Sharif Digital Repository

The immense space of composition-processing parameters leads to numerous trial-and-error experimental works for engineering of novel bulk metallic glasses (BMGs). To tackle this challenging problem, it is required to consider specific guidelines which are able to restrict the productive alloying compositions. In this work, a correlation-based neural network (CBNN) approach was developed, based on a dataset of 7950 alloying compositions, to design potential new MGs through prediction of casting ability, reduced glass transition (Trg) and critical thickness (Dmax). This approach involves individual and mutual characteristics of contributory factors to improve the prediction accuracy. To... 

The quantity and variety of parameters involved in the failure evolutions in solder joints under a thermo-mechanical process directs the reliability assessment of electronic devices to be frustratingly slow and expensive. To tackle this challenge, we develop a novel machine learning framework for reliability assessment of solder joints in electronic systems; we propose a correlation-driven neural network model that predicts the useful lifetime based on the materials properties, device configuration, and thermal cycling variations. The results indicate a high accuracy of the prediction model in the shortest possible time. A case study will evaluate the role of solder material and the joint... 

Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this paper, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforces the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network to fulfill the data diversity. Outcomes show a... 

Costly and time-consuming approaches for solder joint lifetime estimation in electronic systems along with the limited availability and incoherency of data challenge the reliability considerations to be among the primary design criteria of electronic devices. In this article, an iterative machine learning framework is designed to predict the useful lifetime of the solder joint using a set of self-healing data that reinforce the machine learning predictive model with thermal loading specifications, material properties, and geometry of the solder joint. The self-healing dataset is iteratively injected through a correlation-driven neural network (CDNN) to fulfill the data diversity. Outcomes... 

Atomic force microscopy (AFM) is an efficient tool for studying the structural heterogeneity in metallic glasses (MGs). However, time-consuming analysis and limitations in the scanning process are downsides of this experiment. To tackle these problems, a machine learning (ML) model was developed to predict the distribution of energy dissipation on the MG surface with the increase in number of AFM scanning. The results indicated that it was possible to accurately predict the energy of scanning points, leading to a timesaving and reliable study. Moreover, characterization of structural heterogeneity shows that the viscoelastic response of each nanoscale region under sequences of AFM scans... 

Compressive strength, a crucial mechanical property of cement mortars, is typically measured destructively. However, there is a need to evaluate the strength of unique cement-based samples at various ages without causing damage. In this paper, a predictive framework using a genetic algorithm (GA) is proposed for estimating the compressive strength of ordinary cement-based mortars based on their dynamic elastic modulus, measured non-destructively using the impulse excitation technique. By combining the Popovics model (PM) and the Lydon-Balendran model (LBM), the static elastic modulus of samples was calculated using constant coefficients, representing an equivalent compressive strength. A GA... 

This study tries to establish a powerful machine learning (ML) model for predicting the compressive strength of cement-based mortars by using dynamic elasticity data. The ML model was developed on the basis of Bayesian theorem, leading to a decrease in the overfitting problem compared with other conventional neural networks. Moreover, for the first time, the empirical equations were embedded in the ML model, enhancing the correlation between dynamic elasticity and compressive strength in the cement-based mortars. The results showed that the ML model efficiently predicted the compressive strength with determination coefficient (R2) of 95.2% and root mean square error (RMSE) of 0.0488 for... 

Depending on the application in which power electronic converters (PECs) are deployed, failure processes may endanger the desirable performance of PECs. This paper offers holistic insights on reliability modeling of PECs considering dependencies in two simultaneous failure processes, namely gradual wearing-out degradation and vibration sudden degradation. While sudden and gradual degradation processes may individually affect the useful lifetime of PECs, their mutual interdependencies could significantly accelerate the aging mechanisms. A new analytical model for reliability assessment of PECs is proposed that can capture such mutual interdependence of simultaneous failure processes. The... 

In this paper, having investigated the behavior of GPGPU applications, we present an effcient L2 cache architecture for GPUs based on STT-RAM technology. With the increase of processing cores count, larger on-chip memories are required. Due to its high density and low power characteristics, STT-RAM technology can be utilized in GPUs where numerous cores leave a limited area for on-chip memory banks. They have however two important issues, high energy and latency of write operations, that have to be addressed. Low data retention time STT-RAMs can reduce the energy and delay of write operations. However, employing STT-RAMs with low retention time in GPUs requires a thorough investigation on... 

Future GPUs should have larger L2 caches based on the current trends in VLSI technology and GPU architectures toward increase of processing core count. Larger L2 caches inevitably have proportionally larger power consumption. In this article, having investigated the behavior of GPGPU applications, we present an efficient L2 cache architecture for GPUs based on STT-RAM technology. Due to its high-density and low-power characteristics, STT-RAM technology can be utilized in GPUs where numerous cores leave a limited area for on-chip memory banks. They have, however, two important issues, high energy and latency of write operations, that have to be addressed. Low retention time STT-RAMs can... 

There exists an extensive range of applications for elevated gain DC/DC converters, as numerous low-voltage resources are exploited for power supply. Therefore, this study introduces a groundbreaking magnetically coupled DC/DC converter specifically designed for resources with low voltage, including micro PV or fuel cell systems. By enduring low current and voltage stresses, the power devices in this converter ensure remarkable efficiency while maintaining proven voltage ratio capability. The operational principles of the converter are thoroughly discussed and supported by the implementation of a 200W-400V prototype. In order to confirm the effectiveness of the converter, a range of... 

Secure routing is a major key to service maintenance in ad hoc networks. Ad hoc nature exposes the network to several types of node misbehavior or attacks. As a result of the resource limitations in such networks nodes may have a tendency to behave selfishly. Selfish behavior can have drastic impacts on network performance. We have proposed a Misbehavior-Tolerant Multipath Routing protocol (MTMR) which detects and punishes all types of misbehavior such as selfish behavior, wormhole, sinkhole and grey-hole attacks. The protocol utilizes a proactive approach to enforce cooperation. In addition, it uses a novel data redirection method to mitigate the impact of node misbehavior on network...