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相关概念视频

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

225
Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....
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Atomic Emission Spectroscopy: Lab01:29

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Atomic Emission Spectroscopy: Interference01:30

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In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
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基于AIS的运营阶段识别使用渐进式废弃特征选择与机器学习来改进船舶排放估计.

Kuiquan Duan1, Qingbo Li1, Shangheng Liu1

  • 1Green Shipping and Carbon Neutrality Laboratory, College of Environmental Science and Engineering, Dalian Maritime University, Dalian, People's Republic of China.

Journal of the Air & Waste Management Association (1995)
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概括

本研究介绍了一种机器学习模型,使用自动识别系统数据准确识别船舶运营阶段. 该模型显著改善了排放估计,并有助于港口管理和排放控制.

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科学领域:

  • 海洋技术的海洋技术
  • 环境科学环境科学
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 船舶发动机和炉工作状态严重影响排放估计,与运营阶段直接相关.
  • 准确识别船舶运营阶段对于精确的排放计算至关重要.
  • 自动识别系统 (AIS) 数据提供了丰富的船舶行为信息.

研究的目的:

  • 开发和验证基于机器学习的分类模型,用于识别船舶运营阶段.
  • 通过改进运营阶段识别,提高船舶排放估计的准确性.
  • 为此目的探索自动识别系统 (AIS) 数据的实用性.

主要方法:

  • 从散货船的AIS数据中提取了12个与运动行为和地理空间特征相关的特征.
  • 开发并比较了五种机器学习模型,其中随机森林 (RF) 显示出卓越的性能.
  • 采用渐进式除特征选择 (PAFS) 来优化RF模型的特征集.

主要成果:

  • 随机森林模型在确定运营阶段时实现了高准确度 (96.66%),F1得分 (93.34%) 和AUC (99.93%).
  • 使用PAFS从12个特征减少到8个特征对模型性能影响最小,保持精度高于96%.
  • 与传统算法相比,射频模型的NOx排放估计准确度提高了57.83% (主发动机) 和93.89% (辅助发动机).

结论:

  • 拟议的机器学习方法有效地识别了船舶的运营阶段,从而获得更准确的排放估计.
  • 该模型在多艘散货船上的验证证明了它的稳定性和适用性.
  • 这种方法为港口交通管理,船舶排放控制和碳税预测提供了显著的好处.