Energy efficient building envelope using novel RBF neural network integrated affinity propagation

Han, Yongming; Fan, Chenyu; Geng, Zhiqiang; Ma, Bo; Cong, Di; Chen, Kai; Yu, Bin

doi:10.1016/j.energy.2020.118414

Cited by 32 publications

(15 citation statements)

References 26 publications

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“…In this way, the information data is combined with the tracking model for the first time, and the correlation filter is used to detect the responsiveness in the frame number of the behavior of the moving person, and the feature points of the data are captured. It further improves the recognition efficiency of the tracking model, and the speed can exceed 100 frames [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Gao

2021

Computational Intelligence and Neuroscience

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With the rapid development of deep learning algorithms, it is gradually applied in UAV (Unmanned Aerial Vehicle) driving, visual recognition, target tracking, behavior recognition, and other fields. In the field of sports, many scientists put forward the research of target tracking and recognition technology based on deep learning algorithms for athletes’ trajectory and behavior capture. Based on the target tracking algorithm, a regional proposal network RPN algorithm combined with the twin regional proposal network Siamese algorithm is proposed to study the tracking and recognition technology of athletes’ behavior. Then, the adaptive updating network is used to track the behavior target of athletes, and the simulation model of behavior recognition is established. This algorithm is different from the traditional twin network algorithm. It can accurately take the athlete’s behavior as the target candidate box in model training and reduce the interference of environment and other factors on model recognition. The results show that the Siamese-RPN algorithm can reduce the interference from the background and environment when tracking the athletes’ target behavior trajectory. This algorithm can improve the training behavior recognition model, ignore the background interference elements of the behavior image, and improve the accuracy and overall performance of the model. Compared with the traditional twin network method for sports behavior recognition, the Siamese-RPN algorithm studied in this paper can perform offline operations and distinguish the interference factors of athletes’ background environment. It can quickly capture the characteristic points of athletes’ behavior as the data input of the tracking model, so it has excellent popularization and application value.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Gao

2021

Computational Intelligence and Neuroscience

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“…A radial basis function ANN (RBF-ANN) has a simple structure, fast convergence speed, and the ability to approximate arbitrary nonlinear functions and can perform relatively accurate estimation with a small number of samples [31][32][33][34]. An RBF-ANN is a forward network with a three-layer structure: the first layer is the input layer, where the number of nodes is equal to the dimension of the input; the second layer is a hidden layer, where the number of nodes depends on the complexity of the problem; and the third layer is the output layer, where the number of nodes is equal to the dimensionality of the output data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Novel Method for Analyzing the Factors Influencing Ground Settlement during Shield Tunnel Construction in Upper-Soft and Lower-Hard Fissured Rock Strata considering the Coupled Hydromechanical Properties

et al. 2020

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Focusing on the special stratum conditions of upper-soft and lower-hard fissured rock strata, this paper conducts quantitative research and analysis on the factors influencing the ground settlement caused by shield tunnel construction considering the coupled hydromechanical properties and presents a corresponding numerical simulation-artificial neural network-Bayesian network- (NS-ANN-BN-) based method. In this method, with a subway shield tunnel as the engineering context, three numerical models are established, in which the shield tunnel is located in soft strata, the shield tunnel is located in semisoft and semihard strata, or the shield tunnel is located in hard strata. According to the numerical simulation (NS) calculations, the ground settlement values under the three types of strata are 38.96 mm, 10.42 mm, and 3.13 mm, respectively. A radial basis function artificial neural network (RBF-ANN) is used to establish the nonlinear mapping relationship between the stratum parameters and the ground settlement, and the training samples and test samples are generated through NS to train the RBF-ANN. After the training is completed, the accuracy of the neural network meets the requirements. The elastic moduli of coarse sand~gravel sand k3 and moderately weathered rhyolite k4 and the cohesion of moderately weathered rhyolite c4 are selected as the key parameters. A large number of training cases are generated through the RBF-ANN, and the Bayesian network (BN) prior probability is calculated by self-learning. A BN model of ground settlement for shield tunnel construction in the upper-soft and lower-hard fissured rock strata is established. The BN back analysis method is used to quantitatively analyze the influencing factors of the ground settlement. The results show that when the tunnel is located in soft strata, the surface settlement is mainly affected by parameter k3. When the ground settlement increases considerably, the three parameters all have a strong influence. When the tunnel is located in semisoft and semihard strata, the influence of the three parameters on the ground settlement is weak. When the tunnel is located in hard strata, the ground settlement is mainly affected by parameter k4. When the ground settlement greatly increases, parameters k3 and c4 have less influence. When the tunnel is located in strata with different soft-hard ratios, the ground settlement is mainly affected by the elastic moduli of coarse sand~gravel sand and moderately weathered rhyolite. This method can provide a reference for the ground settlement analysis of shield tunnel construction in areas with similar fissured rock strata.

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“…Bhamare et al developed a machine learning and deep learning-based model in their study for the thermal performance prediction of a structure with a phase change material integrated into its shell [5]. Han et al proposed a model for affinity propagation-based new radial basis function (RBF) of an energy-efficient building envelope using neural network-integrated affinity propagation in their study [13]. Meanwhile, Cha et al worked on a random forest-based machine learning model that predicts the generation of construction and demolition waste and monitors the waste management efficiency of facilities in their study [7].…”

Section: Introductionmentioning

confidence: 99%

Research on the Use of Machine Learning on Building Facades

Günay

Mayuk

2022

EJRnD

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Artificial intelligence and its sub-branch, machine learning technologies, have developed rapidly in recent years and their use for various purposes has seemed to be increased in various sectors from automotive to medicine, from law to marketing. Similarly, these technologies have begun to be used in the building sector and in the field of architecture. These technologies are being used in many fields in architecture such as feasibility studies, building design, project control, occupational safety, earthquake resistant building design and applications, energy efficient system design, construction with smart construction equipment, smart building design, and smart facade design. Despite this increasing use in the field, it has been determined by the literature review that the number of studies focusing on the use of machine learning in architecture, especially on building facades, is low. In this sense in the study, it is aimed to examine the relationship between artificial intelligence and machine learning technologies with architecture in the context of building facades. Initially in the study the topics and the historical process related to artificial intelligence and machine learning were explained, subsequently the use of technologies on the building facades was examined through examples. In this way, a guiding resource has been created for those who want to work on this subject in the future.

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Energy efficient building envelope using novel RBF neural network integrated affinity propagation

Cited by 32 publications

References 26 publications

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

A Novel Method for Analyzing the Factors Influencing Ground Settlement during Shield Tunnel Construction in Upper-Soft and Lower-Hard Fissured Rock Strata considering the Coupled Hydromechanical Properties

Research on the Use of Machine Learning on Building Facades

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