Engineering Commons^™

Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson

Electrical & Computer Engineering Theses & Dissertations

Raman spectroscopy is a powerful analysis technique that has found applications in fields such as analytical chemistry, planetary sciences, and medical diagnostics. Recent studies have shown that analysis of Raman spectral profiles can be greatly assisted by use of computational models with achievements including high accuracy pure sample classification with imbalanced data sets and detection of ideal sample deviations for pharmaceutical quality control. The adoption of automated methods is a necessary step in streamlining the analysis process as Raman hardware becomes more advanced. Due to limits in the architectures of current machine learning based Raman classification models, transfer from pure …

Recent Trends, Current Research In Cyberpsychology: A Literature Review, Amarjit Kumar Singh, Pawan Kumar Singh

Library Philosophy and Practice (e-journal)

Cyberpsychology refers to the study of the mind and behavior in the context of interactions with technology. It is an emerging branch, which has focused on the psychological aspects connected to the increasing presence and usages of technology in modern lives. This paper traces recent advancement and trends of Cyberpsychology is an emerging domain of knowledge and goes on the give a literature review of the same. An analysis of the recent research and literature covering 300 most relevant research papers from the period of 2012 to 15, August 2019 was conducted to determine and shape the research pattern based …

Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders

Faculty Scholarship

Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of …

Ambient-Intelligent Decision Support System For Smart Manufacturing, Mahdi Fathi, Marzieh Khakifirooz, Yiannis Ampatzidis, Panos M. Pardalos

Bagley College of Engineering Publications and Scholarship

Ambient Intelligence (AmI) refers to a networked environment of computing devices for implementing a "smart" system. AmI is built using sensors and actuators connected through real-time networks. The data and signals captured from sensors are ambiguous for both human and machine. Artificial Intelligence (AI) is merged into an ambient environment to translate data and signals into a language understandable by human users and can help transform an operational setting from machine-centered to human-centered. However, the implementation of AI technology into an ambient environment requires quantitative modeling approaches to emphasize system requirements analysis and more detailed design specifications. This article tries …

Inverted Cone Convolutional Neural Network For Deboning Mris, Oliver John Palumbo

Theses and Dissertations

Data plenitude is the power but also the bottleneck for data-driven approaches, including neural networks. In particular, Convolutional Neural Networks (CNNs) require an abundant database of training images to achieve a desired high accuracy. Current techniques employed for boosting small datasets are data augmentation and synthetic data generation, which suffer from computational complexity and imprecision compared to original datasets. In this thesis, we intercalate prior knowledge based on the temporal relation between the images in the third dimension. Specifically, we compute the gradient of subsequent images in the dataset to remove extraneous information and highlight subtle variations between the images. …

Exploring The Behavior Repertoire Of A Wireless Vibrationally Actuated Tensegrity Robot, Zongliang Ji

Honors Theses

Soft robotics is an emerging field of research due to its potential to explore and operate in unstructured, rugged, and dynamic environments. However, the properties that make soft robots compelling also make them difficult to robustly control. Here at Union, we developed the world’s first wireless soft tensegrity robot. The goal of my thesis is to explore effective and efficient methods to explore the diverse behavior our tensegrity robot. We will achieve that by applying state-of-art machine learning technique and a novelty search algorithm.

The Future Robo-Advisor, Catalin Burlacu

MITB Thought Leadership Series

The accelerated digitalisation of both people and business around the world today is having a huge impact on the investment management and advisory space. The addition of new and vastly larger data sets, as well as exponentially more sophisticated analytical tools to turn that data into usable information is constantly changing the way investments are decided on, made and managed.

Receptive Fields Optimization In Deep Learning For Enhanced Interpretability, Diversity, And Resource Efficiency., Babajide Odunitan Ayinde

Electronic Theses and Dissertations

In both supervised and unsupervised learning settings, deep neural networks (DNNs) are known to perform hierarchical and discriminative representation of data. They are capable of automatically extracting excellent hierarchy of features from raw data without the need for manual feature engineering. Over the past few years, the general trend has been that DNNs have grown deeper and larger, amounting to huge number of final parameters and highly nonlinear cascade of features, thus improving the flexibility and accuracy of resulting models. In order to account for the scale, diversity and the difficulty of data DNNs learn from, the architectural complexity and …

Intelligent Machine Learning: Tailor-Making Macromolecules, Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, Philippe Zinck, Krzysztof Matyjaszewski

Faculty Publications

Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and …

Intelligent Machine Learning: Tailor-Making Macromolecules, Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, Philippe Zinck, Krzysztof Matyjaszewski

Faculty Publications

Nowadays, polymer reaction engineers seek robust and effective tools to synthesize complex macromolecules with well-defined and desirable microstructural and architectural characteristics. Over the past few decades, several promising approaches, such as controlled living (co)polymerization systems and chain-shuttling reactions have been proposed and widely applied to synthesize rather complex macromolecules with controlled monomer sequences. Despite the unique potential of the newly developed techniques, tailor-making the microstructure of macromolecules by suggesting the most appropriate polymerization recipe still remains a very challenging task. In the current work, two versatile and powerful tools capable of effectively addressing the aforementioned questions have been proposed and …

