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  • 1
    Online Resource
    Online Resource
    A Distributed Stochastic Energy Management Framework Based-Fuzzy-PDMM for Smart Grids Considering Wind Park and Energy Storage Systems
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Access Vol. 9 ( 2021), p. 46674-46685
    Details
    In: IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), Vol. 9 ( 2021), p. 46674-46685
    Type of Medium: Online Resource
    ISSN: 2169-3536
    URL: Article
    DOI: 10.1109/ACCESS.2021.3067501
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
    detail.hit.zdb_id: 2687964-5
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  • 2
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    A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment
    Institute of Electrical and Electronics Engineers (IEEE) ; 2021
    In:  IEEE Access Vol. 9 ( 2021), p. 92083-92100
    Details
    In: IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), Vol. 9 ( 2021), p. 92083-92100
    Type of Medium: Online Resource
    ISSN: 2169-3536
    URL: Article
    DOI: 10.1109/ACCESS.2021.3092834
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2021
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  • 3
    Online Resource
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    Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 8 ( 2022-04-14), p. 1312-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 8 ( 2022-04-14), p. 1312-
    Abstract: In this paper, a hybrid gradient simulated annealing algorithm is guided to solve the constrained optimization problem. In trying to solve constrained optimization problems using deterministic, stochastic optimization methods or hybridization between them, penalty function methods are the most popular approach due to their simplicity and ease of implementation. There are many approaches to handling the existence of the constraints in the constrained problem. The simulated-annealing algorithm (SA) is one of the most successful meta-heuristic strategies. On the other hand, the gradient method is the most inexpensive method among the deterministic methods. In previous literature, the hybrid gradient simulated annealing algorithm (GLMSA) has demonstrated efficiency and effectiveness to solve unconstrained optimization problems. In this paper, therefore, the GLMSA algorithm is generalized to solve the constrained optimization problems. Hence, a new approach penalty function is proposed to handle the existence of the constraints. The proposed approach penalty function is used to guide the hybrid gradient simulated annealing algorithm (GLMSA) to obtain a new algorithm (GHMSA) that finds the constrained optimization problem. The performance of the proposed algorithm is tested on several benchmark optimization test problems and some well-known engineering design problems with varying dimensions. Comprehensive comparisons against other methods in the literature are also presented. The results indicate that the proposed method is promising and competitive. The comparison results between the GHMSA and the other four state-Meta-heuristic algorithms indicate that the proposed GHMSA algorithm is competitive with, and in some cases superior to, other existing algorithms in terms of the quality, efficiency, convergence rate, and robustness of the final result.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10081312
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704244-3
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  • 4
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    Online Resource
    A stochastic machine learning based approach for observability enhancement of automated smart grids
    Elsevier BV ; 2021
    In:  Sustainable Cities and Society Vol. 72 ( 2021-09), p. 103071-
    Details
    In: Sustainable Cities and Society, Elsevier BV, Vol. 72 ( 2021-09), p. 103071-
    Type of Medium: Online Resource
    ISSN: 2210-6707
    URL: Article
    DOI: 10.1016/j.scs.2021.103071
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2021
    detail.hit.zdb_id: 2573417-9
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  • 5
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    Interpretable Deep Learning for Discriminating Pneumonia from Lung Ultrasounds
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 21 ( 2022-11-06), p. 4153-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 21 ( 2022-11-06), p. 4153-
