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... 

Functional design of coating system for the primary mirror of the Iranian National Observatory (INO) is done. This telescope has a 3.4 m primary mirror in diameter, made of Zerodur from Schott. Although similar coating systems are in use since years ago, this is the first attempt to design a system for such a large surface case in Iran. Systematic approach led us first to clarify and identifying the design requirements. Lower limit of 80% for reflectance on all wavelength range from 325-1000 nm, thickness of 100 nm with un-uniformity of 5 nm, adhesion enough to pass the selotape test and surface roughness of less than 8 nm. Study of the available methods for coating and selection of the best... 

The use of braced frames has become more popular in seismic design of buildings because of the simplicity in design and construction. The first event that occurs in the earthquake for a concentrically brace frame (CBF), is the buckling of the strut. The inelastic behavior of CBF systems is dominated by brace buckling, yielding and the post buckling behavior. The Inelastic performance of the brace is nonsymmetrical, because of the difference in the tensile and compressive strength of the brace and the deterioration in the resistance after buckling. so we have too complexity in modeling the inelastic performance of brace members. Also the subject of the near fault ground motion and design of... 

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... 

The quality of service in Wireless Multimedia Sensor Networks (WMSNs) is related to packet loss rate. Recently dierent studies have been done on developing ecient protocols in the transport layer for controlling packet loss in WMSNs. However, all of these protocols are independent of the characteristics of the multimedia content. In this thesis, a novel transport layer protocol, called User Datagram Dispatcher Protocol (UDDP), is proposed to minimize the packet loss ratio in WMSNs by considering trac characteristics, the inter-arrival pattern of packets and packet priority. UDDP is a new cross-layer transport layer that uses information of MAC and application layers to distribute packet... 

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... 

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.... 

Deep learning is one of the most powerful branches of machine learning, demonstrating exceptional performance in tasks such as image and speech recognition with the help of neural networks. However, deep learning networks are highly vulnerable to small and intentional changes in input data, known as adversarial perturbations. These perturbations, despite having a significant impact on the model's output, are usually imperceptible to humans and can pose serious security challenges in sensitive areas such as autonomous vehicles and medical diagnostics. To counter these perturbations, extensive research has been conducted, most of which has utilized a single deep learning network to create a...