825 research outputs found
The Possible Negative Outcomes of Putting Learners in Spotlight
No full textAbstractDespite the vast research on the benefits of learner-fronted language learning and teaching, little is studied and known about the possible negative consequences of pure learner-centered language learning. It will be useful to look at the matter from a different angle, though, the overall image emerging from the literature is positive; independent responsible active learners with great learning motivation. This study reveals some of the facts regarding learner‘s autonomy. We are going to see whether there are any negative points concerning giving learner‘s infinite amount of authority. The participants, on which this survey has been carried out, two groups of 20 male upper intermediate learners of English, were selected and treated completely different. One group was given sort of unlimited authority while the other group was somehow reliant on the teacher. After almost three months of instruction, it was revealed that giving learners too much authority would result in serious and severe problems both for learners and specifically for the teachers. Moreover, by the shift of responsibility, anxiety among learners has drastically influenced
Rhodeus caspius, a new bitterling from Iran (Teleostei: Cypriniformes Acheilognathidae)
No full textEsmaeili, Hamid Reza, Sayyadzadeh, Golnaz, Japoshvili, Bella, Eagderi, Soheil, Abbasi, Keivan, Mousavi-Sabet, Hamed (2020): Rhodeus caspius, a new bitterling from Iran (Teleostei: Cypriniformes Acheilognathidae). Zootaxa 4851 (2): 319-337, DOI: https://doi.org/10.11646/zootaxa.4851.2.
Rhodeus caspius, a new bitterling from Iran (Teleostei: Cypriniformes Acheilognathidae)
No full textEsmaeili, Hamid Reza, Sayyadzadeh, Golnaz, Japoshvili, Bella, Eagderi, Soheil, Abbasi, Keivan, Mousavi-Sabet, Hamed (2020): Rhodeus caspius, a new bitterling from Iran (Teleostei: Cypriniformes Acheilognathidae). Zootaxa 4851 (2): 319-337, DOI: 10.11646/zootaxa.4851.2.
Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques-A comparative study
No full textThis paper presents a combined data-driven framework for fault detection and isolation (FDI) based on the ensemble of diverse classification schemes. The proposed FDI scheme is configured in series and parallel forms in the sense that in series form the decision on the occurrence of fault is made in FD module, and subsequently, the FI module coupled to the FD module will be activated for fault indication purposes. On the other hand, in parallel form a single module is employed for FDI purposes, simultaneously. In other words, two separate multiple-classifiers schemes are presented by using fourteen various statistical and non-statistical classification schemes. Furthermore, in this study, a novel ensemble classification scheme namely blended learning (BL) is proposed for the first time where single and boosted classifiers are blended as the local classifiers in order to enrich the classification performance. Single-classifier schemes are also exploited in FDI modules along with the ensemble-classifier methods for comparison purposes. In order to show the performance of proposed FDI method, it was also tested and validated on DAMADICS actuator system benchmark. Besides, comparative study with the related works done on this benchmark is provided to show the pros and cons of the proposed FDI method
Novel Non-Model-Based Fault Detection and Isolation of Satellite Reaction Wheels Based on a Mixed-Learning Fusion Framework
No full textThis paper suggests a model-free framework for Fault Detection and Isolation (FDI) of satellite reaction wheels for the first time. The proposed FDI method is based on multi-classifier fusion with diverse learning algorithms and configured in a parallel form where a unique module simultaneously performs both detection and isolation tasks. In other words, a multi-classifier-based arrangement is presented on the basis of Mixed Learning strategy where four classic and well-practised classification schemes including Random Forest, Support Vector Machine, Partial Least Square, and Naïve Bayes are incorporated into FDI module in order to make a decision on the occurrence of a fault and its location. Extensive simulation results with a high-fidelity nonlinear spacecraft simulator considering gyroscopic effects, measurement noise, and exogenous aerodynamic disturbance signals show that the proposed FDI scheme can cope with faults affecting reaction wheel torques and obtain promising FDI performances in most of the designed scenarios
