ADVANCES IN ENGINEERING SOFTWARE工程软件进展
ADVANCES IN ENGINEERING SOFTWARE(英文缩写 ADV ENG SOFTW),ISSN 0965-9978,eISSN 1873-5339,中文译名:工程软件进展 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 4.255 | Q1 |
| 2022 | 4.800 | Q1 |
| 2023 | 4.000 | Q1 |
| 2024 | 5.700 | Q1 |
| 2025 | 6.300 | Q1 |
ADVANCES IN ENGINEERING SOFTWARE 最新收录文献
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1. Optimizing foam padding of the advanced combat helmet to maximize protection of blast-induced brain injury and wearing comfort.
PMID:日期:2025-06-23The Advanced combat helmet (ACH) is critical for mitigating the risk of blast-induced traumatic brain injury (bTBI). Helmet foam pads are in continuous contact with the head to provide mechanical support. They are essential for helmet bTBI mitigation effectiveness and wearing comfort. In this study, we parametrically investigate the significance of foam pad thickness and relative density on reducing the peak intracranial pressure (ICP) from blast. In addition, we study how they influence the perceived comfort, by quantifying the distribution uniformity of ACH-to-scalp pressure resulting from gravity, referred to as the Comfort Index. Three specific pad thicknesses and random relative densities coupled with a range of trinitrotoluene (TNT) masses placed to the front or side of the helmet-head complex were used for simulation. The incidence pressures from the ConWep model were used as input for blast loading. The ratios between peak ICP in the corpus callosum and the peak incident pressure as well as the comfort indices were analyzed using a data-driven approach. A multi-functional design method, Pareto front, was used to identify sets of optimal parameters based on user preferred weighting factors for ICP reduction and head surface pressure distribution. Finally, a decision tree was applied to refine the rules for optimal designs. For an equal weighting on ICP reduction and surface pressure distribution, a pad thickness of 10 mm and relative density of 7.7 % were identified. This study demonstrates the effectiveness of combining Pareto front and decision trees for the identification of optimal design parameters for the ACH.
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2. UMAT4COMSOL: An Abaqus user material (UMAT) subroutine wrapper for COMSOL.
PMID:日期:2024-04We present a wrapper that allows Abaqus user material subroutines (s) to be used as an library in the software COMSOL Multiphysics. The wrapper, written in C language, transforms COMSOL's external material subroutine inputs and outputs into Fortran-coded Abaqus inputs and outputs, by means of a consistent variable transformation. This significantly facilitates conducting coupled, multi-physics studies employing the advanced material models that the solid mechanics community has developed over the past decades. We exemplify the potential of our new framework, UMAT4COMSOL, by conducting numerical experiments in the areas of elastoplasticity, hyperelasticity and crystal plasticity. The source code, detailed documentation and example tutorials are made freely available to download at www.empaneda.com/codes.
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3. Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm.
PMID:日期:2022-11-25The COVID-19 pandemic made robot manufacturers explore the idea of combining mobile robotics with UV-C light to automate the disinfection processes. But performing this process in an optimum way introduces some challenges: on the one hand, it is necessary to guarantee that all surfaces receive the radiation level to ensure the disinfection; at the same time, it is necessary to minimize the radiation dose to avoid the damage of the environment. In this work, both challenges are addressed with the design of a complete coverage path planning (CCPP) algorithm. To do it, a novel architecture that combines the glasius bio-inspired neural network (GBNN), a motion strategy, an UV-C estimator, a speed controller, and a pure pursuit controller have been designed. One of the main issues in CCPP is the deadlocks. In this application they may cause a loss of the operation, lack of regularity and high peaks in the radiation dose map, and in the worst case, they can make the robot to get stuck and not complete the disinfection process. To tackle this problem, in this work we propose a preventive deadlock processing algorithm (PDPA) and an escape route generator algorithm (ERGA). Simulation results show how the application of PDPA and the ERGA allow to complete complex maps in an efficient way where the application of GBNN is not enough. Indeed, a 58% more of covered surface is observed. Furthermore, two different motion strategies have been compared: boustrophedon and spiral motion, to check its influence on the performance of the robot navigation.
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4. A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.
