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Full-Text Articles in Engineering
Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi
Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi
Graduate Theses, Dissertations, and Problem Reports
One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and …
Development Of A Machine Learning Model To Characterize The Performance Of A Selective Catalytic Reduction On Filter After-Treatment System For A Heavy-Duty Diesel Engine, Samuel A. Okeleye
Development Of A Machine Learning Model To Characterize The Performance Of A Selective Catalytic Reduction On Filter After-Treatment System For A Heavy-Duty Diesel Engine, Samuel A. Okeleye
Graduate Theses, Dissertations, and Problem Reports
Particulate matter (PM) and Oxides of Nitrogen (NOx) are the major pollutants in diesel engines, an attempt to control one leads to an increase in the other, a phenomenon known as PM-NOx trade-off in diesel engine emission control. Currently, these two pollutants are controlled by the Diesel Particulate Filter (DPF) and the Selective Catalytic Reduction (SCR) after-treatment system respectively, in addition to the Diesel Oxidation Catalyst (DOC) which helps to provide 1:1 split of NO/NO2 and helps with raising exhaust gas temperatures. Today, heavy-duty diesel engines feature a DPF, a primary SCR and a secondary SCR. Despite this complex …
Dynamic Hvac Energy Management Using Commercial Building Occupancy Metrics & Neural Networks, Krishna Chaitanya Jagadeesh Simma
Dynamic Hvac Energy Management Using Commercial Building Occupancy Metrics & Neural Networks, Krishna Chaitanya Jagadeesh Simma
Civil Engineering ETDs
With the rise of technology use in buildings, it is now possible to collect data that can be used to improve building energy consumption. One factor that has significant impact on building energy consumption is occupancy. Recent studies have shown promising results in obtaining occupancy information from existing infrastructure such as WiFi router networks. However, these existing frameworks require additional investments through software upgrades, added infrastructure, computational resources, and may raise occupant privacy concerns. Additionally, with occupant thermal comfort statistics being lower than ASHAREA specified standards, a novel approach for indoor climate control is needed. To address the limitations in …
Artificial Intelligence Approaches For Structural Health Monitoring Of Aerospace Structures, Kimberly A. Cardillo
Artificial Intelligence Approaches For Structural Health Monitoring Of Aerospace Structures, Kimberly A. Cardillo
Theses and Dissertations
Structural health monitoring (SHM) and non-destructive evaluation (NDE) have been a significant research topic to help with damage detection in aerospace structures. SHM and NDE techniques are based on extracting damage sensitive features to determine the criticality of damage and lifetime of a structure. Acoustic emission (AE) signal detection is an important technique in SHM and NDE especially for fatigue crack growth. AE signals for thin aerospace structures consist of ultrasonic guided Lamb waves that propagate through the structure. This thesis focuses on AE signal repeatability, load at which AE signals occur, feature extraction, artificial intelligence and electro-mechanical impedance of …
Short Term Energy Forecasting For A Microgird Load Using Lstm Rnn, Akhil Soman
Short Term Energy Forecasting For A Microgird Load Using Lstm Rnn, Akhil Soman
Masters Theses
Decentralization of the electric grid can increase resiliency (during natural disasters) and can reduce T&D energy losses and emissions. Microgrids and DERs can enable this to happen. It is important to optimally control microgrids and DERs to extract the greatest economic, environmental and resiliency benefits. This is enabled by robust forecasting to optimally control loads and energy sources. An integral part of microgrid control is power side and load side demand forecasting.
