Computational Engineering Commons^™

The Development And Validation Of Sinatra: A Three-Dimensional Direct Simulation Monte Carlo (Dsmc) Code Written In Object-Oriented C++ And Performed On Cartesian Grids, David Matthew Galvez

Master's Theses

The field of Computational Fluid Dynamics (CFD) primarily involves the approximation of the Navier-Stokes equations. However, these equations are only valid when the flow is considered continuous such that molecular interactions are abundant and predictable. The Knudsen number, $Kn$, which is defined as the ratio of the flow's mean free path, $\lambda$, to some characteristic length, $L$, quantifies the continuity of any flow, and when this parameter is large enough, alternative methods must be employed to simulate gases. The Direct Simulation Monte Carlo (DSMC) method is one which simulates rarefied gas flows by directly simulating the particles that compose the …

New Methods For Understanding And Controlling The Self-Assembly Of Reacting Systems Using Coarse-Grained Molecular Dynamics, Stephen Thomas

Boise State University Theses and Dissertations

This research aims at developing new computational methods to understand the molecular self-assembly of reacting systems whose complex structures depend on the thermodynamics of mixing, reaction kinetics, and diffusion kinetics. The specific reacting system examined in this study is epoxy, cured with linear chain thermoplastic tougheners whose complex microstructure is known from experiments to affect mechanical properties and to be sensitive to processing conditions. Mesoscale simulation techniques have helped to bridge the length and time scales needed to predict the microstructures of cured epoxies, but the prohibitive computational cost of simulating experimentally relevant system sizes has limited their impact. In …

Pine Flower Waste Innovation As A Plant Based Program Augmented Reality, Ayik Bela Saputra, Imam Muhtarom, Ni’Matul Rochmaniyah, Ardhana Iswari Citra Padmi, Ika Rahmawati

Journal of Mechanical Engineering Science and Technology (JMEST)

Indonesia one of the tropical countries in the world can not be separated from environment problems like other developing countries. One of the causes of declining environment quality is the decline of forests in Indonesia. In 2009, Indonesia’s forest area was by more than half, to about 88 million hectares. By the year 2013 the number is about 82 million hectares. One of the efforts to promote environment education is to familiarize planting plants to children from an early age from the school environment to the family environment. Therefore it is necessary an alternative in the form of planting media …

Properties Of Cassava Starch Based Bioplastic Reinforced By Nanoclay, Nanang Eko Wahyuningtiyas, Heru Suryanto

Journal of Mechanical Engineering Science and Technology (JMEST)

Synthetic Synthetic plastic is chemical materials which cause severe environmental problems. Incinerating plastic waste leads to release of hazardous gases, which is not good for humans. Bioplastic can help reduce the dependence on fossil fuels and petroleum, that bioplastic can solve the problem of synthetic plastic use. This research aims to define the properties of the cassava starch-based bioplastic reinforced by nanoclay. Methods were experimental with bioplastic component of cassava starch, glycerol as plasticizer and nanoclay as reinforcement. The bioplastic was analyzed using XRD, tensile test, moisture absorption, biodegradability, and compared with another bioplastic. The results show that the addition …

Damage Simulation Of Pre-Forming V-Neck Plates With Variations In Material Type, Time And Temperature Using The Taguchi Method, Avita Ayu Permanasari, Rr. Poppy Puspitasari, Kevin Valiant Cahya, Mahfud Ihsan

Journal of Mechanical Engineering Science and Technology (JMEST)

In metal forming, damage can be caused by several factors: load on the workpiece, initial heating temperature, and temperature due to the friction between the die and the material for pre-forming. The metal forming process can be executed in 2 ways, namely by hot working and cold working. During these two types of mechanical working process, the metal undergoes plastic deformation. In hot working, the required deformation force is relatively low, and changes in mechanical properties are also insignificant. In cold working, a higher force is required, but the strength of the metal will increase significantly. The use of simulation …

Mechanical Properties And Permeability Of Sand Casting Moulds With Eggshells Binder, Maryam Sharifi Jebeli Sharifi Jebeli, Wahyu Kurnianto, Wahono Wahono

Journal of Mechanical Engineering Science and Technology (JMEST)

This research aimed to analyse whether or not eggshell could be a potential addition to sand mould composition as a binding material together with bentonite. Different amounts of eggshell material, i.e. 4%, 7%, 10% and 3%, were added to each sample. This study was a type of pre-experimental design called the one-shot case study. Among all sand moulds under study, sample 3 10% ES in dry conditions had the highest tensile, compressive and shear strengths of 0.09 Kg/cm2, 3.11 Kg/cm2 and 1.13 Kg/cm2, respectively. The results of the permeability test with heat treatment at 110˚C for 60 minutes showed that …

Effect Of Mnfe2o4 Nanoparticles To Reduce Co And Hc Levels On Vehicle Exhaust Gas Emissions, Poppy Puspitasari, Sukarni Sukarni, Amir Hamzah

Journal of Mechanical Engineering Science and Technology (JMEST)

The air pollution particle emitted from transportation in Indonesia Bay 2015 consist of 70.50% CO; 18.34% HC; 8.89% NOx; 0.88% SOx; 1.33%. These gases are very harmful to humans. To reduce the toxic gas effect, special treatment is needed, one of them is by applying catalyst on exhaust system. MnFe2O4 has the potential to bind the CO and HC elements contained in the exhaust gas. This study aims to investigate the effect of MnFe2O4 addition on catalityc converter towards CO gas and HC emissions of motor vehicles. Therefore, the percentage of exhaust reduction with and without applying MnFe2O4 on varied …

The Effect Of Rotational Speed, Friction Duration, And Pressure On Tensile Strength Of Aisi 6061 Welding Joints, Dewi Puspitasari, Poppy Puspitasari, M. Rizka Gita Firmansyah, Solichin Solichin

Journal of Mechanical Engineering Science and Technology (JMEST)

Electric arc welding with covered electrodes is ideally suited for flat plates. If this technique is employed for welding rigid bodies, the results would be of poor quality. One viable solution to weld rigid bodies is through friction welding. The study aimed to investigate the tensile strength of specimens subjected to friction welding with variations in rotational speed, friction time, and pressure and to examine the microstructure of friction welding joints. This research used the one-shot case study design and involved descriptive analysis. The descriptive analysis described the results of tensile testing and microstructure of welding joints. Results showed that …

Advanced Recurrent Network-Based Hybrid Acoustic Models For Low Resource Speech Recognition, Jian Kang, Wei-Qiang Zhang, Wei-Wei Liu, Jia Liu, Michael T. Johnson

Electrical and Computer Engineering Faculty Publications

Recurrent neural networks (RNNs) have shown an ability to model temporal dependencies. However, the problem of exploding or vanishing gradients has limited their application. In recent years, long short-term memory RNNs (LSTM RNNs) have been proposed to solve this problem and have achieved excellent results. Bidirectional LSTM (BLSTM), which uses both preceding and following context, has shown particularly good performance. However, the computational requirements of BLSTM approaches are quite heavy, even when implemented efficiently with GPU-based high performance computers. In addition, because the output of LSTM units is bounded, there is often still a vanishing gradient issue over multiple layers. …

Data-Driven Uncertainty Quantification Interpretation With High Density Regions, Matthew Gregor Peterson

Computer Science ETDs

In a time when data is being constantly generated by phones, vehicles, sensor net- works, social media, etc. detecting anomalies with in the data can be very crucial. In cases where we know little prior knowledge about the data, it becomes difficult to extract uncertainty about our results. In this thesis, we will propose a framework in which we can extract uncertainty distributions from data-driven modeling prob- lems. We will show some concrete examples of how to apply framework and provide some insight into what the uncertainty distributions are telling us using High Density Regions (HDRs).

Predicting River Stage Using Recurrent Neural Networks, Eric Rohli

LSU Master's Theses

River stage prediction is an important problem in the water transportation industry. Accurate river stage predictions provide crucial information to barge and tow boat operators, port terminal captains, and lock management officials. Shallow river levels caused by prolonged drought impact the loading capacity of barges and tow boats. High river levels caused by excessive rainfall or snowmelt allow for greater tow capacities but make downstream transportation and lock management risky. Current academic river height prediction systems utilize either time series statistical analysis or machine learning algorithms to forecast future river heights, but systems that combine these two areas often limit …

Formalizing Schoenberg’S Fundamentals Of Musical Composition Through Petri Nets, A. Baratè, G. Haus, L. A. Ludovico, Davide Andrea Mauro

Computer Sciences and Electrical Engineering Faculty Research

The formalization of musical composition rules is a topic that has been studied for a long time. It can lead to a better understanding of the underlying processes, and provide a useful tool for musicologist to aid and speed up the analysis process. In our attempt we introduce Schoenberg’s rules from Fundamentals of Musical Composition using a specialized version of Petri nets, called Music Petri nets. Petri nets are a formal tool for studying systems that are concurrent, asynchronous, distributed, parallel, nondeterministic, and/or stochastic. We present some examples highlighting how multiple approaches to the analysis task can find counterparts in …

A Study Of Scalability And Cost-Effectiveness Of Large-Scale Scientific Applications Over Heterogeneous Computing Environment, Arghya K. Das

LSU Doctoral Dissertations

Recent advances in large-scale experimental facilities ushered in an era of data-driven science. These large-scale data increase the opportunity to answer many fundamental questions in basic science. However, these data pose new challenges to the scientific community in terms of their optimal processing and transfer. Consequently, scientists are in dire need of robust high performance computing (HPC) solutions that can scale with terabytes of data.

