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  • 1
    Online Resource
    Online Resource
    Point Defects in Buckled Honeycomb PAs Monolayer: A Systematic Study of Stability, Electronic, and Magnetic Properties
    Wiley ; 2023
    In:  Advanced Theory and Simulations
    Details
    In: Advanced Theory and Simulations, Wiley
    Abstract: In this work, the effects of point defects on the electronic and magnetic properties of PAs monolayer are investigated. PAs monolayer is stable in a buckled hexagonal structure, exhibiting indirect gap semiconductor character and is metallized under the presence of vacancies due to the dangling bonds around defect sites. Meanwhile, the non‐magnetic semiconductor character is preserved upon creating antisite defects with a negligible variation of the band gap. Significant magnetism as well as feature‐rich electronic properties are induced by p ‐ and n ‐type defects. Specifically, doping with Si and Ge atoms leads to the emergence of the magnetic semiconductor nature. In these cases, the total magnetic moment of 1 is obtained, where dopant atoms are mainly responsible for the magnetization. Besides, doping with Br induce the half‐metallic nature with a total magnetic moment of 2 , where magnetic properties are produced by the dopant and its neighbor due to the strong hybridization. In contrast, weak hybridization is the main reason for the absence of magnetism in the Cl‐doped PAs monolayer. Results presented herein introduce the creation of point defects in buckled semiconductor PAs monolayer as efficient functionalization approaches for optoelectronic and spintronic applications.
    Type of Medium: Online Resource
    ISSN: 2513-0390 , 2513-0390
    URL: Article
    DOI: 10.1002/adts.202300416
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2894557-8
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  • 2
    Online Resource
    Online Resource
    An Efficient Classification Algorithm for Traditional Textile Patterns from Different Cultures Based on Structures
    Association for Computing Machinery (ACM) ; 2021
    In:  Journal on Computing and Cultural Heritage Vol. 14, No. 4 ( 2021-12-31), p. 1-22
    Details
    In: Journal on Computing and Cultural Heritage, Association for Computing Machinery (ACM), Vol. 14, No. 4 ( 2021-12-31), p. 1-22
    Abstract: Textiles have an important role in many cultures and have been digitised. They are three-dimensional objects and have complex structures, especially archaeological fabric specimens and artifact textiles created manually by traditional craftsmen. In this article, we propose a novel algorithm for textile classification based on their structures. First, a hypergraph is used to represent the textile structure. Second, multisets of k -neighbourhoods are extracted from the hypergraph and converted to one feature vector for representation of each textile. Then, the k -neighbourhood vectors are classified using seven most popular supervised learning methods. Finally, we evaluate experimentally the different variants of our approach on a data set of 1,600 textile samples with the 4-fold cross-validation technique. The experimental results indicate that comparing the variants, the best classification accuracies are 0.999 with LR, 0.994 with LDA, 0.996 with KNN, 0.994 with CART, 0.998 with NB, 0.974 with SVM, and 0.999 with NNM.
    Type of Medium: Online Resource
    ISSN: 1556-4673 , 1556-4711
    URL: Article
    DOI: 10.1145/3465381
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2021
    detail.hit.zdb_id: 2432355-X
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  • 3
    Online Resource
    Online Resource
    Adaptive tracking control for an uncertain chaotic permanent magnet synchronous motor based on fuzzy neural networks
    SAGE Publications ; 2015
    In:  Journal of Vibration and Control Vol. 21, No. 3 ( 2015-02), p. 580-590
    Details
    In: Journal of Vibration and Control, SAGE Publications, Vol. 21, No. 3 ( 2015-02), p. 580-590
    Abstract: In this paper, an adaptive controller is developed to suppress chaos and track the desired speed in an uncertain chaotic permanent magnet synchronous motor (PMSM) drive system. The controller consists of computational and supervisory control schemes. The computational controller, based on fuzzy neural networks, is used to approximate the unknown nonlinear control signal, while the supervisory controller is employed to attenuate the approximation error effects of the neural network and ensure the system is robust. Simulation results demonstrate that the proposed controller can successfully quash chaotic oscillation in a PMSM and allow speeds to follow the desired trajectory despite the existence of uncertainties.
