Engineering Commons^™

An Investigation Of Information Structures In Dna, Joel Mohrmann

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

The information-containing nature of the DNA molecule has been long known and observed. One technique for quantifying the relationships existing within the information contained in DNA sequences is an entity from information theory known as the average mutual information (AMI) profile. This investigation sought to use principally the AMI profile along with a few other metrics to explore the structure of the information contained in DNA sequences.

Treating DNA sequences as an information source, several computational methods were employed to model their information structure. Maximum likelihood and maximum a posteriori estimators were used to predict missing bases in DNA sequences. …

Synthesis Algorithm Of Control System Based On Neural Network For The Gas And Oil Process, Jasur Sevinov, Abdishukurov Maqsudovich Shavkat, A.Z. Shodiyorov

Chemical Technology, Control and Management

Automatic control systems in the oil and gas processing are considered. After extraction of natural gas condensate, management of technological parameters plays an important role in its processing. Among the parameter of regulating algorithms, control based on neural network technology was considered. These control systems are created on the basis of algorithms of neural network technology. There are several structures of neural networks according to their structure. Differences in the process of information processing of these branching structures are considered.

Synthesize A Neural Network Parameter Optimizer For An Adaptive Pid Controller, Nashvandova Gulruxsor Murot Qizi

Chemical Technology, Control and Management

Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters superstructuring. In the paper, the questions of optimization of PID-regulator parameters with application of methods of neural network technology are considered. A methodology for selecting the architecture of neural network optimizer designed to determine the tuned parameters of PID regulator is proposed. The algorithm of training of the neural network, with the set on the basis of the method of inverse gradient propagation is offered. The proposed improved PID-neural regulator allowed to provide stabilization of neural network operation and its trainability in the control loop …

Grey Wolf Optimization Algorithm-Based Robust Neural Learning Control Of Passive Torque Simulators With Predetermined Performance, Seyyed Amirhossein Saadat, Mohammad Mehdi Fateh, Javad Keighobadi

Turkish Journal of Electrical Engineering and Computer Sciences

In flight control systems, the actuators need to tolerate aerodynamic torques and continue their operations without interruption. To this end, using the simulators to test the actuators in conditions close to the real flight is efficient. On the other hand, achieving the guaranteed performance encounters some challenges and practical limitations such as unknown dynamics, external disturbances, and state constraints in reality. Thus, this article attempts to present a robust adaptive neural network learning controller equipped with a disturbance observer for passive torque simulators (PTS) with load torque constraints. The radial basis function networks (RBFNs) are employed to identify the unknown …

Research And Comparison Of Pavement Performance Prediction Based On Neural Networks And Fusion Transformer Architecture, Hui Yao, Ke Han, Yanhao Liu, Dawei Wang, Zhanping You

Michigan Tech Publications, Part 2

The decision-making process for pavement maintenance from a scientific perspective is based on accurate predictions of pavement performance. To improve the rationality of pavement performance indicators, comprehensive consideration of various influencing factors is necessary. To this end, four typical pavement performance indicators (i.e., Rutting Depth, International Roughness Index, Longitudinal Cracking, and Alligator Cracking) were predicted using the Long Term Pavement Performance (LTPP) database. Two types of data, i.e., local input variables and global input variables, were selected, and S-ANN and L-ANN models were constructed using a fully connected neural network. A comparative analysis of the predictive outcomes reveals the superior …

Neural Network-Based Fault Distance Estimation For Multi-Terminal Dc Microgrids, Mohamed Elmadawy, Abdelhady Ghanem, Sayed Abulanwar, Ahmed Shahin

Mansoura Engineering Journal

Fault distance estimation in DC microgrids is a critical issue due to the growing adoption of DC-based distribution systems. Current methods face limitations like sensitivity to system parameters and high-resistance fault detection, necessitating improved accuracy. This study proposes a neural network approach to accurately locate fault distances in multi-terminal DC microgrids. Three different structures based on backpropagation algorithms are developed and trained to estimate fault distances with high precision. These structures can handle various fault scenarios, including different fault resistances and the presence of noise. Two of the structures can predict fault distances from one side locally, achieving low error …

