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Vulnerability Analysis of Power Systems Under Physical Deliberate Attacks Considering Geographic-Cyber Interdependence of the Power System and Communication Network
Discrete-Time, Markov Chain Analysis of Energy Efficiency in a CR Network Regarding Primary and Secondary Traffic With Primary User Returns
Discrete-Time, Markov Chain Analysis of Energy Efficiency in a CR Network Regarding Primary and Secondary Traffic With Primary User Returns
Using (t-Bu)5PW11CoO39 to fabricate a sponge graphene network for energy storage in seawater and acidic solutions
Using (t-Bu)5PW11CoO39 to fabricate a sponge graphene network for energy storage in seawater and acidic solutions
Conjugate active and reactive power management in a smart distribution network through electric vehicles: A mixed integer-linear programming model
Conjugate active and reactive power management in a smart distribution network through electric vehicles: A mixed integer-linear programming model
Optimization of Water Distribution Networks Using a New Entropy-based Mixed Reliability Index and a Fuzzy-based Constraint Handling Technique
EXPERIMENTAL AND NUMERICAL INVESTIGATION OF THE DEEP DRAWING PROCESS FOR AN AUTOMOBILE PANEL AND PREDICTION OF APPROPRIATE AMOUNT OF PARAMETERS BY MULTI-LAYER NEURAL NETWORK
Artificial Neural Network Models for Production of Nano-Grained Structure in AISI 304L Stainless Steel by Predicting Thermo-Mechanical Parameters
Artificial Neural Network Models for Production of Nano-Grained Structure in AISI 304L Stainless Steel by Predicting Thermo-Mechanical Parameters
Mapping Environmental Conditions in the St. Lawrence River onto Ice Parameters using Artificial Neural Networks to Predict Ice Jams
Extending the Dynamic Range of the Determination of Copper by Adsorption Differential Pulse Stripping Method Using a Principal componet artificial neural network
Extending the Dynamic Range of the Determination of Copper by Adsorption Differential Pulse Stripping Method Using a Principal componet artificial neural network
Prediction of solubility for polycyclic aromatic hydrocarbons in supercritical carbon dioxide using wavelet neural networks in quantitative structure property relationship