In this thesis, I address the challenges in three major aspects of scientific big data processing as follows: 1) Developing scalable software and algorithms for data- and compute-intensive scientific applications. 2) Proposing new cluster architectures …

Vehicle Tracking And Speed Estimation From Traffic Videos, Shuai Hua, Manika Kapoor, David Anastasiu

Faculty Publications

The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. Traffic flow prediction, anomaly detection, vehicle re-identification, and vehicle tracking are basic components in traffic analysis. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most important research topics of recent years. Good solutions to this problem could prevent traffic collisions and help improve road planning by better estimating transit demand. In the 2018 NVIDIA AI City Challenge, we combine modern deep learning models with classic computer …

Assessing The Accuracy Of Four Popular Face Recognition Tools For Inferring Gender, Age, And Race, Soon-Gyu Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

In this research, we evaluate four widely used face detection tools, which are Face++, IBM Bluemix Visual Recognition, AWS Rekognition, and Microsoft Azure Face API, using multiple datasets to determine their accuracy in inferring user attributes, including gender, race, and age. Results show that the tools are generally proficient at determining gender, with accuracy rates greater than 90%, except for IBM Bluemix. Concerning race, only one of the four tools provides this capability, Face++, with an accuracy rate of greater than 90%, although the evaluation was performed on a high-quality dataset. Inferring age appears to be a challenging problem, as …

Vehicle Pseudonym Association Attack Model, Pierson Yieh

Master's Theses

With recent advances in technology, Vehicular Ad-hoc Networks (VANETs) have grown in application. One of these areas of application is Vehicle Safety Communication (VSC) technology. VSC technology allows for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications that enhance vehicle safety and driving experience. However, these newly developing technologies bring with them a concern for the vehicular privacy of drivers. Vehicles already employ the use of pseudonyms, unique identifiers used with signal messages for a limited period of time, to prevent long term tracking. But can attackers still attack vehicular privacy even when vehicles employ a pseudonym change strategy? The major contribution …

Artificial Neural Network-Based Robotic Control, Justin Ng

Master's Theses

Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.

N-Slope: A One-Class Classification Ensemble For Nuclear Forensics, Justin Kehl

Master's Theses

One-class classification is a specialized form of classification from the field of machine learning. Traditional classification attempts to assign unknowns to known classes, but cannot handle novel unknowns that do not belong to any of the known classes. One-class classification seeks to identify these outliers, while still correctly assigning unknowns to classes appropriately. One-class classification is applied here to the field of nuclear forensics, which is the study and analysis of nuclear material for the purpose of nuclear incident investigations. Nuclear forensics data poses an interesting challenge because false positive identification can prove costly and data is often small, high-dimensional, …

Towards Autonomous Localization Of An Underwater Drone, Nathan Sfard

Master's Theses

Autonomous vehicle navigation is a complex and challenging task. Land and aerial vehicles often use highly accurate GPS sensors to localize themselves in their environments. These sensors are ineffective in underwater environments due to signal attenuation. Autonomous underwater vehicles utilize one or more of the following approaches for successful localization and navigation: inertial/dead-reckoning, acoustic signals, and geophysical data. This thesis examines autonomous localization in a simulated environment for an OpenROV Underwater Drone using a Kalman Filter. This filter performs state estimation for a dead reckoning system exhibiting an additive error in location measurements. We evaluate the accuracy of this Kalman …

Jasmint: Language To User-Friendly Ast With Emphasis On Translation, John E. Bradbury

Computer Science and Software Engineering

The goal of this project was to create a language (JASMINT) which would be easily transformable into other languages. With this, a library could be built which provides a rich set of functions, including typechecking, interpreting, and serialization, in order to make user modules easy to write. These modules are able to translate this AST into other languages and through the translation blocks can add new functionalities to JASMINT. The final state of the project at submission includes a library which handles all features except dynamic memory, transpilers (JasmintCxxTranspiler and JasmintPythonTranspiler) which handle most features except classes and dynamic memory, …