A Memory Efficient Deep Recurrent Q-Learning Approach For Autonomous Wildfire Surveillance, Jeremy A. Cantor

UNF Graduate Theses and Dissertations

Previous literature demonstrates that autonomous UAVs (unmanned aerial vehicles) have the po- tential to be utilized for wildfire surveillance. This advanced technology empowers firefighters by providing them with critical information, thereby facilitating more informed decision-making processes. This thesis applies deep Q-learning techniques to the problem of control policy design under the objective that the UAVs collectively identify the maximum number of locations that are under fire, assuming the UAVs can share their observations. The prohibitively large state space underlying the control policy motivates a neural network approximation, but prior work used only convolutional layers to extract spatial fire information from …

Convolution And Autoencoders Applied To Nonlinear Differential Equations, Noah Borquaye

Electronic Theses and Dissertations

Autoencoders, a type of artificial neural network, have gained recognition by researchers in various fields, especially machine learning due to their vast applications in data representations from inputs. Recently researchers have explored the possibility to extend the application of autoencoders to solve nonlinear differential equations. Algorithms and methods employed in an autoencoder framework include sparse identification of nonlinear dynamics (SINDy), dynamic mode decomposition (DMD), Koopman operator theory and singular value decomposition (SVD). These approaches use matrix multiplication to represent linear transformation. However, machine learning algorithms often use convolution to represent linear transformations. In our work, we modify these approaches to …

Simulation And Research Of Manipulator Motion Strategy Based On Adaptive Dynamic Programming, Ming Li, Qun Xu, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the difficulty of manipulator to realize high-precision motion tracking in complex and harsh environment, a strategy method based on the combination of adaptive dynamic programming (ADP) and sliding mode admittance control is proposed. The unknown environment is modeled as a linear model and based on quasi, a sliding mode admittance controller is derived to resist disturbance interference. An optimal control method that combines ADP with sliding mode admittance controller is proposed, in which the definition of R-matrix in value function is optimized and improved to further improve the tracking accuracy. The neural network based on ADP is …

Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni

Turkish Journal of Electrical Engineering and Computer Sciences

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …

Prediction Of Crack Width Of Drilling Fluid Leakage Based On Neural Network, Wang Jian, Xu Jiafang, Zhao Mifu, Wang Bowen, Wang Yahua, Chen Jie, Wang Xiaohui, Yang Gang, Ma Tengfei

Coal Geology & Exploration

During the drilling, it is difficult to select the appropriate leakage prevention and plugging methods and materials as the development of reservoir fractures is unknown. Herein, a reservoir crack width prediction method based on neural network was proposed with reference to the actual history data of wells. Firstly, the main influencing factors of reservoir fracture width were explored and ranked by correlation analysis, and seven main influencing factors, including pump pressure, drilling fluid displacement and drilling speed, were selected as the input parameters. The rate of convergence of the model was improved using the additional momentum algorithm and the variable …

Dual-Site Photoplethysmography Sensing For Noninvasive Continuous-Time Blood Pressure Monitoring Using Artificial Neural Network, Anas Mohmmad Rabab’Ah

Theses

Millions of people worldwide struggle from high blood pressure, often known as hypertension, and it is a major health concern that can lead to serious cardiovascular diseases, including heart attacks and many other consequences. Blood pressure monitoring that is reliable and accurate is crucial to the detection and management of hypertension. Although invasive techniques, such as arterial catheterization, are considered to be the most accurate means of evaluating blood pressure, they can be painful, time-consuming and carry a risk of complications.
This thesis presents the development of a real time non-invasive blood pressure monitoring system based on commercially available microcontroller …

Use Of Neural Networks In Intelligent Measurement Tools, N.R Yusupbekov, Y.Sh. Avazov, Umidjon Ruziev Phd

