Computational Engineering Commons^™

Hardness Evaluation On Ss 316l Joined With Gas Tungsten Arc Welding Under Constant Heat Treatment, Duwi Leksono, Imam Sudjono

Journal of Mechanical Engineering Science and Technology (JMEST)

The purpose of this study is to show the hardness of the GTAW welding results on SS 316L metal with surface heating during the welding process. Observations in this study used SS 316L material with heat temperature regulation on the metal surface of the welding process using heating variations of 100 ℃, 120 ℃ and 140 ℃. The welding process of SS 316L material used a welding joint model uses single V-type welding joint with an angle of 60°, a spacing of 2 mm, a root surface of 1 mm and a thickness of 5 mm. Vickers hardness test was …

The Effect Of Solution Treatment Temperature And Quenching Media Variation In Heat Treatment Process Cu-Zn-Al Shape Memory Alloys On Shape Memory Effect And Microstructures, Wikan Jatimurti, Monica Gayatri K, Mavindra Ramadhani, Rochman Rochiem

Journal of Mechanical Engineering Science and Technology (JMEST)

Shape memory alloys (SMAs) are metal alloys with a reversible ability to recover their shape at a certain temperature after being deformed. This ability referred to as Shape Memory Effect (SME). The application of SMAs such as Ni-Ti and Cu-Zn-Al alloys usually used on automotive, biomedical, etc. The commonly used SMA is Ni-Ti because of its superior SME properties than Cu-Zn-Al, even though the price is quite higher. The SME of Cu-Zn-Al might be improved by increasing the presence of the martensite phase in its microstructure by heat treatment. The heat treatment process given to Cu-21Zn-5Al alloy is a homogenizing, …

The Effect Of Heat Treatment To The Erosion Rate Of Brass Composite, Aminnudin Aminnudin, Solichin Solichin

Journal of Mechanical Engineering Science and Technology (JMEST)

Brass composites can be improved their mechanical properties by the heat treatment process. The improvement of the mechanical properties of this technique is expected to increase the resistance of the composite to erosion that occurs in the environment of flow water. Brass composites used are compositeswith fly ash 5, 10, 15, and 20%. The heat treatment process was carried out using electric furnace withoutprotective gas. Composite heat up to 350°C and 400°C for 30 min. and quenching with water. Before and after the erosion test, the weight of the test specimen was weighed with analytical scales. The treatment process affects …

The Aesthetics Of Multicoding Esolangs, Daniel J. Temkin

Electronic Literature Organization Conference 2020

In the influential 2005 paper "A Box, Darkly," Michael Mateas and Nick Montfort introduced the idea of multicoding: when one text holds multiple meanings, depending on context. Examples include polyglots, programs that can run successfully in two different languages, as well as sentences that can be read in French or English. Esoteric programming has a history of explicit multicoding languages. The best known are Piet, whose programs are images, and commands are the change in color from one pixel to the next; Shakespeare, with code written as plays, and Chef, whose programs are recipes that work in the kitchen. While …

Efficient Data Mining Algorithm Network Intrusion Detection System For Masked Feature Intrusions, Kassahun Admkie, Kassahun Admkie Tekle

African Conference on Information Systems and Technology

Most researches have been conducted to develop models, algorithms and systems to detect intrusions. However, they are not plausible as intruders began to attack systems by masking their features. While researches continued to various techniques to overcome these challenges, little attention was given to use data mining techniques, for development of intrusion detection. Recently there has been much interest in applying data mining to computer network intrusion detection, specifically as intruders began to cheat by masking some detection features to attack systems. This work is an attempt to propose a model that works based on semi-supervised collective classification algorithm. For …

Automatic Delamination Segmentation For Bridge Deck Based On Encoder-Decoder Deep Learning Through Uav-Based Thermography, Chongsheng Cheng, Zhexiong Shang, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

Concrete deck delamination often demonstrates strong variations in size, shape, and temperature distribution under the influences of outdoor weather conditions. The strong variations create challenges for pure analytical solutions in infrared image segmentation of delaminated areas. The recently developed supervised deep learning approach demonstrated the potentials in achieving automatic segmentation of RGB images. However, its effectiveness in segmenting thermal images remains under-explored. The main challenge lies in the development of specific models and the generation of a large range of labeled infrared images for training. To address this challenge, a customized deep learning model based on encoder-decoder architecture is proposed …

An Optimization Approach For Automated Unit Test Generation Tools Using Multi-Objective Evolutionary Algorithms, Samar Ali Abdallah, Ramdan Mowad, Esaam Eldeen Fawzy