    Abstract: Lung ultrasound images have shown great promise to be an operative point-of-care test for the diagnosis of COVID-19 because of the ease of procedure with negligible individual protection equipment, together with relaxed disinfection. Deep learning (DL) is a robust tool for modeling infection patterns from medical images; however, the existing COVID-19 detection models are complex and thereby are hard to deploy in frequently used mobile platforms in point-of-care testing. Moreover, most of the COVID-19 detection models in the existing literature on DL are implemented as a black box, hence, they are hard to be interpreted or trusted by the healthcare community. This paper presents a novel interpretable DL framework discriminating COVID-19 infection from other cases of pneumonia and normal cases using ultrasound data of patients. In the proposed framework, novel transformer modules are introduced to model the pathological information from ultrasound frames using an improved window-based multi-head self-attention layer. A convolutional patching module is introduced to transform input frames into latent space rather than partitioning input into patches. A weighted pooling module is presented to score the embeddings of the disease representations obtained from the transformer modules to attend to information that is most valuable for the screening decision. Experimental analysis of the public three-class lung ultrasound dataset (PCUS dataset) demonstrates the discriminative power (Accuracy: 93.4%, F1-score: 93.1%, AUC: 97.5%) of the proposed solution overcoming the competing approaches while maintaining low complexity. The proposed model obtained very promising results in comparison with the rival models. More importantly, it gives explainable outputs therefore, it can serve as a candidate tool for empowering the sustainable diagnosis of COVID-19-like diseases in smart healthcare.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10214153
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704244-3
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  • 6
    Online Resource
    Online Resource
    A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines
    MDPI AG ; 2022
    In:  Mathematics Vol. 10, No. 15 ( 2022-08-05), p. 2780-
    Details
    In: Mathematics, MDPI AG, Vol. 10, No. 15 ( 2022-08-05), p. 2780-
    Abstract: Power quality has emerged as a sincere denominator in the planning and operation of a power system. Various events affect the quality of power at the distribution end of the system. Detection of these events has been a major thrust area in the last decade. This paper presents the application of Support Vector Machine (SVM) in classifying the power quality events. Well-known signal processing techniques, namely Hilbert transform and Wavelet transform, are employed to extract the potential features from the observation sets of voltages. Supervised architecture consisting of SVM has been constructed by tuning the parameters of SVM by various algorithms. It has been observed that Augmented Crow Search Algorithm (ACSA) yields the best accuracy compared to other contemporary optimizers. Further, Principal Component Analysis (PCA) is employed to choose the most significant features from the available features. On the basis of PCA, three different models of tuned SVMs are constructed. Comparative analysis of these three models, along with recently published approaches, is exhibited. Results are validated by the statistical one-way analysis of variance (ANOVA) method. It is observed that SVM, which contains attributes from both signal-processing techniques, gives satisfactory results.
    Type of Medium: Online Resource
    ISSN: 2227-7390
    URL: Article
    DOI: 10.3390/math10152780
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704244-3
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  • 7
    Online Resource
    Online Resource
    NIPUNA: A Novel Optimizer Activation Function for Deep Neural Networks
    MDPI AG ; 2023
    In:  Axioms Vol. 12, No. 3 ( 2023-02-28), p. 246-
    Details
    In: Axioms, MDPI AG, Vol. 12, No. 3 ( 2023-02-28), p. 246-
    Abstract: In recent years, various deep neural networks with different learning paradigms have been widely employed in various applications, including medical diagnosis, image analysis, self-driving vehicles and others. The activation functions employed in deep neural networks have a huge impact on the training model and the reliability of the model. The Rectified Linear Unit (ReLU) has recently emerged as the most popular and extensively utilized activation function. ReLU has some flaws, such as the fact that it is only active when the units are positive during back-propagation and zero otherwise. This causes neurons to die (dying ReLU) and a shift in bias. However, unlike ReLU activation functions, Swish activation functions do not remain stable or move in a single direction. This research proposes a new activation function named NIPUNA for deep neural networks. We test this activation by training on customized convolutional neural networks (CCNN). On benchmark datasets (Fashion MNIST images of clothes, MNIST dataset of handwritten digits), the contributions are examined and compared to various activation functions. The proposed activation function can outperform traditional activation functions.
