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Engineering

2019

Machine learning

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Full-Text Articles in Physical Sciences and Mathematics

An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza Dec 2019

An Application Of Deep Learning Models To Automate Food Waste Classification, Alejandro Zachary Espinoza

Dissertations and Theses

Food wastage is a problem that affects all demographics and regions of the world. Each year, approximately one-third of food produced for human consumption is thrown away. In an effort to track and reduce food waste in the commercial sector, some companies utilize third party devices which collect data to analyze individual contributions to the global problem. These devices track the type of food wasted (such as vegetables, fruit, boneless chicken, pasta) along with the weight. Some devices also allow the user to leave the food in a kitchen container while it is weighed, so the container weight must also …

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Chaotic Time Series Prediction Based On Gaussian Processes Mixture, Zhenjie Feng, Fan Yu Dec 2019

Chaotic Time Series Prediction Based On Gaussian Processes Mixture, Zhenjie Feng, Fan Yu

Journal of System Simulation

Abstract: Aiming at the problem that the existing learning algorithms of Gaussian processes mixture (GPM) model, such as Markov Chain Monte Carlo (MCMC), variation or leave one out, have high computational complexity, a hidden variables posterior hard-cut iterative training algorithm is proposed, which simplifies the training process of the model. The GPM model based on the proposed algorithm is applied to chaotic time series prediction. The effects of embedding dimension, time delay, learning sample number, and testing sample numbers on predictive ability are discussed. It is demonstrated by the experimental results that the prediction of the GPM model is more …

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Characteristic Index Digging Of Combat Sos Capability Based On Machine Learning, Yongli Yang, Xiaofeng Hu, Rong Ming, Xiaojing Yin, Wenxiang Wang Dec 2019

Characteristic Index Digging Of Combat Sos Capability Based On Machine Learning, Yongli Yang, Xiaofeng Hu, Rong Ming, Xiaojing Yin, Wenxiang Wang

Journal of System Simulation

Abstract: Aiming at the two difficulties in characteristic index digging of combat system of systems (CSoS), namely operation data generation and digging method selection, this paper proposes a new digging method, that is, using the simulation testbed to generate operation data, then adopting the machine learning to dig characteristic index. Two methods of characteristic index digging based on machine learning are researched: (1) the method based on network convergence, divides the communities for fundamental indexes based on their relationship, and obtains the characteristic indexes by principal component analysis (PCA); this method is applied to dig the characteristic indexes of …

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Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque Dec 2019

Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …

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Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson Oct 2019

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 …

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Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan Aug 2019

Machine Learning In Support Of Electric Distribution Asset Failure Prediction, Robert D. Flamenbaum, Thomas Pompo, Christopher Havenstein, Jade Thiemsuwan

SMU Data Science Review

In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence …

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Computational Studies Of Thermal Properties And Desalination Performance Of Low-Dimensional Materials, Yang Hong Aug 2019

Computational Studies Of Thermal Properties And Desalination Performance Of Low-Dimensional Materials, Yang Hong

Department of Chemistry: Dissertations, Theses, and Student Research

During the last 30 years, microelectronic devices have been continuously designed and developed with smaller size and yet more functionalities. Today, hundreds of millions of transistors and complementary metal-oxide-semiconductor cells can be designed and integrated on a single microchip through 3D packaging and chip stacking technology. A large amount of heat will be generated in a limited space during the operation of microchips. Moreover, there is a high possibility of hot spots due to non-uniform integrated circuit design patterns as some core parts of a microchip work harder than other memory parts. This issue becomes acute as stacked microchips get …

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Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga Jun 2019

Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga

LSU Master's Theses

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different …

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Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm Jun 2019

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

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Exploring The Behavior Repertoire Of A Wireless Vibrationally Actuated Tensegrity Robot, Zongliang Ji Jun 2019

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.

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Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors, Param Bidja May 2019

Depressiongnn: Depression Prediction Using Graph Neural Network On Smartphone And Wearable Sensors, Param Bidja

Honors Scholar Theses

Depression prediction is a complicated classification problem because depression diagnosis involves many different social, physical, and mental signals. Traditional classification algorithms can only reach an accuracy of no more than 70% given the complexities of depression. However, a novel approach using Graph Neural Networks (GNN) can be used to reach over 80% accuracy, if a graph can represent the depression data set to capture differentiating features. Building such a graph requires 1) the definition of node features, which must be highly correlated with depression, and 2) the definition for edge metrics, which must also be highly correlated with depression. In …

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Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher May 2019

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher

Student Research Symposium

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

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Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre May 2019

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis has …

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A Data-Driven Approach For Modeling Agents, Hamdi Kavak Apr 2019

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 …

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Confidence Inference In Defensive Cyber Operator Decision Making, Graig S. Ganitano Mar 2019

Confidence Inference In Defensive Cyber Operator Decision Making, Graig S. Ganitano

