The interference of humidity on a shear horizontal surface acoustic wave ammonia sensor

Shen, Chien-Hua; Hsu, Cheng-Liang; Hwang, Rey-Chue; Jeng, Jin-Shing

doi:10.1016/j.snb.2006.06.017

Cited by 26 publications

(14 citation statements)

References 13 publications

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“…Recently, due to the powerful learning and modeling capabilities, NN has been widely employed into different areas such as the signal processing and control [11][12][13][14][15][16][17][18]. Through the learning, NN is able to generate a mapping between input and output pairs bypassing the complicated statistic steps such as model hypothesis, identification, estimation of model parameters, and model verification.…”

Section: Methodsmentioning

confidence: 99%

“…As previous mentioned, the relationship among the actual resistance, ideal resistance and the relevant control parameters of printing and etching processes is expected to be obtained through the learning of NN model. The major steps of BP algorithm can be summarized as follows [14][15][16][17][18][19]. 1 st step: All weights ( ij ) are firstly initialized to the small random values.…”

Section: Methodsmentioning

confidence: 99%

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The intelligent control of ITO bar's resistance for touch panel

Chen

Shen

et al. 2013

2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)

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The paper presents an intelligent control mechanism for the ITO bar's resistance of touch panel (TP). The artificial neural network is used to catch the complex relationship between bar's resistance and its relevant manufacturing parameters during the printing and etching processes. An effective and accurate manufacturing mechanism of ITO bar is expected to be developed. This mechanism can be taken as a tool which is able to help the engineer to precisely control the manufacturing process of ITO bar such that the ideal touch panel with desired film thickness and resistance can be produced. Not only the rate of excellent product could be greatly improved, but also the business cost could be effectively saved.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

The intelligent control of ITO bar's resistance for touch panel

Chen

Shen

et al. 2013

2013 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA)

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“…In our previous studies [9][10][11][12], L-glutamic acid hydrochloride had high sensitivity, selectivity, reversibility and repeatability to ammonia in dry nitrogen. We have applied the shear horizontal surface acoustic wave (SH-SAW) sensor coated with L-glutamic acid hydrochloride to detect ppb-level ammonia [13].…”

Section: Introductionmentioning

confidence: 91%

High‐sensitive neural network ammonia sensor based on shear horizontal surface acoustic wave devices

Shen

Huang

Hwang

2008

Journal of Chemometrics

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In this paper, a shear horizontal surface acoustic wave devices coated with L-glutamic acid hydrochloride were applied as ammonia sensors. This sensor has shown high sensitivity and fast responses to ppb-level ammonia. The frequency shift linearly increased as the ammonia concentration increased from 40 to 400 ppb in dry environment. In the humid environment, the frequency shift gradually decreased with ammonia concentration increasing. In order to precisely estimate the ammonia in humid environment, two different neural models, the conventional feedforward neural network and quantum neural network, were used as the identifier and their performances were reported and compared.

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“…The learning algorithm utilized in the neural models is "BackPropagation Algorithm (BP)". The major steps of BP algorithm are listed as follows [4,7,[8][9].…”

Section: Neural Network Estimatormentioning

confidence: 99%

“…Undoubtedly, such a setting way based on human's experience easily makes the quality of steel bar be failure. Recently, due to the powerful learning and adaptive capabilities, neural network technique has been widely applied into many areas, such as control system, system identification, decision making, pattern recognition and so on [3][4][5][6][7][8][9]. Through a simple training process, NN model can automatically develop the complex and nonlinear relationship between input and output pairs.…”

Section: Introductionmentioning

confidence: 99%

The Neural Network Estimator for Mechanical Property of Rolled Steel Bar

Huang

Chen

et al. 2009

2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC)

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In this paper, the neural network estimator for mechanical property of rolled steel bar was proposed. Based on the learning capability of neural network, the nonlinear, complex relationships among the steel bar, the billet materials and the control parameters of production are expected to be automatically developed. Such a neural network estimator can help the technician to make a precise judgment for setting the related control parameters of rolling process. Not only the quality of steel bars can meet the standard asked for, but also can reduce the running cost caused by failure production.

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The interference of humidity on a shear horizontal surface acoustic wave ammonia sensor

Cited by 26 publications

References 13 publications

The intelligent control of ITO bar's resistance for touch panel

The intelligent control of ITO bar's resistance for touch panel

High‐sensitive neural network ammonia sensor based on shear horizontal surface acoustic wave devices

The Neural Network Estimator for Mechanical Property of Rolled Steel Bar

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