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Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model
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Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of The Mechanical Behavior Of Materials
A spherical fuzzy AHP model for contractor assessment during project life cycle
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Abstract<p>Measurement of construction performance is essential to a clear image of the present situation. This monitoring by the management team is necessary to identify locations where performance is exceptionally excellent or poor and to identify the primary reasons so that the lessons gained may be exported to the firm and its progress strengthened. This research attempts to construct an integrated mathematical model utilizing one of the recent methodologies for dealing with the fuzzy representation of experts’ knowledge and judgment considering hesitancy called spherical fuzzy analytic hierarchy process (SFAHP) method to assess the contractor’s performance per the project performance pa</p> ... Show More
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Publication Date
Sun Dec 10 2017
Journal Name
Al-academy
The Composition of design according to norm renovation in the interior design space
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The interior spaces represent a true reflection of the concepts and values ​​of humanity and requirements; were interested in current research ongoing changes that have occurred in those human values ​​and requirements over time, even pat each time warp its values ​​and its own requirements. Those changes, which receive Bdilalha the spaces linked by a renewal which guarantees the interior design of the spaces of sustainability and bio-coordinated. Launched search from an initial perception that those associated with the changes of the spaces through time that have not been subject to examination and supervision, they will constitute a continuing threat of losing the ties with the past under the pretext of modernization and mo

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Publication Date
Wed Mar 15 2023
Journal Name
Al-academy
Design requirements according to the formal integration in the design of interior spaces
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The formal integration of the interior spaces in general and the commercial spaces of the watch shops in the large commercial centers in particular is the goal that the designers aim to reach in order for the interior space to become successful in terms of the design idea and its characteristics. Implementation mechanism. One of the reasons for achieving formal integration in the interior spaces of watch shops is the requirements of the design that must be available in these spaces to reach a state of formal integration between the interior and the exterior so that the space becomes fully integrated in all respects. Because of the aforementioned reasons for dealing with the research, through four chapters: The first chapter included the

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Publication Date
Tue Jul 01 2025
Journal Name
Mastering The Minds Of Machines
Recurrent Neural Networks and its Applications in Time Series Data
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Publication Date
Sun Nov 26 2017
Journal Name
Journal Of Engineering
Compression Index and Compression Ratio Prediction by Artificial Neural Networks
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Information about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites

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Publication Date
Tue Jan 01 2019
Journal Name
International Journal Of Machine Learning And Computing
Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks
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Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

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Publication Date
Tue Dec 01 2015
Journal Name
Journal Of Engineering
Modeling and Control of Fuel Cell Using Artificial Neural Networks
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This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

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Publication Date
Mon Jan 01 2024
Journal Name
Aims Mathematics
Solving quaternion nonsymmetric algebraic Riccati equations through zeroing neural networks
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<abstract><p>Many variations of the algebraic Riccati equation (ARE) have been used to study nonlinear system stability in the control domain in great detail. Taking the quaternion nonsymmetric ARE (QNARE) as a generalized version of ARE, the time-varying QNARE (TQNARE) is introduced. This brings us to the main objective of this work: finding the TQNARE solution. The zeroing neural network (ZNN) technique, which has demonstrated a high degree of effectiveness in handling time-varying problems, is used to do this. Specifically, the TQNARE can be solved using the high order ZNN (HZNN) design, which is a member of the family of ZNN models that correlate to hyperpower iterative techniques. As a result, a novel

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Publication Date
Thu Jun 01 2023
Journal Name
Baghdad Science Journal
In vitro isolation and expansion of neural stem cells NSCs
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   Neural stem cells (NSCs) are progenitor cells which have the ability to self‑renewal and potential for differentiating into neurons, oligodendrocytes, and astrocytes. The in vitro isolation, culturing, identification, cryopreservation were investigated to produce neural stem cells in culture as successful sources for further studies before using it for clinical trials. In this study, mouse bone marrow was the source of neural stem cells. The results of morphological study and immunocytochemistry of isolated cells showed that NSCs can be produced successfully and maintaining their self‑renewal and successfully forming neurosphere for multiple passages. The spheres preserved their morphology in culture and cryopreserved t

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Publication Date
Thu Dec 31 2020
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
SYNTHESIS NEW LIQUID ELECTRODES FOR DETERMINATION DOMPERIDONE MALEATE BASED ON A MOLECULARLY IMPRINTED POLYMER: SYNTHESIS NEW LIQUID ELECTRODES FOR DETERMINATION DOMPERIDONE MALEATE BASED ON A MOLECULARLY IMPRINTED POLYMER
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Liquid electrodes of domperidone maleate (DOMP) imprinted polymer were synthesis based on precipitation polymerization mechanism. The molecularly imprinted (MIP) and non-imprinted (NIP) polymers were synthesized using DOMP as a template. By methyl methacrylate (MMA) as monomer, N,Nmethylenebisacrylamide (NMAA) and ethylene glycol dimethacrylate (EGDMA) as cross-linkers and benzoyl peroxide (BP) as an initiator. The molecularly imprinted membranes were synthesis using acetophenone (APH), di-butyl sabacate (DBS), Di octylphthalate (DOPH) and triolyl phosphate (TP)as plasticizers in PVC matrix. The slopes and limit of detection of l

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