Multi-objective optimization design of steel structure building energy consumption simulation based on genetic algorithm

Yuan, Rong; Rubaiee, Saeed; Ahmed, Anas; Othman, Asem Majed; Arora, Sandeep

doi:10.1515/nleng-2022-0012

Cited by 5 publications

(4 citation statements)

References 50 publications

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“…Knowledge graph construction technology is a complex technical system that requires knowledge extraction, knowledge fusion, knowledge processing, and other techniques to support it. Starting from heterogeneous data and ending with the formation of a knowledge graph, many key technologies are involved in the middle, including knowledge acquisition, processing, and structured representation [ 18 ]. Knowledge extraction refers to extracting valuable knowledge from multiple heterogeneous information sources.…”

Section: Construction Of a Comprehensive Quantitative And Biological ...mentioning

confidence: 99%

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

Wang

Gao

Li³

2022

Computational Intelligence and Neuroscience

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In this paper, a comprehensive quantitative and biological neural network optimization model of sports industry structure is thoroughly studied and analyzed using knowledge graphs. To address the problems of poor performance interpretability deficiency of knowledge graph-based recommendation methods in the face of relational sparse graphs, a pretraining-based implicit characterization algorithm strategy is proposed for the recall stage, which can solve the problems of difficulty in going online and high delay in the recall stage of the recommendation system while improving the accuracy, and not only this can be applied in the recall stage, but also the sorting and postsorting modules can be used as features. To study the relationship between signaling activity and energy metabolism of pyramidal neurons, an empirical model of the synaptic vesicle cycle is proposed to simulate the synaptic transmission process, the role played by energy metabolism in synaptic transmission is studied from the perspective of feedback control, and the quantitative relationship between neuronal pulse discharge frequency, energy consumption, and information quantity in dendritic integration is analyzed using the cable theory and atrial chamber model. It was found that, when 0 ≤ ε ≤ 0.6 , the chaotic region shrinks and eventually disappears with the increase of the memory factor ε ; however, when 0.6 ≤ ε ≤ 1 is used, chaos is recreated and the chaotic area gradually increases with the increase of the memory factor ε . This paper conducts comparative experiments on data sets in the recommendation domain and verifies that the proposed model and the feature intersection module can effectively perform feature interaction between items and entities, thus enhancing the recommendation effect.

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Section: Construction Of a Comprehensive Quantitative And Biological ...mentioning

confidence: 99%

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

Wang

Gao

Li³

2022

Computational Intelligence and Neuroscience

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“…It can be divided into the following four categories: planning and design cost, construction and production cost, warehousing and logistics cost, and construction and installation cost. Therefore, lean management of these processes is a key to reducing the cost of prefabricated buildings [30]. The concept of the multidimensional model is depicted in figure 1.…”

Section: Cost Composition Of the Prefabricated Buildingmentioning

confidence: 99%

Construction of lean control system of prefabricated mechanical building cost based on Hall multi-dimensional structure model

Fan

Sharma

2022

IJCAI

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Based on the systematic idea of Hall's multidimensional structure and the theory and practice of prefabricated building cost lean management, the prefabricated mechanical building cost lean control system based on Hall's multidimensional structure model is proposed and constructed. The application of the lean management of the hall multidimensional structure model from the perspective of the time dimension, logic, and knowledge dimension. The example analysis results show that the original design components and the number of open modes is 72, the optimized types of components and the number of open modes is 51, reduce 21 mold machining, mold costs were reduced by about 25%. The number of original design components and the lifting times of components is 129 kinds, the number of components and lifting of components is 103, the number of components per layer was decreased by 26, lifting time is shortened by about 20%, the comprehensive construction period is shortened by more than 40 days, improve the management efficiency, lean cost control of the project plays a positive role. It provides a reference for the lean control system management of the hall multidimensional structural model Povzetek: Razvit je vitki nadzorni sistem za gradnje na osnovi Hallovega multi-dimenzionalnega modela.

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“…In this paper, the data with the same chaotic correlation characteristics are divided into one class through the above clustering analysis process, so as to realize the effective clustering of big data [29][30][31]. This work is also considered for the industrial applications and contributing towards social life with the integration of the Internet of Things, AI, and robotics [32][33][34][35].…”

Section: F End Iterationmentioning

confidence: 99%

Chaotic association feature extraction of big data clustering based on Internet of Things

Liu

Gupta

Onyema

2022

IJCAI

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This article addresses the stabilization of chaotic characteristics in abnormal data by proposing chaotic correlation feature extraction of big data clustering based on the Internet of things. The chaotic features in big data usually show complex folding and distortion without obvious rules and order and nonsynchronization. In this article, the dimension of extracted correlation is utilized as the chaotic feature for the clustering of big data. The one-dimensional time series that can be extended in multi-dimensional space is analysed based on phase space reconstruction, to extract the chaotic correlation dimension (CCD) features. After the relevant experimental analysis, this paper mainly compares the energy consumption and processing time of the two respective algorithms. In the simulation parameter design, the time interval of big data packet generation is 0.1s, and the data is generated from the simulation time of 300s. The results obtained show that when dealing with the same amount of data, the energy consumption of this algorithm is significantly lower than that of the traditional algorithm. When dealing with the same amount of data, the time required by this algorithm is significantly lower than that of the traditional algorithm. This is because this algorithm is easy to implement and has good clustering efficiency for data, so the clustering time is short. With the gradual increase in the amount of data, the correlation dimension of this algorithm tends to be stable. While the correlation dimension of the traditional algorithm fluctuates greatly, it is revealed that the proposed approach has high data clustering efficiency and verifies the effectiveness of this algorithm.Povzetek: Za internet stvari je analizirana možnost stabilizacije nenavadnih podatkov znotraj velikih podatkov.

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Multi-objective optimization design of steel structure building energy consumption simulation based on genetic algorithm

Cited by 5 publications

References 50 publications

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping

Construction of lean control system of prefabricated mechanical building cost based on Hall multi-dimensional structure model

Chaotic association feature extraction of big data clustering based on Internet of Things

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