Theses and Dissertations

Cyber defense analysts face the challenge of validating machine generated alerts regarding network-based security threats. Operations tempo and systematic manpower issues have increased the importance of these individual analyst decisions, since they typically are not reviewed or changed. Analysts may not always be confident in their decisions. If confidence can be accurately assessed, then analyst decisions made under low confidence can be independently reviewed and analysts can be offered decision assistance or additional training. This work investigates the utility of using neurophysiological and behavioral correlates of decision confidence to train machine learning models to infer confidence in analyst decisions. Electroencephalography …

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Spectral Clustering For Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series, Logan Blakely Jan 2019

Spectral Clustering For Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series, Logan Blakely

Dissertations and Theses

The increasing demand for and prevalence of distributed energy resources (DER) such as solar power, electric vehicles, and energy storage, present a unique set of challenges for integration into a legacy power grid, and accurate models of the low-voltage distribution systems are critical for accurate simulations of DER. Accurate labeling of the phase connections for each customer in a utility model is one area of grid topology that is known to have errors and has implications for the safety, efficiency, and hosting capacity of a distribution system. This research presents a methodology for the phase identification of customers solely using …

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Assessment Of Post-Wildfire Debris Flow Occurrence Using Classifier Tree, Priscilla Addison, Thomas Oommen, Qiuying Sha Jan 2019

Assessment Of Post-Wildfire Debris Flow Occurrence Using Classifier Tree, Priscilla Addison, Thomas Oommen, Qiuying Sha

Michigan Tech Publications

Besides the dangers of an actively burning wildfire, a plethora of other hazardous consequences can occur afterwards. Debris flows are among the most hazardous of these, being known to cause fatalities and extensive damage to infrastructure. Although debris flows are not exclusive to fire affected areas, a wildfire can increase a location’s susceptibility by stripping its protective covers like vegetation and introducing destabilizing factors such as ash filling soil pores to increase runoff potential. Due to the associated dangers, researchers are developing statistical models to isolate susceptible locations. Existing models predominantly employ the logistic regression algorithm; however, previous studies have …

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Intelligent Diagnosis Of Aircraft Electrical Faults Based On Rmbp Neural Network, Lishan Jia, Zhe Liu, Sun Yi Jan 2019

Intelligent Diagnosis Of Aircraft Electrical Faults Based On Rmbp Neural Network, Lishan Jia, Zhe Liu, Sun Yi

Journal of System Simulation

Abstract: To the characteristics of multiple properties, hard to remove and high cost of time and manpower of aircraft electrical faults maintenance in aircraft maintenance of civil aviation, construction of intelligent aircraft electrical faults diagnosis system using RMBP neural network is proposed. RMBP algorithm is used to study sample data in the intelligent faults diagnosis system as it can overcome the faults of long time of convergence and easy to go into local minima of common BP algorithm, and is suitable for training large-scale neural network,. Experience data are collected, samples are made, samples training and experiment are carried out. …

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Research Of Nonlinear Time Series Prediction Method For Motion Capture, Tianyu Huang, Yunying Guo Jan 2019

Research Of Nonlinear Time Series Prediction Method For Motion Capture, Tianyu Huang, Yunying Guo

Journal of System Simulation

Abstract: In this paper, we study the nonlinear time series prediction method for action capture. A prediction method based on the capture data is studied and implemented by analyzing human motion data to solve the data loss and correction problem caused by sensor failure. Based on this research purpose, the simulation experiment assumes that a sensor in the sequence of actions fails, then uses eight kinds of machine learning methods, and evaluates them with six indexes. The prediction results of different methods are compared and the predicted motions are visualized. Through the experiments, data prediction accuracy by random forest, decision …

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Optimizing Control Of Total Heat Supply Based On Machine Learning, Li Qi, Xingqi Hu, Jianmin Zhao Jan 2019

Optimizing Control Of Total Heat Supply Based On Machine Learning, Li Qi, Xingqi Hu, Jianmin Zhao

Journal of System Simulation

Abstract: The central heating system has complex structure, along with the characteristics of hysteresis, strong coupling and nonlinear. Contraposing the problem that the process is difficult to be identified and controlled by the mechanism modeling, an optimal control method of heat source total heat production based on machine learning is proposed. The heat source model of central heating system is established by BP neural network and long short-term memory neural network. Under the premise of meeting the demand of heating quality, with the total energy consumption as the optimization objective, the optimal control sequence of water supply temperature and water …

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Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli Jan 2019

Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli

Turkish Journal of Electrical Engineering and Computer Sciences

Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (GridSearch), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn package in the Python language. The proposed model uses a multilayer …

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An Improved Tree Model Based On Ensemble Feature Selection For Classification, Chandralekha M, Shenbagavadivu N Jan 2019

An Improved Tree Model Based On Ensemble Feature Selection For Classification, Chandralekha M, Shenbagavadivu N

