Optimized Air Quality Prediction Model Based on Neural Network

Zhu, Wei; Liu, Xiuwen; Zhu, Zhen

doi:10.1109/iccea50009.2020.00124

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…20 presents the real and predicted temperatures for comparison. The root-mean-square error is defined in (10), in which N is the number of data points, yi is the ith actual value, and i y ˆis the ith predicted value [24].…”

Section: A Bidirectional I/o Port Verificationmentioning

confidence: 99%

Front-End Gateway System With Serial Communication Protocol Conversion and Edge Computing Platforms

Sung,

Tung,

Huang

et al. 2023

IEEE Access

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This study presents a front-end gateway system with asynchronous and synchronous serial communication protocol conversion between the universal asynchronous receiver/transmitter (UART) protocol and inter-integrated circuit (I 2 C) protocol. The valid data and sensing data can be integrated using the I 2 C protocol and sent to an edge computing platform for automated data analysis. Our system reduces data processing time and the number of buses. The system has an edge computing platform that handles simple linear regression, base conversion, a neural network, and a text database and communicates with multiple peripheral devices. The I 2 C master and slave are constructed on the edge computing platform and implement arbitration by using the carrier-sense multiple access with collision avoidance protocol to prevent data collision. According to the results obtained from the Signal Tap logic analyzer in experiments conducted using a field-programmable gate array board, a completed 330-bit UART packet requires 755.2 s to be received, and the throughput is 436.97 kbps. By contrast, a 90-bit I 2 C packet requires 184.6 s to be received, and the throughput is 487.54 kbps. The front-end gateway sends integrated packets by using the I 2 C protocol, and the operating freuency (serial clock) of the I 2 C slave can reach up to 3.6 MHz bidirectionally. An integrated 153-bit packet requires 42.96 s to be received by the edge computing platform, and the throughput is 3.5614 Mbps, which is approximately 8.15 times higher than that of the UART packet. We also fabricated a front-end gateway ASIC by using the TSMC 90-nm 1P9M CMOS process.INDEX TERMS Serial communication protocol, universal asynchronous receiver/transmitter (UART), inter-integrated circuit (I 2 C), ZigBee, front-end gateway, edge computing platform, IoT network, application-specific integrated circuit (ASIC).

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Section: A Bidirectional I/o Port Verificationmentioning

confidence: 99%

Front-End Gateway System With Serial Communication Protocol Conversion and Edge Computing Platforms

Sung,

Tung,

Huang

et al. 2023

IEEE Access

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show abstract

“…The texture feature is calculated using the Tamura texture feature algorithm, and then combined with other texture features such as local binary pattern (LBP) and Gabor texture feature as the objective feature of the research object, then the image is classified or retrieved. There are many methods for classification and prediction of research objects, such as linear regression model, BP neural network (Li et al , 2011; Zhang et al , 2017), support vector machine (SVM) prediction model, MLP neural network model (Xing et al , 2019; Zhu, 2018), etc. The construction of predictive model combined with image processing technology has achieved certain research results in fabric flatness and wrinkling (Liu and Xu, 2012).…”

Section: Introductionmentioning

confidence: 99%

Objective evaluation of optical illusion skirt based on image texture features

Wei

Liu

Wang

2021

IJCST

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PurposeNowadays, most methods of illusion garment evaluation are based on the subjective evaluation of experienced practitioners, which consumes time and the results are too subjective to be accurate enough. It is necessary to explore a method that can quantify professional experience into objective indicators to evaluate the sensory comfort of the optical illusion skirt quickly and accurately. The purpose of this paper is to propose a method to objectively evaluate the sensory comfort of optical illusion skirt patterns by combining texture feature extraction and prediction model construction.Design/methodology/approachFirstly, 10 optical illusion sample skirts are produced, and 10 experimental images are collected for each sample skirt. Then a Likert five-level evaluation scale is designed to obtain the sensory comfort level of each skirt through the questionnaire survey. Synchronously, the coarseness, contrast, directionality, line-likeness, regularity and roughness of the sample image are calculated based on Tamura texture feature algorithm, and the mean, contrast and entropy are extracted of the image transformed by Gabor wavelet. Both are set as objective parameters. Two final indicators T1 and T2 are refined from the objective parameters previously obtained to construct the predictive model of the subjective comfort of the visual illusion skirt. The linear regression model and the MLP neural network model are constructed.FindingsResults show that the accuracy of the linear regression model is 92%, and prediction accuracy of the MLP neural network model is 97.9%. It is feasible to use Tamura texture features, Gabor wavelet transform and MLP neural network methods to objectively predict the sensory comfort of visual illusion skirt images.Originality/valueCompared with the existing uncertain and non-reproducible subjective evaluation of optical illusion clothing based on experienced experts. The main advantage of the authors' method is that this method can objectively obtain evaluation parameters, quickly and accurately obtain evaluation grades without repeated evaluation by experienced experts. It is a method of objectively quantifying the experience of experts.

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Machine learning algorithms to forecast air quality: a survey

2023

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Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011–2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.

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Optimized Air Quality Prediction Model Based on Neural Network

Cited by 6 publications

References 3 publications

Front-End Gateway System With Serial Communication Protocol Conversion and Edge Computing Platforms

Front-End Gateway System With Serial Communication Protocol Conversion and Edge Computing Platforms

Objective evaluation of optical illusion skirt based on image texture features

Machine learning algorithms to forecast air quality: a survey

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