Sharif Digital Repository

For measuring damping capacity of shape memory alloy, samples containing Fe- 27Mn-6Si-4Cr-4Ni were prepared. In order to produce the alloy 99% pure elemental powders were mixed and pressed in a matrix rod. The packed powders put in 15cc alumina crucibles were held in a 200 °C oven for 10 hours before they were melted inside two–layered alumina and graphite grucible an induction furnace. Resulting samples went under hot and cold rolling, thermomechanical treatment and were shaped into strips by wirecut. Chemical composition ascertainment, Tensile, bending, differential scanning calorimetry (DSC), micro hardness and modal tests were operated. Microstructural investigation was done using... 

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... 

In the present study, the electrochemical oxidation process was introduced as a green process for complete degradation and removal of non-biodegradable pollutants from aquatic environments and effluents of various industries without the need to use chemicals and toxins. Research stages include comprehensive preliminary studies in the field of electrochemistry and wastewater treatment, software design of electro-oxidation reactor using titanium mesh anode and stainless steel cathode mesh inside a body made of polypropylene, designed cell construction and then evaluation of device performance in polluting environment Are different. In software simulation using COMSOL MULTIPHYSICS software, it... 

We introduce a refinement of the Ozsváth-Szabó complex associated by Juhász [8] to a balanced sutured manifold (X; ). An algebra A is associated to the boundary of a sutured manifold. For a fixed class s of a Spinc structure over the manifold X, which is obtained from X by filling out the sutures, the Ozsváth-Szabó chain complex CF(X; ; s) is then defined as a chain complex with coefficients in A and filtered by the relative Spinc classes in Spinc(X; ). The filtered chain homotopy type of this chain complex is an invariant of (X; ) and the Spinc class s 2 Spinc(X). The construction generalizes the construction of Juhász. It plays the role of CF (X; s) when X is a closed three-manifold,... 

Let a surfaces K be embedded in a closed, symplectic 4-manifold (X, ω). Investigating the restrictions of the manifold X and its symplectic structure on K, is a challenging issue for some topologists. During decades 1990 and 2000, some solutions were given for this problem. Most of them were based on very advanced techniques and topics like gauge theory. But in 2020, Peter Lambert-Cole found a new solution for this problem, based on some combinatorical methods and no gauge theory. He showed that there is an upper bound for the Euler-characteristic of K, and named it a version of the adjunction inequality. This upper bound is in terms of homological information of X, its symplectic... 

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... 

Suppose that K and K^' are knots inside the homology spheres Y and Y^', respectively. Let X=Y(K,K^') be the 3-manifold obtained by splicing the complements of K and K^' and Z be the three-manifold obtained by 0-surgery on K. When Y^' is an L-space, we use Eftekhary's splicing formula to show that the rank of (HF) ̂(X) is bounded below by the rank of (HF) ̂(Y) if τ_(K^' )=0 and is bounded below by rank((HF) ̂(Z))-2 rank((HF) ̂(Y))+1 if τ_(K^' )≠0.
 

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 first define the pseudo-distance p on the group of Hamiltonian diffeomorphisms.Using the concept of displacement energy, we show that the pseudo-distance p is degenerate and if the manifold is closed, p will be zero for each p = 1; 2; 3; : : : Then, we introduce Lagrangian submanifolds and prove that if L R2n is a rational Lagrangian submanifold, we have the following inequality e(L) ≥ 1γ(L). : Finally, using the above inequality and the concept of displacement energy, for M = R2n we prove that 1 is non-degenerate. Therefore, the hypothesis for 1 to be a metric, are satisfied. This metric is called Hofer’s metric  

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.... 

In this thesis, we study a homological invariant in Knot theory, called Khovanov homology. The main property of this invariants is that it gives us the Jones polynomial, as its graded Euler characteristic. Besides, the functor (1+1) TQFT, from the category of closed one-manifolds to the category of vector spaces is employed in its construction. By making some changes to this functor and defining another functor and some other steps, the so-called Lee spectral sequence is derived which starts from Khovanov homology and converges to another homological invariant of links, called Lee-Khovanov homology. Computation of this homology is very simple. By using this spectral sequence, a numerical... 

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...