217 skills found · Page 5 of 8
StevenShaw98 / Artificial Lemming AlgorithmArtificial lemming algorithm: A novel bionic meta-heuristic technique for solving real-world engineering optimization problems
SinaGhanbarii / Numerical Methods And Their Application In Chemical EngineeringExplore the world of Numerical Methods and their practical applications in Chemical Engineering through this repository. Dive into algorithms, simulations, and computations that empower chemical engineers to solve complex problems efficiently and gain insights into various processes. Files have been provided in (.mlx) and (.m) format!
borkob / Git LabsThis is a GitHub archive for the best known solutions and solvers of the low autocorrelation binary sequence (labs) problem. It also is a testbed for rigorous, transparent, and reproducible performance testing of labs solvers. Maintained by Borko Bošković, Faculty of Electrical Engineering and Computer Science, University of Maribor, Slovenia.
pngts / Nonlinear Parameter Estimation In Thermodynamic ModelsThe reliable solution of nonlinear parameter estimation problems is an essential computational and mathematical problem in process systems engineering, both in on-line and off-line applications. Parameter estimation in semi-empirical models for vapor – liquid equilibrium (VLE) data modelling plays an important role in design, optimization and control of separation units. Conventional optimisation methods may not be reliable since they do not guarantee convergence to the global optimum sought in the parameter estimation problem. In this work we demonstrate a technique, based on genetic algorithms (GA), that can solve the nonlinear parameter estimation problem with complete reliability, providing a high probability that the global optimum is found. Two versions of stochastic optimization techniques are evaluated and compared for nine vapour - liquid equilibrium problems: our genetic base algorithm and a hybrid algorithm. Reliable experimental data from the literature on vapor - liquid equilibrium systems were correlated using the UNIQUAC equation for activity coefficients. Our results indicate that this method, when properly implemented, is a robust procedure for nonlinear parameter estimation in thermodynamic models. Considering that new globally optimal parameter values are found by using the proposed method we can surmise by our results that several sets of parameter values published in the DECHEMA VLE Data Collection correspond to local instead of global minima.
mthd98 / Project Algorithm For A Dog Identification AppProject Overview Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. Sample Output Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! Project Instructions Instructions Clone the repository and navigate to the downloaded folder. git clone https://github.com/udacity/dog-project.git cd dog-project Download the dog dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/dogImages. Download the human dataset. Unzip the folder and place it in the repo, at location path/to/dog-project/lfw. If you are using a Windows machine, you are encouraged to use 7zip to extract the folder. Download the VGG-16 bottleneck features for the dog dataset. Place it in the repo, at location path/to/dog-project/bottleneck_features. (Optional) If you plan to install TensorFlow with GPU support on your local machine, follow the guide to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. (Optional) If you are running the project on your local machine (and not using AWS), create (and activate) a new environment. Linux (to install with GPU support, change requirements/dog-linux.yml to requirements/dog-linux-gpu.yml): conda env create -f requirements/dog-linux.yml source activate dog-project Mac (to install with GPU support, change requirements/dog-mac.yml to requirements/dog-mac-gpu.yml): conda env create -f requirements/dog-mac.yml source activate dog-project NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/dog-windows.yml to requirements/dog-windows-gpu.yml): conda env create -f requirements/dog-windows.yml activate dog-project (Optional) If you are running the project on your local machine (and not using AWS) and Step 6 throws errors, try this alternative step to create your environment. Linux or Mac (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 source activate dog-project pip install -r requirements/requirements.txt NOTE: Some Mac users may need to install a different version of OpenCV conda install --channel https://conda.anaconda.org/menpo opencv3 Windows (to install with GPU support, change requirements/requirements.txt to requirements/requirements-gpu.txt): conda create --name dog-project python=3.5 activate dog-project pip install -r requirements/requirements.txt (Optional) If you are using AWS, install Tensorflow. sudo python3 -m pip install -r requirements/requirements-gpu.txt Switch Keras backend to TensorFlow. Linux or Mac: KERAS_BACKEND=tensorflow python -c "from keras import backend" Windows: set KERAS_BACKEND=tensorflow python -c "from keras import backend" (Optional) If you are running the project on your local machine (and not using AWS), create an IPython kernel for the dog-project environment. python -m ipykernel install --user --name dog-project --display-name "dog-project" Open the notebook. jupyter notebook dog_app.ipynb (Optional) If you are running the project on your local machine (and not using AWS), before running code, change the kernel to match the dog-project environment by using the drop-down menu (Kernel > Change kernel > dog-project). Then, follow the instructions in the notebook. NOTE: While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. Unless requested, do not modify code that has already been included. Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload: The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder.