In this thesis, we look at the ability of a powerful neural network algorithm to forecast the load side demand for a microgrid using the UMass campus as …
Development Of Horizontal Axis Hydrokinetic Turbine Using Experimental And Numerical Approaches, Abdulaziz Abutunis
Development Of Horizontal Axis Hydrokinetic Turbine Using Experimental And Numerical Approaches, Abdulaziz Abutunis
Doctoral Dissertations
“Hydrokinetic energy conversion systems (HECSs) are emerging as viable solutions for harnessing the kinetic energy in river streams and tidal currents due to their low operating head and flexible mobility. This study is focused on the experimental and numerical aspects of developing an axial HECS for applications with low head ranges and limited operational space. In Part I, blade element momentum (BEM) and neural network (NN) models were developed and coupled to overcome the BEM’s inherent convergence issues which hinder the blade design process. The NNs were also used as a multivariate interpolation tool to estimate the blade hydrodynamic characteristics …
An Application Of Clustering And Cluster Update Methods To Boiler Sensor Prediction And Case-Based-Reasoning To Boiler Repair, Timothy Edward Rooney
An Application Of Clustering And Cluster Update Methods To Boiler Sensor Prediction And Case-Based-Reasoning To Boiler Repair, Timothy Edward Rooney
Theses and Dissertations
Driven by demand from both consumers and manufacturers alike, Internet of Things (IoT)
capabilities are being built into more products. Consumers want more control and access to their
devices, while manufacturers can find data gathered from IoT-capable products invaluable. In
this thesis, we use data from a growing fleet of IoT-connected boilers in the residential, lightcommercial, and medium-commercial ranges to demonstrate a framework for cluster initialization
and updating. We compare two methods of dynamically updating clusters: a sequential method
inspired by sequential K-means clustering and a cohesion-based method called DYNC. A predictive
artificial neural network system demonstrates the effectiveness of …
Manufacturing Feature Recognition With 2d Convolutional Neural Networks, Yang Shi
Manufacturing Feature Recognition With 2d Convolutional Neural Networks, Yang Shi
Theses and Dissertations
Feature recognition is a critical sub-discipline of CAD/CAM that focuses on the design and implementation of algorithms for automated identification of manufacturing features. The development of feature recognition methods has been active for more than two decades for academic research. However, in this domain, there are still many drawbacks that hinder its practical applications, such as lack of robustness, inability to learn, limited domain of features, and computational complexity. The most critical one is the difficulty of recognizing interacting features, which arises from the fact that feature interactions change the boundaries that are indispensable for characterizing a feature. This research …
Approximate Dynamic Programming Based Solutions For Fixed-Final-Time Optimal Control And Optimal Switching, Ali Heydari
Approximate Dynamic Programming Based Solutions For Fixed-Final-Time Optimal Control And Optimal Switching, Ali Heydari
Doctoral Dissertations
"Optimal solutions with neural networks (NN) based on an approximate dynamic programming (ADP) framework for new classes of engineering and non-engineering problems and associated difficulties and challenges are investigated in this dissertation. In the enclosed eight papers, the ADP framework is utilized for solving fixed-final-time problems (also called terminal control problems) and problems with switching nature. An ADP based algorithm is proposed in Paper 1 for solving fixed-final-time problems with soft terminal constraint, in which, a single neural network with a single set of weights is utilized. Paper 2 investigates fixed-final-time problems with hard terminal constraints. The optimality analysis of …
Nonlinear Control Concepts For A Ua, Vijayakumar Janardhan, Derek Schmitz, S. N. Balakrishnan
Nonlinear Control Concepts For A Ua, Vijayakumar Janardhan, Derek Schmitz, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A reconfigurable flight control method is developed to be implemented on an Unmanned Aircraft (UA), a thirty percent scale model of the Cessna 150. This paper presents the details of the UA platform, system identification, reconfigurable controller design, development, and implementation on the UA to analyze the performance metrics. A Crossbow Inertial Measurement Unit provides the roll, pitch, and yaw accelerations and rates along with the roll and pitch. The 100-400 mini-air data boom from SpaceAge Control provides the airspeed, altitude, angle of attack, and the side slip angles. System identification is accomplished by commanding preprogrammed inputs to the control …
Modeling And Control Of Re-Entry Heat Transfer Problem Using Neural Networks, Katie Grantham, Radhakant Padhi, S. N. Balakrishnan, Dwight C. Look
Modeling And Control Of Re-Entry Heat Transfer Problem Using Neural Networks, Katie Grantham, Radhakant Padhi, S. N. Balakrishnan, Dwight C. Look
Engineering Management and Systems Engineering Faculty Research & Creative Works
A nonlinear optimal re-entry temperature control problem is solved using single network adaptive critic (SNAC) technique. The nonlinear model developed and used accounts for conduction, convection and radiation at high temperature, represents the dynamics of heat transfer in a cooling fin for an object re-entering the earth's atmosphere. Simulation results demonstrate that the control synthesis technique presented is very effective in obtaining a desired temperature profile over a wide envelope of initial temperature distribution.