PMID:日期:2022-10-24The Coronavirus (COVID-19) has become a critical and extreme epidemic because of its international dissemination. COVID-19 is the world's most serious health, economic, and survival danger. This disease affects not only a single country but the entire planet due to this infectious disease. Illnesses of Covid-19 spread at a much faster rate than usual influenza cases. Because of its high transmissibility and early diagnosis, it isn't easy to manage COVID-19. The popularly used RT-PCR method for COVID-19 disease diagnosis may provide false negatives. COVID-19 can be detected non-invasively using medical imaging procedures such as chest CT and chest x-ray. Deep learning is the most effective machine learning approach for examining a considerable quantity of chest computed tomography (CT) pictures that can significantly affect Covid-19 screening. Convolutional neural network (CNN) is one of the most popular deep learning techniques right now, and its gaining traction due to its potential to transform several spheres of human life. This research aims to develop conceptual transfer learning enhanced CNN framework models for detecting COVID-19 with CT scan images. Though with minimal datasets, these techniques were demonstrated to be effective in detecting the presence of COVID-19. This proposed research looks into several deep transfer learning-based CNN approaches for detecting the presence of COVID-19 in chest CT images.VGG16, VGG19, Densenet121, InceptionV3, Xception, and Resnet50 are the foundation models used in this work. Each model's performance was evaluated using a confusion matrix and various performance measures such as accuracy, recall, precision, f1-score, loss, and ROC. The VGG16 model performed much better than the other models in this study (98.00 % accuracy). Promising outcomes from experiments have revealed the merits of the proposed model for detecting and monitoring COVID-19 patients. This could help practitioners and academics create a tool to help minimal health professionals decide on the best course of therapy.
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5. Improved seagull optimization algorithm of partition and XGBoost of prediction for fuzzy time series forecasting of COVID-19 daily confirmed.
PMID:日期:2022-08-01The establishment of fuzzy relations and the fuzzification of time series are the top priorities of the model for predicting fuzzy time series. A lot of literature studied these two aspects to ameliorate the capability of the forecasting model. In this paper, we proposed a new method(FTSOAX) to forecast fuzzy time series derived from the improved seagull optimization algorithm(ISOA) and XGBoost. For increasing the accurateness of the forecasting model in fuzzy time series, ISOA is applied to partition the domain of discourse to get more suitable intervals. We improved the seagull optimization algorithm(SOA) with the help of the Powell algorithm and a random curve action to make SOA have better convergence ability. Using XGBoost to forecast the change of fuzzy membership in order to overcome the disadvantage that fuzzy relation leads to low accuracy. We obtained daily confirmed COVID-19 cases in 7 countries as a dataset to demonstrate the performance of FTSOAX. The results show that FTSOAX is superior to other fuzzy forecasting models in the application of prediction of COVID-19 daily confirmed cases.
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6. COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model.
PMID:日期:2022-07-29World Health Organization has defined COVID-19 as a contagious, communicable and fast spreading disease engendered by the Corona virus, SARS-CoV-2, is a respirational microorganism. Computerized Tomography (CT) scan images of the chest helps in detecting COVID 19 infection in a fast way with much reliability. In this paper, chest CT scan images of COVID and Non-COVID categories are considered to train the supervised classifier, Iterative convolution Neural Network. The training process is done with six different training data size. The trained models are iterated for the fixed size of testing data (20 images). The same set of training and testing processes are done with two different Iterative Convolutional Neural Network architectures, one with two hidden layers (CNN1) and another with three hidden layers (CNN2). The iterations are extended up to 7, but the model performance is degraded after the 6th iteration, which makes to fix the iteration level as 5 for both CNN models. Six different training sets with five iterations have led into 30 CNN models. For two different CNN architectures, which lead to 60 different models. The model designed with 100 training sets in both CNN1 and CNN2, have produced the high accuracy in COVID classification than any other models. The better classification accuracy 89% is achieved from CNN2 model with its 5th iteration.
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7. Mathematical conditions for and physical meaning of a maximum of the determinant of [Formula: see text] in the prebuckling regime.
PMID:日期:2013-08It is shown that the determinant of the tangent stiffness matrix has a maximum in the prebuckling regime if and only if the determinant of a specific linear combination of the first and the third derivative of this matrix with respect to a dimensionless load factor vanishes. The mathematical tool for this proof is the so-called consistently linearized eigenproblem in the frame of the Finite Element Method. The physical meaning of the mentioned maximum is the one of a minimum of the percentage bending energy of the total strain energy. The paper provides mathematical and physical background knowledge on numerical results that were obtained 35 years ago.
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8. Computational Development of Jacobian Matrices for Complex Spatial Manipulators.
PMID:日期:2012-03-24Current methods for developing manipulator Jacobian matrices are based on traditional kinematic descriptions such as Denavit and Hartenberg parameters. The resulting symbolic equations for these matrices become cumbersome and computationally inefficient when dealing with more complex spatial manipulators, such as those seen in the field of biomechanics. This paper develops a modified method for Jacobian development based on generalized kinematic equations that incorporates partial derivatives of matrices with Leibniz's Law (the product rule). It is shown that a set of symbolic matrix functions can be derived that improve computational efficiency when used in MATLAB(®) M-Files and are applicable to any spatial manipulator. An articulated arm subassembly and a musculoskeletal model of the hand are used as examples.