Core loading pattern optimization of a typical two-loop 300MWe PWR using Simulated Annealing (SA), novel crossover Genetic Algorithms (GA) and hybrid GA(SA) schemes

Zameer, Aneela; Mirza, Sikander M.; Mirza, Nasir M.

doi:10.1016/j.anucene.2013.10.024

Cited by 68 publications

(14 citation statements)

References 24 publications

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“…If the substance cools slowly, annealing will achieve its goal. In contrast, if the substance cools quickly, the object will be brought into a local optimal state, which does not have minimal energy [26].…”

Section: Selecting the Most Effective Featuresmentioning

confidence: 99%

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Sabzi

Abbaspour‐Gilandeh

Garcı́a-Mateos

et al. 2018

Water

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Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies.

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Section: Selecting the Most Effective Featuresmentioning

confidence: 99%

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Sabzi

Abbaspour‐Gilandeh

Garcı́a-Mateos

et al. 2018

Water

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“…On the basis of the literature (Dahal and Chakpitak, 2007;Zameer et al, 2014), some strategies are proposed to merge the population in GAs. In one strategy called predefined shares, each set has the same portion and n(pop) is a combination of P(t) + Q(t) + R(t) in which P(t), Q(t) and R(t) are the main population, crossover population and mutation population, respectively.…”

Section: Creation Of New Generation By Sa Rulesmentioning

confidence: 99%

Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

Goudarzi

Hassan

Soleymani

et al. 2017

IJAHUC

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To provide the seamless mobility in heterogeneous wireless networks two significant methods, Simulated annealing (SA) and genetic algorithms (GAs) are hybrid. In this paradigm, vertical handovers (VHs) are necessary for seamless mobility. In this paper, the hybrid algorithm has the ability to find the optimal network to connect with a good quality of service (QoS) in accordance with the user's preferences. The intelligent algorithm was developed to provide solutions near to real time and to avoid slow and considerable computations according to the features of the mobile devices. Moreover, a cost function is used to sustain the chosen QoS during transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR and monetary cost. Simulation results presented that choosing the SA rules would minimise the cost function and the GA-SA algorithm could reduce the number of unnecessary handovers, and thereby avoid the 'Ping-Pong' effect.

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“…Recently, the International Atomic Energy Agency (IAEA) has urged the nuclear community to integrate ML in the industry within the framework of emerging technologies, given its superior capability in handling big-data (IAEA, 2020). In fact, the potential of using ML technology has been explored to estimate some key figures of merit such as the power pin peaking factor (Bae et al, 2008), the wall temperature at critical heat flux (Park et al, 2020), the flow pattern identification (Lin, 2020), to detect anomalies and warn of equipment failure (Ahsan and Hassan, 2013;Chen and Jahanshahi, 2018;Devereux et al, 2019); to determine core configuration and core loading pattern optimization (Siegelmann et al, 1997;Faria and Pereira, 2003;Erdogan and Gekinli, 2003;Zamer et al, 2014;Nissan, 2019), to identify initiating events and categorize accidents (Santosh et al, 2003;Na et al, 2004;Lee and Lee, 2006;Ma and Jiang, 2011;Pinheiro et al, 2020;Farber and Cole, 2020) and to determine of key performance metrics and safety parameters (Ridlluan et al, 2009;Montes et al, 2009;Farshad Faghihi and Seyed, 2011;Patra et al, 2012;Young, 2019;Park et al, 2020;Alketbi and Diab, 2021), and in radiation protection for isotope identification and classification (Keller and Kouzes, 1994;Abdel-Aal and Al-Haddad, 1997;Chen, 2009;Kamuda and Sullivan, 2019), etc. However, it is worth noting that the application of ML in nuclear safety is still limited despite its potential to enhance performance, safety, as well as economics of plant operation (Chai et al, 2003) which warrants further research (Gomez Fernandez et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

Sallehhudin

Diab

2021

Front. Energy Res.

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In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty quantification. A machine learning algorithm is developed to predict the peak cladding temperature (PCT) under the conditions of a large break loss of coolant accident given the various underlying uncertainties. The best estimate approach is used to simulate the thermal-hydraulic system of APR1400 large break loss of coolant accident (LBLOCA) scenario using the multidimensional reactor safety analysis code (MARS-KS) lumped parameter system code developed by Korea Atomic Energy Research Institute (KAERI). To generate the database necessary to train the ML model, a set of uncertainty parameters derived from the phenomena identification and ranking table (PIRT) is propagated through the thermal hydraulic model using the Dakota-MARS uncertainty quantification framework. The developed ML model uses the database created by the uncertainty quantification framework along with Keras library and Talos optimization to construct the artificial neural network (ANN). After learning and validation, the ML model can predict the peak cladding temperature (PCT) reasonably well with a mean squared error (MSE) of ∼0.002 and R2 of ∼0.9 with 9 to 11 key uncertain parameters. As a bounding accident scenario analysis of the LBLOCA case paves the way to using machine learning as a decision making tool for design extension conditions as well as severe accidents.

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Core loading pattern optimization of a typical two-loop 300MWe PWR using Simulated Annealing (SA), novel crossover Genetic Algorithms (GA) and hybrid GA(SA) schemes

Cited by 68 publications

References 24 publications

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Hybridisation of genetic algorithm with simulated annealing for vertical-handover in heterogeneous wireless networks

Using Machine Learning to Predict the Fuel Peak Cladding Temperature for a Large Break Loss of Coolant Accident

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