regarding the functionality, effectiveness, accuracy, and importance of digital forensic tools and data science technologies from practitioners who use them. This study focused specifically on examining the value practitioners give to the tools …

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Assessment Of Adaptability Of A Supply Chain Trading Agent’S Strategy: Evolutionary Game Theory Approach, Yoon Sang Lee, Riyaz T. Sikora Jan 2019

Assessment Of Adaptability Of A Supply Chain Trading Agent’S Strategy: Evolutionary Game Theory Approach, Yoon Sang Lee, Riyaz T. Sikora

Journal of International Technology and Information Management

With the increase in the complexity of supply chain management, the use of intelligent agents for automated trading has gained popularity (Collins, Arunachalam, B, et al. 2006). The performance of supply-chain agents depends on not just the market environment (supply and demand patterns) but also on what types of other agents they are competing with. For designers of such agents it is important to ascertain that their agents are robust and can adapt to changing market and competitive environments. However, to date there has not been any work done that assesses the adaptability of a trading agent’s strategy in the …

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Cloud-Supported Machine Learning System For Context-Aware Adaptive M-Learning, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq Jan 2019

Cloud-Supported Machine Learning System For Context-Aware Adaptive M-Learning, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq

Turkish Journal of Electrical Engineering and Computer Sciences

It is a knotty task to amicably identify the sporadically changing real-world context information of a learner during M-learning processes. Contextual information varies greatly during the learning process. Contextual information that affects the learner during a learning process includes background knowledge, learning time, learning location, and environmental situation. The computer programming skills of learners improve rapidly if they are encouraged to solve real-world programming problems. It is important to guide learners based on their contextual information in order to maximize their learning performance. In this paper, we proposed a cloud-supported machine learning system (CSMLS), which assists learners in learning practical …

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Feasible Form Parameter Design Of Complex Ship Hull Form Geometry, Thomas L. Mcculloch Dec 2018

Feasible Form Parameter Design Of Complex Ship Hull Form Geometry, Thomas L. Mcculloch

LSU New Orleans Theses and Dissertations

This thesis introduces a new methodology for robust form parameter design of complex hull form geometry via constraint programming, automatic differentiation, interval arithmetic, and truncated hierarchical B- splines. To date, there has been no clearly stated methodology for assuring consistency of general (equality and inequality) constraints across an entire geometric form parameter ship hull design space. In contrast, the method to be given here can be used to produce guaranteed narrowing of the design space, such that infeasible portions are eliminated. Furthermore, we can guarantee that any set of form parameters generated by our method will be self consistent. It …

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Sdnet2018: An Annotated Image Dataset For Non-Contact Concrete Crack Detection Using Deep Convolutional Neural Networks, Sattar Dorafshan, Robert J. Thomas, Marc Maguire Nov 2018

Sdnet2018: An Annotated Image Dataset For Non-Contact Concrete Crack Detection Using Deep Convolutional Neural Networks, Sattar Dorafshan, Robert J. Thomas, Marc Maguire

Civil and Environmental Engineering Faculty Publications

SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field …

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Combining Algorithms For More General Ai, Mark Robert Musil May 2018

Combining Algorithms For More General Ai, Mark Robert Musil

Maseeh Summer Undergraduate Research Experience

Two decades since the first convolutional neural network was introduced the AI sub-domains of classification, regression and prediction still rely heavily on a few ML architectures despite their flaws of being hungry for data, time, and high-end hardware while still lacking generality. In order to achieve more general intelligence that can perform one-shot learning, create internal representations, and recognize subtle patterns it is necessary to look for new ML system frameworks. Research on the interface between neuroscience and computational statistics/machine learning has suggested that combined algorithms may increase AI robustness in the same way that separate brain regions specialize. In …

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A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau May 2018

A Qualitative Research On Marketing And Sales In The Artificial Intelligence Age, Yin Yang, Keng Siau

Research Collection School Of Computing and Information Systems

The age of artificial intelligence is here! Artificial Intelligence, robotics, machine learning, and automation are impacting the field of marketing and sales in an unprecedented way. In this study, the qualitative research methodology will be used to better understand the revolution and evolution of marketing and sales field in the AI age. Multiple case studies will be performed in various marketing and sales units in different organizations. This research is of value to both academics and practitioners as it aims to provide a detailed analysis and documentation of the changes in marketing and sales functionalities and job markets as AI …

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Plant Identification Using Tensorflow, Kody G. Dangtongdee Mar 2018

