Imitative learning control of a LSTM-NMPC controller on PEM fuel cell for computational cost reduction

Abstract: In this paper, the imitative learning control is studied to address the problem of high computational cost of a Nonlinear Model Predictive Controller (NMPC) designed for controlling the output voltage of a Proton Exchange Membrane Fuel Cell (PEMFC) stack. The NMPC is already designed with an embedded Long Short-Term Memory (LSTM) network that provides the required predictions for solving the optimization problem. The LSTM-NMPC controller offers the desired performance in voltage tracking and minimizing fuel consumption, however, its long run-time makes it impractical for real-time implementation. Therefore, an imitative-based controller is designed to learn the behavior of the LSTM-NMPC and replace it, resulting in a noticeably lower computational cost while the desired performance is maintained. The generalization and adaptability of the imitative-based controller are also studied in this work. Finally, different simulations are reported for elaborating the process of designing imitative-based controller and the associated considerations.Communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023

Elementary Students’ Computational Thinking Practice in a Bridge Design and Building Challenge (Fundamental)

The increased focus on computational thinking (CT) has grown in recent years for various reasons, such as a general concern about (a) a lack of global competitiveness among American students and general literacy in science, technology, engineering, and math (STEM) fields (Hsu & Cardella, 2013), (b) maintaining the economic competitiveness of the U.S. (Yadav, Hong, & Stephenson, 2016), and (c) preparing students adequately for a society that is increasingly technological (NRC, 2011). CT can help individuals analyze and understand multiple dimensions of a complex problem and identify and apply appropriate tools or techniques to address a complex problem (Wing, 2010). Furthermore, children can benefit from improved technological literacy, content knowledge, and problem-solving skills (Hsu & Cardella, 2013) while practicing CT

A Twenty-Year Look at “Computational Geology,” an Evolving, In-Discipline Course in Quantitative Literacy at the University of South Florida

Since 1996, the Geology (GLY) program at the USF has offered “Computational Geology” as part of its commitment to prepare undergraduate majors for the quantitative aspects of their field. The course focuses on geological-mathematical problem solving. Over its twenty years, the course has evolved from a GATC (geometry-algebra-trigonometry-calculus) in-discipline capstone to a quantitative literacy (QL) course taught within a natural science major. With the formation of the new School of Geosciences in 2013, the merging departments re-examined their various curricular programs. An online survey of the Geology Alumni Society found that “express quantitative evidence in support of an argument” was more favorably viewed as a workplace skill (4th out of 69) than algebra (51st), trig (55th) and calculus 1 and 2 (59th and 60th). In that context, we decided to find out from successful alumni, “What did you get out of Computational Geology?” To that end, the first author carried out a formal, qualitative research study (narrative inquiry protocol), whereby he conducted, recorded, and transcribed semi-structured interviews of ten alumni selected from a list of 20 provided by the second author. In response to “Tell me what you remember from the course,” multiple alumni volunteered nine items: Excel (10 out of 10), Excel modules (8), Polya problem solving (5), “important” (4), unit conversions (4), back-of-the-envelope calculations (4), gender equality (3). In response to “Is there anything from the course that you used professionally or personally since graduating?” multiple alumni volunteered seven items: Excel (9 out of 10), QL/thinking (6), unit conversions (5), statistics (5), Excel modules (3), their notes (2). Outcome analysis from the open-ended comments arising from structured questions led to the identification of alumni takeaways in terms of elements of three values: (1) understanding and knowledge (facts such as conversion factors, and concepts such as proportions and log scales); (2) abilities and skills (communication, Excel, unit conversions); and (3) traits and dispositions (problem solving, confidence, and QL itself). The overriding conclusion of this case study is that QL education can have a place in geoscience education where the so-called context of the QL is interesting because it is in the students’ home major, and that such a course can be tailored to any level of program prerequisites

