Sharif Digital Repository

This research work presents a novel integrated structure for the cogeneration of electricity and renewable syngas. The base structure of the process is developed by solid oxide cells in which electricity is generated by the natural gas-fueled fuel cell unit, and renewable syngas is produced by the electrolyzer cell unit. Direct integration between fuel cell and electrolyzer cell units is established for optimal use of fuel cell off-gases. To improve system's sustainability, a solar power cycle, including solar collectors coupled with an organic Rankine cycle (ORC), is designed to provide renewable electricity for steam and CO2 co-electrolysis operation. 1D mathematical approaches are... 

The power-to-liquid concept is a promising strategy to convert the power plants' flue gas to value-added liquid fuels using renewable energy. This technology could potentially reduce global greenhouse gases emissions and mitigate the environmental problems associated with the fossil fuels industry. In this regard, the main objective of the present study is to propose a novel power-to-liquid plant for the synthesis of formic acid and ammonia from power plants' flue gas, emphasizing the role of electrochemical technologies and renewable energy. The system's basis is developed by the integration of CO2 electroreduction cell, alkaline water electrolysis cell, and photovoltaic panel technologies.... 

Predicting the hydrodynamic coefficients of an autonomous underwater vehicle (AUV) is important during vehicle design. SUT-2 is an AUV. being developed by the Marine Engineering Research Center of Sharif University of Technology in Iran (MERC). Model tests are done in the marine engineering laboratory towing tank. In tins research, hydrodynamic coefficients are calculated using model test results of an autonomous underwater vehicle. Hydrodynamic forces are also analyzed. These coefficients are used for dynamic modeling and autonomous controller design  

Convolutional neural networks (CNNs) are at the heart of several machine learning applications, while they suffer from computational complexity due to their large number of parameters and operations. Recently, all-optical implementation of the CNNs has achieved many attentions, however, the recently proposed optical architectures for CNNs cannot fully utilize the tremendous capabilities of optical processing, due to the required electro-optical conversions in-between successive layers. To implement an all-optical multi-layer CNN, it is essential to optically implement all required operations, namely convolution, summation of channels' output for each convolutional kernel feeding the... 

The classification performance of all-optical Convolutional Neural Networks (CNNs) is greatly influenced by components’ misalignment and translation of input images in the practical applications. In this paper, we propose a free-space all-optical CNN (named Trans-ONN) which accurately classifies translated images in the horizontal, vertical, or diagonal directions. Trans-ONN takes advantages of an optical motion pooling layer which provides the translation invariance property by implementing different optical masks in the Fourier plane for classifying translated test images. Moreover, to enhance the translation invariance property, global average pooling (GAP) is utilized in the Trans-ONN... 

The mathematical techniques of decision making are among the most valuable outcomes of this research activity, which is generally referred to as realization in the operations, operational research or quantitative methods of decision making. Over time, with the increase in the complexity and the variety of decision making problems, the methods of decision making become more varied and will have more capability of problem solving. Multi-Criteria Decision Making (MCDM) is a collection of methodologies to compare, select, or rank multiple alternatives that involve incommensurate attributes. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is a multiple criteria... 

4H-chromenes play a significant role in natural and pharmacological products. Despite continuous advances in the synthesis methodology of these compounds, there is still a lack of a green and efficient method. In this study, we have designed cysteic acid chemically attached to magnetic graphene oxide (MNPs·GO-CysA) as an efficient and reusable solid acid catalyst to synthesize 4H-chromene skeletons via a one-pot three components reaction of an enolizable compound, malononitrile, an aldehyde or isatin, and a mixture of water–ethanol as a green solvent. This new heterogeneous catalyst provides desired products with a good to excellent yield, short time, and mild condition. This procedure... 

Optimal Reactive Power Dispatch (ORPD) has a salient impact on decreasing the power loss of transmission lines and adjusting the voltage deviation. The parameters that are used for the ORPD are tap settings of transformers, voltages of generating plants and the output of compensating device such as capacitor banks and synchronous condensors. In this paper settings of FACTS devices are considered as additional parameters in solving the ORPD problem. Also the genetic algorithm has been used to find the optimal settings of the controlling parameters. The results of the ORPD have been obtained on a 30 bus IEEE network. © 2009 IEEE  

Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems. In this regard, this paper proposes an optical saturable absorption nonlinearity and its atomic-level model, as well as two various optical pooling operations, namely optical average pooling and optical motion pooling, by means of 4f optical correlators. Proposing these optical building blocks not only speed up the neural networks due to negligible optical processing latency, but also facilitate the concatenation of optical convolutional layers with no optoelectrical conversions in-between, as the... 

One of the most important obligations of privacy-enhancing technologies is to bring confidentiality and privacy to users' browsing activities on the Internet. The website fingerprinting attack enables a local passive eavesdropper to predict the target user's browsing activities even she uses anonymous technologies, such as VPNs, IPsec, and Tor. Recently, the growth of deep learning empowers adversaries to conduct the website fingerprinting attack with higher accuracy. In this paper, we propose a new defense against website fingerprinting attack using adversarial deep learning approaches called Adversarial Website Adaptation (AWA). AWA creates a transformer set in each run so that each...