n-Heptane Chemical Mechanism Evaluation for Conterflow Nonpremixed Flame Structure on 1D and 2D Open Source Codes

This work deals with implementation of an experimental flowrate control unit using free and low-cost hardware and software. The open-source software Processing was used to develop the source codes and user graphical interface and the open-source electronic prototyping platform Arduino was used to acquire data from an experimental unit. Work presents descriptions of the experimental setup, the real-time PID controllers used and theoretical/conceptual issues of Arduino. PID controllers based on internal model control, minimization of the integral of time-weighted absolute error, Ziegler-Nichols, and others were tuned for setpoint and load changes and real-time runs were carried out in order to make real-time use of control theory learned in academy. Results showed the developed platform proved to be suitable for use in experimental setups allowing users compare their ideas and expectations with the experimental evidence in a real and low-cost fashion. In addition, the instrumentation is simple to configure with acceptable level noise and particularly useful for control/automation learning with educational purposes.

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There is more to transparency in research than open-source codes of data processing

10.5194/hess-2016-215-rc1 ◽

2016 ◽

Author(s):

Olaf Arie Cirpka

Keyword(s):

Data Processing ◽

Open Source ◽

Source Codes

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Optimization Scenarios for Open Source Software Used in E-Learning Activities

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Optimizing Contemporary Application and Processes in Open Source Software ◽

10.4018/978-1-5225-5314-4.ch005 ◽

2018 ◽

pp. 102-123

Author(s):

Utku Köse

Keyword(s):

Open Source ◽

Open Source Software ◽

Learning Experience ◽

Learning Activities ◽

Machine Learning Techniques ◽

Software Systems ◽

Source Codes ◽

E Learning ◽

Additional Costs ◽

Teaching Learning

Using open software in e-learning application is one of the most popular ways of improving effectiveness of e-learning-based processes without thinking about additional costs and even focusing on modifying the software according to needs. Because of that, it is important to have an idea about what is needed while using an e-learning-oriented open software system and how to deal with its source codes. At this point, it is a good option to add some additional features and functions to make the open source software more intelligent and practical to make both teaching-learning experiences during e-learning processes. In this context, the objective of this chapter is to discuss some possible applications of artificial intelligence to include optimization processes within open source software systems used in e-learning activities. In detail, the chapter focuses more on using swarm intelligence and machine learning techniques for this aim and expresses some theoretical views for improving the effectiveness of such software for a better e-learning experience.

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AtChem, an open source box-model for the Master Chemical Mechanism

10.5194/gmd-2019-192 ◽

2019 ◽

Author(s):

Roberto Sommariva ◽

Sam Cox ◽

Chris Martin ◽

Kasia Borońska ◽

Jenny Young ◽

...

Keyword(s):

Open Source ◽

Atmospheric Chemistry ◽

Web Application ◽

Box Model ◽

Chemical Mechanism ◽

Control Software ◽

Sensitivity Studies ◽

Master Chemical Mechanism ◽

Field Campaigns ◽

Set Up

Abstract. AtChem is an open source zero-dimensional box-model for atmospheric chemistry. Any general set of chemical reactions can be used with AtChem, but the model was designed specifically for use with the Master Chemical Mechanism (MCM, http://mcm.york.ac.uk/). AtChem was initially developed within the EUROCHAMP project as a web application (AtChem-online, https://atchem.leeds.ac.uk/webapp/) for modelling environmental chamber experiments; it was recently upgraded and further developed into a standalone offline version (AtChem2) which allows the user to run complex and long simulations, such as those needed for modelling of intensive field campaigns, as well as to perform batch model runs for sensitivity studies. AtChem is installed, set up and configured using semi-automated scripts and simple text configuration files, making it easy to use even for non-experienced users. A key feature of AtChem is that it can easily be constrained to observational data which may have different timescales, thus retaining all the information contained in the observations. Implementation of a continuous integration workflow, coupled with a comprehensive suite of tests and version control software, makes the AtChem codebase robust, reliable and traceable. The AtChem2 code and documentation are available at https://github.com/AtChem/, under the open source MIT license.

