scholarly journals Microcontroller Training Kit Design Compatible with Drawings of the ISIS Simulation Program

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Real World ◽  
Electronic Circuit ◽  
System Simulation ◽  
Real Life ◽  
Simulation Software ◽  
Simulation Environment ◽  
Training Set ◽  
Learning And Teaching ◽  
Input System ◽  
Simple Systems

Microcontrollers are inside all areas of industry today. They are being used in many electronic circuit designs, ranging from very simple systems to highly complex systems. For that reason, microcontrollers have a great importance in the electronics sector. Microcontrollers are especially important in terms of students enrolled in engineering departments in the fields of computers, electrical engineering, electronics, and mechatronics. Experimental sets and simulation programs are used in the learning and teaching of microcontrollers in real-world simulations. In this study, a 16F877 microcontroller training set, designed for use in microcontroller courses, was used. The designed training kit allows experimentation with the 16F877 microcontroller circuits as a subprogram of the Proteus program of Lab center Electronics, which is electronic circuit drawing in Proteus ISIS (Intelligent Schematic Input System) simulation software tested in a real environment. The microcontroller basic hardware connections and the application circuits on the training kit were implemented as modules similar to the ISIS program drawings. Transition from the simulation environment to the real-life environment can be done easily by using these modules, and the information learned in this way becomes concrete.

A Vessel's Mathematical Model and its Real Counterpart: A Comparative Methodology Based on a Real-world Study

2016 ◽  
Vol 69 (6) ◽  
pp. 1379-1392 ◽  
Author(s):  
Krzysztof Czaplewski ◽  
Piotr Zwolan
Keyword(s):  
Real World ◽  
Real Life ◽  
Simulation Models ◽  
Weather Conditions ◽  
Simulation Software ◽  
Naval Academy ◽  
Wave Models ◽  
Marine Navigation ◽  
Marine Environmental ◽  
Research Centres

Navigation and manoeuvring simulators are increasingly being used in research centres to conduct complex navigation experiments and analyses. The structure and capabilities of simulation software make it possible to reproduce any conditions, including weather conditions. The complex mathematical models of marine environmental conditions that are being implemented nowadays take into account various sea wave models, which makes simulation tests more realistic. This paper deals with issues related to evaluating and verifying vessel simulation models based on real-world studies. As a result of the present research project, a methodology for comparing vessel simulation models with their real-life counterparts was developed. A measurement platform was created for the purpose of carrying out real-world studies; it is available at the Institute of Marine Navigation and Hydrography of the Polish Naval Academy. One important research step involved developing a procedural algorithm for making real-world measurements. This paper presents the results of using this platform in comparative tests of the manoeuvring elements of real and simulated vessels.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

The Influence of Assessment on Learning and Teaching

2019 ◽  
pp. 166-185
Author(s):  
Susan Hallam
Keyword(s):  
Communities Of Practice ◽  
Teaching And Learning ◽  
Formal Education ◽  
Peer Assessment ◽  
Real Life ◽  
Summative Assessment ◽  
Learning And Teaching ◽  
The Arts ◽  
Assessment Procedures ◽  
Assessment Research

It is debatable whether it is appropriate to assess performance in the arts. However, formal education institutions and the systems within which they operate continue to require summative assessment to take place in order to award qualifications. This chapter considers the extent to which such summative assessment systems in music determine not only what is taught but also what learners learn. The evidence suggests that any learning outcome in formal education that is not assessed is unlikely to be given priority by either learners or teachers. To optimize learning, the aims and the processes of learning, including formative, self-, and peer assessment procedures, should be aligned with summative assessment. Research addressing the roles, methods, and value of formative, self-, and peer assessment in enhancing learning is considered. A proposal is made that the most appropriate way of enhancing learning is to ensure that summative assessment procedures are authentic and have real-life relevance supporting the teaching and learning process, to ensure that learners are motivated and see the relevance of what they are learning. This might take many forms depending on musical genre, communities of practice, and the wider cultural environment.

scholarly journals Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

2021 ◽  
Author(s):  
Amarildo Likmeta ◽  
Alberto Maria Metelli ◽  
Giorgia Ramponi ◽  
Andrea Tirinzoni ◽  
Matteo Giuliani ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Real Life ◽  
User Preferences ◽  
Inverse Reinforcement Learning ◽  
Water Release ◽  
Reward Function ◽  
Model Free ◽  
Conflicting Objectives ◽  
Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

scholarly journals Improving the performance of a radio-frequency localization system in adverse outdoor applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  
Keyword(s):  
Random Forest ◽  
Ray Tracing ◽  
Real World ◽  
Practical Implication ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Gradient Boosting ◽  
Real World Data ◽  
Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