Turkish Journal of Electrical Engineering and Computer Sciences

Researchers train and build specific models to classify the presence and absence of a disease and the accuracy of such classification models is continuously improved. The process of building a model and training depends on the medical data utilized. Various machine learning techniques and tools are used to handle different data with respect to disease types and their clinical conditions. Classification is the most widely used technique to classify disease and the accuracy of the classifier largely depends on the attributes. The choice of the attribute largely affects the diagnosis and performance of the classifier. Due to growing large volumes …

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Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee Jan 2019

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 …

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Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar Jan 2019

Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar

Legacy Theses & Dissertations (2009 - 2024)

Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …

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Identifying Criminal Organizations From Their Social Network Structures, Muhammet Serkan Çi̇nar, Burkay Genç, Hayri̇ Sever Jan 2019

Identifying Criminal Organizations From Their Social Network Structures, Muhammet Serkan Çi̇nar, Burkay Genç, Hayri̇ Sever

Turkish Journal of Electrical Engineering and Computer Sciences

Identification of criminal structures within very large social networks is an essential security feat. By identifying such structures, it may be possible to track, neutralize, and terminate the corresponding criminal organizations before they act. We evaluate the effectiveness of three different methods for classifying an unknown network as terrorist, cocaine, or noncriminal. We consider three methods for the identification of network types: evaluating common social network analysis metrics, modeling with a decision tree, and network motif frequency analysis. The empirical results show that these three methods can provide significant improvements in distinguishing all three network types. We show that these …

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Improving Anomaly Detection In Bgp Time-Series Data By New Guide Features And Moderated Feature Selection Algorithm, Mahmoud Hashem, Ahmed Bashandy, Samir Shaheen Jan 2019

Improving Anomaly Detection In Bgp Time-Series Data By New Guide Features And Moderated Feature Selection Algorithm, Mahmoud Hashem, Ahmed Bashandy, Samir Shaheen

Turkish Journal of Electrical Engineering and Computer Sciences

The Internet infrastructure relies on the Border Gateway Protocol (BGP) to provide essential routing information where abnormal routing behavior impairs global Internet connectivity and stability. Hence, employing anomaly detection algorithms is important for improving the performance of BGP routing protocol. In this paper, we propose two algorithms; the first is the guide feature generator (GFG), which generates guide features from traditional features in BGP time-series data using moving regression in combination with smoothed moving average. The second is a modified random forest feature selection algorithm which is employed to automatically select the most dominant features (ASMDF). Our mechanism shows that …

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Dop: Discover Objects And Paths, A Model For Automated Navigation Andselection In Virtual Environments, Muhammad Raees, Sehat Ullah Jan 2019

Dop: Discover Objects And Paths, A Model For Automated Navigation Andselection In Virtual Environments, Muhammad Raees, Sehat Ullah

Turkish Journal of Electrical Engineering and Computer Sciences

Navigation and selection are the two interaction tasks often needed for the manipulation of an object in a synthetic world. An interface that supports automatic navigation and selection may increase the realism of a virtual reality (VR) system. Such an engrossing interface of a VR system is possible by incorporating machine learning (ML) into the realm of the virtual environment (VE). The use of intelligence in VR systems, however, is a milestone yet to be achieved to make seamless realism in a VE possible. To improve the believability of an intelligent virtual agent (IVA), this research work presents DOP (Discover …

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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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A Hybrid Feature-Selection Approach For Finding The Digital Evidence Of Web Application Attacks, Mohammed Babiker, Eni̇s Karaarslan, Yaşar Hoşcan Jan 2019

A Hybrid Feature-Selection Approach For Finding The Digital Evidence Of Web Application Attacks, Mohammed Babiker, Eni̇s Karaarslan, Yaşar Hoşcan

Turkish Journal of Electrical Engineering and Computer Sciences

The most critical challenge of web attack forensic investigations is the sheer amount of data and level of complexity. Machine learning technology might be an efficient solution for web attack analysis and investigation. Consequently, machine learning applications have been applied in various areas of information security and digital forensics, and have improved over time. Moreover, feature selection is a crucial step in machine learning; in fact, selecting an optimal feature subset could enhance the accuracy and performance of the predictive model. To date, there has not been an adequate approach to select optimal features for the evidence of web attack. …

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Energy Saving Scheduling In A Fog-Based Iot Application By Bayesian Task Classification Approach, Gholamreza Heydari, Dadmehr Rahbari, Mohsen Nickray Jan 2019

Energy Saving Scheduling In A Fog-Based Iot Application By Bayesian Task Classification Approach, Gholamreza Heydari, Dadmehr Rahbari, Mohsen Nickray

Turkish Journal of Electrical Engineering and Computer Sciences

The Internet of things increases information volume in computer networks and the concept of fog will help us to control this volume more efficiently. Scheduling resources in such an environment would be an NP-Hard problem. This article has studied the concept of scheduling in fog with Bayesian classification which could be applied to gain the task requirements like the processing ones. After classification, virtual machines will be created in accordance with the predicted requirements. The ifogsim simulator has been applied to study our fog-based Bayesian classification scheduling (FBCS) method performance in an EEG tractor application. Algorithms have been evaluated on …

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