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Transplantation of U-Boot on NAND Flash Based on S3C2440 Processor

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.716-717.1199 ◽

2014 ◽

Vol 716-717 ◽

pp. 1199-1202

Author(s):

Peng Zhang ◽

Zhi Wei Peng ◽

Jian Wei Lv ◽

Zhao Yu Ding

Keyword(s):

Operating System ◽

Open Source ◽

High Reliability ◽

Main Task ◽

Nand Flash ◽

Embedded Operating System ◽

Source Codes ◽

Start Up ◽

Target Platform

The main task of bootloader is to copy the kernel image to RAM , and then jump to the entrance of the kernel to run the operating system . It is the first-running code when the system is powered on . U-boot , with its open-source codes , supporting for varieties of embedded operating system kernel , high reliability and stability , has been in the mainstream of bootloader . However , it has not yet provided the original support for the S3C2440 processor , neither does its NAND flash . This paper analyzes two flashes in reference to the corresponding start-up mode , and introduces the U-boot start-up procedures briefly and figures out the major steps of transplanting U-boot to the target platform . U-boot was transplanted successfully .

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TagNN: A Code Tag Generation Technology for Resource Retrieval from Open-Source Big Data

Wireless Communications and Mobile Computing ◽

10.1155/2021/9956207 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Lingbin Zeng ◽

Xin Guo ◽

Cheng Yang ◽

Yao Lu ◽

Xiao Li

Keyword(s):

Big Data ◽

Open Source ◽

Open Source Software ◽

Learning Algorithm ◽

Difficult Problem ◽

Empirical Knowledge ◽

Huge Number ◽

Source Codes ◽

Deep Learning Algorithm ◽

Generation Technology

With the vigorous development of open-source software, a huge number of open-source projects and open-source codes have been accumulated in open-source big data, which contains a wealth of code resources. However, effectively and efficiently retrieving the relevant code snippets in such a large amount of open-source big data is an extremely difficult problem. There are usually large gaps between the user’s natural language description and the open-source code snippets. In this paper, we propose a novel code tag generation and code retrieval approach named TagNN, which combines software engineering empirical knowledge and a deep learning algorithm. The experimental results show that our method has good effects on code tag generation and code snippet retrieval.

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Science Family skills: An Alexa Assistant Tailored for Laboratory Routine

10.1101/484147 ◽

2018 ◽

Author(s):

Tiago Lubiana-Alves ◽

André A.N.A. Gonçalves ◽

Helder I Nakaya

Keyword(s):

Open Source ◽

User Interfaces ◽

3D Printer ◽

Laboratory Environment ◽

Source Codes ◽

Work Related ◽

Voice User ◽

Related Stress ◽

Printer Model ◽

Biology Laboratory

ABSTRACTVoice User Interfaces such as Amazon Alexa and Google Home are already widely available and used for personal purposes. These services could be used to improve experimental biology laboratory routine, facilitate troubleshooting and increase efficiency. Till date, no applications that are tailored to enhance laboratory routine have been made available. Here, we present a set of free-to-use, open source tools adapted to Alexa for application in the laboratory environment, with prospects of enhancing productivity and reducing work-related stress. All skills, 3D printer model and source codes are freely available in the Alexa app store and in GitHub.

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Implementation and Validation of the Jet Flame Model Using C++ Open Source Codes Computational Fluid Dynamics Software

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.656-657.676 ◽

2015 ◽

Vol 656-657 ◽

pp. 676-681 ◽

Cited By ~ 1

Author(s):

Panit Kamma ◽

Chakrit Suvanjumrat

Keyword(s):

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Open Source ◽

Turbulence Model ◽

Mixture Fraction ◽

Average Error ◽

Jet Flame ◽

Source Codes ◽

Eddy Simulation ◽

Fast Chemistry

The open source code software, OpenFOAM, based on the computational fluid dynamics (CFD) was implemented to simulate the methane jet flame. The large eddy simulation (LES) of turbulence model was written using C++ language. The mixture fraction approach and infinitely-fast chemistry assumption was combined with this LES turbulence model. The results of jet flame simulation were validated with Yi Zeng etal., (2011) experiments based on flame lengths under flow rate and pressure conditions of 5.95-23.81 mg/s and 50-100 kPa, respectively. It was found that an average error of flame lengths obtained from the developed CFD model was 8.57% when referred the 1% O2 remaining was a flame shape.

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