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 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 On the Development of SCILAB Compatible Software for the Analysis and Control of Repetitive Processes

2008 ◽  
Vol 18 (3) ◽  
pp. 377-387
Author(s):  
Łukasz Hładowski ◽  
Błażej Cichy ◽  
Krzysztof Gałkowski ◽  
Eric Rogers
Keyword(s):  
Software Package ◽  
Reference Signal ◽  
Control Law ◽  
Simulation Tool ◽  
Repetitive Processes ◽  
The Core ◽  
Input Design ◽  
And Control

On the Development of SCILAB Compatible Software for the Analysis and Control of Repetitive ProcessesIn this paper further results on the development of a Scilab compatible software package for the analysis and control of repetitive processes is described. The core of the package consists of a simulation tool which enables the user to inspect the response of a given example to an input, design a control law for stability and/or performance, and also simulate the response of a controlled process to a specified reference signal.

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>

The Methodical Complex of Laboratory Works on the Study of Neural Network Technologies

2019 ◽  
pp. 358-367
Author(s):  
Artem Borodkin ◽  
Vladimir Eliseev ◽  
Gennady Filaretov ◽  
Alireza Aghvami Seyed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Undergraduate Students ◽  
Software Package ◽  
Practical Experience ◽  
Software Packages ◽  
Network Methods ◽  
Network Technologies ◽  
And Control ◽  
Neural Network Technologies

The chapter considers a task of teaching undergraduate students practical skills using artificial neural networks to solve problems of information processing and control systems. It represents and proves the methods of teaching, based on the gradual increase in the complexity of tasks to be solved by students. The developed complex of laboratory works includes classical problems and methods of their solutions, as well as original methods for solving problems of automatic control. The technology base of the laboratory works are both well-known programs and software package developed by the authors. In addition to the practical experience in the use of software packages, students obtain experience in conducting comparative studies of traditional and neural network methods for solving control problems.

Composite right/left-handed transmission line with array of thermocouples for generating terahertz radiation

2020 ◽  
Vol 92 (2) ◽  
pp. 20502
Author(s):  
Behrokh Beiranvand ◽  
Alexander S. Sobolev ◽  
Anton V. Kudryashov
Keyword(s):  
Transmission Line ◽  
Terahertz Radiation ◽  
Transmission Lines ◽  
Software Package ◽  
Optical Pulses ◽  
Capacitively Coupled ◽  
Voltage Difference ◽  
Left Handed ◽  
Microwave Transmission ◽  
Commercial Software Package

We present a new concept of the thermoelectric structure that generates microwave and terahertz signals when illuminated by femtosecond optical pulses. The structure consists of a series array of capacitively coupled thermocouples. The array acts as a hybrid type microwave transmission line with anomalous dispersion and phase velocity higher than the velocity of light. This allows for adding up the responces from all the thermocouples in phase. The array is easily integrable with microstrip transmission lines. Dispersion curves obtained from both the lumped network scheme and numerical simulations are presented. The connection of the thermocouples is a composite right/left-handed transmission line, which can receive terahertz radiation from the transmission line ports. The radiation of the photon to the surface of the thermocouple structure causes a voltage difference with the bandwidth of terahertz. We examined a lossy composite right/left-handed transmission line to extract the circuit elements. The calculated properties of the design are extracted by employing commercial software package CST STUDIO SUITE.

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 Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 400 ◽  
Author(s):  
Zelin Nie ◽  
Feng Gao ◽  
Chao-Bo Yan
Keyword(s):  
Optimization Model ◽  
Air Conditioning ◽  
State Of The Art ◽  
Hvac Systems ◽  
Practical Significance ◽  
Hvac System ◽  
Bilinear Model ◽  
Optimization And Control ◽  
And Control ◽  
Slow Timescale

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

scholarly journals Drone Deep Reinforcement Learning: A Review

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 999
Author(s):  
Ahmad Taher Azar ◽  
Anis Koubaa ◽  
Nada Ali Mohamed ◽  
Habiba A. Ibrahim ◽  
Zahra Fathy Ibrahim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
Real Life ◽  
Environment Monitoring ◽  
Simulated Environments ◽  
Infrastructure Inspection ◽  
Remote Sensing Mapping ◽  
And Control ◽  
The Military ◽  
Uav Navigation

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios.

scholarly journals MLatom 2: An Integrative Platform for Atomistic Machine Learning

2021 ◽  
Vol 379 (4) ◽  
Author(s):  
Pavlo O. Dral ◽  
Fuchun Ge ◽  
Bao-Xin Xue ◽  
Yi-Fan Hou ◽  
Max Pinheiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Software Package ◽  
State Of The Art ◽  
Kernel Method ◽  
Automatic Learning ◽  
Hyperparameter Optimization ◽  
Input And Output ◽  
Spectrum Simulation ◽  
Farthest Point ◽  
Theoretical Foundations

AbstractAtomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input and output. Thus, here we give an overview of our MLatom 2 software package, which provides an integrative platform for a wide variety of AML simulations by implementing from scratch and interfacing existing software for a range of state-of-the-art models. These include kernel method-based model types such as KREG (native implementation), sGDML, and GAP-SOAP as well as neural-network-based model types such as ANI, DeepPot-SE, and PhysNet. The theoretical foundations behind these methods are overviewed too. The modular structure of MLatom allows for easy extension to more AML model types. MLatom 2 also has many other capabilities useful for AML simulations, such as the support of custom descriptors, farthest-point and structure-based sampling, hyperparameter optimization, model evaluation, and automatic learning curve generation. It can also be used for such multi-step tasks as Δ-learning, self-correction approaches, and absorption spectrum simulation within the machine-learning nuclear-ensemble approach. Several of these MLatom 2 capabilities are showcased in application examples.

scholarly journals Interoperability in Smart Manufacturing: Research Challenges

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 21 ◽  
Author(s):  
Abe Zeid ◽  
Sarvesh Sundaram ◽  
Mohsen Moghaddam ◽  
Sagar Kamarthi ◽  
Tucker Marion
Keyword(s):  
Data Exchange ◽  
Research Area ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Successful Implementation ◽  
Integrated Networks ◽  
Software Packages ◽  
Industrial Internet ◽  
Logical Semantics ◽  
And Control

Recent advances in manufacturing technology, such as cyber–physical systems, industrial Internet, AI (Artificial Intelligence), and machine learning have driven the evolution of manufacturing architectures into integrated networks of automation devices, services, and enterprises. One of the resulting challenges of this evolution is the increased need for interoperability at different levels of the manufacturing ecosystem. The scope ranges from shop–floor software, devices, and control systems to Internet-based cloud-platforms, providing various services on-demand. Successful implementation of interoperability in smart manufacturing would, thus, result in effective communication and error-prone data-exchange between machines, sensors, actuators, users, systems, and platforms. A significant challenge to this is the architecture and the platforms that are used by machines and software packages. A better understanding of the subject can be achieved by studying industry-specific communication protocols and their respective logical semantics. A review of research conducted in this area is provided in this article to gain perspective on the various dimensions and types of interoperability. This article provides a multi-faceted approach to the research area of interoperability by reviewing key concepts and existing research efforts in the domain, as well as by discussing challenges and solutions.

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