Reliability-driven distribution power network dynamic reconfiguration in presence of distributed generation by the deep reinforcement learning method

Malekshah, Soheil; Rasouli, Ali; Malekshah, Yaser; Ramezani, Afsaneh; Malekshah, Arezoo

doi:10.1016/j.aej.2021.12.012

Cited by 46 publications

(15 citation statements)

References 27 publications

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“…Security of the system is the key assessment (Malekshah and Javad, 2021) which is implemented for environmentalrelated data distribution. Deep reinforcement learning (Malekshah et al, 2022) is used for distributing the power in the dynamic network with high reliability.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

Liu

Huang

2022

Front. Psychol.

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COVID-19 has altered our lifestyle, communication, employment, and also our emotions. The pandemic and its devastating implications have had a significant impact on higher education, as well as other sectors. Numerous researchers have utilized typical statistical methods to determine the effect of COVID-19 on the psychological wellbeing of young people. Moreover, the primary aspects that have changed in the psychological condition of children and young adults during COVID lockdown is analyzed. These changes are analyzed using machine learning and AI techniques which should be established for the alterations. This research work mainly concentrates on children's and young people's mental health in the first lockdown. There are six processes involved in this work. Initially, it collects the data using questionnaires, and then, the collected data are pre-processed by data cleaning, categorical encoding, and data normalization method. Next, the clustering process is used for grouping the data based on their mood state, and then, the feature selection process is done by chi-square, L1-Norm, and ReliefF. Then, the machine learning classifiers are used for predicting the mood state, and automatic calibration is used for selecting the best model. Finally, it predicts the mood state of the children and young adults. The findings revealed that for a better understanding of the effects of the COVID-19 pandemic on children's and youths' mental states, a combination of heterogeneous data from practically all feature groups is required.

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Section: Related Workmentioning

confidence: 99%

RETRACTED: Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

Liu

Huang

2022

Front. Psychol.

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“…Using the fuzzy Laplace method coupled with the Adomian decomposition transform obtained numerical results for a better understanding of the dynamic structures of the physical behavior of COVID-19 [22] . Soheil Malekshah et al [23] proposed a new approach based on dynamic reconfiguration to improve the reliability of distribution networks in the presence of distributed generation by reinforcement learning algorithms. Rohit Kundu et al [24] proposed an automatic detection system for COVID-19 using lung CT scan images and classified them into COVID and non-COVID cases.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: SIR model for the spread of COVID-19: A case study

Salimipour

Mehraban

Ghafour

et al. 2023

Operations Research Perspectives

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“…The ROIs enclosed by the red curve are the two instances of the largest GGO lesions and were used for radiomics extracting features, whereas the areas enclosed by the green curve are other smaller lesions and were used to calculate some significant medical factors, including the position of all of the other lesions on this piece, the ratio of any of the lesion regions in the thoracic cavity on this piece, and the total number of lesions on this slice. 35,36 The PSNR is the ratio of the maximum signal power to the undesirable noise power, the equation for it shown in Eq. ( 1).…”

Section: Resultsmentioning

confidence: 99%

(Retracted) Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Abbas

2022

J. Electron. Imag.

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Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space.

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Reliability-driven distribution power network dynamic reconfiguration in presence of distributed generation by the deep reinforcement learning method

Cited by 46 publications

References 27 publications

RETRACTED: Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

RETRACTED: Mental Health Identification of Children and Young Adults in a Pandemic Using Machine Learning Classifiers

RETRACTED: SIR model for the spread of COVID-19: A case study

(Retracted) Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

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