Sharif Digital Repository

Additive manufacturing (AM) has gained significant attention in recent years as a novel approach to fabricate three-dimensional parts. Its advantages, including high speed, low-cost production for small-scale batches, and greater design freedom, have made it a competitive alternative to traditional manufacturing methods. However, the application of AM for components subjected to fretting fatigue requires careful evaluation. Fretting fatigue is a major cause of failure in turbines, particularly at the contact interface between turbine blade roots and disk. In this study, the influence of AM and post-processing treatments on the fretting fatigue life of Ti-6Al-4V alloy was investigated. Five... 

One of the most important problems in machine learning is investigating the performance of the learning algorithms, and especially deep neural networks, on adversarial examples, which are generated by imperceptibly perturbing input images, so that cause the model make a wrong prediction. Not only this line of research is important for making deep neural networks dependable, but also can help with understanding the fundamental limitations of deep neural networks, and the nature of their operation, which can in turn provide researchers with valuable insights into artificial intelligence. In this research work, we have tried to approach the topic with a mainly theoretical mindset. The method we... 

Existing face forgery detection methods mainly focus on certain features of images, such as features related to image noise, local textures or frequency statistics of images for forgery detection. This makes the extracted representations and the final decision depend on the data in the database and makes it difficult to detect forgery with unknown manipulation methods. Solving this challenge, which is called the generalization challenge in artificial intelligence literature, has become the main goal of researchers in this field. In this thesis, the focus is on extracting effective features for success in forgery detection and preventing the performance of the forgery detection network from... 

Free access to large-scale public datasets, together with the rapid advancement of deep learning techniques and networks, particularly generative models, has led to the production of forged content whose distinction from authentic content has become impossible for humans and many classical forgery detection methods. The well-known term deepfake refers to a deep learning–based technique capable of generating forged images and videos by manipulating their content. A common example of deepfake forgery is identity manipulation in images and videos through facial alteration. In this research, three main objectives related to deepfake methods are considered. First, deepfake generation techniques... 

Quantum mechanics is very succesful in the description and prediction of the microphysical world. So nowadays very few doubt its authenticity. Nevertheless, there are some conceptual issues that are still under debate, such as action at a distance. It took a while after the introduction of the EPR paradox in 1935 until John Stewart Bell derived an inequality, using local hidden variables. This inequality was in contrast with predictions of quantum mechanics. Experiments also contradicted this inequality. This implies that there is action at distance which, in turn suggests, the violation of the special relativity. In this thesis, we consider this issue and shall see that on the one hand we... 

Nowadays, with the increasing size of data,it’s impossible to collect data and fast classification by human, and needs for an automated classification and data analysis, is more interested. Data classification is a process of giving the training data along with their class labels to the learning agent, which learns the relation between the instances and the labels. Then make a prediction to the label of the training data.In this thesis we will observe the classification of the multi-label data. Multi-label data have more than one label. In other words, each instance appears with a vector of labels.In this thesis, a method based on nearest neighbor is proposed to classify the multi-label... 

Credit scoring is a mechanism used to quantify the risk factors relevant for an obligor’s ability and willingness to pay. Credit scoring has become the norm in modern banking, due to the large number of applications received on a daily basis and the increased regulatory requirements for banks. The meaning of credit scoring is to assign scores to the characteristics of debt and borrowers, historical default, and other loss experienced as an indication of the risk level of the borrower. The aim of the credit score model is to build a single aggregate risk indicator for a set of risk factors.
In this study, current method in which Maskan bank will give a loan is explained. We developed a... 

Recent progress in developing Artificial Neural Network (ANN) meta-models has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture remain key challenges to their practical use. This study’s main objectives are: selecting proper input parameters for ANN meta-models, optimizing the ANN models to achieve the most accurate hourly prediction for a case study (City of Tehran), coupling the ANN method with filtered input data and evaluating its privilege. In the current study the ANNs were constructed to predict criteria... 

In this thesis, static and dynamic analysis of nano beams based on second strain gradient theory is presented. Due to their small sizes, nano electro mechanical devices (NEMS) hold tremendous promise for novel, versatile and very sensitive devices for different applications ranging from actuators, transducers and also mass, force, light and frequency detectors. Therefore accurate modeling and analysis of such devices has an important role in their design and performance improvement. Neglecting the size effect, traditional theory of elasticity can not be suitable to predict mechanical behavior of these systems and so, it should be used non-classical theories which include size dependency... 

In recent years, deep learning has experienced rapid and remarkable advancements, demonstrating exceptional performance in various applications such as computer vision, natural language processing, and autonomous vehicles. These models are continually evolving and expanding, yet they harbor inherent vulnerabilities that prevent complete trust in their reliability. One of the most critical issues is their susceptibility to adversarial attacks, where a clean image is subtly manipulated to create an adversarial example. Adversarial examples, generated by adding imperceptible perturbations to input data, can easily mislead deep learning models into making highly confident yet incorrect... 

In this thesis, we study the task of sign language translation using a non-end-to-end framework by utilizing large language models. The proposed method initially generates a sequence of glosses corresponding to a sign language video through a continuous sign language recognition model, which is then considered as input for a language model. The role of a language model in this framework is to translate the sequence of glosses into coherent sentences in spoken language. Given the limited training resources available for machine translation models on the specific problem of sign language to text translation, leveraging large language models can be highly beneficial due to the accumulated... 

Wind energy technologies guarantee low pollution and operational costs. Using a DFIG and a fractionally rated power electronics converter gives variable speed operation with a low cost drive train. As energy policy organizations have allocated a considerable quota of wind energy generation to offshore wind farms, the absence of slip rings and brushes in the brushless DFIG (BDFIG) is an advantage for offshore wind turbines where maintenance is vital and expensive. With increasing wind power penetration in power systems, grid code requirements are an important consideration for the ride-through capability of wind farms through voltage dips, particularly for multi- MW wind turbine generators.... 

This study explores hysteresis in cardiac electrical and mechanical responses to heart rate changes. Specifically, the focus is on the RT interval (as a surrogate for QT) and the systolic interval (S1S2), extracted from ECG and PCG signals, respectively. Long Short-Term Memory (LSTM) models were trained to capture the dynamic relationship between RR intervals and each of these modalities. To evaluate temporal memory, synthetic step-like RR inputs were used, and the corresponding delays in model outputs were analyzed. The results showed clear hysteresis in both RT and S1S2 intervals, with delays increasing at higher workloads (i.e., shorter RR), indicating slower cardiovascular adaptation...