Peirce on learning and teaching

2021 ◽  
Vol 17 (1) ◽  
pp. 163-174 ◽  
Author(s):  
Winfried Nöth
Keyword(s):  
Life Experience ◽  
Philosophy Of Education ◽  
Real Life ◽  
Abductive Reasoning ◽  
Learning And Teaching ◽  
Theory Of Education ◽  
New Information ◽  
New Knowledge ◽  
Sensory Qualities ◽  
Semiotic Theory

Abstract The paper is a precis of C. S. Peirce’s semiotic theory of education. It presents this theory of learning and teaching from the perspective of Peirce’s phenomenological categories of Firstness, Secondness, and Thirdness. In the domain of Thirdness, learning is mediation between ignorance and knowledge, new information and old knowledge. Teaching has its focus on laws, symbols, legisigns, and reasoning. In the domain of Secondness, learners acquire new knowledge from the “hard realities” of real-life experience, from obstacles, and from the resistance caused by error and doubt. Teaching takes place by means of sinsigns (singular signs) and indexical signs. In the domain of Firstness, the learner acquires familiarity with the sensory qualities of objects of experience and learns from free associations, imagination, and acts of creativity. The instruments of teaching are qualisigns, icons, and abductive reasoning. The paper concludes that Peirce’s philosophy of education is holistic insofar as it states that most efficient signs are those signs in which “the iconic, indicative, and symbolic characters are blended as equally as possible.”

scholarly journals An Adaptive Game-Based Learning Strategy for Children Road Safety Education and Practice in Virtual Space

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3661
Author(s):  
Noman Khan ◽  
Khan Muhammad ◽  
Tanveer Hussain ◽  
Mansoor Nasir ◽  
Muhammad Munsif ◽  
...  
Keyword(s):  
Real World ◽  
Road Safety ◽  
Learning Strategy ◽  
Real Life ◽  
Safety Training ◽  
Virtual Space ◽  
Safety Education ◽  
Average Output ◽  
Training Environment ◽  
The Road

Virtual reality (VR) has been widely used as a tool to assist people by letting them learn and simulate situations that are too dangerous and risky to practice in real life, and one of these is road safety training for children. Traditional video- and presentation-based road safety training has average output results as it lacks physical practice and the involvement of children during training, without any practical testing examination to check the learned abilities of a child before their exposure to real-world environments. Therefore, in this paper, we propose a 3D realistic open-ended VR and Kinect sensor-based training setup using the Unity game engine, wherein children are educated and involved in road safety exercises. The proposed system applies the concepts of VR in a game-like setting to let the children learn about traffic rules and practice them in their homes without any risk of being exposed to the outside environment. Thus, with our interactive and immersive training environment, we aim to minimize road accidents involving children and contribute to the generic domain of healthcare. Furthermore, the proposed framework evaluates the overall performance of the students in a virtual environment (VE) to develop their road-awareness skills. To ensure safety, the proposed system has an extra examination layer for children’s abilities evaluation, whereby a child is considered fit for real-world practice in cases where they fulfil certain criteria by achieving set scores. To show the robustness and stability of the proposed system, we conduct four types of subjective activities by involving a group of ten students with average grades in their classes. The experimental results show the positive effect of the proposed system in improving the road crossing behavior of the children.

scholarly journals Multi-Objective Optimization of Traffic Signal Timing for Oversaturated Intersection

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Li ◽  
Lijie Yu ◽  
Siran Tao ◽  
Kuanmin Chen
Keyword(s):  
Simulation Software ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Simulation Environment ◽  
Multi Objective Optimization ◽  
Good Efficiency ◽  
Multi Objective ◽  
Traffic Signal Timing

For the purpose of improving the efficiency of traffic signal control for isolate intersection under oversaturated conditions, a multi-objective optimization algorithm for traffic signal control is proposed. Throughput maximum and average queue ratio minimum are selected as the optimization objectives of the traffic signal control under oversaturated condition. A simulation environment using VISSIM SCAPI was utilized to evaluate the convergence and the optimization results under various settings and traffic conditions. It is written by C++/CRL to connect the simulation software VISSIM and the proposed algorithm. The simulation results indicated that the signal timing plan generated by the proposed algorithm has good efficiency in managing the traffic flow at oversaturated intersection than the commonly utilized signal timing optimization software Synchro. The update frequency applied in the simulation environment was 120 s, and it can meet the requirements of signal timing plan update in real filed. Thus, the proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain under both normal condition and over-saturated condition.

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