WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Enhancement of process modelling and simulation evaluation by deploying a test for assessment and feedback individualisation

This paper demonstrates a novel approach for a computer-based course assessment. A test is introduced in which computers are deployed. This significantly contributes to the enhancement of the marking consistency, individual performance distinction and feedbacks, and widen the questions range for computer-based modules. The proposed test method, for the first time, uses the simulation files marking for individualised evaluation purposes. The methodology has successfully been implemented in practice for three modules including Process Simulation (CE2105), Advanced Process Simulation (CE4023), and Process Computation (CE3021) at Aston University (UK) over three academic years, from 2016 to 2019. The effectiveness of the proposed approach has been evaluated using several factors, including final marks, consistency multiple academic years, and mark distribution. In contrast to the common teamwork assessments, individualised feedback became possible. While ASPEN has been used for CE4023 and CE2105 tests, MATLAB has been applied as the computation platform for CE3021 module. This reveals the applicability of different software in proposed methodology. The number of students in the cohorts studied was from 52 to 204, demonstrating the applicability of the method for various cohort sizes. Even though the methodology has been demonstrated based on the chemical engineering discipline modules, it allows digitalising the delivery and assessment of a wide range of simulation techniques in many disciplines.     

A comprehensive survey on computer vision based concepts,methodologies, analysis and applications for automatic gun/knife detection

The ability to detect gun and gun held in hand or other body parts is a typical human skill. The same problem presents an imperative task for computer vision system. Automatic observer independent detection of hand held gun or gun held in the other body part, whether it is visible or concealed, provides enhance security in vulnerable places and initiates appropriate action there. Compare to the automatic object detection systems, automatic detection of gun has very few successful attempts. In the present scope of this paper, we present an extensive survey on automatic detection of gun and define a taxonomy for this particular detection system. We also describe the inherent difficulties related with this problem. In this survey of published papers, we examine different approaches used in state-of-the-art attempts and compare performances of these approaches. Finally, this paper concludes pointing to the possible research gaps in related fields.     

Past is important: Improved image captioning by looking back in time

A major development in the area of image captioning consists of trying to incorporate visual attention in the design of language generative model. However, most previous studies only emphasize its role in enhancing visual composition at the current moment, while neglect its role in global sequence reasoning. This problem appears not only in captioning model, but also in reinforcement learning structure. To tackle this issue, we first propose a Visual Reserved model that enables previous visual context to be considered for the current sequence reasoning. Next, a Attentional-Fluctuation Supervised model is also proposed in reinforcement learning structure. Compared against the traditional strategies that only take non-differentiable Natural Language Processing (NLP) metrics as the incentive standard, the proposed model regards the fluctuation of previous attention matrix as an important indicator to judge the convergence of the captioning model. The proposed methods have been tested on MS-COCO captioning dataset and achieve competitive results evaluated by the evaluation server of MS COCO captioning challenge.     

稀土灼烧过程温湿度场仿真分析

影响稀土灼烧工艺的因素十分复杂,关系产品质量稳定及能耗,现行工艺存在优化空间。通过剖析灼烧窑中温度和湿度分布状况,运用κ-ε双方程湍流模型、流体传热、多孔介质传热等理论,按特定组分运输模式,建立灼烧过程质量、动量和能量耦合传递数学模型。设置不同边界导入Fluent环境对数学模型进行仿真试验,完成数据处理实现工艺参数优化。结果表明所建模型能准确反映灼烧窑中温湿度场分布及变化,且最终仿真结果与实际灼烧后的产品湿度含量相符合。     

MP PFGun脉冲爆燃压裂技术在阿曼SSM-XX井的应用

简要叙述了MP PFGun技术工作原理、系统结构特点以及PropFrac脉冲压裂模拟器软件基本功能,并对SSM-XX井的模拟计算P-T曲线和井下压力仪实测曲线进行了对比。结果表明:SM-XX井采用MP PFGun电缆输送工艺安全,脉冲压裂持续时间长,且利用PropFrac模拟软件指导施工设计,取得了较好的应用效果。     

Commentary on “A continuous hydrogen absorption/desorption model for metal hydride reactor coupled with PCM as heat management and its application in the fuel cell power system” [Yao J,Zhu P,Guo L,Duan L,Zhang Z,Kurko S et al. Int J Hydrogen Energy 2020. https://doi.org/10.1016/j.ijhydene.2020.05.089]

A recent paper titled “A continuous hydrogen absorption/desorption model for metal hydride reactor coupled with PCM as heat management and its application in the fuel cell power system”, published in the International Journal of Hydrogen Energy, describes the desorption kinetics modification without which the authors of this paper was unable to obtain experimental dependencies in the simulation during desorption. According to the opinion of the author of this commentary, modification of the kinetics is not required, and experimental results can be obtained using the standard kinetics by replacing the inlet/outlet boundary condition with a better one.     

Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Main trends on the simulation of the drying of refractory castables - Review

Refractory castables develop microstructures after curing that behave as partially saturated porous media. Upon heating (during its drying stage), the steam generated by the physical and chemically bond water can result in pore pressurization and explosive spalling. Numerical modeling can provide guidelines for designing safer heat-up profiles and also a better understanding of the mechanisms that lead to catastrophic damage. This work aims to review the fundamentals and models available, providing insightful thoughts on the current trends of the drying phenomena of ceramic compositions. The review also highlights that there are models better oriented to result in reasonable predictions of pore pressure values and others focused on a more accurate representation of the main physical phenomena that take place during heating. According to the findings, there are still various challenges to attain accurate models with high applicability capable of yielding safer and more efficient drying of refractory castables.     

How Mixing and Light Heterogeneity Impact the Overall Growth Rate in Photobioreactors

The microalgae growth rate in photobioreactors responds with inertia to light stimuli. Here, light variations experienced by the algae are accessed through a coupling of an irradiance field calculation and a Lagrangian particle tracking. The response of algae to fluctuating light is then described by a relaxation model involving a single time constant, the value of which is identified from published data. The overall growth rate is calculated as the sum of individual growth rates of all particles. Instantaneous adaptation and full integration asymptotic behaviors are recovered whilst a finite time constant reveals that the overall growth rate is dependent both on mixing and light distribution. This methodology thus quantitatively relates the design parameters to the photobioreactor performance.