Plant Identification Using Tensorflow, Kody G. Dangtongdee

Computer Engineering

Exploring the diverse nature that is available in San Luis Obispo is one of my favorite activities, however, I often am frustrated that I am unable to identify or learn more about the plants that I appreciate. This project is an attempt at using the concepts of neural networks to create an image classifier which can identify plants. Machine learning is one of the biggest topics in computer science at the moment, and its many uses make it a topic that will continue to be researched for a very long time. Convolutional neural networks are a popular realm of machine …

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Cyber-Physical Embedded Systems With Transient Supervisory Command And Control: A Framework For Validating Safety Response In Automated Collision Avoidance Systems, Daniel K. Trembley Jan 2018

Cyber-Physical Embedded Systems With Transient Supervisory Command And Control: A Framework For Validating Safety Response In Automated Collision Avoidance Systems, Daniel K. Trembley

Graduate Dissertations and Theses

The ability to design and engineer complex and dynamical Cyber-Physical Systems (CPS) requires a systematic view that requires a definition of level of automation intent for the system. Since CPS covers a diverse range of systemized implementations of smart and intelligent technologies networked within a system of systems (SoS), the terms “smart” and “intelligent” is frequently used in describing systems that perform complex operations with a reduced need of a human-agent. The difference between this research and most papers in publication on CPS is that most other research focuses on the performance of the CPS rather than on the correctness …

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Using Artificial Intelligence And Machine Learning To Develop Synthetic Well Logs, Marwan Mohammed Alnuaimi Jan 2018

Using Artificial Intelligence And Machine Learning To Develop Synthetic Well Logs, Marwan Mohammed Alnuaimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

There has been an increase in the need for energy in the recent past. Oil and gas stand as the source of energy that are widely used. The oil and gas reservoirs are targeted for the purposes of field development. The conventional methods of reservoir characteristics require computing techniques that are unique and complex, some of which are labor and time intensive. Mohaghegh argues that all efforts must be tried and made possible to apply Petroleum Data analytics in production and management of reservoir so as to earn a maximum return (Mohaghegh, Shale Analytics, 2017). Different methodologies have been applied …

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Special Issue: Neutrosophic Information Theory And Applications, Florentin Smarandache, Jun Ye Jan 2018

Special Issue: Neutrosophic Information Theory And Applications, Florentin Smarandache, Jun Ye

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophiclogic,symboliclogic,set,probability,statistics,etc.,are,respectively,generalizations of fuzzy and intuitionistic fuzzy logic and set, classical and imprecise probability, classical statistics, and so on. Neutrosophic logic, symbol logic, and set are gaining significant attention in solving many real-life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistency, and indeterminacy. A number of new neutrosophic theories have been proposed and have been applied in computational intelligence, multiple-attribute decision making, image processing, medical diagnosis, fault diagnosis, optimization design, etc. This Special Issue gathers original research papers that report on the state of the art, as well as on recent advancements in neutrosophic information theory in soft computing, artificial intelligence, …

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Fundamentals Of Neutrosophic Logic And Sets And Their Role In Artificial Intelligence (Fundamentos De La Lógica Y Los Conjuntos Neutrosóficos Y Su Papel En La Inteligencia Artificial ), Florentin Smarandache, Maykel Leyva-Vazquez Jan 2018

Fundamentals Of Neutrosophic Logic And Sets And Their Role In Artificial Intelligence (Fundamentos De La Lógica Y Los Conjuntos Neutrosóficos Y Su Papel En La Inteligencia Artificial ), Florentin Smarandache, Maykel Leyva-Vazquez

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophy is a new branch of philosophy which studies the origin, nature and scope of neutralities. This has formed the basis for a series of mathematical theories that generalize the classical and fuzzy theories such as the neutrosophic sets and the neutrosophic logic. In the paper, the fundamental concepts related to neutrosophy and its antecedents are presented. Additionally, fundamental concepts of artificial intelligence will be defined and how neutrosophy has come to strengthen this discipline.

