Wood classification study based on thermal physical parameters with intelligent method of artificial neural networks

Cao, Shiwei; Zhou, Shiyu; Liu, Jiying; Li, Xiaoping; Zhou, Yucheng

doi:10.15376/biores.17.1.1187-1204

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…These layers are fully interconnected with no interneuronal connections within the same layer. The functioning of BP neural networks is facilitated by a two-phase process: the forward propagation of the input signal and the subsequent backpropagation of the error which allows the adjustment of the underlying weights [51]. The genetic algorithm is a model that simulates the natural selection and genetic mechanisms of biological evolution in Charles Darwin's theory.…”

Section: Algorithmsmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Lu,

Cui,

Gao

et al. 2024

Buildings

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Machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (Top) and energy consumption (Eh). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting Top and Eh, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in Eh prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field.

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Section: Algorithmsmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Lu,

Cui,

Gao

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…To compare the heat release performance of different flooring materials, and also to study the influence of different surface materials to the room heating effect, the paper conducts a study about the comparison among these commonly used surface materials by simulating their effect on the indoor thermal environment. Previously, Cao et al (2022) measured thermal properties of 65 kinds of wood species, including thermal conductivity, specific heat capacity, and surface emissivity. Based on these measured values, they constructed wood classification models using an artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Thermal properties of radiant floor surface materials and numerical evaluation of the thermal performance

Zhou

Cao

Wang

et al. 2023

BioRes

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A Hot Disk thermal constant analyzer was used to obtain the thermal parameters of composite boards, solid wood floor, and ceramic tiles (CT). FLUENT software was used for the model establishment and the temperature field simulation, and the effects caused by different surface materials were analyzed. A 2D unsteady model was constructed to analyze floor surface temperature and indoor temperature fields in an enclosure space. Comparison of temperature fields caused by different materials showed that both steady indoor temperature and surface temperature of CT were the highest, which is due to its good thermal properties. Thermal conductivity and thermal capacity are the two main factors affecting floor thermal performance in the initial hours, while thermal conductivity is the key factor in the steady period. For the compared floor materials, CT and Sindora glabra (SG) are the optimal choices from the perspective of thermal performance, while composite boards are almost the same in thermal performance.

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“…Their research anticipated the potential of computer vision to enhance the accessibility of wood identification to the public and make significant contributions to the field of wood science. Cao et al (2022) utilized an artificial neural network trained using species-specific thermal conductivity trends to classify different species.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network performance and the factors affecting performance for classification of seven Quercus species using sclereid characteristics in the bark

Kim,

Purusatama,

Savero

et al. 2023

BioRes

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Based on the sclereids in the bark of oak species, a convolutional neural network (CNN) was employed to validate species classification performance and its influencing factors. Three optimizers including stochastic gradient descent (SGD), adaptive moment estimation (Adam), root mean square propagation (RMSProp), and dataset augmentation were adopted. The accuracy and loss stabilized at approximately 15 to 20 and 70 to 80 epochs for the augmented and non-augmented condition, respectively. In the last five epochs, the RMSProp-augmented condition achieved the highest accuracy of 89.8%, whereas the Adam-augmented condition achieved the lowest accuracy of 73.8%. Regarding the loss, SGD-non-augmented condition was the lowest at 0.498, whereas Adam-augmented condition was the highest at 2.740. The highest accuracy was influenced by RMSProp at 0.194. Dataset augmentation had a significant influence on accuracy at 0.456. Homogeneous subsets among the validation conditions indicated that the accuracy and loss were classified into the same subset using an augmented dataset during the training, regardless of the optimizer. Only Adam and RMSProp with non-augmented datasets were categorized into the same subset during the test. Hence, species classification using CNN and sclereid characteristics in the bark was feasible, and RMSProp with augmented datasets showed optimal performance for species classification.

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Wood classification study based on thermal physical parameters with intelligent method of artificial neural networks

Cited by 9 publications

References 14 publications

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Thermal properties of radiant floor surface materials and numerical evaluation of the thermal performance

Convolutional neural network performance and the factors affecting performance for classification of seven Quercus species using sclereid characteristics in the bark

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