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Prediction Of Solid Oxide Fuel Cell Performance Using Artificial Neural Network, M. A. Rafe Biswas, Kamwana N. Mwara 2017 University of Texas at Tyler

Prediction Of Solid Oxide Fuel Cell Performance Using Artificial Neural Network, M. A. Rafe Biswas, Kamwana N. Mwara

M. A. Rafe Biswas

NASA’s Johnson Space Center has recently begun efforts to eventually integrate air-independent Solid Oxide Fuel Cell (SOFC) systems, with landers that can be propelled by LOX-CH4, for long duration missions. Using landers that utilize such propellants, provides the opportunity to use SOFCs as a power option, especially since they are able to process methane into a reactant through fuel reformation. Various lead-up activities, such as hardware testing and computational modelling, have been initiated to assist with this developmental effort.
One modeling approach, currently being explored to predict SOFC behavior, involves the usage of artificial neural networks (ANN). Since SOFC ...


Open Source Artificial Intelligence In A Biological/Ecological Context, Trevor Grant 2017 Illinois State University

Open Source Artificial Intelligence In A Biological/Ecological Context, Trevor Grant

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


Distributed Evolution Of Spiking Neuron Models On Apache Mahout For Time Series Analysis, Andrew Palumbo 2017 Cylance, Inc.

Distributed Evolution Of Spiking Neuron Models On Apache Mahout For Time Series Analysis, Andrew Palumbo

Annual Symposium on Biomathematics and Ecology: Education and Research

No abstract provided.


Content-Based Top-N Recommendations With Perceived Similarity, Charlie Wang, Arpita Agrawal, Xiaojun Li, Tanima Makkad, Ejaz Veljee, Ole J. Mengshoel, Alvin Jude 2017 Ericsson

Content-Based Top-N Recommendations With Perceived Similarity, Charlie Wang, Arpita Agrawal, Xiaojun Li, Tanima Makkad, Ejaz Veljee, Ole J. Mengshoel, Alvin Jude

Ole J Mengshoel

Similarity-based recommender systems can be used to pre-compute distance between item pairs, and then to quickly recommend similar items to users. The content-based approach to similarity uses the item’s description, which in movies could mean genre, director or cast. These similarity methods are often built with unsupervised learning, which means the notion of similarity is defined by those who write the method. This notion may not match that of the users, resulting in poor user experience. In this paper we used user-contributed labels representing perceived similarity between movies to build a supervised content-based (CB) model for movie recommendations. Our ...


Developing Grounded Goals Through Instant Replay Learning, Lisa Meeden, Douglas S. Blank 2017 Swarthmore College

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 ...


Designing An Ai That Cares, 2017 Vocational Training Council

Designing An Ai That Cares

SIGNED: The Magazine of The Hong Kong Design Institute

The breakthrough that could change AI from being a plaything to being a playmate with which humans can have meaningful interations may be about to come from a seemingly unlikely source.


3½ Problems For Digital Assistants, 2017 Vocational Training Council

3½ Problems For Digital Assistants

SIGNED: The Magazine of The Hong Kong Design Institute

Digital home assistants promise to make life easier and happier. But a few problems stand in their way


Ai Centaurs, 2017 Vocational Training Council

Ai Centaurs

SIGNED: The Magazine of The Hong Kong Design Institute

As artificial intelligence outstrips human intelligence, AIs can now beat humans at any game. A pessimist might claim that this marks the end of the line for play. But a mythical beast has come to save playtime from the robots.


Footprintid: Indoor Pedestrian Identification Through Ambient Structural Vibration Sensing, Shijia Pan, Tong Yu, Mostafa Mirshekari, Jonathon Fagert, Amelie Bonde, Ole J. Mengshoel, Hae Young Noh, Pei Zhang 2017 Carnegie Mellon University

Footprintid: Indoor Pedestrian Identification Through Ambient Structural Vibration Sensing, Shijia Pan, Tong Yu, Mostafa Mirshekari, Jonathon Fagert, Amelie Bonde, Ole J. Mengshoel, Hae Young Noh, Pei Zhang

Ole J Mengshoel

We present FootprintID, an indoor pedestrian identification system that utilizes footstep-induced structural vibration to infer pedestrian identities for enabling various smart building applications. Previous studies have explored other sensing methods, including vision-, RF-, mobile-, and acoustic-based methods. They often require specific sensing conditions, including line-of-sight, high sensor density, and carrying wearable devices. Vibration-based methods, on the other hand, provide easy-to-install sparse sensing and utilize gait to distinguish different individuals. However, the challenge for these methods is that the signals are sensitive to the gait variations caused by different walking speeds and the floor variations caused by structural heterogeneity.

We present ...


A Developmental Robotics Manifesto, Douglas Blank, James Marshall, Lisa Meeden 2017 Bryn Mawr College

A Developmental Robotics Manifesto, Douglas Blank, James Marshall, Lisa Meeden

Douglas Blank

We are largely in agreement with Tani’s approach to developmental robotics as elucidated in this dialog and his recent book. The basic assumptions inherent in his approach, such as that agents are embodied in the world and that neural systems are capable of complex learning, are now established wisdom. Although this has been a relatively recent shift in AI and Cognitive Science, we consider these underlying assumptions to be a given and thus do not address them further. Here we expand on Tani’s questions and offer a broader set of principles for guiding developmental robotics research.