Agent based decision support systems in medicine

Embedding Machine Learning technology into Agent Driven Diagnosis Systems adds a new potential to the realm of Medicine, and in particular to the imagiology one. However, despite all the research done in the last years on the development of new methodologies for problem solving, in terms of the design of MultiAgent Systems (MAS) there is none where both the agent and the organizational view can be modelled. Current multi-agent approaches to problem solving either take a centralist, static approach to organizational design or take an emergent view in which agent interactions are not pre-determined, thus making it impossible to make any predictions on the behavior of the whole systems. Most of them also lack a model of the norms in the environment that should rule the behaviour of the agent society as a whole and/or the actions of the individuals. In this paper, it is proposed not only a framework for modelling and run agent organizations, but also to depict the different components of such societies. To illustrate these premises, we will evoke a society with one modality, the Axial Computed Tomography one, where two different but complementary computational paradigms, the Artificial Neural Networks and the Case Based Reasoning are object of attention

Inter-organization cooperation for care of the elderly

With the growing numbers of the elderly population, the society is face to face with a set of new problems, namely the lack of resources to assist their living in a noble mode. Nevertheless, with the use of new computational technologies and novel methodologies for problem solving, some solutions to these problems are emerging (e.g., remote sensing/assistance/supervision). Therefore, it is our goal to show that under such scenarios, it is possible to bring into play different interconnected virtual organizations, through which will be provided to the population, in general, and the elderly, in particular, a number of services (e.g., healthcare, entertainment, learning), without delocalization or messing up with their routine

A Computational Model of Memetic Evolution: Optimizing Collective Intelligence

The purpose of this study was to create an adaptive agent based simulation modeling the processes of creative collaboration. This model aided in the development of a new evolutionary based framework through which education scholars, academics, and professionals in all disciplines and industries can work to optimize their collective ability to find creative solutions to complex problems. The basic premise follows that the process of idea exchange, parallels the role sexual reproduction in biological evolution and is essential to society\u27s collective ability to solve complex problems. The study outlined a set of assumptions used to develop a new theory of collective intelligence. These assumptions were then translated into design requirements that were designated as parameters for a computational simulation that utilizes two types of machine learning algorithms. This model was developed, and 200 simulations were run for each of 48 different combinations of four independent variables for a total of 9,600 simulations. Statistical analysis of the data revealed a number of patterns enhancing the simulation agents\u27 collective problem solving abilities. Most notably, agents\u27 collective problem solving abilities were optimized when idea exchange between agents was balanced with individual agent time contemplating new creative strategies. Additionally, the agents\u27 collective problem solving abilities were optimized when simulation constraints did not force the agents to converge upon one potential solution

In Memoriam: Edward Cameron Kirby (1934 – 2019)

Edward Cameron Kirby (August 25th 1934–January 19th 2019) was a Scottish scientist, a Fellow of the (British) Royal Society of Chemistry and a member of the International Academy of Mathematical Chemistry, who made contributions to a unique combination of areas: problem solving in practical Chemistry, editorial work in Nutrition and Health, Chemical Graph Theory, and the use of small personal computers in Computational Chemistry, of which he was an early pioneer. For a period of some forty years, he was a keen and dependable supporter of Mathematical Chemistry in Croatia, even in the dark days of 1991–1995 and the post-war years 1996–2000. This work is licensed under a Creative Commons Attribution 4.0 International License

On the independence between phenomenal consciousness and computational intelligence

Consciousness and intelligence are properties commonly understood as dependent by folk psychology and society in general. The term artificial intelligence and the kind of problems that it managed to solve in the recent years has been shown as an argument to establish that machines experience some sort of consciousness. Following the analogy of Russell, if a machine is able to do what a conscious human being does, the likelihood that the machine is conscious increases. However, the social implications of this analogy are catastrophic. Concretely, if rights are given to entities that can solve the kind of problems that a neurotypical person can, does the machine have potentially more rights that a person that has a disability? For example, the autistic syndrome disorder spectrum can make a person unable to solve the kind of problems that a machine solves. We believe that the obvious answer is no, as problem solving does not imply consciousness. Consequently, we will argue in this paper how phenomenal consciousness and, at least, computational intelligence are independent and why machines do not possess phenomenal consciousness, although they can potentially develop a higher computational intelligence that human beings. In order to do so, we try to formulate an objective measure of computational intelligence and study how it presents in human beings, animals and machines. Analogously, we study phenomenal consciousness as a dichotomous variable and how it is distributed in humans, animals and machines. As phenomenal consciousness and computational intelligence are independent, this fact has critical implications for society that we also analyze in this work