    Type of Medium: Online Resource
    ISSN: 1077-5463 , 1741-2986
    URL: Article
    DOI: 10.1177/1077546313487761
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2015
    detail.hit.zdb_id: 2070247-4
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  • 4
    Online Resource
    Online Resource
    A big data smart agricultural system: recommending optimum fertilisers for crops
    Springer Science and Business Media LLC ; 2023
    In:  International Journal of Information Technology Vol. 15, No. 1 ( 2023-01), p. 249-265
    Details
    In: International Journal of Information Technology, Springer Science and Business Media LLC, Vol. 15, No. 1 ( 2023-01), p. 249-265
    Abstract: Nutrients are important to promote plant growth and nutrient deficiency is the primary factor limiting crop production. However, excess fertilisers can also have a negative impact on crop quality and yield, cause an increase in pollution and decrease producer profit. Hence, determining the suitable quantities of fertiliser for every crop is very useful. Currently, the agricultural systems with internet of things make very large data volumes. Exploiting agricultural Big Data will help to extract valuable information. However, designing and implementing a large scale agricultural data warehouse are very challenging. The data warehouse is a key module to build a smart crop system to make proficient agronomy recommendations. In our paper, an electronic agricultural record (EAR) is proposed to integrate many separate datasets into a unified dataset. Then, to store and manage the agricultural Big Data, we built an agricultural data warehouse based on Hive and Elasticsearch. Finally, we applied some statistical methods based on our data warehouse to extract fertiliser information such as a case study. These statistical methods propose the recommended quantities of fertiliser components across a wide range of environmental and crop management conditions, such as nitrogen ( N ), phosphorus ( P ) and potassium ( K ) for the top ten most popular crops in EU.
    Type of Medium: Online Resource
    ISSN: 2511-2104 , 2511-2112
    URL: Article
    DOI: 10.1007/s41870-022-01150-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2878562-9
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  • 5
    Online Resource
    Online Resource
    Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks
    Hindawi Limited ; 2015
    In:  Mathematical Problems in Engineering Vol. 2015 ( 2015), p. 1-13
    Details
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2015 ( 2015), p. 1-13
    Abstract: The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach.
    Type of Medium: Online Resource
    ISSN: 1024-123X , 1563-5147
    URL: Article
    DOI: 10.1155/2015/351524
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2015
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 6
    Online Resource
    Online Resource
    Automated evaluation of Tuberculosis using Deep Neural Networks
    European Alliance for Innovation n.o. ; 2022
    In:  EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Vol. 8, No. 30 ( 2022-04-14), p. e4-
    Details
    In: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, European Alliance for Innovation n.o., Vol. 8, No. 30 ( 2022-04-14), p. e4-
    Abstract: INTRODUCTION: Tuberculosis (TB) is a chronic, progressive infection that often has a latent period after the initial infection period. Early awareness from those period to have better prevention steps becomes an indispensable part for patients who want to lengthen their lives. Hence, applying cutting-edge technologies to support the medical business domain plays a key role in improving speed and accuracy in methods of diagnosis. Deep Neural Network-based technique (DNN) is one of such methods which offers positive results by leveraging the advantages of analyzing deeply the data, especially image data format via tons of deep layers of the neural networks. Our study was wrapped up by objectively assessing the performance of modern Deep Neural Network approaches and suggesting a model offering good results in terms of the selected metrics as defined later. In order to achieve optimized results, the chosen model must adapt well to the datasets but require less hardware and computational resources.OBJECTIVES: Our objective is to pick up and train a Deep Neural Network architecture which is highly trusted and flexibly fitted and applied to various datasets with minimum configurations. This will be used to produce good predictions based on the input data which are Chest X-ray images retrieved from the published datasets.METHODS: We have been approaching this problem by using the recognized datasets which have already been published before, then resizing them to the consistent input data for training purposes. In terms of Deep Neural Networks, we picked up VGG16 as the baseline network architecture, then use other ones which are state-of-the-art networks for comparison purposes. After all, we recommend the neural network architecture offering the most positive results based on accuracy and recall measurements. So that, this network architecture will show flexibility when fitting into diverse datasets representing different areas in the world that suffered from Tuberculosis before.RESULTS: After conducting the experiments, we observed that the Mobilenet model produced great results based on the predefined metrics for most of the proposed datasets. It shows the versatility which is applicable to all CXR datasets, especially for the Tuberculosis ones.CONCLUSION: Tuberculosis is still one of the most dangerous illnesses in the world that needs vital methods to prevent and detect soon so that patients are able to keep their lives longer. After this research, we are constantly improving the current accuracy of the models and applying the current results of this research for later problems such as detecting the Tuberculosis areas in real-time and supporting doctors to make decisions based on the current status of patients.
