Estimation of the Synchronization Time of a Transmission System through Multi Body Dynamic Analysis

The essential component in transmission system is synchronizer. Synchronizer developed to obtain gear changing smoothly. Reducing the transmission time will increase the efficiency of the transmission system and minimize the energy loss during the shifting process. In order to achieve the optimized design, the time estimation for synchronizing process is necessary. In this present study, the multi body dynamic model is proposed to predict the synchronization time. For validation of the results two different synchronizer types, single cone and double cone were used in the test rig machine under different loading conditions. The results of multi body dynamic analysis were compared to experimental and analytical results and show that there is a good agreement between simulation and experimental results. Using the multi body dynamic analysis makes more accurate result to predict the synchronization dynamic behavior, especially synchronization time

A study on the different finite element approaches for laser cutting of aluminum alloy sheet

The effectiveness of finite element simulation techniques for laser cutting of 1.2-mm-thick aluminium sheets has been studied. Lagrangian and Arbitrary Lagrangian-Eulerian techniques were used to model and simulate laser cutting process. The reliability of finite element results were evaluated by general energy balance analysis and experimental results. Temperature and stress distribution along with heat-affected zone were predicted during the laser-induced process in line with experimental conditions under ABAQUS finite element code. Heat transfer analysis relying on thermal loading was employed to reach the best efficiency. By using field-emission scanning electron microscope, morphological, structural, and elemental changes in the cutting sections were analyzed along with the X-ray diffraction technique. Obtained stress and heat-affected zone are highly dependent on the element type as well as numerical method. Both numerical method, ALE and Lagrangian, are compared to each other in terms of power absorption, cut surface morphology, and cutting efficiency. The results show that ALE method is in good agreement with experimental data. A study on the different finite element approaches for laser cutting of aluminum alloy sheet. Available from: https://www.researchgate.net/publication/317579195_A_study_on_the_different_finite_element_approaches_for_laser_cutting_of_aluminum_alloy_sheet [accessed Jul 3, 2017]

Modal dynamic analysis of a synchronizer mechanism: A numerical study

The present paper shows the modal dynamic behaviour of a single cone synchronizer mechanism. A 3D finite element model is proposed to calculate the natural frequency of the system. By using an efficient method, the natural frequencies of every single component as well as the full system are extracted under different boundary conditions. The natural frequencies of the system in neutral and engaged conditions are extracted and the effective modes are identified. The finite element model is extended to evaluate transient response of the synchronizer mechanism. The effect of different boundary conditions on the modal response is presented. The results show that changing the actuator position can have a significant effect on the dynamic responses of the system. This methodology can be implemented to examine the transient behaviour of other shifting mechanisms

Enhancing the Performance of Automated Grade Prediction in MOOC using Graph Representation Learning

In recent years, Massive Open Online Courses (MOOCs) have gained significant traction as a rapidly growing phenomenon in online learning. Unlike traditional classrooms, MOOCs offer a unique opportunity to cater to a diverse audience from different backgrounds and geographical locations. Renowned universities and MOOC-specific providers, such as Coursera, offer MOOC courses on various subjects. Automated assessment tasks like grade and early dropout predictions are necessary due to the high enrollment and limited direct interaction between teachers and learners. However, current automated assessment approaches overlook the structural links between different entities involved in the downstream tasks, such as the students and courses. Our hypothesis suggests that these structural relationships, manifested through an interaction graph, contain valuable information that can enhance the performance of the task at hand. To validate this, we construct a unique knowledge graph for a large MOOC dataset, which will be publicly available to the research community. Furthermore, we utilize graph embedding techniques to extract latent structural information encoded in the interactions between entities in the dataset. These techniques do not require ground truth labels and can be utilized for various tasks. Finally, by combining entity-specific features, behavioral features, and extracted structural features, we enhance the performance of predictive machine learning models in student assignment grade prediction. Our experiments demonstrate that structural features can significantly improve the predictive performance of downstream assessment tasks. The code and data are available in \url{https://github.com/DSAatUSU/MOOPer_grade_prediction

CLASSIFICATION OF RICE GRAIN VARIETIES USING TWO ARTIFICIAL NEURAL NETWORKS (MLP AND NEURO-FUZZY)

ABSTRACT Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively

Mutation analysis of GJB2 and GJB6 genes and the genetic linkage analysis of five common DFNB loci in the Iranian families with autosomal recessive non-syndrom

The incidence of pre-lingual hearing loss (HL) is about 1 in 1000 neonates. More than 60% of cases are inherited. Non-syndromic HL (NSHL) is extremely heterogeneous: more than 130 loci have been identified so far. The most common form of NSHL is the autosomal recessive form (ARNSHL). In this study, a cohort of 36 big ARNSHL pedigrees with 4 or more patients from 7 provinces of Iran was investigated. All of the families were examined for the presence of GJB2 and GJB6 (del D13S1830 and del D13S1854) mutations using direct sequencing and multiplex PCR methods, respectively. The negative pedigrees for the above-named genes were then tested for the linkage to 5 known loci including DFNB3 (MYO7A), DFNB4 (SLC26A4), DFNB7/11 (TMC1), DFNB21 (TECTA) and DFNB59 (PJVK) by genotyping the corresponding STR markers using PCR and PAGE. Six families had GJB2 mutations. No GJB6 mutation was found. Totally, 3 families showed linkage to DFNB4, 1 family to DFNB7/11 and 1 family to DFNB21. No family was linked to DFNB59. GJB2 included 16.6% of the causes of ARNSHL in our study. In the remaining negative families, DFNB4 accounted for 10% of the causes. Other loci including DFNB7/11 and DFNB21 were each responsible for 3.3% of the etiology. Thus, DFNB1(GJB2) and DFNB4 are the main causes of ARNSHL in our study and GJB6 mutations (del D13S1830, del D13S1854), DFNB3 and DFNB59 were absent. Totally, 30.5% of the ARNSHL etiology was found in this study

Towards energy aware cloud computing application construction

The energy consumption of cloud computing continues to be an area of significant concern as data center growth continues to increase. This paper reports on an energy efficient interoperable cloud architecture realised as a cloud toolbox that focuses on reducing the energy consumption of cloud applications holistically across all deployment models. The architecture supports energy efficiency at service construction, deployment and operation. We discuss our practical experience during implementation of an architectural component, the Virtual Machine Image Constructor (VMIC), required to facilitate construction of energy aware cloud applications. We carry out a performance evaluation of the component on a cloud testbed. The results show the performance of Virtual Machine construction, primarily limited by available I/O, to be adequate for agile, energy aware software development. We conclude that the implementation of the VMIC is feasible, incurs minimal performance overhead comparatively to the time taken by other aspects of the cloud application construction life-cycle, and make recommendations on enhancing its performance