GreatDevelopers / CivilOctaveUse of Octave (MatLAB) in Civil Engineering Problems
MathWorks-Teaching-Resources / Engineering Problem SolvingThis curriculum module contains interactive examples that emphasize the general approach and methodologies of problem-solving within the field of engineering. The fundamental concepts of systems thinking/engineering will be used as the basis to solve problems.
shazaaly / Technical Interview PrepPrepare to ace your technical interviews with our focused and collaborative study group. Whether you're aiming for a role in software engineering, data science, or any tech-driven field, our group offers a structured pathway to sharpen your problem-solving skills, coding proficiency, and understanding of key concepts.
cadcheme / TenProbsInChEA Collection of Ten Problems in Chemical Engineering
Chao-Dang / Reliability Analysis Using Laplace Transform And Mixture DistribtutionSource code of the paper: Dang C., Xu, J. Unified reliability assessment for problems with low- to high-dimensional random inputs using the Laplace transform and a mixture distribution. Reliabiliy Engineering & System Safety (2020). https://doi.org/10.1016/j.ress.2020.107124
TannerClarkLee / Finite Element MethodThe finite element method (FEM) is a numerical analysis method for finding the solution to boundary value problems for partial differential equations (PDE). The finite element method has a variety of applications. The most popular application for this method is used for numerical modeling of physical systems in engineering and physics. Using FEM, computers can model thermodynamics, electromagnetism and fluid dynamics.
arthurmrodriguez / Advanced Metaheuristics LSGOThis repo contains my Computer Engineering Degree's Final Project, studied at the University of Granada, Spain. The main focus is to apply state-of-the-art metaheuristic algorithms into a Big Optimization problem with thousands of variables. Our task is to find out how accurate are theoretical benchmark results compared to real EEG (Electroencephalography) data
7ORVS / Cyber Security ResourcesThis repo will contain resources in different topics in cyber security especially in reverse engineering and malware analysis, such tools, YouTube channels, blogs, courses, articles related to this field, and websites you can solve RE and MA problems on it
sanjayg0 / PLoMPLoM is an open source python package that implements the algorithm of Probabilistic Learning on Manifolds with and without constraints (Soize and Ghanem, 2016; Soize and Ghanem, 2019) for generating realizations of a random vector in a finite Euclidean space that are statistically consistent with a given dataset of that vector. The package mainly consists of python modules and invokes a dynamic library for more efficiently computing the gradient of the potential, and can be imported and run on Linux, macOS, and Windows platform. This repository also archives the unit/integration tests and examples of applying the algorithm to practical engineering problems.
Web-By-Xhicko / PassionPagesThe ALX SE Project for the third trimester is a pivotal stage before entering the specialized phase. It focuses on honing software engineering skills through practical application, integrating various technologies, and problem-solving. The project aims to fortify foundational knowledge, fostering an environment for comprehensive learning and skill
safedep-hiring / Swe Fe Intern Problem 2SWE Frontend Engineering Intern Hiring Problem
MimAhmed / Leet Code DSA Interview Bootcamp Tecognize This Boot Camp Covers Data Structure and Algorithm Real World Practice How To Design and and Solve Competitive Programming Problems and Real World Engineering Problems
barbagroup / JITcode MechEOnline learning modules to learn computing in a problem-based context within Mechanical Engineering
TP-Coder-Innovation-Hub / Project And Problem ListList of possible business/engineering problems that are applied in real-world business
yomaokobiah / YonoYono is a python numerical methods library. The aim of Yono is to provide solutions to engineering problems that different numerical methods can solve.
