Digitalisation in Higher Education: A Flipped Classroom Arrangement to foster Internationalisation

This practical paper presents a successful international teaching & learning project in Higher Education (HE), which can be used as blue print for similar international HE teaching/learning cooperations. A virtual module, delivering 5 ECTS to participants from Germany and Iran, was organized as flipped classroom (FC), consisting of 2 phases: (1) online phase of 7 weeks, having started at April 12, 2019, with 15 students from Shiraz University, Iran, and 23 students from TU Dresden, Germany, collaborating in mixed teams of 5–6 participants each on a complex business case under tight guidance by qualified learning community managers, and (2) a follow-up on-site meeting at TU Dresden in the first week of July with 3 intensive workshops applying different techniques to consolidate the prior online collaboration results

Internet of Everything in the Teaching-Learning Approach: An Integrative Review

Introduction: The fourth industrial revolution or industry 4.0 has brought a variety of technologies to different societies. One of these technologies is the Internet of Things (IoT), primarily conceptualized in engineering fields and then found its way to the field of education. Internet of Everything (IoE) has been discussed in the evolution of the IoT concept. IoE mainly focuses on things, people, processes, and data. This paper aims to investigate different studies from the emergence of IoT concept and its development to IoE based teaching-learning process.Methods: The integrative review was applied as the research method, Web of Science and Scopus databases were directly investigated and 139 articles were finalized as the result of this integrative review.Results: Findings of this study demonstrated that the teaching learning process with the focus on IoE could be categorized into logic models, including inputs, activities, outputs, outcomes, and external factors. Based on extracted components, the final model showed that the teaching-learning approach with the focus on IoE is a process that mainly occurs through integration and connection of IoT-based infrastructures, stakeholder’s interactions, teaching and learning activities. Eventually, this has brought personal and general outputs to achieve sustainability, Green IoT, and meeting the needs of industry. Simultaneously with the implementation or application of this system, several challenges can arise in the process, namely Security, Privacy, Financing, Reliable connectivity, and Cloud infrastructure.Conclusion: Therefore, this model can help policymakers or educators to be aware of the different parts of an IoE-based education system

Conquering the Jet Lag Era: Experiences from Virtual Interdisciplinary Collaboration across Continents

Mit der Ausschreibung International Virtual Academic Collaboration (IVAC) verfolgt der Förderträger DAAD die Ziele, die Studienangebote an deutschen Hochschulen und deren ausländischen Kooperationshochschulen zu flexibilisieren und den Studierenden einen erweiterten Zugang zur internationalen Hochschulbildung zu ermöglichen. In dem geförderten Projekt Collaborative International, Intercultural & Interdisciplinary Learning (COIIIL) zwischen den Partnerinstitutionen Technische Universität Dresden, Stellenbosch University, Shiraz University und Bucknell University wurden diese Ziele adressiert

Recent Survey of Large-Scale Systems: Architectures, Controller Strategies, and Industrial Applications

Complex, dynamical systems, often of a high order, composed of several interconnected subsystems, are referred to as large-scale systems (LSSs). This article presents a survey of the most common LSS architectures. The article then proceeds to discuss conventional control schemes, including decentralized, distributed, and hierarchical structures for these LSSs. Finally, some relevant and recent application domains such as power systems, transportation systems, and industrial processes are outlined. The article concludes by outlining some possible future research and development directions

Harmonic Fault Diagnosis in Power Quality System Using Harmonic Wavelet

The increasing use of non-linear loads such as power electronics, converters, arc furnaces, transformers, fluorescent and high intensity discharge lights have caused harmonics distortion in power quality (PQ) systems. On the other hand, harmonics have numerous effects on electrical systems. For examples, they can be troublesome to communication systems, they increase heating in the transformers and motors, and consequently decrease their life cycle. The first step to address these issues is to diagnose harmonic faults in power distribution systems. This paper introduces a new method for detecting harmonic faults using harmonic wavelets. For this purpose, harmonic wavelet transform (HWT) is used to decompose the faulty signal at different levels. Then, the energies of the decomposition levels based on parseval\u27s theorem are computed. Finally, the faulty signal is reconstructed with harmonics wavelets. Simulation results show that the suggested fault detection and diagnosis (FDD) system can successfully identify the maximum harmonic in the faulty signal and the amount of harmonics in the faulty signal compared to fundamental signal

Fault tolerant control of rhine-meuse delta water system: A performance assessment based approach