Proper Orthogonal Decomposition Based Feedback Optimal Control Synthesis Of Distributed Parameter Systems Using Neural Networks, Radhakant Padhi, S. N. Balakrishnan
Proper Orthogonal Decomposition Based Feedback Optimal Control Synthesis Of Distributed Parameter Systems Using Neural Networks, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A new method for optimal control design of distributed parameter systems is presented in this paper. The concept of proper orthogonal decomposition is used for the model reduction of distributed parameter systems to form a reduced order lumped parameter problem. The optimal control problem is then solved in the time domain, in a state feedback sense, following the philosophy of ''adaptive critic'' neural networks. The control solution is then mapped back to the spatial domain using the same basis functions. Numerical simulation results are presented for a linear and nonlinear one-dimensional heat equation problem in an infinite time regulator framework.
Numerical Modeling And Optimization Of Waterjet Based Surface Decontamination, Konstantin Babets
Numerical Modeling And Optimization Of Waterjet Based Surface Decontamination, Konstantin Babets
Dissertations
The mission of this study is to investigate the high-pressure waterjet based surface decontamination. Our specific objective is to develop a practical procedure for selection of process conditions at given constraints and available knowledge. This investigation is expected to improve information processing in the course of material decontamination and assist in the implementation of the waterjet decontamination technology into practice. The development of a realistic procedure for processing of a chaotic and non-accurate information constitutes the main accomplishment of this study.
The research involved acquisition of representative information about removal of brittle, elastic and viscous deposits. As a result an …
An Optimal Control Based Treatment Strategy For Parturient Paresis Using Neural Networks, Radhakant Padhi, S. N. Balakrishnan
An Optimal Control Based Treatment Strategy For Parturient Paresis Using Neural Networks, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
An optimal online feedback treatment strategy is developed for the parturient paresis of cows, based on nonlinear optimal control theory. A limitation in the development of an existing mathematical model for calcium homeostasis is addressed and the model is extended to incorporate control inputs. An optimal feedback controller is synthesized for the nonlinear system using neural networks. Though the main aim of this paper is to solve the biomedical control problem, the methodology presented in this paper is a general computational tool, which can be applied to solve a fairly general class nonlinear optimal control problems.
Convergence Analysis Of Adaptive Critic Based Optimal Control, S. N. Balakrishnan, Xin Liu
Convergence Analysis Of Adaptive Critic Based Optimal Control, S. N. Balakrishnan, Xin Liu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Adaptive critic based neural networks have been found to be powerful tools in solving various optimal control problems. The adaptive critic approach consists of two neural networks which output the control values and the Lagrangian multipliers associated with optimal control. These networks are trained successively and when the outputs of the two networks are mutually consistent and satisfy the differential constraints, the controller network output produces optimal control. In this paper, we analyze the mechanics of convergence of the network solutions. We establish the necessary conditions for the network solutions to converge and show that the converged solution is optimal.
Online Identification And Control Of Aerospace Vehicles Using Recurrent Networks, Zhenning Hu, S. N. Balakrishnan
Online Identification And Control Of Aerospace Vehicles Using Recurrent Networks, Zhenning Hu, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Methods for estimating the aerospace system parameters and controlling them through two neural networks are presented in this study. We equate the energy function of Hopfield neural network to integral square of errors in the system dynamics and extract the parameters of a system. Parameter convergence is proved. For control, we equate the equilibrium status of a "modified" Hopfield neural network to the steady state Riccati solution with the system parameters as inputs. Through these two networks, we present the online identification and control of an aircraft using its nonlinear dynamics.