    Type of Medium: Online Resource
    ISSN: 2075-1680
    URL: Article
    DOI: 10.3390/axioms12030246
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2661511-3
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  • 8
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    Online Resource
    Efficient Modified Meta-Heuristic Technique for Unconstrained Optimization Problems
    MDPI AG ; 2022
    In:  Axioms Vol. 11, No. 9 ( 2022-09-19), p. 483-
    Details
    In: Axioms, MDPI AG, Vol. 11, No. 9 ( 2022-09-19), p. 483-
    Abstract: In this paper, a new Modified Meta-Heuristic algorithm is proposed. This method contains some modifications to improve the performance of the simulated-annealing algorithm (SA). Most authors who deal with improving the SA algorithm presented some improvements and modifications to one or more of the five standard features of the SA algorithm. In this paper, we improve the SA algorithm by presenting some suggestions and modifications to all five standard features of the SA algorithm. Through these suggestions and modifications, we obtained a new algorithm that finds the approximate solution to the global minimum of a non-convex function. The new algorithm contains novel parameters, which are updated at each iteration. Therefore, the variety and alternatives in choosing these parameters demonstrated a noticeable impact on the performance of the proposed algorithm. Furthermore, it has multiple formulas by which the candidate solutions are generated. Diversity in these formulas helped the proposed algorithm to escape a local point while finding the global minimizer of a non-convex function. The efficiency of the proposed algorithm is reported through extensive numerical experiments on some well-known test problems. The performance profiles are used to evaluate and compare the performance of our proposed algorithm against the other five meta-heuristic algorithms. The comparison results between the performance of our suggested algorithm and the other five algorithms indicate that the proposed algorithm is competitive with, and in all cases superior to, the five algorithms in terms of the efficiency, reliability, and effectiveness for finding the global minimizers of non-convex functions. This superiority of the new proposed algorithm is due to those five modified standard features.
    Type of Medium: Online Resource
    ISSN: 2075-1680
    URL: Article
    DOI: 10.3390/axioms11090483
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2661511-3
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  • 9
    Online Resource
    Online Resource
    Local Grey Predictor Based on Cubic Polynomial Realization for Market Clearing Price Prediction
    MDPI AG ; 2022
    In:  Axioms Vol. 11, No. 11 ( 2022-11-08), p. 627-
    Details
    In: Axioms, MDPI AG, Vol. 11, No. 11 ( 2022-11-08), p. 627-
    Abstract: With the development of restructured power markets, the profit-making competitive business environment has emerged. With the help of different advanced technologies, generating companies are taking decisions regarding trading electricity with imperfect information about marketing operating conditions. The forecasting of the market clearing price (MCP) is a potential issue in these markets. Early information on the MCP can be a proven beneficial tool for accumulating profit. In this work, a local grey prediction model based on a cubic polynomial function is presented to estimate the MCP with the help of historical data. The mathematical framework of this grey model was established and evaluated for different market conditions and databases. The comparison between traditional grey models and some advanced grey models reveals that the proposed model yields accurate results.
    Type of Medium: Online Resource
    ISSN: 2075-1680
    URL: Article
    DOI: 10.3390/axioms11110627
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2661511-3
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  • 10
    Online Resource
    Online Resource
    An Amended Whale Optimization Algorithm for Optimal Bidding in Day Ahead Electricity Market
    MDPI AG ; 2022
    In:  Axioms Vol. 11, No. 9 ( 2022-09-05), p. 456-
    Details
    In: Axioms, MDPI AG, Vol. 11, No. 9 ( 2022-09-05), p. 456-
    Abstract: Successful privatization in other sectors leads to a restructuring in the power sector. The same practice has been adopted in the electrical industry with a deregulated electricity market (EM). This enables competition among generating companies (Genco’s) for maximizing their profit and it plays a central role. With this aim, each Genco gives a higher bid that may result in a risk of losing the opportunity to get selected at auction. The big challenge in front of a Genco is to acquire an optimal bid and this process is known as the Optimal Bidding Strategy (OBS) of a Genco. In this manuscript, a new variant of whale optimization (WOA) termed the Amended Whale Optimization Algorithm (AWOA) is proposed, to attain the OBS of thermal Genco in an EM. Once the effectiveness of new AWOA is proved on 23 benchmark functions, it is applied to five Genco strategic bidding problems in a spot market with uniform price. The results obtained from the proposed AWOA are compared with other competitive algorithms. The results reflect that AWOA outperforms in terms of the profit and convergence rate. Simulations also indicate that the proposed AWOA can successfully be used for an OBS in the EM.
    Type of Medium: Online Resource
    ISSN: 2075-1680
    URL: Article
    DOI: 10.3390/axioms11090456
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2661511-3
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