Future Computing and Informatics Journal

High code coverage is measured by the process of software testing typically using automatic test case generation tools. This standard approach is usually used for unit testing to improve software reliability. Most automated test case generation tools focused just on code coverage without considering its cost and redundancy between generated test cases. To obtain optimized high code coverage and to ensure minimum cost and redundancy a Multi-Objectives Evolutionary Algorithm approach (MOEA) is set in motion. An efficient approach is proposed and applied to different algorithms from MOEA Frame from the separate library with three fitness functions for Coverage, Cost, and …

A Thresholding Based Technique To Extract Retinal Blood Vessels From Fundus Images, Jyotiprava Dash, Nilamani Bhoi

Future Computing and Informatics Journal

Retinal imaging has become the significant tool among all the medical imaging technology, due to its capability to extract many data which is linked to various eye diseases. So, the accurate extraction of blood vessel is necessary that helps the eye care specialists and ophthalmologist to identify the diseases at the early stages. In this paper, we have proposed a computerized technique for extraction of blood vessels from fundus images. The process is conducted in three phases: (i) pre-processing where the image is enhanced using contrast limited adaptive histogram equalization and median filter, (ii) segmentation using mean-C thresholding to extract …

A Nove L F U Z Z Y L O G I C B A S E D R Eve R S E E N G I N E E R I N G O F G E N E R Eg U L A T O R Y N E T W O R K, Raviajot Kaur, Abhishek Bhola, Shailendra Singh

Future Computing and Informatics Journal

Genes of an organism play a very crucial role in the working of various cellular activities. Genes and other biological molecules like DNA, RNA do not operate alone but they all are correlated. Their relationships are shown with the help of networks commonly known as Gene Regulatory Networks. Gene Regulatory Networks are complex control networks that show the map of interactions among the genes. They provide very useful contribution to the genomic science and increase the understanding of various biological processes. In this paper, fuzzy logic based method is proposed for the reverse engineering of gene regulatory network from microarray …

A Supporting Tool For Requirements Change Management In Distributed Agile Development, Ramdan Mowad, Domia Lloyd, Mona Kadry

Future Computing and Informatics Journal

Software development industry has witnessed the growth of the agile movement and approaches. Applying the agile approaches and practices in the distributed environment will lead to gain a lot of benefits such as reduced costs, higher efficiency and better customization, on the other hand it will face many challenges for example working in different time zones, requirements changes, personal selection and knowledge management. In order to gain these benefits, it should address the challenges that will face the agile approaches in a distributed environment. One of the main challenges is managing the requirements changes during the process of distributed agile …

Electricity Price Prediction With Computational Intelligence, Toan Hoang

Electrical Engineering

Electricity price depends on numerous factors including the weather, location, time of year/month/day (summer, holidays, day/night, etc.), consumption, and market changes or disruptions. The power loss in the transmission lines and the use of electricity also affect the load's price. Forecasting about the electricity price provides future trends and patterns consumption of the users. Forecasting electricity price is crucially important for producers and consumers in the energy trading markets. It is a complicated task because of the uncertainty behaviors and demand fluctuation. The maximization of profit for participants is highly associated with the bidding strategies. Multiple forecasting electricity price tools …

Evaluating Driving Performance Of A Novel Behavior Planning Model On Connected Autonomous Vehicles, Keyur Shah

Honors Scholar Theses

Many current algorithms and approaches in autonomous driving attempt to solve the "trajectory generation" or "trajectory following” problems: given a target behavior (e.g. stay in the current lane at the speed limit or change lane), what trajectory should the vehicle follow, and what inputs should the driving agent apply to the throttle and brake to achieve this trajectory? In this work, we instead focus on the “behavior planning” problem—specifically, should an autonomous vehicle change lane or keep lane given the current state of the system?