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Measuring Goal Similarity Using Concept, Context And Task Features, Vahid Eyorokon Jan 2018

Measuring Goal Similarity Using Concept, Context And Task Features, Vahid Eyorokon

Browse all Theses and Dissertations

Goals can be described as the user's desired state of the agent and the world and are satisfied when the agent and the world are altered in such a way that the present state matches the desired state. For physical agents, they must act in the world to alter it in a series of individual atomic actions. Traditionally, agents use planning to create a chain of actions each of which altering the current world state and yielding a new one until the final action yields the desired goal state. Once this goal state has been achieved, the goal is said …

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Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi Jan 2018

Development Of A Locomotion And Balancing Strategy For Humanoid Robots, Emile Bahdi

Electronic Theses and Dissertations

The locomotion ability and high mobility are the most distinguished features of humanoid robots. Due to the non-linear dynamics of walking, developing and controlling the locomotion of humanoid robots is a challenging task. In this thesis, we study and develop a walking engine for the humanoid robot, NAO, which is the official robotic platform used in the RoboCup Spl. Aldebaran Robotics, the manufacturing company of NAO provides a walking module that has disadvantages, such as being a black box that does not provide control of the gait as well as the robot walk with a bent knee. The latter disadvantage, …

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Convolutional Neural Network Based Localized Classification Of Uterine Cervical Cancer Digital Histology Images, Haidar A. Almubarak, R. Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier Oct 2017

Convolutional Neural Network Based Localized Classification Of Uterine Cervical Cancer Digital Histology Images, Haidar A. Almubarak, R. Joe Stanley, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier

Electrical and Computer Engineering Faculty Research & Creative Works

In previous research, we introduced an automated localized, fusion-based algorithm to classify squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN). The approach partitioned the epithelium into 10 segments. Image processing and machine vision algorithms were used to extract features from each segment. The features were then used to classify the segment and the result was fused to classify the whole epithelium. This research extends the previous research by dividing each of the 10 segments into 3 parts and uses a convolutional neural network to classify the 3 parts. The result is then fused to …

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Mechanism Design For Strategic Project Scheduling, Pradeep Varakantham, Na Fu Aug 2017

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 …

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Proactive And Reactive Coordination Of Non-Dedicated Agent Teams Operating In Uncertain Environments, Pritee Agrawal, Pradeep Varakantham Aug 2017

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 …

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Real-Time Traffic State Estimation With Connected Vehicles, Sakib Mahmud Khan, Kaken Dey, Mashrur Chowdhury Jul 2017

Real-Time Traffic State Estimation With Connected Vehicles, Sakib Mahmud Khan, Kaken Dey, Mashrur Chowdhury

Publications

A novel framework is developed in this paper, to increase the real-time roadway traffic condition assessment accuracy, which integrates connected vehicle technology (CVT) with artificial intelligence (AI) paradigm forming a CVT-AI method. Traffic density is a major indicator of traffic conditions. In this paper, the traffic operational condition is assessed based on traffic density. A simulated network of Interstate 26 in South Carolina is developed to investigate the effectiveness of the method. The assumption is that the vehicle onboard units will forward the CV generated data to the edge devices (e.g., roadside units) for further processing. CV generated distance headway …

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Models For Pedestrian Trajectory Prediction And Navigation In Dynamic Environments, Jeremy N. Kerfs May 2017

Models For Pedestrian Trajectory Prediction And Navigation In Dynamic Environments, Jeremy N. Kerfs

Master's Theses

Robots are no longer constrained to cages in factories and are increasingly taking on roles alongside humans. Before robots can accomplish their tasks in these dynamic environments, they must be able to navigate while avoiding collisions with pedestrians or other robots. Humans are able to move through crowds by anticipating the movements of other pedestrians and how their actions will influence others; developing a method for predicting pedestrian trajectories is a critical component of a robust robot navigation system. A current state-of-the-art approach for predicting pedestrian trajectories is Social-LSTM, which is a recurrent neural network that incorporates information about neighboring …

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A Comparison On The Classification Of Short-Text Documents Using Latent Dirichlet Allocation And Formal Concept Analysis, Noel Rogers, Luca Longo Jan 2017

A Comparison On The Classification Of Short-Text Documents Using Latent Dirichlet Allocation And Formal Concept Analysis, Noel Rogers, Luca Longo

Books/Book Chapters

With the increasing amounts of textual data being collected online, automated text classification techniques are becoming increasingly important. However, a lot of this data is in the form of short-text with just a handful of terms per document (e.g. Text messages, tweets or Facebook posts). This data is generally too sparse and noisy to obtain satisfactory classification. Two techniques which aim to alleviate this problem are Latent Dirichlet Allocation (LDA) and Formal Concept Analysis (FCA). Both techniques have been shown to improve the performance of short-text classification by reducing the sparsity of the input data. The relative performance of classifiers …

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Adapting The Search Space While Limiting Damage During Learning In A Simulated Flapping Wing Micro Air Vehicle, Monica Sam Jan 2017

Adapting The Search Space While Limiting Damage During Learning In A Simulated Flapping Wing Micro Air Vehicle, Monica Sam