From Decoder Rings To Deep Fakes: Translating Complex Technologies For Legal Education, Rachel S. Evans, Jason Tubinis

Presentations

“Technological developments are disrupting the practice of law” is a common refrain, but the last few years has seen some particularly complex pieces of technology become the hot new thing in legal tech. This session will look at blockchain, quantum computing, artificial intelligence, and ‘Deep Fakes’ as examples of how librarians can stay abreast of technological developments and inform themselves about their impacts in the legal profession. Then we will look at how to translate the complexities and jargon of these examples into lessons for for-credit courses, one-off informational sessions, or meetings with stakeholders.

Recipe For Disaster, Zac Travis

MFA Thesis Exhibit Catalogs

Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.

The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as a …

Research On Interaction Model Of Hand Tracking Based On Cognitive Theory, Shaobai Zhang, Zhang Teng

Journal of System Simulation

Abstract: This paper aims to solve the problems in a vector integration to endpoint (VITE) model of human reaching and grasping under perturbations of object size, distance and orientation. We discuss how to reduce the numbers of disturbances of three main kinds of components: hand/wrist transport, grip aperture and hand orientation. Based on the achievements of cognitive psychology, and a tracking and cognitive model for operational 3D gestures, this paper proposes a new divide-and-conquer model that is used for indicating current grasping status and to trigger three main kinds of methods of when to start or stop working. The model …

Preliminary Study Of Modeling And Simulation Technology Oriented To Neo-Type Artificial Intelligent Systems, Libo Hu, Xudong Chai, Zhang Lin, Li Tan, Duzheng Qing, Tingyu Lin, Liu Yang

Journal of System Simulation

Abstract: A brief interpretation of the rapidly developing “New Internet+ Big Data+ Artificial Intelligence+” era is given in the paperand the essence and the architectureof neo-type artificial intelligence systems are explained. The meaning of neo-type artificial intelligence system oriented modelling and simulation technology is proposed and the new challenges they are facing are discussed. The research contents and preliminaryresults on neo-type artificial intelligence system oriented modelling and simulation technology are given, which includeneo-type artificial intelligence system oriented modelling/secondary modelling, intelligent simulation computer, smart cloud simulation and intelligent simulation hardware/software supporting system technology, and intelligent simulation system application engineering technology. Several …

From Situation Cognition Stepped Into Situation Intelligent Cognition, Zhu Feng, Xiaofeng Hu, Wu Lin, Xiaoyuan He, Xuezhi Lü, Liao Ying

Journal of System Simulation

Abstract: Aimed at operational situation cognition and some relevant problems under the background of the Joint-Tactical, some deep researches are carried out in this paper. The concept models of combat situation cognition and situation intelligent cognition are proposed respectively, and some related concepts are clarified. The situation intelligent cognition technology framework is proposed, and five key problems which should be solved are analyzed, and the possible technical routes are given. These research contents and achievements build a foundation for stepping into the situation intelligent cognition from combat situation cognition.

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 …

Smart Proxy Modeling Of Sacroc Co2-Eor, Gholami Vida, Mohaghegh D. Shahab, Maysami Mohammad

Faculty & Staff Scholarship

Large CO2-enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO2-EOR projects. The use of …

Distribution Level Building Load Prediction Using Deep Learning, Abdulaziz S. Almalaq

Electronic Theses and Dissertations

Load prediction in distribution grids is an important means to improve energy supply scheduling, reduce the production cost, and support emission reduction. Determining accurate load predictions has become more crucial than ever as electrical load patterns are becoming increasingly complicated due to the versatility of the load profiles, the heterogeneity of individual load consumptions, and the variability of consumer-owned energy resources. However, despite the increase of smart grids technologies and energy conservation research, many challenges remain for accurate load prediction using existing methods. This dissertation investigates how to improve the accuracy of load predictions at the distribution level using artificial …

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 …

Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue

Dissertations

Basketball teams at all levels of the game invest a considerable amount of time and effort into collecting, segmenting, and analysing footage from their upcoming opponents previous games. This analysis helps teams identify and exploit the potential weaknesses of their opponents and is commonly cited as one of the key elements required to achieve success in the modern game. The growing importance of this type of analysis has prompted research into the application of computer vision and audio classification techniques to help teams classify scoring sequences and key events using game footage. However, this research tends to focus on classifying …

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …

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 …

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 …

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 …

Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee

Legacy Theses & Dissertations (2009 - 2024)

Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …

Communications Using Deep Learning Techniques, Priti Gopal Pachpande

Legacy Theses & Dissertations (2009 - 2024)

Deep learning (DL) techniques have the potential of making communication systems

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 …

Should Robots Prosecute And Defend?, Stephen E. Henderson

Stephen E Henderson