Chemical Technology, Control and Management

The paper presents algorithms for processing the measurement signal with the possibilities of adaptation, learning and decision making. A comparative analysis of the methods of intellectual processing of measurement data is carried out. A model of a measuring instrument for determining the structure of a neural network has been developed. The problem of error reduction due to measurement noise filtering with the use of neural networks is considered. The structure of the neural network has been developed for intelligent processing of the measurement signal and ensuring the implementation of the functions of reconfiguration, calibration, self-diagnosis and self-control. A neural network …

Stochastic Modeling Of Physical Drag Coefficient – Its Impact On Orbit Prediction And Space Traffic Management, Smriti Nandan Paul, Phillip Logan Sheridan, Richard J. Licata, Piyush M. Mehta

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Ambitious satellite constellation projects by commercial entities and the ease of access to space in recent times have led to a dramatic proliferation of low-Earth space traffic. It jeopardizes space safety and long-term sustainability, necessitating better space domain awareness (SDA). Correct modeling of uncertainties in force models and orbital states, among other things, is an essential part of SDA. For objects in the low-Earth orbit (LEO) region, the uncertainty in the orbital dynamics mainly emanate from limited knowledge of the atmospheric drag-related parameters and variables. In this paper, which extends the work by Paul et al. (2021), we develop a …

A Long-Term Funds Predictor Based On Deep Learning, Shuiyi Kuang

Electronic Theses, Projects, and Dissertations

Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, we try to find a suitable long-term predictor for the funds market by testing different kinds of neural network models, including the Long Short-Term Memory(LSTM) model with different layers, the Gated Recurrent Units(GRU) model with different layers, and the combination …

Deep Learning For Power Flow Estimation And High Impedance Fault Detection, Kun Yang

Electronic Theses and Dissertations

My thesis is divided into two parts.

The first part is: “Optimal Power Flow Estimation Using One-Dimensional Convolutional Neural Network [1]“. Optimal power flow (OPF) is an important research topic in power system operation and control decisions. Traditional OPF problems are solved through dynamic optimization with nonlinear programming techniques. For a large power system with large amounts of variables and constraints, the solving process would take a long time. This paper presents a new method to quickly estimate the OPF results using a one-dimensional convolutional neural network (1D-CNN). The OPF problem is treated as a high-dimensional mapping between the load …

Development Of A Hospital Discharge Planning System Augmented With A Neural Clinical Decision Support Engine, David Mulqueen

Dissertations

The process of discharging patients from a tertiary care hospital, is one of the key activities to ensure the efficient and effective operation of a hospital. However, the decision to discharge a patient from a hospital is complex, as it requires multiple interactions with nurses, family, consultants, health information records and doctors, which can be very time consuming and prone to error. This thesis descries how a neural network based Clinical Decision Support system can be developed, to help in the decision making process and dramatically reduce the time and effort in running the discharge process in a hospital. A …

Prediction Of Blast-Induced Ground Vibrations: A Comparison Between Empirical And Artificial-Neural-Network Approaches, Luis F. Velasquez

Theses and Dissertations--Mining Engineering

Ground vibrations are a critical factor in the rock blasting process. The instantaneous load application exerted by the gas pressure during the detonation process acts on the blasthole walls creating dynamic stresses in the adjacent rock. This triggers different sorts of stress waves, mainly divided into two categories: body and surface waves. The first comprises the P and the S waves, while the second comprises Rayleigh waves. These waves spread concentrically starting at the blast location and move along the ground surface and its interior, being attenuated as they reach further distances.

