Software Impacts最新文献

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Plant diseases classification with Spectral Signature Taxonomy & Analysis Software (SSTAS) 基于光谱特征分类分析软件（SSTAS）的植物病害分类

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-03-01 DOI: 10.1016/j.simpa.2025.100744

Hardik Jayswal, Hetvi Desai, Hasti Vakani, Mithil Mistry, Nilesh Dubey

This paper investigates a novel approach to plant disease classification, addressing cases where symptoms are not visually apparent. Traditional machine learning methods, reliant on observable symptoms, face challenges such as limited training data, high costs, and low interpretability. To overcome these limitations, a spectroscopy-based classification technique was developed. Experimental data, collected over 15 months at Anand Agriculture University, Gujarat, and Charotar University Space Research Centre, utilized spectral signatures (400–1000 nm) to detect mango diseases. The SSTAS Software, developed with a fine-tuned deep learning model, Deep-Spectro, demonstrated superior accuracy using an 80:20 training-to-testing ratio, surpassing existing models reported in prior research.

本文研究了植物病害分类的一种新方法，解决了症状不明显的情况。传统的机器学习方法依赖于可观察到的症状，面临着训练数据有限、成本高、可解释性低等挑战。为了克服这些限制，开发了一种基于光谱的分类技术。在古吉拉特邦阿南德农业大学和夏洛塔大学空间研究中心收集了15个多月的实验数据，利用光谱特征（400-1000 nm）检测芒果疾病。SSTAS软件采用微调深度学习模型deep - spectro开发，使用80:20的训练与测试比例显示出卓越的准确性，超过了先前研究报告的现有模型。

引用次数: 0

hvarma: Autoregressive moving average model of microtremor H/V spectral ratio hvarma：微颤H/V谱比的自回归移动平均模型

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-03-01 DOI: 10.1016/j.simpa.2025.100745

Aleix Seguí , Arantza Ugalde , Juan José Egozcue

hvarma is a Python software for estimating the horizontal-to-vertical (H/V) spectral ratio through seismic ambient vibration measurements. It employs a parametric approach to model the H/V transfer function using an AutoRegressive Moving Average (ARMA) filter. Compared to traditional methods, this technique enhances accuracy and reliability in spectral estimates, determining the ground fundamental resonance frequency with high spectral resolution, which is important for engineering geology projects. The program inverts to find optimal filter coefficients and computes coherence between horizontal and vertical components, generating H/V transfer function visualizations across both negative and positive frequencies. Results are saved as image and text files.

hvarma是一个Python软件，用于通过地震环境振动测量估计水平与垂直（H/V）频谱比。它采用参数化方法使用自回归移动平均（ARMA）滤波器对H/V传递函数建模。与传统方法相比，该技术提高了频谱估计的精度和可靠性，以高光谱分辨率确定了地面基共振频率，对工程地质工程具有重要意义。该程序通过反向查找最佳过滤系数，并计算水平和垂直分量之间的相干性，从而在负频率和正频率上生成H/V传递函数可视化。结果保存为图像和文本文件。

引用次数: 0

KNNOR-Reg: A python package for oversampling in imbalanced regression 一个python包，用于不平衡回归中的过采样

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-01-03 DOI: 10.1016/j.simpa.2024.100740

Samir Brahim Belhaouari , Ashhadul Islam , Khelil Kassoul , Ala Al-Fuqaha , Abdesselam Bouzerdoum

KNNOR-Reg is a Python package designed to address the challenge of imbalanced regression. While popular Python packages exist for tackling imbalanced classification, support for imbalanced regression remains limited. Imbalanced regression involves the underrepresentation of important ranges within a continuous target variable. KNNOR-Reg implements an oversampling technique that generates synthetic samples through interpolation between minority class samples and their nearest neighbors. The labels for synthetic samples are computed based on the inverse distance-weighted average of the nearest neighbors’ labels. KNNOR-Reg offers a user-friendly and extensible Python implementation for oversampling imbalanced regression data, aiming to reduce regressor bias and enhance model outcomes.

knor - reg是一个Python包，旨在解决不平衡回归的挑战。虽然存在用于处理不平衡分类的流行Python包，但对不平衡回归的支持仍然有限。不平衡回归涉及连续目标变量内重要范围的代表性不足。knor - reg实现了一种过采样技术，通过在少数类样本和它们最近的邻居之间插值来生成合成样本。合成样本的标签是基于最近邻居标签的逆距离加权平均值计算的。knor - reg提供了一个用户友好且可扩展的Python实现，用于对不平衡回归数据进行过采样，旨在减少回归偏差并增强模型结果。

引用次数: 0

pff-oc: A space–time phase-field fracture optimal control framework pff-oc：时空相场断裂最优控制框架

IF 1.3 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Software Impacts

Pub Date : 2025-01-02 DOI: 10.1016/j.simpa.2024.100734

Denis Khimin, Marc Christian Steinbach, Thomas Wick

This codebase is developed to address optimal control problems in phase-field fracture, aiming to achieve a desired fracture pattern in brittle materials through the application of external forces. Built alongside our recent work (Khimin et al., 2022), this framework provides an efficient and precise approach for simulating space–time phase-field optimal control problems. In this setup, the fracture is controlled via Neumann boundary conditions, with the cost functional designed to minimize the difference between the actual and desired fracture states. The implementation relies on the open-source libraries DOpElib (Goll et al., 2017) and deal.II (Arndt et al. [1], [2])

这个代码库是为了解决相场断裂的最优控制问题而开发的，旨在通过施加外力来实现脆性材料的理想断裂模式。该框架与我们最近的工作（Khimin et al., 2022）一起构建，为模拟时空相场最优控制问题提供了一种有效而精确的方法。在这种设置中，裂缝是通过Neumann边界条件控制的，成本函数的设计是为了最小化实际和期望的裂缝状态之间的差异。实现依赖于开源库DOpElib （Goll et al., 2017）和deal。II （Arndt et al. [1], [2]）

引用次数: 0