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- Liu, Zhenxia, et al.
(författare)
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Large deviations for longest runs in Markov chains
- 2020
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Ingår i: JOURNAL OF APPLIED ANALYSIS. - : WALTER DE GRUYTER GMBH. - 1425-6908 .- 1869-6082. ; 26:2, s. 309-314
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Tidskriftsartikel (refereegranskat)abstract
- We continue our investigation on general large deviation principles (LDPs) for longest runs. Previously, a general LDP for the longest success run in a sequence of independent Bernoulli trails was derived in [Z. Liu and X. Yang, A general large deviation principle for longest runs, Statist. Probab. Lett. 110 (2016), 128-132]. In the present note, we establish a general LDP for the longest success run in a two-state (success or failure) Markov chain which recovers the previous result in the aforementioned paper. The main new ingredient is to implement suitable estimates of the distribution function of the longest success run recently established in [Z. Liu and X. Yang, On the longest runs in Markov chains, Probab. Math. Statist. 38 (2018), no. 2, 407-428].
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| 2. |
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| 3. |
- Wang, Xiaohuan, et al.
(författare)
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Using Machine Learning Method to Discover Hygrothermal Transfer Patterns from the Outside of the Wall to Interior Bamboo and Wood Composite Sheathing
- 2022
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Ingår i: Buildings. - : MDPI AG. - 2075-5309. ; 12:7, s. 898-898
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Tidskriftsartikel (refereegranskat)abstract
- To identify hygrothermal transfer patterns of exterior walls is a crucial issue in the design, assessment, and construction of buildings. Temperature and relative humidity, as sensor monitoring data, were collected from the outside of the wall to interior bamboo and wood composite sheathing over the year in Huangshan Mountain District, Anhui Province, China. Combining the machine learning method of reservoir computing (RC) with agglomerative hierarchical clustering (AHC), a novel clustering framework was built for better extraction of the characteristics of hygrothermal transfer on the time series data. The experimental results confirmed the hypothesis that the change in the temperature and relative humidity of the outside of the wall (RHT12) dominated the change of the interior sheathing (RHT11). The delay time between two adjacent peaks in temperature was 1 to 2 h, while that in relative humidity was 1 to 4 h from the outside of the wall to interior bamboo and wood composite sheathing. There was no significant difference in temperature peak delay time between April and July. Temperature peak delay time was 50 to 120 min. However, relative humidity peak delay time was 100 to 240 min in April, whereas it was 20 to 120 min in July. The impact formed a relatively linear relationship between outdoor temperature and relative humidity peak delay time. The hygrothermal transfer patterns were characterized effectively by the peak delays. The discovery of the hygrothermal transfer patterns for the bamboo and wood composite walls using the machine learning method will facilitate the development of energy-efficient and durable bamboo and wood composite wall materials and structures.
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| 4. |
- Zhu, Yurong, et al.
(författare)
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A Novel Approach to Discovering Hygrothermal Transfer Patterns in Wooden Building Exterior Walls
- 2023
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Ingår i: Buildings. - : MDPI. - 2075-5309. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- To maintain the life of building materials, it is critical to understand the hygrothermal transfer mechanisms (HTM) between the walls and the layers inside the walls. Due to the extreme instability of weather data, the actual data models of the HTM—the data being collected for actual buildings using modern sensor technologies—would appear to be a great difference from any theoretical models, in particular, for wood building materials. In this paper, we aim to consider a variety of data analysis tools for hygrothermal transfer features. A novel approach for peak and valley detection is proposed based on the discrete differentiation of the original data. Not to be limited to the measure of peak and valley delays for HTM, we propose a cross-correlation analysis to obtain the general delay between two daily time series, which seems to be representative of the delay in the daily time series. Furthermore, the seasonal pattern of the hygrothermal transfer combined with the correlation analysis reveals a reasonable relationship between the delays and the indoor and outdoor climates. © 2023 by the authors.
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| 5. |
- Zhu, Yurong, et al.
(författare)
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A Review on Data-driven Methods for Studying Hygrothermal Transfer in Building Exterior Walls
- 2023
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Ingår i: ICBDT '23: Proceedings of the 2023 6th International Conference on Big Data Technologies. - : ACM Press. ; , s. 33-41
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Konferensbidrag (refereegranskat)abstract
- This review aims to comprehensively assess and synthesize the existing literature on the use of data-driven methods for studying hygrothermal transfer in building exterior walls. The review is conducted by an exhaustive search strategy to identify relevant articles from Web of Science and Scopus databases. There are 20 eligible studies included in this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The most used data-driven methods are traditional neural networks, such as Multi-Layer Perceptrons and 2D Convolutional Neural Networks. Results suggested that neural network models hold potential for accurately predicting hygrothermal attributes of building exteriors. However, a conspicuous gap in the literature is the absence of studies drawing direct comparisons between data-driven methodologies and conventional simulation techniques. © 2023 ACM.
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