A Fuzzy Expression Way for Air Quality Index with More Comprehensive Information

Wang, Yujie; Zhao, Mingxuan; Han, Yulin; Zhou, Jian

doi:10.3390/su9010083

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…According to Javid et al [15] and the remaining summary in Table 1, TVOC and CO2 are gases that are frequently used to construct IAQI. Additionally, some studies use Fuzzy Logic to classify the various types of gas parameters [14], [17], [18], but only a few IAQI studies employ forecasting techniques, either statistical or machine learning based. TVOC and CO2 were used as gas parameters in this study to calculate the IAQI.…”

Section: A Principle Of Indoor Air Quality Indexmentioning

confidence: 99%

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Erfianto

Rahmatsyah²

2022

JOIV : Int. J. Inform. Visualization

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Air quality monitoring is a process that determines the number of pollutants in the air, one of which is indoor air quality. The Fuzzy Indoor Air Quality Index was developed in this research. It is a method for determining the indoor air quality index using sensor fusion and fuzzy logic. By combining several different time series determinants of air quality, a fuzzy logic-based sensor fusion method is used to build a knowledge base about indoor air quality levels. Without the use of complicated calculation models, fuzzy logic-based fusion will make it easier to determine indoor air quality levels based on various sensor parameters. The input for fuzzy-based data fusion is obtained from the ARIMA method with Kalman Filter's air quality parameter values estimation. The application of ARIMA with a Kalman Filter was used to improve the accuracy of indoor air quality estimation in this study. ARIMA(3,1,3) had a MAPE of 0.1 percent on the CO2 dataset, and ARIMA(1,0,1) had a MAPE of 0.63 percent on the TVOC dataset based on approximately three experimental days. ARIMA (3,1,3) estimation with a Kalman Filter results in a MAPE of 0.03 percent for the CO2 dataset and a MAPE of 0.24 percent for ARIMA(1,0,1) Kalman Filter estimation on TVOC dataset. As a result, the Fuzzy Indoor Air Quality Index (FIAQI) developed in this research reasonably estimates indoor air quality. This can be seen by examining the percentage of estimation errors obtained from the experiment.

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Section: A Principle Of Indoor Air Quality Indexmentioning

confidence: 99%

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Erfianto

Rahmatsyah²

2022

JOIV : Int. J. Inform. Visualization

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“…Yang et al (2019) developed an improved method to tackle a fuzzy facility location problem by using the operational law proposed in Zhou et al (2016). Besides, the research findings in are also applied to other fields such as location problem (Soltanpour et al 2019), quality function deployment , evaluation for the air quality (Wang et al 2017), green-fuzzy vehicle routing problem (Wang et al 2018), diagnosis of prostate cancer (Kar and Majumder 2017), and so on.…”

Section: Introductionmentioning

confidence: 99%

An Extensive Operational Law for Monotone Functions of LR Fuzzy Intervals with Applications to Fuzzy Optimization

Zhao

Han

Zhou

2021

Preprint

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The operational law put forward by Zhou et al. on strictly monotone functions with regard to regular LR fuzzy numbers makes a valuable push to the development of fuzzy set theory. However, its applicable conditions are confined to strictly monotone functions and regular LR fuzzy numbers, which restricts its application in practice to a certain degree. In this paper, we propose an extensive operational law that generalizes the one proposed by Zhou et al. to apply to monotone (but not necessarily strictly monotone) functions with regard to regular LR fuzzy intervals (LR-FIs), of which regular fuzzy numbers can be regarded as particular cases. By means of the extensive operational law, the inverse credibility distributions (ICDs) of monotone functions regarding regular LR-FIs can be calculated efficiently and effectively. Moreover, the extensive operational law has a wider range of applications, which can deal with the situations hard to be handled by the original operational law. Subsequently, based on the extensive operational law, the computational formulae for expected values (EVs) of LR-FIs and monotone functions with regard to regular LR-FIs are presented. Furthermore, the proposed operational law is also applied to dispose fuzzy optimization problems with regular LR-FIs, for which a solution strategy is provided, where the fuzzy programming is converted to a deterministic equivalent first and then a newly-devised solution algorithm is utilized. Finally, the proposed solution strategy is applied to a purchasing planning problem, whose performances are evaluated by comparing with the traditional fuzzy simulation-based genetic algorithm. Experimental results indicate that our method is much more efficient, yielding high-quality solutions within a short time.

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“…One is a parametric method that uses a set of parameters for prior risk-neutral probability distribution and calculates option prices by varying the parameters to minimize the pricing error, such as expansion methods [15], generalized distribution methods [16][17][18], and mixture methods [19,20]. The other category is nonparametric methods, which search the risk-neutral probability distribution without a prior assumption of a specific distribution, such as maximum entropy methods [21,22], kernel methods [23] and curve-fitting methods [24][25][26][27]. The implied binomial tree of Rubinstein [11], hereinafter referred to as "IBT model", is a classical model of the nonparametric approach.…”

Section: Introductionmentioning

confidence: 99%

An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution

Jiang

Lei

Chung³

2018

Sustainability

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This paper investigates the approximated arbitrage bounds of option prices in an incomplete market setting and draws implications for option pricing and risk management. It gives consideration to periods of global financial crisis and European sovereign debt crisis. To this end, we employ the gain-loss ratio method combined with the market-implied risk-neutral distribution calculated by binomial tree to investigate the options price bounds. Our implied gain-loss bounds of option prices are preference-free and parametric-free to avoid the misspecification error of subjective choice on the benchmark model of gain-loss ratio, and consequently, greatly reduce model risk and market risk. The empirical results show that there are option prices breaking the gain-loss bounds, even after taking into account the market information. This means that a good risk management technique and good-deal investment opportunities exist if the implied binomial tree is used as a benchmark model in the gain-loss bounds.

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A Fuzzy Expression Way for Air Quality Index with More Comprehensive Information

Cited by 7 publications

References 31 publications

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

An Extensive Operational Law for Monotone Functions of LR Fuzzy Intervals with Applications to Fuzzy Optimization

An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution

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