Adaptive Critic Based Neurocontroller For Autolanding Of Aircraft With Varying Glideslopes, Gaurav Saini, S. N. Balakrishnan
Adaptive Critic Based Neurocontroller For Autolanding Of Aircraft With Varying Glideslopes, Gaurav Saini, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this paper, adaptive critic based neural networks have been used to design a controller for a benchmark problem in aircraft autolanding. The adaptive critic control methodology comprises successive adaptations of two neural networks, namely `action' and `critic' networks until closed loop optimal control is achieved. The autolanding problem deals with longitudinal dynamics of an aircraft which is to be landed in a specified touchdown region in the presence of wind disturbances and gusts using elevator deflection as the control for glideslope and flare modes. The performance of the neurocontroller is compared to that of a conventional PID controller. Neurocontroller's …
Window-On-Europe, S. R. Leclair, A. G. Jackson
Window-On-Europe, S. R. Leclair, A. G. Jackson
Mechanical and Materials Engineering Faculty Publications
This report covers a three week visit to the following locations: 1.) A.A. Baikov Institute of Metallurgy of the Russian Academy of Science, Moscow, Russia; 2.) Institute for Applied Informatics, of the Ukrainian Academy of Science, Kiev, Ukraine; 3.)Engineering Design Center.Cambridge University, Cambridge, England- and 4) Artificial Intelligence Department, University of Edinburgh, Edinburgh, Scotland. The visits involved sites whose current research involved either empirical methods for the design of materials and resultant products and/or the development of empirical methods of potential benefit in automating empirical research of materials and/or processing. The report address, for each site, 1) a brief overview …
A New Neural Architecture For Homing Missile Guidance, S. N. Balakrishnan, Victor Biega
A New Neural Architecture For Homing Missile Guidance, S. N. Balakrishnan, Victor Biega
Mechanical and Aerospace Engineering Faculty Research & Creative Works
We present a new neural architecture which imbeds dynamic programming solutions to solve optimal target-intercept problems. They provide feedback guidance solutions, which are optimal with any initial conditions and time-to-go, for a 2D scenario. The method discussed in this study determines an optimal control law for a system by successively adapting two networks - an action and a critic network. This method determines the control law for an entire range of initial conditions; it simultaneously determines and adapts the neural networks to the optimal control policy for both linear and nonlinear systems. In addition, it is important to know that …
Planning And Control Of A Robotic Manipulator Using Neural Networks, Xavier J. R. Avula, Heng Ma, Anil Malkani, Jay-Shinn Tsai, Luis C. Rabelo
Planning And Control Of A Robotic Manipulator Using Neural Networks, Xavier J. R. Avula, Heng Ma, Anil Malkani, Jay-Shinn Tsai, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
An architecture which utilizes two artificial neural systems for planning and control of a robotic arm is presented. The first neural network system participates in the trajectory planning and the motion decision-making process. The second neural network system provides the correct sequence of control actions with a high accuracy due to the utilization of an unsupervised/supervised neural network scheme. The utilization of a hybrid hierarchical/distributed organization, supervised/unsupervised learning models, and forward modeling yielded an architecture with capabilities of high level functionality.
Moving Object Recognition And Guidance Of Robots Using Neural Networks, Abhijit Neogy, S. N. Balakrishnan, Cihan H. Dagli
Moving Object Recognition And Guidance Of Robots Using Neural Networks, Abhijit Neogy, S. N. Balakrishnan, Cihan H. Dagli
Mechanical and Aerospace Engineering Faculty Research & Creative Works
The design of a robust guidance system for a robot is discussed. The two major tasks for this guidance system are the online recognition of a moving object invariant to rotation and translation, and tracking the moving object using a neural-network-driven vision system. This system included computer software ported to the IBM PC and interfaced with an IBM 7535 robot. The operation of this guidance system involved recognition of a moving object and the ability to track it till the robot and effector was in close proximity of the object. It was found that the robot was able to track …
Hierarchical Neurocontroller Architecture For Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Hierarchical Neurocontroller Architecture For Robotic Manipulation, Xavier J. R. Avula, Luis C. Rabelo
Chemical and Biochemical Engineering Faculty Research & Creative Works
A hierarchical neurocontroller architecture consisting of two artificial neural network systems for the manipulation of a robotic arm is presented. The higher-level network system participates in the delineation of the robot arm workspace and coordinates transformation and the motion decision-making process. The lower-level network provides the correct sequence of control actions. A straightforward example illustrates the architecture''s capabilities, including speed, adaptability, and computational efficiency