Моделирование парожидкостного равновесия в системе: α,γ-дихлоргидрин глицерина– α-монохлоргидрин глицерина–глицерин– хлористый водород–вода

. Both the experimental LLE and VLE data were compared with the correlated values obtained by means of the NRTL equation. Satisfactory agreement between the calculated values and experimental data was achievedполучена модель парожидкостного равновесия для пятикомпонентной промышленной системы α,γ-дихлоргидрин глицерина–α-монохлоргидрин глицерина–глицерин–хлористый водород–вода

Simulation of vapor-liquid equilibrium in system: a,g-dichlorohydrin–Α-monochlorohydrin–glycerol–hydrogen chloride–water

. Both the experimental LLE and VLE data were compared with the correlated values obtained by means of the NRTL equation. Satisfactory agreement between the calculated values and experimental data was achieve

Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)<i>_x</i>(LiNbO₃)_{100−<i>x</i>} Nanocomposite Memristors

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements