MX2022012449A - Sistemas y metodos para interpretar la interaccion de alta energia. - Google Patents
Sistemas y metodos para interpretar la interaccion de alta energia.Info
- Publication number
- MX2022012449A MX2022012449A MX2022012449A MX2022012449A MX2022012449A MX 2022012449 A MX2022012449 A MX 2022012449A MX 2022012449 A MX2022012449 A MX 2022012449A MX 2022012449 A MX2022012449 A MX 2022012449A MX 2022012449 A MX2022012449 A MX 2022012449A
- Authority
- MX
- Mexico
- Prior art keywords
- methods
- quantitative
- systems
- detector
- energy interactions
- Prior art date
Links
- 230000003993 interaction Effects 0.000 title abstract 4
- 238000000034 method Methods 0.000 title abstract 4
- 230000005855 radiation Effects 0.000 abstract 3
- 239000012491 analyte Substances 0.000 abstract 1
- 238000004458 analytical method Methods 0.000 abstract 1
- 238000010801 machine learning Methods 0.000 abstract 1
- 238000007781 pre-processing Methods 0.000 abstract 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/22—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material
- G01N23/225—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material using electron or ion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/22—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material
- G01N23/223—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by measuring secondary emission from the material by irradiating the sample with X-rays or gamma-rays and by measuring X-ray fluorescence
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
- G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
- G01T1/16—Measuring radiation intensity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2201/00—Features of devices classified in G01N21/00
- G01N2201/12—Circuits of general importance; Signal processing
- G01N2201/129—Using chemometrical methods
- G01N2201/1296—Using chemometrical methods using neural networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/07—Investigating materials by wave or particle radiation secondary emission
- G01N2223/076—X-ray fluorescence
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/30—Accessories, mechanical or electrical features
- G01N2223/306—Accessories, mechanical or electrical features computer control
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Chemical & Material Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Molecular Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- High Energy & Nuclear Physics (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
La presente invención describe sistemas y métodos para interpretar interacciones de alta energía en una muestra. En particular, esta solicitud describe sistemas y métodos de análisis, que comprenden la radiación incidente a partir de una fuente sobre un analito, la detección de interacciones de energía resultantes de la radiación incidente usando un detector, el ajuste de una señal emitida por el detector de radiación usando un método de preprocesamiento para enfatizar características de esa señal, el uso de un módulo de aprendizaje automático para interpretar partes específicas de la señal ajustada, la producción de un modelo cuantitativo y/o cualitativo usando el módulo de aprendizaje automático, y la aplicación del modelo cuantitativo y/o cualitativo a una interacción de energía separada. Los modelos cuantitativos y cualitativos derivados de esta formación se pueden aplicar a nuevas entradas de detectores del mismo instrumento o de instrumentos similares.
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/US2020/026698 WO2021201883A1 (en) | 2020-04-03 | 2020-04-03 | Systems and methods for interpreting high energy interactions |
Publications (1)
Publication Number | Publication Date |
---|---|
MX2022012449A true MX2022012449A (es) | 2022-10-27 |
Family
ID=77929756
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
MX2022012449A MX2022012449A (es) | 2020-04-03 | 2020-04-03 | Sistemas y metodos para interpretar la interaccion de alta energia. |
Country Status (8)
Country | Link |
---|---|
US (1) | US20230204527A1 (es) |
EP (1) | EP4127662A4 (es) |
AU (1) | AU2020439463A1 (es) |
CA (1) | CA3178717A1 (es) |
CL (1) | CL2022002710A1 (es) |
MX (1) | MX2022012449A (es) |
PE (1) | PE20230272A1 (es) |
