scholarly journals ChemOS: An Orchestration Software to Democratize Autonomous Discovery

2018 ◽  
Author(s):  
Loı̈c M. Roch ◽  
Florian Häse ◽  
Christoph Kreisbeck ◽  
Teresa Tamayo-Mendoza ◽  
Lars P. E. Yunker ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Software Package ◽  
Scientific Community ◽  
State Of The Art ◽  
Scientific Discovery ◽  
Considerable Effort ◽  
Machine Learning Methods ◽  
Software Packages ◽  
Autonomous Discovery ◽  
Traditional Approaches

<div>Autonomous or “self-driving” laboratories combine robotic platforms with artificial intelligence to increase the rate of scientific discovery. They have the potential to transform our traditional approaches to experimentation. Although autonomous laboratories recently gained increased attention, the requirements imposed by engineering the software packages often prevent their development. Indeed, autonomous laboratories require considerable effort in designing and writing advanced and robust software packages to control, orchestrate and synchronize automated instrumentations, cope with databases, and interact with various artificial intelligence algorithms. To overcome this limitation, we introduce ChemOS, a portable, modular and versatile software package, which supplies the structured layers indispensable for operating autonomous laboratories. Additionally, it enables remote control of laboratories, provides access to distributed computing resources, and comprises state-of-the-art machine learning methods. We believe that ChemOS will reduce the time-to-deployment from automated to autonomous discovery, and will provide the scientific community with an easy-to-use package to facilitate novel discovery, at a faster pace.</div>

scholarly journals ChemOS: An Orchestration Software to Democratize Autonomous Discovery

2018 ◽  
Author(s):  
Loı̈c M. Roch ◽  
Florian Häse ◽  
Christoph Kreisbeck ◽  
Teresa Tamayo-Mendoza ◽  
Lars P. E. Yunker ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Software Package ◽  
Scientific Community ◽  
State Of The Art ◽  
Scientific Discovery ◽  
Considerable Effort ◽  
Machine Learning Methods ◽  
Software Packages ◽  
Autonomous Discovery ◽  
Traditional Approaches

<div>Autonomous or “self-driving” laboratories combine robotic platforms with artificial intelligence to increase the rate of scientific discovery. They have the potential to transform our traditional approaches to experimentation. Although autonomous laboratories recently gained increased attention, the requirements imposed by engineering the software packages often prevent their development. Indeed, autonomous laboratories require considerable effort in designing and writing advanced and robust software packages to control, orchestrate and synchronize automated instrumentations, cope with databases, and interact with various artificial intelligence algorithms. To overcome this limitation, we introduce ChemOS, a portable, modular and versatile software package, which supplies the structured layers indispensable for operating autonomous laboratories. Additionally, it enables remote control of laboratories, provides access to distributed computing resources, and comprises state-of-the-art machine learning methods. We believe that ChemOS will reduce the time-to-deployment from automated to autonomous discovery, and will provide the scientific community with an easy-to-use package to facilitate novel discovery, at a faster pace.</div>

scholarly journals Reviewing artificial intelligence methods for predictive modeling applications

2021 ◽  
Author(s):  
Andreas Sepp
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Modeling ◽  
Significant Contribution ◽  
Predictive Analytics ◽  
State Of The Art ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Intelligence Methods

Artificial intelligence and machine learning methods had significant contribution to the advancement and progress of predictive analytics. This article presents a state of the art of methods and applications of artificial intelligence and machine learning.

scholarly journals TOWARDS IMPROVED LEARNING OF FLUID MECHANICS VIA INTEGRATION OF A COMMERCIAL SOFTWARE PACKAGE INTO AN UNDERGRADUATE COURSE

2015 ◽  
Author(s):  
Scott Campbell ◽  
David R. Latulippe
Keyword(s):  
Fluid Mechanics ◽  
Software Package ◽  
State Of The Art ◽  
Deep Understanding ◽  
Simulation Tool ◽  
Software Packages ◽  
Commercial Software Package ◽  
Improved Learning ◽  
And Control ◽  
Computer Labs

The integration of software packages intochemical engineering courses is widely regarded tobenefit students in two ways. First, the active learningenvironment encourages a deep understanding of thecourse material. Second, it gives students practicalexperience with ‘state of the art’ tools that are used inindustry. However, surveys of chemical engineeringprograms have shown that the use of software packagesinto traditional fluid mechanics courses is quite low (lessthan 10%). Recently, the software package PIPE-FLO(from Engineered Software Inc.) was integrated into thesecond-year fluid mechanics course (ChE 2O04) atMcMaster. The software performs a full hydraulicnetwork analysis for a variety of piping configurationswith numerous piping components such as pumps,compressors, and control valves. The implementation ofPIPE-FLO as a simulation tool is in accordance with therecent initiative by the Canadian EngineeringAccreditation Board (CEAB) to determine directions forprogram improvement. A set of ten self-guided tutorialswere prepared to teach the students how to use the fullprofessional version of PIPE-FLO that was available inthe campus computer labs. Each tutorial was developedto enhance the understanding of the theory learned inclass and included references to the appropriateequations from the course textbook. Feedback from thestudents was overwhelmingly positive and encouragedgreater integration of the software into future offerings ofthe course.

scholarly journals Assessment of the efficiency of educational project management using neuro-fuzzy system

2019 ◽  
Vol 110 ◽  
pp. 02070
Author(s):  
Mikhail Krichevsky ◽  
Artyr Bydagov ◽  
Julia Martynova
Keyword(s):  
Artificial Intelligence ◽  
Project Management ◽  
Fuzzy System ◽  
Software Package ◽  
Fuzzy Inference ◽  
Type System ◽  
Inference System ◽  
Machine Learning Methods ◽  
System Type ◽  
Neuro Fuzzy

The project represents the introduction of elements and methods of artificial intelligence in the work programs of disciplines in the direction of “Management”. To assess the efficiency of such project management, it was proposed to use tools related to machine learning methods that include neural networks and fuzzy logic. The results of such an assessment are obtained using a neuro-fuzzy anfis (adaptive neuro-fuzzy inference system) type system, which is implemented using the MATLAB R2018b software package.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

State-of-the-art of graphic software packages

1977 ◽  
Vol 11 (3) ◽  
pp. 1-117 ◽  
Author(s):  
Compuater Graphics staff
Keyword(s):  
State Of The Art ◽  
Software Packages ◽  
Graphic Software

scholarly journals A magnetoencephalography dataset for motor and cognitive imagery-based brain-computer interface

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dheeraj Rathee ◽  
Haider Raza ◽  
Sujit Roy ◽  
Girijesh Prasad
Keyword(s):  
Scientific Community ◽  
Brain Computer Interface ◽  
Sampling Frequency ◽  
Computer Interface ◽  
Word Generation ◽  
Machine Learning Methods ◽  
Computer Interfaces ◽  
Signal Processing Algorithms ◽  
Processing Algorithms ◽  
Healthy Participants

AbstractRecent advancements in magnetoencephalography (MEG)-based brain-computer interfaces (BCIs) have shown great potential. However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of novel pattern recognition machine learning methods to detect brain activities related to motor imagery and cognitive imagery tasks using MEG signals.

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