原油从何而来?近红外光谱仪在准确探测油源方面的作用

IF 3.3 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shifan Xu, Zhibin Xu, Jiannan Zheng, Hai Lin, Liang Zou, Meng Lei
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引用次数: 0

摘要

准确追踪原油原产地对于挫败欺骗性贸易行为(包括伪造原产地逃税)至关重要,从而防止进口国遭受经济损失和安全威胁。传统的原油产地测定方法需要复杂的样品制备、昂贵的仪器和稳定的测试环境,因此无法在港口等地点进行实时分析。本文介绍了一种利用近红外光谱(NIRS)与深度学习算法相结合的新方法,以加快并提高原油来源识别的精度。为了有效消除异常值,本文引入了改进的 Mahalanobis 距离,并结合了正则化原则和全局-局部概念。这种方法解决了在高维光谱中倒转协方差矩阵的难题,并排除了具有局部畸变的样本。此外,将多感知场感知、基于 Transformer 的全局信息交互和 scSE 注意力机制整合在一起,开发出了 MG-Unet 模型,旨在解决光谱峰值重叠问题并捕捉长程特征依赖性。所提出的方法达到了最先进的 96.92% 的准确率,为可靠的原油源追踪提供了巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Where does the crude oil originate? The role of near-infrared spectroscopy in accurate source detection

Where does the crude oil originate? The role of near-infrared spectroscopy in accurate source detection

Accurate tracing of crude oil origins is essential for thwarting deceptive trade practices, including origin falsification to evade taxes, thereby preventing economic losses and security threats for importing nations. Traditional crude oil origin determination methods require complex sample preparation, expensive instrumentation, and stable testing environments, rendering them impractical for real-time analysis at locations such as ports. This paper introduces a novel approach utilizing near-infrared spectroscopy (NIRS) combined with deep learning algorithms to expedite and enhance the precision of crude oil source identification. To effectively eliminate outliers, an improved Mahalanobis distance is introduced, incorporating regularization principles and global-local concepts. This approach addresses the challenges of inverting covariance matrices in high-dimensional spectra and excludes samples with localized aberrations. Furthermore, the integration of multi-receptive fields perception, Transformer-based global information interaction, and the scSE attention mechanism has led to the development of an MG-Unet model, designed to resolve spectral peak overlap issues and capture long-range feature dependencies. The proposed method achieves state-of-the-art accuracy of 96.92%, demonstrating significant potential for reliable crude oil source tracing.

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来源期刊
Memetic Computing
Memetic Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE
CiteScore
6.80
自引率
12.80%
发文量
31
期刊介绍: Memes have been defined as basic units of transferrable information that reside in the brain and are propagated across populations through the process of imitation. From an algorithmic point of view, memes have come to be regarded as building-blocks of prior knowledge, expressed in arbitrary computational representations (e.g., local search heuristics, fuzzy rules, neural models, etc.), that have been acquired through experience by a human or machine, and can be imitated (i.e., reused) across problems. The Memetic Computing journal welcomes papers incorporating the aforementioned socio-cultural notion of memes into artificial systems, with particular emphasis on enhancing the efficacy of computational and artificial intelligence techniques for search, optimization, and machine learning through explicit prior knowledge incorporation. The goal of the journal is to thus be an outlet for high quality theoretical and applied research on hybrid, knowledge-driven computational approaches that may be characterized under any of the following categories of memetics: Type 1: General-purpose algorithms integrated with human-crafted heuristics that capture some form of prior domain knowledge; e.g., traditional memetic algorithms hybridizing evolutionary global search with a problem-specific local search. Type 2: Algorithms with the ability to automatically select, adapt, and reuse the most appropriate heuristics from a diverse pool of available choices; e.g., learning a mapping between global search operators and multiple local search schemes, given an optimization problem at hand. Type 3: Algorithms that autonomously learn with experience, adaptively reusing data and/or machine learning models drawn from related problems as prior knowledge in new target tasks of interest; examples include, but are not limited to, transfer learning and optimization, multi-task learning and optimization, or any other multi-X evolutionary learning and optimization methodologies.
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