WO (1) | WO2021201883A1 (es) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP7130267B2 (ja) * | 2020-09-03 | 2022-09-05 | 株式会社リガク | 全反射蛍光x線分析装置及び推定方法 |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2002065090A2 (en) * | 2001-01-26 | 2002-08-22 | Sensys Medical | Noninvasive measurement of glucose through the optical properties of tissue |
WO2006002027A2 (en) * | 2004-06-15 | 2006-01-05 | Griffin Analytical Technologies, Inc. | Portable mass spectrometer configured to perform multidimensional mass analysis |
US10697953B2 (en) * | 2014-06-18 | 2020-06-30 | Texas Tech University System | Portable apparatus for liquid chemical characterization |
CN111373256B (zh) * | 2017-11-09 | 2022-07-22 | 株式会社岛津制作所 | 波形分析装置 |
US11627895B2 (en) * | 2018-08-10 | 2023-04-18 | Samsung Electronics Co., Ltd. | Apparatus and method for estimating analyte concentration, and apparatus and method for generating analyte concentration estimation model |
US11874240B2 (en) * | 2018-10-04 | 2024-01-16 | Decision Tree, Llc | Systems and methods for interpreting high energy interactions |
-
2020
- 2020-04-03 AU AU2020439463A patent/AU2020439463A1/en active Pending
- 2020-04-03 EP EP20928169.0A patent/EP4127662A4/en active Pending
- 2020-04-03 PE PE2022002185A patent/PE20230272A1/es unknown
- 2020-04-03 US US17/995,335 patent/US20230204527A1/en active Pending
- 2020-04-03 MX MX2022012449A patent/MX2022012449A/es unknown
- 2020-04-03 WO PCT/US2020/026698 patent/WO2021201883A1/en active Application Filing
- 2020-04-03 CA CA3178717A patent/CA3178717A1/en active Pending
-
2022
- 2022-10-03 CL CL2022002710A patent/CL2022002710A1/es unknown
Also Published As
Publication number | Publication date |
---|---|
EP4127662A4 (en) | 2023-12-27 |
CA3178717A1 (en) | 2021-10-07 |
CL2022002710A1 (es) | 2023-05-26 |
US20230204527A1 (en) | 2023-06-29 |
PE20230272A1 (es) | 2023-02-08 |
EP4127662A1 (en) | 2023-02-08 |
WO2021201883A1 (en) | 2021-10-07 |
AU2020439463A1 (en) | 2022-12-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors | |
Shahamiri et al. | Real-time frequency-based noise-robust Automatic Speech Recognition using Multi-Nets Artificial Neural Networks: A multi-views multi-learners approach | |
CL2021000836A1 (es) | Sistemas y métodos para interpretar interacciones de alta energía | |
CA2718564A1 (en) | Systems and methods of improving automated speech recognition accuracy using statistical analysis of search terms | |
MX2022012449A (es) | Sistemas y metodos para interpretar la interaccion de alta energia. | |
Cai et al. | End-to-End Deep Learning Framework for Speech Paralinguistics Detection Based on Perception Aware Spectrum. | |
Xian et al. | A multi-scale feature recalibration network for end-to-end single channel speech enhancement | |
CN109036468A (zh) | 基于深度信念网络和核非线性psvm的语音情感识别方法 | |
Wang et al. | Speech signal feature parameters extraction algorithm based on PCNN for isolated word recognition | |
Dixit et al. | Review on speech enhancement techniques | |
Denk et al. | Enhanced forensic multiple speaker recognition in the presence of coloured noise | |
Šimko et al. | Analysis of speech prosody using WaveNet embeddings: The Lombard effect | |
Ma et al. | Combining speech fragment decoding and adaptive noise floor modeling | |
Cheng et al. | A study on emotional feature analysis and recognition in speech signal | |
Jang et al. | Efficient spectrum estimation of noise using line spectral pairs for robust speech recognition | |
Zhang et al. | Speech endpoint detection in noisy environments using EMD and teager energy operator | |
PE20230761A1 (es) | Sistemas y metodos para analisis mejorado de muestras de nucleo | |
Koniaris et al. | Phoneme level non-native pronunciation analysis by an auditory model-based native assessment scheme | |
de Cheveigné | Time-domain auditory processing of speech | |
Kollmeier et al. | Modelling human speech recognition in challenging noise maskers using machine learning | |
Shi et al. | Study about Chinese speech synthesis algorithm and acoustic model based on wireless communication network | |
Mahar et al. | Prosody Generation Using Back Propagation Neural Networks for Sindhi Speech Processing Applications | |
RU2589851C2 (ru) | Система и способ перевода речевого сигнала в транскрипционное представление с метаданными | |
Sun et al. | Unsupervised speaker segmentation framework based on sparse correlation feature | |
CN112420022B (zh) | 一种噪声提取方法、装置、设备和存储介质 |