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Electronic Nose: Algorithmic Challenges
by 作者: Lei Zhang - Fengchun Tian - David Zhang
ISBN-10 书号: 9811321663
ISBN-13 书号: 9789811321665
Edition 版本: 1st ed. 2018
Publisher Finelybook 出版日期: 2018-09-12
Pages: 339
Book Description
This book presents the key technology of electronic noses,and systematically describes how e-noses can be used to automatically analyse odours. Appealing to readers from the fields of artificial intelligence,computer science,electrical engineering,electronics,and instrumentation science,it addresses three main areas: First,readers will learn how to apply machine learning,pattern recognition and signal processing algorithms to real perception tasks. Second,they will be shown how to make their algorithms match their systems once the algorithms don’t work because of the limitation of hardware resources. Third,readers will learn how to make schemes and solutions when the acquired data from their systems is not stable due to the fundamental issues affecting perceptron devices (e.g. sensors).
In brief,the book presents and discusses the key technologies and new algorithmic challenges in electronic noses and artificial olfaction. The goal is to promote the industrial application of electronic nose technology in environmental detection,medical diagnosis,food quality control,explosive detection,etc. and to highlight the scientific advances in artificial olfaction and artificial intelligence.
The book offers a good reference guide for newcomers to the topic of electronic noses,because it refers to the basic principles and algorithms. At the same time,it clearly presents the key challenges – such as long-term drift,signal uniqueness,and disturbance – and effective and efficient solutions,making it equally valuable for researchers engaged in the science and engineering of sensors,instruments,chemometrics,etc.
Partl.Overview
1.Introduction
2.E-Nose Algorithms and Challenges
Part ll.E-Nose Odor Recognition and Prediction: Challengel
3.Heuristic and Bio-inspired Neural Network Model
4.Chaos-Based Neural Network Optimization Approach
5.Multilayer Perceptron-Based Concentration Estimation
6.Discriminative Support Vector Machine-Based Odor Classification
7.Local Kernel Discriminant Analysis-Based Odor Recognition
8.Ensemble of Classifiers for Robust Recognition
Part ll.E-Nose Drift Compensation: Challenge ll
9.Chaotic Time Series-Based Sensor Drift Prediction
10.Domain Adaptation Guided Drift Compensation
11.Domain Regularized Subspace Projection Method
12.Cross-Domain Subspace Learning Approach
13.Domain Correction-Based Adaptive Extreme Learning Machine
14.Multi-feature Semi-supervised Learning Approach
Part IV.E-Nose Disturbance Elimination: Challenge ll
15.Pattern Recognition-Based Interference Reduction
16.Pattern Mismatch Guided Interference Elimination
17.Self-expression-Based Abnormal Odor Detection
Part V.E-Nose Discreteness Correction: ChallengeV
18.Affine Calibration Transfer Model
19.Instrumental Batch Correction
20.Book Review and Future Work