    Type of Medium: Online Resource
    ISSN: 2410-0218
    URL: Article
    DOI: 10.4108/eetinis.v8i30.478
    Language: Unknown
    Publisher: European Alliance for Innovation n.o.
    Publication Date: 2022
    detail.hit.zdb_id: 3003127-8
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  • 7
    Online Resource
    Online Resource
    An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor
    Hindawi Limited ; 2014
    In:  Abstract and Applied Analysis Vol. 2014 ( 2014), p. 1-12
    Details
    In: Abstract and Applied Analysis, Hindawi Limited, Vol. 2014 ( 2014), p. 1-12
    Abstract: This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM) drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of the controlled system. Moreover, due to improvement in controller design, the singularity problem is surely avoided. Finally, numerical simulations are carried out to demonstrate that the proposed control scheme can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existence of unknown models and uncertainties.
    Type of Medium: Online Resource
    ISSN: 1085-3375 , 1687-0409
    URL: Article
    DOI: 10.1155/2014/987308
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2014
    detail.hit.zdb_id: 2064801-7
    SSG: 17,1
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  • 8
    Online Resource
    Online Resource
    Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks
    Hindawi Limited ; 2014
    In:  Mathematical Problems in Engineering Vol. 2014 ( 2014), p. 1-11
    Details
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2014 ( 2014), p. 1-11
    Abstract: In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM) drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.
    Type of Medium: Online Resource
    ISSN: 1024-123X , 1563-5147
    URL: Article
    DOI: 10.1155/2014/868415
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2014
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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  • 9
    Online Resource
    Online Resource
    Automating attendance management in human resources: A design science approach using computer vision and facial recognition
    Elsevier BV ; 2024
    In:  International Journal of Information Management Data Insights Vol. 4, No. 2 ( 2024-11), p. 100253-
    Details
    In: International Journal of Information Management Data Insights, Elsevier BV, Vol. 4, No. 2 ( 2024-11), p. 100253-
    Type of Medium: Online Resource
    ISSN: 2667-0968
    URL: Article
    DOI: 10.1016/j.jjimei.2024.100253
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 3063513-5
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  • 10
    Online Resource
    Online Resource
    An Optimal Cluter Head Parameter in Leach Protocol of Wireless Sensor Networks
    The University of Danang ; 2020
    In:  Journal of Science and Technology: Issue on Information and Communications Technology Vol. 18, No. 4.2 ( 2020-04-30), p. 8-
    Details
    In: Journal of Science and Technology: Issue on Information and Communications Technology, The University of Danang, Vol. 18, No. 4.2 ( 2020-04-30), p. 8-
    Abstract: In wireless sensor networks, LEACH is often used as an energy saving protocol and extends the network life. However, there are many parameters that affect the performance of the LEACH protocol, one of which is the number of cluster heads. This paper proposes a simple and efficient solution to determine the optimal number of cluster heads in the LEACH protocol. For the proposed solution, the system can achieve the optimal performance between the longest lifetime in the constraint as the largest amount of data transmitted in the network.   
    Type of Medium: Online Resource
    ISSN: 1859-1531
    URL: Article
    DOI: 10.31130/ict-ud.2020.92
    Language: Unknown
    Publisher: The University of Danang
    Publication Date: 2020
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