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Full-Text Articles in Engineering
A Generalized Machine Learning-Based Classifier Considering Cost-Effective Features For Automated Fault Detection And Diagnosis (Afdd) Of Packaged Rooftop Units, Md Rasel Uddin
Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–
Packaged rooftop units (RTUs) are widely used for space conditioning in commercial buildings and manufacturing facilities. The typical soft faults related to RTUs degrade the system's performance in terms of cooling capacity, power consumption, and Coefficient of Performance (COP), detrimentally affecting both the equipment and energy consumption and the environment. Previous research in soft fault detection for rooftop units lacked classifier validation using lab and field data, developing a generalizable algorithm, and analyzing its performance across varying fault intensities. Using a simulated data library for multiple rooftop units, this study proposes a machine-learning classifier with a reduced set of 9 …
The Stability Of Pre-Enrolment Prediction Of Academic Achievement: Criterion-Referencing Versus Norm-Referencing, Jolan Hanssens, Carolien Van Soom, Greet Langie
The Stability Of Pre-Enrolment Prediction Of Academic Achievement: Criterion-Referencing Versus Norm-Referencing, Jolan Hanssens, Carolien Van Soom, Greet Langie
Research Papers
Positioning tests are organized in Flanders for prospective STEM students. They provide a low-stakes opportunity to assess their level of starting competences before enrolment. Predictive validity for subsequent academic achievement is an important quality measure of these positioning tests. However, the content of the tests varies over the years. This could be problematic for making accurate predictions based on data from previous years. Therefore, the objective of this study is to compare the stability over time of the predictions of academic achievement using either criterionreferenced (absolute grading) or norm-referenced (relative grading) positioning test grades of engineering and science students. Comparisons …
Assessment Of E-Senses Performance Through Machine Learning Models For Colombian Herbal Teas Classification, Jeniffer Katerine Carrillo, Cristhian Manuel Durán, Juan Martin Cáceres, Carlos Alberto Cuastumal, Jordana Ferreira, José Ramos, Brian Bahder, Martin Oates, Antonio Ruiz
Assessment Of E-Senses Performance Through Machine Learning Models For Colombian Herbal Teas Classification, Jeniffer Katerine Carrillo, Cristhian Manuel Durán, Juan Martin Cáceres, Carlos Alberto Cuastumal, Jordana Ferreira, José Ramos, Brian Bahder, Martin Oates, Antonio Ruiz
CCE Faculty Articles
This paper describes different E-Senses systems, such as Electronic Nose, Electronic Tongue, and Electronic Eyes, which were used to build several machine learning models and assess their performance in classifying a variety of Colombian herbal tea brands such as Albahaca, Frutos Verdes, Jaibel, Toronjil, and Toute. To do this, a set of Colombian herbal tea samples were previously acquired from the instruments and processed through multivariate data analysis techniques (principal component analysis and linear discriminant analysis) to feed the support vector machine, K-nearest neighbors, decision trees, naive Bayes, and random forests algorithms. The results of the E-Senses were validated using …
Machine-Learning-Based Head Impact Subtyping Based On The Spectral Densities Of The Measurable Head Kinematics, Xianghao Zhan, Yiheng Li, Yuzhe Liu, Nicholas J. Cecchi, Samuel J. Raymond, Zhou Zhou, Hossein Vahid Alizadeh, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
Machine-Learning-Based Head Impact Subtyping Based On The Spectral Densities Of The Measurable Head Kinematics, Xianghao Zhan, Yiheng Li, Yuzhe Liu, Nicholas J. Cecchi, Samuel J. Raymond, Zhou Zhou, Hossein Vahid Alizadeh, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo
Articles
Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. Methods: Data was analyzed from 3,262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, …
Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo
Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo
Articles
Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …
Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner
Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner
Electrical & Computer Engineering Faculty Publications
This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …