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Song, William Wei, ProfessorORCID iD iconorcid.org/0000-0003-3681-8173
Publications (10 of 50) Show all publications
Zhu, Y., Song, W. W., Wang, X., Rybarczyk, Y., Nyberg, R. G. & Fei, B. (2023). A Novel Approach to Discovering Hygrothermal Transfer Patterns in Wooden Building Exterior Walls. Buildings, 13(9), Article ID 2151.
Open this publication in new window or tab >>A Novel Approach to Discovering Hygrothermal Transfer Patterns in Wooden Building Exterior Walls
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2023 (English)In: Buildings, E-ISSN 2075-5309, Vol. 13, no 9, article id 2151Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
building exterior wall, data-driven approach, hygrothermal transfer mechanisms, transfer patterns
National Category
Building Technologies
Identifiers
urn:nbn:se:du-47087 (URN)10.3390/buildings13092151 (DOI)001076493300001 ()2-s2.0-85172805131 (Scopus ID)
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2024-01-17Bibliographically approved
Xu, Y., Wu, G. & Song, W. W. (2023). A Predictive Study of ARIMA Model Based on Multi-Bayesian Estimation Method Fused Data on Building Materials Environment. In: ACM International Conference Proceeding Series: . Paper presented at 6th International Conference on Big Data Technologies, ICBDT 2023 (pp. 22-27). ACM Digital Library
Open this publication in new window or tab >>A Predictive Study of ARIMA Model Based on Multi-Bayesian Estimation Method Fused Data on Building Materials Environment
2023 (English)In: ACM International Conference Proceeding Series, ACM Digital Library, 2023, p. 22-27Conference paper, Published paper (Refereed)
Abstract [en]

Due to the uncertainty and inconsistency of measurement data from multiple sensors in the same space, a multi-sensor data fusion algorithm is used to fuse the measurement data of multiple nodes. We propose a multi-Bayesian estimation method for fusing multi-sensor data, and combine Bayesian estimation with ARIMA model to predict the ambient temperature of bamboo and wood building materials. It can utilize the redundancy of data to reduce this uncertainty and improve the reliability of subsequent predictions. © 2023 ACM.

Place, publisher, year, edition, pages
ACM Digital Library, 2023
Keywords
ARIMA, Building material environment prediction, Data fusion, Multi-Bayesian estimation
National Category
Information Systems
Identifiers
urn:nbn:se:du-47621 (URN)10.1145/3627377.3627381 (DOI)2-s2.0-85180132052 (Scopus ID)
Conference
6th International Conference on Big Data Technologies, ICBDT 2023
Available from: 2024-01-02 Created: 2024-01-02 Last updated: 2024-01-02Bibliographically approved
Zhu, Y., Song, W. W., Nyberg, R. G., Rybarczyk, Y. & Wang, X. (2023). A Review on Data-driven Methods for Studying Hygrothermal Transfer in Building Exterior Walls. In: ICBDT '23: Proceedings of the 2023 6th International Conference on Big Data Technologies: . Paper presented at 6th International Conference on Big Data Technologies, ICBDT 2023 (pp. 33-41). ACM Press
Open this publication in new window or tab >>A Review on Data-driven Methods for Studying Hygrothermal Transfer in Building Exterior Walls
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2023 (English)In: ICBDT '23: Proceedings of the 2023 6th International Conference on Big Data Technologies, ACM Press, 2023, p. 33-41Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
ACM Press, 2023
Series
ACM International Conference Proceeding Series
Keywords
Hygrothermal performance, Machine learning, Statistical learning, Systematic review
National Category
Energy Systems
Identifiers
urn:nbn:se:du-47617 (URN)10.1145/3627377.3627409 (DOI)2-s2.0-85180131187 (Scopus ID)
Conference
6th International Conference on Big Data Technologies, ICBDT 2023
Available from: 2024-01-02 Created: 2024-01-02 Last updated: 2024-01-02Bibliographically approved
Song, W. W. (2023). An Approximation Computation Approach to Big Data Analysis with a Case Analysis of PV System. In: 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023: . Paper presented at 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023, Chengdu, 26-28 April 2023 (pp. 44-52). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>An Approximation Computation Approach to Big Data Analysis with a Case Analysis of PV System
2023 (English)In: 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 44-52Conference paper, Published paper (Refereed)
Abstract [en]

In the era of big data, it is indispensable to apply data science and technology for big data analysis to solve the big data problems. With the advancement of the big data technologies, we are also facing many problems when dealing with the big data and their studies. It is obvious that big data become "bigger"and "bigger", more complex than before, with a good number of attributes and features in various formats and styles. On the other hand, many data analysis techniques have been proposed for various application domain problems in different purposes. This worsens the situation of choosing an appropriate method for a right problem of right data. In this paper, the author intends to propose an approximation approach toward this problem, through discussing the ways of identification of patterns of the original data, be they of data features or analysis methods. The author attempts to apply the idea to a case of fault detection of a household photovoltaic system. © 2023 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
approximation, big data, data analysis, fault detection, methodologies, photovoltaic systems, Data handling, Information analysis, Case analysis, Data analysis techniques, Data problems, Data technologies, Faults detection, Methodology, PV system, Science and Technology
National Category
Information Systems
Identifiers
urn:nbn:se:du-46631 (URN)10.1109/ICCCBDA56900.2023.10154583 (DOI)001021400000009 ()2-s2.0-85164675555 (Scopus ID)9781665455336 (ISBN)
Conference
8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023, Chengdu, 26-28 April 2023
Available from: 2023-08-04 Created: 2023-08-04 Last updated: 2023-08-24Bibliographically approved
Chen, J., Mao, C. & Song, W. W. (2023). QoS prediction for web services in cloud environments based on swarm intelligence search. Knowledge-Based Systems, 259, Article ID 110081.
Open this publication in new window or tab >>QoS prediction for web services in cloud environments based on swarm intelligence search
2023 (English)In: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 259, article id 110081Article in journal (Refereed) Published
National Category
Computer Sciences
Identifiers
urn:nbn:se:du-43350 (URN)10.1016/j.knosys.2022.110081 (DOI)000935578900013 ()2-s2.0-85142156067 (Scopus ID)
Available from: 2022-11-28 Created: 2022-11-28 Last updated: 2023-03-24Bibliographically approved
Wang, J., Ji, Y., Wei, L., Chen, H. & Song, W. W. (2022). A dynamic firefly algorithm based on two-way guidance and dimensional mutation. International Journal of Bio-Inspired Computation (IJBIC), 20(2), 126-126
Open this publication in new window or tab >>A dynamic firefly algorithm based on two-way guidance and dimensional mutation
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2022 (English)In: International Journal of Bio-Inspired Computation (IJBIC), ISSN 1758-0366, E-ISSN 1758-0374, Vol. 20, no 2, p. 126-126Article in journal (Refereed) Published
Abstract [en]

As a stochastic optimiser, the firefly algorithm (FA) has been successfully and widely used in the solutions to various optimisation problems. Recent related research shows that the standard FA does not sufficiently balance between exploration and exploitation. Especially in high-dimensional problems, it is easy for the standard FA to fall into the local optimum and lead to premature convergence. To overcome the problems as mentioned above, DMTgFA uses three strategies: dynamic step length setting strategy (DS), non-elite two-way guidance model (TG) and elites dimensional mutation strategy (DM). The dynamic step length setting strategy makes the algorithm convergence speed faster. The non-elite two-way guidance model and the elite dimensional mutation strategy cooperate to solve the balance problem between global search and local search. Experimental results show that DMTgFA has stronger optimisation ability and faster convergence speed than other state-of-the-art FA variants.

Keywords
firefly algorithm, single-objective optimisation, non-elite two-way guidance model, elite dimensional mutation strategy
National Category
Control Engineering
Identifiers
urn:nbn:se:du-44771 (URN)10.1504/ijbic.2022.126772 (DOI)000881809200006 ()2-s2.0-85142527556 (Scopus ID)
Available from: 2022-12-30 Created: 2022-12-30 Last updated: 2023-03-17Bibliographically approved
Song, W. W., Zhu, Y., Wang, X. & Peng, X. (2022). An Investigation into Effective Data Analysis Methods for Sensor Datasets of a Sample Building. In: Proceedings of ICBDT 2022: . Paper presented at ICBDT 2022: 2022 5th International Conference on Big Data Technologies (ICBDT) September 2022 (pp. 125-130).
Open this publication in new window or tab >>An Investigation into Effective Data Analysis Methods for Sensor Datasets of a Sample Building
2022 (English)In: Proceedings of ICBDT 2022, 2022, p. 125-130Conference paper, Published paper (Refereed)
National Category
Information Systems
Identifiers
urn:nbn:se:du-44363 (URN)10.1145/3565291.3565311 (DOI)2-s2.0-85145881596 (Scopus ID)
Conference
ICBDT 2022: 2022 5th International Conference on Big Data Technologies (ICBDT) September 2022
Available from: 2022-12-16 Created: 2022-12-16 Last updated: 2023-03-17Bibliographically approved
Peng, X., Shu, W., Pan, C., Ke, Z., Zhu, H., Zhou, X. & Song, W. W. (2022). DSCSSA: A Classification Framework for Spatiotemporal Features Extraction of Arrhythmia Based on the Seq2Seq Model With Attention Mechanism. IEEE Transactions on Instrumentation and Measurement, 71, Article ID 2515112.
Open this publication in new window or tab >>DSCSSA: A Classification Framework for Spatiotemporal Features Extraction of Arrhythmia Based on the Seq2Seq Model With Attention Mechanism
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2022 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 71, article id 2515112Article in journal (Refereed) Published
Abstract [en]

In the field of arrhythmia classification, classification accuracy has always been a research hotspot. However, the noises of electrocardiogram (ECG) signals, the class imbalance of ECG data, and the complexity of spatiotemporal features of ECG data are all important factors affecting the accuracy of ECG arrhythmias classification. In this article, a novel DSCSSA ECG arrhythmias classification framework is proposed. First, discretewavelet transform (DWT) is used to denoise and reconstruct ECG signals to improve the feature extraction ability of ECG signals.Then, the synthetic minority oversampling technique (SMOTE) oversampling method is used to synthesize a new minority sample ECG signal to reduce the impact of ECG data imbalance on classification. Finally, a convolutional neural network (CNN) and sequence-to-sequence (Seq2Seq) classification model with attention mechanism based on bi directional long short-term memory(Bi-LSTM) as the codec is used for arrhythmias classification, and the model can give corresponding weight according to the importance of heartbeat features and can improve the ability toextract and filter the spatiotemporal features of heartbeats. In the classification of five heartbeat types, including normal beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V),fusion beat (F), and unknown beat (Q), the proposed method achieved the overall accuracy (OA) value and Macro-F1 score of 99.28% and 95.70%, respectively, in public the Massachusetts Institute of Technology - Boston’s Beth Israel Hospital (MIT-BIH)arrhythmia database. These methods are helpful to improve the effectiveness and clinical reference value of computer-aided ECG automatic classification diagnosis.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:du-42124 (URN)10.1109/tim.2022.3194906 (DOI)000841802700009 ()2-s2.0-85135745508 (Scopus ID)
Available from: 2022-08-15 Created: 2022-08-15 Last updated: 2023-03-17Bibliographically approved
Peng, X., Xiong, P., Song, W. W., Zhou, X., Zhu, H., Zhang, Q., . . . Zheng, T. (2022). ECG Signals Classification Method for Wireless Body Area Network Based on Quantized Residual Network. In: Proceedings of ICBDT: . Paper presented at ICBDT 2022: 2022 5th International Conference on Big Data Technologies (ICBDT) September 2022 (pp. 425-435).
Open this publication in new window or tab >>ECG Signals Classification Method for Wireless Body Area Network Based on Quantized Residual Network
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2022 (English)In: Proceedings of ICBDT, 2022, p. 425-435Conference paper, Published paper (Refereed)
National Category
Information Systems
Identifiers
urn:nbn:se:du-44364 (URN)10.1145/3565291.3565359 (DOI)2-s2.0-85145882088 (Scopus ID)
Conference
ICBDT 2022: 2022 5th International Conference on Big Data Technologies (ICBDT) September 2022
Available from: 2022-12-16 Created: 2022-12-16 Last updated: 2023-03-17Bibliographically approved
Wang, X., Li, H., Zhu, Y., Peng, X., Wan, Z., Xu, H., . . . Fei, B. (2022). Using Machine Learning Method to Discover Hygrothermal Transfer Patterns from the Outside of the Wall to Interior Bamboo and Wood Composite Sheathing. Buildings, 12(7), 898-898
Open this publication in new window or tab >>Using Machine Learning Method to Discover Hygrothermal Transfer Patterns from the Outside of the Wall to Interior Bamboo and Wood Composite Sheathing
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2022 (English)In: Buildings, E-ISSN 2075-5309, Vol. 12, no 7, p. 898-898Article in journal (Refereed) Published
National Category
Civil Engineering
Identifiers
urn:nbn:se:du-42147 (URN)10.3390/buildings12070898 (DOI)000831610300001 ()2-s2.0-85133243973 (Scopus ID)
Available from: 2022-08-16 Created: 2022-08-16 Last updated: 2024-01-17
Projects
Förstudie forsknings och innovationsnätverk
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3681-8173

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