In most cases, and accepting the hypothesis that …

Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou

MSU Graduate Theses

In this study, I developed Deep Learning interatomic potentials to model a multi-phase and multi-component system of Ni-based Superalloys. The system has up to three major phase constituents, namely Gamma, Gamma Prime, and Transition-metal rich Carbide. I utilized invariant scalar-based and/or equivariant, tensor-based neural network (NN) approach as implemented in DEEPMD, NEQUIP/ALLEGRO codes, respectively, and Moment Tensor Potential (MTP). For the training and validation sets, I employed the ab-initio molecular dynamics (AIMD) trajectory results and ground state DFT calculations, including the energy, force, and virial database from highly diverse compositions, temperatures, and pressures following a “High Entropy Strategy.” The Deep …

A Machine Learning Framework For Hypersonic Vehicle Design Exploration, Atticus Beachy

Browse all Theses and Dissertations

The design of Hypersonic Vehicles (HVs) requires meeting multiple unconventional and often conflicting design requirements in a hostile, high-energy environment. The most fundamental difference between ordinary aerospace design and hypersonic flight is that the extreme conditions of hypersonic flight require parts to perform multiple functions and be tightly integrated, resulting in significant coupled effects. Critical couplings among the disciplines of aerodynamics, structures, propulsion, and thermodynamics must be investigated in the early stages of design exploration to reduce the risk of requiring major design changes and cost overruns later. In addition, due to a lack of validated test data within the …

Long Future Frame Prediction Using Optical Flow Informed Deep Neural Networks For Enhancement Of Robotic Teleoperation In High Latency Environments, Md Moniruzzaman, Alexander Rassau, Douglas Chai, Syed M. S. Islam

Research outputs 2022 to 2026

High latency in teleoperation has a significant negative impact on operator performance. While deep learning has revolutionized many domains recently, it has not previously been applied to teleoperation enhancement. We propose a novel approach to predict video frames deep into the future using neural networks informed by synthetically generated optical flow information. This can be employed in teleoperated robotic systems that rely on video feeds for operator situational awareness. We have used the image-to-image translation technique as a basis for the prediction of future frames. The Pix2Pix conditional generative adversarial network (cGAN) has been selected as a base network. Optical …

Estimating Air Pollution Levels Using Machine Learning, Srujay Rao Devaraneni

Master's Projects

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air …

Artificial Neural Network For Predicting Heat Transfer Rates In Supercritical Carbon Dioxide, Vinusha Dasarla Giri Babu

Doctoral Dissertations and Master's Theses

Supercritical carbon dioxide as a working fluid in a closed Brayton cycle is proving to be more efficient than a conventional steam-based Rankine engine. Understanding the heat transfer properties of supercritical fluids is important for the design of a working engine cycle. The thermophysical properties of supercritical fluids tend to vary non-linearly near the pseudo-critical region. Traditionally, empirical correlations are used to calculate the heat transfer coefficient. It has been shown in the literature and within our own studies that these correlations provide inaccurate predictions near the pseudo-critical line, where heat transfer may be deteriorated or enhanced, resulting from strong …

Neural Network Based Reactive Control Of Point Absorber Wave Energy Converters, Abdelmoamen Ali Nasser

Theses

The main objective of this work is to develop a neural-network-based Reactive Control (RC) system for wave energy converters. The ability to maximize the power output of WEC while maintaining operation constraints, which can be physical or thermal, is crucial to the development of deployable control strategies. Having a control method that is robust, which means it handles uncertainty and noise very well, is one of the main performance criteria in evaluating the method. Therefore, this work starts by deriving an averaged WEC model to be simulated in MATLAB/Simulink. Additionally, the concepts of resistive loading control and reactive control (approximate …

Inversion Iterative Correction Method For Estimating Shear Strength Of Rock And Soil Mass In Slope Engineering, Wei Jiang, Ye Ouyang, Jin-Zhou Yan, Zhi-Jian Wang, Li-Peng Liu

Rock and Soil Mechanics

For the slopes that have failed or deformed significantly, the shear strength of rock and soil mass is frequently inversely estimated based on a factor of safety assumed. For the slope with a sliding surface passing through multi-layer rock and soil mass, it is unreasonable to achieve this goal by blind trial. To solve this issue, back propagation (BP) neural network is constructed using shear strength of multi-layer rock and soil mass as the input, and the factor of safety of slope, the entrance and exit positions of the sliding surface obtained by Geoslope as the outputs. Then, based on …