An occurrence of potential faults/hazardous situations could jeopardize the safety and reliability of complex dynamical systems. A new Fault Tolerant Control (FTC) methodology capable of preventing floods in the land areas close to the Rhine-Meuse Delta water system is proposed in this paper. The Delta water network is a Large-Scale System (LSS) with many barriers and sluices and is of enormous economic importance to Europe. Floods in this water network have damaged the system and cities around it. Thus, control of this complex water system is necessary. To monitor this complex system and detect any anomalies in a timely fashion, a fault diagnosis method using a Control Performance Index (CPI) is proposed for this large-scale water system. After fault diagnosis is performed, a switching mode control is devised to prevent potential flood situations. The switching controller is self-modified via the performance index information. Simulation tests are performed using experimental data from the aforementioned water system to examine the effectiveness of the suggested FTC method in comparison with the FTC using historical benchmark performance assessment method and the current controller of this water network system

Application of reinforcement learning in development of a new adaptive intelligent traffic shaper

In this paper, we have taken advantage of reinforcement learning to develop a new traffic shaper in order to obtain a reasonable utilization of bandwidth while preventing traffic overload in other part of the network and as a result, reducing total number of packet dropping in the whole network.. We used a modified version of Q-learning in which a combination of neural networks keeps the data of Q-table in order to make the operation faster while keeping the required storage as small as possible. This method shows satisfactory results in simulations from the aspects of keeping dropping probability low while injecting as many packets as possible into the network in order to utilize the free bandwidth as much as possible. On the other hand the results show that the system can perform in situations that are not originally designed to act i

Maximum Power Point Tracker (MPPT) for Photovoltaic Power Systems-A Systematic Literature Review

Photovoltaic (PV) as a renewable source of energy plays a significant role in generating electricities in the industry and distributed consumers. The output power of the PV device is highly nonlinear which is dependent on I-P and V-P characteristics of the device and also irradiation conditions. Therefore, many research works have been performed to optimize the performance and obtain maximum power from the PV panels. This paper provides a brief literature review on maximum power point tracker (MPPT) for the PV panels. For this purpose, the PV circuit structure with its mathematics model is presented. Then, recent publications on various design methodologies are reviewed

Novel Multiagent Model-Predictive Control Performance Indices for Monitoring of a Large-Scale Distributed Water System

High-order, often distributed, dynamical systems composed of several interconnected subsystems are often referred to as large-scale systems (LSSs). LSSs are often hard to control with a single centralized controller due to the complexity imposed by the system\u27s dimensionality and distributedness. As a result, decentralized or hierarchical control schemes are employed in controlling LSSs. Control performance assessment (CPA) is an important strategy to analyze the efficiency of controllers in LSSs. This paper presents CPA for the Rhine-Meuse Delta water system in The Netherlands. The water system consists of a large number of rivers and sea outlets with barriers and sluices. A flood in this area can damage the ecosystem and cities around it. Thus, it is essential to control this LSS in a way to protect the distributed water system against floods. For this purpose, a multiagent predictive control is developed to control the subsystems in the LSS. Further, two novel control performance indices (CPIs) based on the model-predictive control strategy are introduced to monitor the performance of the controllers and detect any changes in the system. Finally, the root cause of controller deficiencies is diagnosed. The suggested CPIs are compared with a historical performance index. Simulation results show the ability and effectiveness of the proposed CPIs in comparison with the performance measure used in the past

An Adaptive Passive Fault Tolerant Control System for a Steam Turbine Using a PCA Based Inverse Neural Network Control Strategy

Fault tolerant control (FTC) becomes an effective way to defectively control a plant and ensure reliability and safety in the system. This paper presents a new adaptive passive fault tolerant control (FTC) methodology based on inverse control strategy. An adaptive principal component analysis (PCA) algorithm is incorporated as a pretreatment data processing to recursively capture inherent time-varying information embedded in the plant time-series measurements. A multi-layered perceptron (MLP) neural network is then trained online with the reduced PCA extracted features to emulate an adaptive inverse controller based on actual post-fault plant dynamic model. The adaptive MLP-based controller will be able to minimize induced tracking error using an error back propagation (BP) learning algorithm without a priori knowledge of the occurred faults on the basis of the PCA-uncorrelated measurement data. This enhances the generalization capability of the realized controller due to distinctiveness of the PCA-based data representation. An extensive set of test scenarios has been considered to explore effectiveness of the proposed FTC scheme against three major faults in an industrial steam turbine benchmark. The results demonstrate promising capability of the proposed FTC to automatically maintain the steam turbine availability with efficient fault accommodation