In addition, current theory mainly focuses on single-vehicle systems, where vehicles do not communicate with …

Evolution Of Computational Thinking Contextualized In A Teacher-Student Collaborative Learning Environment., John Arthur Underwood

LSU Doctoral Dissertations

The discussion of Computational Thinking as a pedagogical concept is now essential as it has found itself integrated into the core science disciplines with its inclusion in all of the Next Generation Science Standards (NGSS, 2018). The need for a practical and functional definition for teacher practitioners is a driving point for many recent research endeavors. Across the United States school systems are currently seeking new methods for expanding their students’ ability to analytically think and to employee real-world problem-solving strategies (Hopson, Simms, and Knezek, 2001). The need for STEM trained individuals crosses both the vocational certified and college degreed …

Using Case-Level Context To Classify Cancer Pathology Reports, Shang Gao, Mohammed Alawad, Noah Schaefferkoetter, Lynne Penberthy, Xiao-Cheng Wu, Eric B. Durbin, Linda Coyle, Arvind Ramanathan, Georgia Tourassi

Kentucky Cancer Registry Faculty Publications

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence-for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We …

Chaprates, Brinly Xavier, Micole Amanda Marietta, Nidhi Vedantam

Student Scholar Symposium Abstracts and Posters

On the Chapman campus, through taking and choosing various classes, there is a significant need for communication and feedback between students and peers, professors, tutors, and study groups. With this, we wanted to create an application that enables users from various majors to not only easily and effectively communicate with various people in their field, but one that also enables them to give and receive feedback on various classes through a rating system. We believe that the application will aid students in a myriad of specific ways, including being involved in study groups and getting tutoring help, determining which classes …

Evaluating Machine Learning Models For Semantic Segmentation Over Cloud Images For Classification, Harsh Nagarkar

Honors Theses

Due to the increasing number of available approaches nowadays, choosing the most accurate image semantic segmentation model has become hard. The purpose of this research is to find the best-performing image semantic segmentation model for Cloud classification. For the purpose of this study, a data set of cloud images from the Max Planck Institute for meteorology is used. These images were taken from the by two NASA space satellite.Three main models UNet, PSPNet and FPN were used in combination of 4 differ-ent encoder Inception-ResNet-v2, MobileNet-v2, ResNet-34, and ResNet 101. After training all the models in the Mississippi Center for Super …

Minet Magnetic Indoor Localization, Michael Drake

Honors Theses

Indoor localization is a modern problem of computer science that has no unified solution, as there are significant trade-offs involved with every technique. Magnetic localization, though less popular than WiFi signal based localization, is a sub-field that is rooted in infrastructure-free design, which can allow universal setup. Magnetic localization is also often paired with probabilistic programming, which provides a powerful method of estimation, given a limited understanding of the environment. This thesis presents Minet, which is a particle filter based localization system using the Earth's geomagnetic field. It explores the novel idea of state space limitation as a method of …

Qlime-A Quadratic Local Interpretable Model-Agnostic Explanation Approach, Steven Bramhall, Hayley Horn, Michael Tieu, Nibhrat Lohia

SMU Data Science Review

In this paper, we introduce a proof of concept that addresses the assumption and limitation of linear local boundaries by Local Interpretable Model-Agnostic Explanations (LIME), a popular technique used to add interpretability and explainability to black box models. LIME is a versatile explainer capable of handling different types of data and models. At the local level, LIME creates a linear relationship for a given prediction through generated sample points to present feature importance. We redefine the linear relationships presented by LIME as quadratic relationships and expand its flexibility in non-linear cases and improve the accuracy of feature interpretations. We coin …

Performance Optimizations Of Nosql Databases In Distributed Systems, Tristyn Maalouf

Senior Honors Theses

Databases store information about a system and provide a mechanism for data to be accessed and manipulated. While advancements in the 1970s provided a relational database model that has persisted to this day, web-scale era mass data needs surfacing in the 1990s and the early 2000s revealed limitations in the scalability of the relational model. As systems grew and transitioned into distributed architectures to support mass data storage and parallel processing, a complete overhaul of distributed computing technologies evolved that fundamentally departed from the relational data model in favor of the NoSQL data model. The course of this research details …

Sensitivity Analysis Of Data-Driven Groundwater Forecasts To Hydroclimatic Controls In Irrigated Croplands, Alessandro Amaranto, Francesca Pianosi, Dimitri Solomatine, Gerald Corzo-Perez, Francisco Munoz-Arriola

Department of Agricultural and Biological Systems Engineering: Faculty Publications

In the last decades, advancements in computational science have greatly expanded the use of artificial neural networks (ANNs) in hydrogeology, including applications on groundwater forecast, variable selection, extended lead-times, and regime-specific analysis. However, ANN-model performance often omits the sensitivity to ob- servational uncertainties in hydroclimate forcings. The goal of this paper is to implement a data-driven modeling framework for assessing the sensitivity of ANN-based groundwater forecasts to the uncertainties in observational inputs across space, time, and hydrological regimes. The objectives are two-folded. The first objective is to couple an ANN model with the PAWN sensitivity analysis (SA). The second objective …