Improving Speech Recognition For Interviews With Both Clean And Telephone Speech, Sung Woo Choi 2017 Minnesota State University, Mankato

Improving Speech Recognition For Interviews With Both Clean And Telephone Speech, Sung Woo Choi

Journal of Undergraduate Research at Minnesota State University, Mankato

High quality automatic speech recognition (ASR) depends on the context of the speech. Cleanly recorded speech has better results than speech recorded over telephone lines. In telephone speech, the signal is band-pass filtered which limits frequencies available for computation. Consequently, the transmitted speech signal may be distorted by noise, causing higher word error rates (WER). The main goal of this research project is to examine approaches to improve recognition of telephone speech while maintaining or improving results for clean speech in mixed telephone-clean speech recordings, by reducing mismatches between the test data and the available models. The test data includes ...


Comparing And Improving Facial Recognition Method, Brandon Luis Sierra 2017 California State University – San Bernardino

Comparing And Improving Facial Recognition Method, Brandon Luis Sierra

Electronic Theses, Projects, and Dissertations

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine ...


Machine Learning Approach To Retrieving Physical Variables From Remotely Sensed Data, Fazlul Shahriar 2017 The Graduate Center, City University of New York

Machine Learning Approach To Retrieving Physical Variables From Remotely Sensed Data, Fazlul Shahriar

All Graduate Works by Year: Dissertations, Theses, and Capstone Projects

Scientists from all over the world make use of remotely sensed data from hundreds of satellites to better understand the Earth. However, physical measurements from an instrument is sometimes missing either because the instrument hasn't been launched yet or the design of the instrument omitted a particular spectral band. Measurements received from the instrument may also be corrupt due to malfunction in the detectors on the instrument. Fortunately, there are machine learning techniques to estimate the missing or corrupt data. Using these techniques we can make use of the available data to its full potential.

We present work on ...


Machine Learning Algorithms For Automated Satellite Snow And Sea Ice Detection, George Bonev 2017 The Graduate Center, City University of New York

Machine Learning Algorithms For Automated Satellite Snow And Sea Ice Detection, George Bonev

All Graduate Works by Year: Dissertations, Theses, and Capstone Projects

The continuous mapping of snow and ice cover, particularly in the arctic and poles, are critical to understanding the earth and atmospheric science. Much of the world's sea ice and snow covers the most inhospitable places, making measurements from satellite-based remote sensors essential. Despite the wealth of data from these instruments many challenges remain. For instance, remote sensing instruments reside on-board different satellites and observe the earth at different portions of the electromagnetic spectrum with different spatial footprints. Integrating and fusing this information to make estimates of the surface is a subject of active research.

In response to these ...


Computer Vision Problems In 3d Plant Phenotyping, Ayan Chaudhury 2017 The University of Western Ontario

Computer Vision Problems In 3d Plant Phenotyping, Ayan Chaudhury

Electronic Thesis and Dissertation Repository

In recent years, there has been significant progress in Computer Vision based plant phenotyping (quantitative analysis of biological properties of plants) technologies. Traditional methods of plant phenotyping are destructive, manual and error prone. Due to non-invasiveness and non-contact properties as well as increased accuracy, imaging techniques are becoming state-of-the-art in plant phenotyping. Among several parameters of plant phenotyping, growth analysis is very important for biological inference. Automating the growth analysis can result in accelerating the throughput in crop production. This thesis contributes to the automation of plant growth analysis.

First, we present a novel system for automated and non-invasive/non-contact ...


Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal 2017 University of New Orleans, New Orleans

Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal

University of New Orleans Theses and Dissertations

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a ...


Comparison Of Visual Datasets For Machine Learning, Kent Gauen, Ryan Dailey, John Laiman, Yuxiang Zi, Nirmal Asokan, Yung-Hsiang Lu, George K. Thiruvathukal, Mei-Ling Shyu, Shu-Ching Chen 2017 Purdue University

Comparison Of Visual Datasets For Machine Learning, Kent Gauen, Ryan Dailey, John Laiman, Yuxiang Zi, Nirmal Asokan, Yung-Hsiang Lu, George K. Thiruvathukal, Mei-Ling Shyu, Shu-Ching Chen

Computer Science: Faculty Publications and Other Works

One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new ...


Effects Of Anthropomorphism On Trust In Human-Robot Interaction, Keith R. MacArthur, William T. Shugars, Tracy L. Sanders, Peter A. Hancock 2017 University of Central Florida

Effects Of Anthropomorphism On Trust In Human-Robot Interaction, Keith R. Macarthur, William T. Shugars, Tracy L. Sanders, Peter A. Hancock

Keith Reid MacArthur

Robots are being integrated into everyday use, making the evaluation of trust in human-robot interactions (HRI) important to ensure their acceptance and correct usage (Lee & See, 2004; Parasuraman & Riley, 1997). Goetz, Kiesler, and Powers (2003) found that participants preferred robots with an anthropomorphic appearance appropriate for the social context of the task. This preference for robots with human-like appearance may be indicative of increased levels of trust and therefore, the present research evaluates the effects of anthropomorphism on trust.
Eighteen participants (Mage = 34.22, SDage = 10.55, n = 8 male, n =10 female) with subject matter expertise in ...


Applying Machine Learning To Computational Chemistry: Can We Predict Molecular Properties Faster Without Compromising Accuracy?, Hanjing Xu, Pradeep Gurunathan, Lyudmila Slipchenko 2017 Purdue University

Applying Machine Learning To Computational Chemistry: Can We Predict Molecular Properties Faster Without Compromising Accuracy?, Hanjing Xu, Pradeep Gurunathan, Lyudmila Slipchenko

The Summer Undergraduate Research Fellowship (SURF) Symposium

Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions using quantum-mechanical formulations. Our group has proposed previously that the effective fragment potential (EFP) method could serve as an efficient alternative to solve this problem. However, one of the computational bottlenecks of the EFP method is obtaining parameters for each molecule/fragment in the system, before the actual EFP simulations can be carried out. Here we present ...


Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi 2017 University of Louisville

Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi

Electronic Theses and Dissertations

While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race ...


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