Artificial Neural Networks And Their Applications To Intelligent Fault Diagnosis Of Power Transmission Lines, Fatemeh Mohammadi Shakiba

Dissertations

Over the past thirty years, the idea of computing based on models inspired by human brains and biological neural networks emerged. Artificial neural networks play an important role in the field of machine learning and hold the key to the success of performing many intelligent tasks by machines. They are used in various applications such as pattern recognition, data classification, stock market prediction, aerospace, weather forecasting, control systems, intelligent automation, robotics, and healthcare. Their architectures generally consist of an input layer, multiple hidden layers, and one output layer. They can be implemented on software or hardware. Nowadays, various structures with …

Identification Of Switching Operation Based On Lstm And Moe, Xiaoqing Zhang, Wanfang Xiao, Yingjie Guo, Bowen Liu, Xuesen Han, Jingwei Ma, Gao Gao, He Huang, Shihong Xia

Journal of System Simulation

Abstract: Aiming at the individual differences of different personnel in the same operation and differences of the same person in the same operation at different times, a switching operation recognition model(MoE-LSTM) based on Mixture of experts model (MOE) and long short-term memory network(LSTM) is proposed. Based on MoE, LSTM is integrated to learn the feature distribution of different sources data. The acceleration data is collected to build the switching operation dataset and the action sequence is segmented and aligned based on sliding window. The action sequence is input to MoE-LSTM, and the temporal dependencies of different actions are independently learned …

In-Situ Infrared Thermographic Inspection For Local Powder Layer Thickness Measurement In Laser Powder Bed Fusion, Tao Liu, Cody S. Lough, Hossein Sehhat, Yi Ming Ren, Panagiotis D. Christofides, Edward C. Kinzel, Ming-Chuan Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The laser powder bed fusion (LPBF) process is strongly influenced by the characteristics of the powder layer, including its thickness and thermal transport properties. This paper investigates in-situ characterization of the powder layer using thermographic inspection. A thermal camera monitors the temperature history of the powder surface immediately after a layer of new powder is deposited by the recoating system. During this process, thermal energy diffuses from the underlying solid part, eventually raising the temperature of the above powder layer. Guided by 1D modeling of this heat-up process, experiments show how the parameterized thermal history can be correlated with powder …

Recognition Of Land Use On Open-Pit Coal Mining Area Based On Deeplabv3+ And Gf-2 High-Resolution Images, Zhang Chengye, Li Feiyue, Li Jun, Xing Jianghe, Yang Jinzhong, Guo Junting, Du Shouhang

Coal Geology & Exploration

A highly efficient means is provided by remote sensing and deep learning to keep tracking of land use in open-pit coal mining area. Based on the high–resolution images from the domestic GF-2 satellite, a DeepLabv3+ model was utilized to achieve recognition of land use on open-pit coal mining area. In addition, a comparison was made among Deeplabv3+, U-Net, FCN, Random Forest, Support Vector Machine, and Maximum Likelihood Method. Firstly, samples data from high-resolution images were produced and sensitivity tests were conducted to determine the optimal cutting size and mode of the sample. Then, the deep neural network model (DeepLabv3+) was …

Multi-Uavs 3d Path Planning Method Based On Random Strategy Search, Sen Zhang, Mengyan Zhang, Jingping Shao, Jiexin Pu

Journal of System Simulation

Abstract: In view of the difficulty of the traditional path planning method without energy consumption constraints to meet the emergency rescue requirements in the complex mountain operation environment, a three-dimensional path planning algorithm for multi-UAVs is proposed based on LSTM-DPPO(long short-term memory-distributed proximal policy optimization) framework. The LSTM long and short-term memory neural network is used to extract the important characteristic state information sequence of the multiple unmanned aerial vehicles in their respective flight process. After repeated iteration and updating, an optimal network parameter model is obtained. Combined with the energy consumption, the optimal 3D detection path is generated. …