Browse all Theses and Dissertations

Cyber-Physical Systems (CPS) are characterized by closely coupled physical and software components that operate simultaneously on different spatial and temporal scales; exhibit multiple and distinct behavioral modalities; and interact with one another in ways not entirely predictable at the time of design. A commonly appearing type of CPS are systems that contain one or more smart components that adapt locally in response to global measurements of whole system performance. An example of a smart component robotic CPS system is a Flapping Wing Micro Air Vehicle (FW-MAV) that contains wing motion oscillators that control their wing flapping patterns to enable the …

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Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter Jan 2017

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 …

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Evaluating Forecasting Methods By Considering Different Accuracy Measures, Nijat Mehdiyev, David Lee Enke, Peter Fettke, Peter Loos Nov 2016

Evaluating Forecasting Methods By Considering Different Accuracy Measures, Nijat Mehdiyev, David Lee Enke, Peter Fettke, Peter Loos

Engineering Management and Systems Engineering Faculty Research & Creative Works

Choosing the appropriate forecasting technique to employ is a challenging issue and requires a comprehensive analysis of empirical results. Recent research findings reveal that the performance evaluation of forecasting models depends on the accuracy measures adopted. Some methods indicate superior performance when error based metrics are used, while others perform better when precision values are adopted as accuracy measures. As scholars tend to use a smaller subset of accuracy metrics to assess the performance of forecasting models, there is a need for a concept of multiple accuracy dimensions to assure the robustness of evaluation. Therefore, the main purpose of this …

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Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar Feb 2016

Shortest Path Based Decision Making Using Probabilistic Inference, Akshat Kumar

Research Collection School Of Computing and Information Systems

We present a new perspective on the classical shortest path routing (SPR) problem in graphs. We show that the SPR problem can be recast to that of probabilistic inference in a mixture of simple Bayesian networks. Maximizing the likelihood in this mixture becomes equivalent to solving the SPR problem. We develop the well known Expectation-Maximization (EM) algorithm for the SPR problem that maximizes the likelihood, and show that it does not get stuck in a locally optimal solution. Using the same probabilistic framework, we then address an NP-Hard network design problem where the goal is to repair a network of …

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Internet Of Things To Smart Iot Through Semantic, Cognitive, And Perceptual Computing, Amit P. Sheth Jan 2016

Internet Of Things To Smart Iot Through Semantic, Cognitive, And Perceptual Computing, Amit P. Sheth

Publications

Rapid growth in the Internet of Things (IoT) has resulted in a massive growth of data generated by these devices and sensors put on the Internet. Physical-cyber-social (PCS) big data consist of this IoT data, complemented by relevant Web-based and social data of various modalities. Smart data is about exploiting this PCS big data to get deep insights and make it actionable, and making it possible to facilitate building intelligent systems and applications. This article discusses key AI research in semantic computing, cognitive computing, and perceptual computing. Their synergistic use is expected to power future progress in building intelligent systems …

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Pure Fuzzy Hall Effect Sensors For Permanent Magnet Synchronous Motor, İbrahi̇m Alişkan, Rüstem Yilmazel Jan 2016

Pure Fuzzy Hall Effect Sensors For Permanent Magnet Synchronous Motor, İbrahi̇m Alişkan, Rüstem Yilmazel

Turkish Journal of Electrical Engineering and Computer Sciences

An investigation about Hall effect sensors' efficiency is confirmed in permanent magnet synchronous motor (PMSM) drive systems. A fuzzy control algorithm is used as an artificial intelligence controller. Large scale and low slopes are used for creating membership functions and a sensitive controller is obtained. Speed is wanted to be taken under control and a minimum error value is aimed. PMSM drive systems are established using MATLAB-Simulink/SimPower. Simulations are realized with real-time parameters in discrete mode. A fuzzy logic controller is designed by using the MATLAB/Fuzzy Logic Toolbox. A normalization technique and high resolution output of the fuzzy logic controller …

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Pathfinding In Computer Games, Ross Graham, Hugh Mccabe, Stephen Sheridan Nov 2015

Pathfinding In Computer Games, Ross Graham, Hugh Mccabe, Stephen Sheridan

The ITB Journal

One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. This report will highlight pathfinding algorithms used presently in games and their shortcomings especially when dealing with real-time pathfinding. With the advances being made in other components, such as physics engines, it is AI that is impeding the next generation of computer games. This report will focus on how machine learning techniques such as Artificial Neural Networks and Genetic Algorithms can be used to enhance an agents ability …

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