REGULATION OF BURNING SPEED FOR THE GRANULES OF HIGH ENERGY MATERIALS IN MILITARY FIELD (SINGLE-BASED PROPELLANT) USING ABSORPTION OF CAMPHOR METHODS

As launchers for artillery weapon, grains were used that have energy in military equipment (also known as the single-based propellant). Each type of artillery weapon has a special requirement for muzzle velocity and pressure in which for the ammunition used for weapon with long barrel and high power, the term "increasing the efficiency of propellant" means, it is extremely important to improve the muzzle velocity of the projectile while maintaining maximum pressure in the barrel. This article presents one of the key approaches to "increasing the efficiency of propellant" by regulation the grain velocity of the initial particles of propellant by absorbing camphor on their surfaces (phlegmatic propellant)

An Empirical Approach to Sentiment Analysis with Doc2Vec

Faculty advisor: Daniel BoleyThis research was supported by the Undergraduate Research Opportunities Program (UROP)

Condensation Heat Transfer of R410A Inside Multiport Minichannels with Different Cross-sectional Geometry

Condensation heat transfer of R410a in a multiport mini-channels tubes with different cross-sectional geometry is experimentally investigated. Three tubes with aspect ratio of 0.395, 0.385 and 0.446, and hydraulic diameters of 1.147 mm, 1.135 mm and 0.846 mm with number of channels (7, 11 and 18) are tested in this study. The experimented range of heat flux is from 3 to 15 kW/m2, mass flux from 50 to 500 kg/m2s. The data show that the heat transfer coefficient increases with heat flux, mass flux and vapor quality. A performance comparison was conducted among the 3 tested tubes and it was found out that the number of channels increases heat transfer coefficient significantly at low heat flux and mass flux, while this effect is damped at higher heat/mass flux condition. In addition, it was found that heat transfer in small hydraulic diameter and high aspect ratio channels deteriorated. Possible mechanism to this deterioration is proposed. Finally, a new correlation is developed to predict the heat transfer coefficient of R410a in a multiport mini-channels tube

Enhancing Few-shot Image Classification with Cosine Transformer

This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehensively. This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms. In this paper, we tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the relational map between supports and queries is effectively obtained for the few-shot tasks. The FS-CT consists of two parts, a learnable prototypical embedding network to obtain categorical representations from support samples with hard cases, and a transformer encoder to effectively achieve the relational map from two different support and query samples. We introduce Cosine Attention, a more robust and stable attention module that enhances the transformer module significantly and therefore improves FS-CT performance from 5% to over 20% in accuracy compared to the default scaled dot-product mechanism. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and few-shot configurations. We also developed a custom few-shot dataset for Yoga pose recognition to demonstrate the potential of our algorithm for practical application. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications, such as healthcare, medical, and security surveillance. The official implementation code of our Few-shot Cosine Transformer is available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme

Interphase formation with carboxylic acids as slurry additives for Si electrodes in Li-ion batteries. Part 2: a photoelectron spectroscopy study

The mass loading of Si–graphite electrodes is often considered as a parameter of secondary importance when testing their electrochemical performance. However, if a sacrificial additive is present in the electrolyte to improve the electrochemical performance, the electrode loading becomes the battery cycle-life-determining factor. The correlation between mass-loading, electrolyte additive, and binder type was investigated by analyzing the cycling behavior of Si–graphite electrodes, prepared with water-based binders, with mass loading ranging from 3 to 9.5 mg cm-2 and cycled with FEC electrolyte additive, while keeping electrolyte amount constant. A lower loading was obtained by keeping slurry preparation steps unchanged from binder to binder and resulted in a longer lifetime for some of the binders. When the final loading was kept constant instead, the performance became independent of the binder used. Because such results can lead to the misinterpretation of the influence of electrode components on the cycling stability (and to a preference of one binder over another in our case), we propose that a comparison of long-term electrochemical performance data of Si–graphite electrodes needs to be always collected by using the same mass-loading with the constant electrolyte and additive

Vehicle Type Classification with Small Dataset and Transfer Learning Techniques

This study delves into the application of deep learning training techniques using a restricted dataset, encompassing around 400 vehicle images sourced from Kaggle. Faced with the challenges of limited data, the impracticality of training models from scratch becomes apparent, advocating instead for the utilization of pre-trained models with pre-trained weights. The investigation considers three prominent models—EfficientNetB0, ResNetB0, and MobileNetV2—with EfficientNetB0 emerging as the most proficient choice. Employing the gradually unfreeze layer technique over a specified number of epochs, EfficientNetB0 exhibits remarkable accuracy, reaching 99.5% on the training dataset and 97% on the validation dataset. In contrast, training models from scratch results in notably lower accuracy. In this context, knowledge distillation proves pivotal, overcoming this limitation and significantly improving accuracy from 29.5% in training and 20.5% in validation to 54% and 45%, respectively. This study uniquely contributes by exploring transfer learning with gradually unfreeze layers and elucidates the potential of knowledge distillation. It highlights their effectiveness in robustly enhancing model performance under data scarcity, thus addressing challenges associated with training deep learning models on limited datasets. The findings underscore the practical significance of these techniques in achieving superior results when confronted with data constraints in real-world scenario

g-INFO portal: a solution to monitor Influenza A on the Grid for non-grid users

International audienceIn this paper, we introduce a portal for monitoring Influenza A on a grid-based system. Influenza A keeps on being a major threat to public health worldwide; especially if one virus can mutate itself so that it acquires the capacity for human to human transmission of H1N1 as well as the high death rate of H5N1. The existing g-INFO (Grid-based Information Network for Flu Observation) project provides a complete system for monitoring flu virus on the Grid. We present here a portal that operates on top of the g-INFO system as a solution for non-grid users to utilize grid services for analyzing molecular biology data of Influenza A

Mitigating effect of embankment to adjacent pipe with CDM columns

Pipelines are valuable infrastructures that covering a large area or expanding to long distance for the transporting function. This leads to the variety of loads and effects applied on such buried structures. A thread to pipeline integrity is the construction of the embankment on the soft soil which leads to the displacement of the pipe adjacent to the slope. This displacement will effect to the increase of internal force or causing failure of the near-by pipes. The use of concrete pile to improve the soil properties may be a solution; however, the cost for this is expensive. To propose an alternative solution for the problem, this study uses a system of cement deep mixing, CDM, columns as the solution for protecting the pipe. A simple 2D Finite Element, FE, model using Plaxis software has been established based on the equivalent soil approach which considering the CDM columns and their surrounding soil as an unified soil. The effectiveness of the proposed solution has been numerically investigated and proven. The lateral displacement of pipe and the maximum ring bending moment and other internal forces are significantly reduced with the appearance of the CDM columns. The selective parametric study has been implemented revealing the critical input variables are the distance of pipe to the slope and the length of the CDM column