FIGURE 3 in Rhodeus caspius, a new bitterling from Iran (Teleostei: Cypriniformes Acheilognathidae)
No full textFIGURE 3. Rhodeus caspius, Uncatalogued; a, and c, male, b, female, Anzali wetland; d, male, Razavar River; e, female, Tajan River and f, male, Babol River.Published as part of Esmaeili, Hamid Reza, Sayyadzadeh, Golnaz, Japoshvili, Bella, Eagderi, Soheil, Abbasi, Keivan & Mousavi-Sabet, Hamed, 2020, Rhodeus caspius, a new bitterling from Iran (Teleostei: Cypriniformes Acheilognathidae), pp. 319-337 in Zootaxa 4851 (2) on page 326, DOI: 10.11646/zootaxa.4851.2.6, http://zenodo.org/record/440766
A Comparative Study of Narration Levels and Its Elements in the Stories of Surah the Cave (Al-Kahaf)
No full textNarrationology is regarded as a theoretical and practical framework and pattern to examine and analyze various types of stories, including Quranic ones. Using narrationology and its theories such as narrative levels, plot, story, narrative text, narrative method of stories, meanings and foundations is regarded an efficient method to analyze Quranic stories. The Surah Cave includes three specific stories, including the story of the people of the Cave, the story of Prophet Musa and Khezr and the story of Dhul-Qarnayn "he of the two horns." The results of the present paper shows that the narrative plot of the stories in the Surah Cave is noticeable in terms of continuous construction methods of various narrative levels, integration and interconnectivity of these levels with each other, successive fractions in story line and episodic analysis of stories in appearance and their integrity in the inner part of the Surah.
Mahdi Hamed Saghghayan[1]
Reza Abbasi[2]
Abdullah Bekaa[3]
[1] Corresponding author, assistant professor of college of Arts and Architecture, Tarbiat Modarres University, [email protected]
[2] MA student of department of film directing, Tehran University, [email protected]
[3] MA student of department of theater directing, Tehran University, [email protected]
FIGURE 9 in Rhodeus caspius, a new bitterling from Iran (Teleostei: Cypriniformes Acheilognathidae)
No full textFIGURE 9. Ventral view of branchial (a) and hyoid (b) arches in Rhodeus caspius. Bhy: basihyal; Brs;branchiostegal rays; Chy: ceratohyal; Epy: epihyal; Hhy: dorsal and ventral hypohyal; Ihy: interhyal; Uhy: urohyal; Bbr: basibranchial; Cbr: ceratobranchial; Ebr: epibranchial; Hbr: hypobranchial; Pbr: inphrapharyngobranchial.Published as part of Esmaeili, Hamid Reza, Sayyadzadeh, Golnaz, Japoshvili, Bella, Eagderi, Soheil, Abbasi, Keivan & Mousavi-Sabet, Hamed, 2020, Rhodeus caspius, a new bitterling from Iran (Teleostei: Cypriniformes Acheilognathidae), pp. 319-337 in Zootaxa 4851 (2) on page 332, DOI: 10.11646/zootaxa.4851.2.6, http://zenodo.org/record/440766
Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques
No full textThis study proposes a model-based robust fault detection and isolation (RFDI) method with hybrid structure. Robust detection and isolation of the realistic faults of an industrial gas turbine in steady-state conditions is mainly considered. For residual generation, a bank of time-delay multilayer perceptron (MLP) models is used, and in fault detection step, a passive approach based on model error modelling is employed to achieve threshold adaptation. To do so, local linear neuro-fuzzy (LLNF) modelling is utilised for constructing error-model to generate uncertainty interval upon the system output in order to make decision whether a fault occurred or not. This model is trained using local linear model tree (LOLIMOT) which is a progressive tree-construction algorithm. Simple thresholding is also used along with adaptive thresholding in fault detection phase for comparative purposes. Besides, another MLP neural network is utilised to isolate the faults. In order to show the effectiveness of proposed RFDI method, it was tested on a single-shaft industrial gas turbine prototype model and has been evaluated based on the gas turbine data. A brief comparative study with the related works done on this gas turbine benchmark is also provided to show the pros and cons of the presented RFDI method
Guest Editorial: Introduction to IEEE Control Systems Letters Special Section on Multi-Agent Coordination for Energy Systems: From Model Based to Data-Driven Methods
Get PDFEditorialGreen Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Sergio GrammaticoTeam Bart De Schutte
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