Methods and Applications of Space Understanding in Indoor Environment—A Decade Survey DOI Creative Commons
Sebastian Pokuciński, Dariusz Mrozek

Applied Sciences, Год журнала: 2024, Номер 14(10), С. 3974 - 3974

Опубликована: Май 7, 2024

The demand for digitizing manufacturing and controlling processes has been steadily increasing in recent years. Digitization relies on different techniques equipment, which produces various data types further influences the process of space understanding area recognition. This paper provides an updated view these structures high-level categories methods leading to indoor environment segmentation discovery its semantic meaning. To achieve this, we followed Systematic Literature Review (SLR) methodology covered a wide range solutions, from floor plan through 3D model reconstruction scene recognition navigation. Based obtained SLR results, identified three taxonomies (the taxonomy underlying type, performed analysis process, accomplished task), constitute perspectives can adopt study existing works field understanding. Our investigations clearly show that progress this is accelerating, more sophisticated rely multidimensional complex representations, while processing itself become focused artificial intelligence-based methods.

Язык: Английский

Air quality and ventilation: exploring solutions for healthy and sustainable urban environments in times of climate change DOI Creative Commons
Iasmin Lourenço Niza, Ana Maria Bueno, Manuel Gameiro da Silva

и другие.

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103157 - 103157

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

9
Damage detection of civil structures based on hybrid optimization algorithm and combined correlation function of heterogeneous responses DOI
Guangcai Zhang, Chunfeng Wan, Zhiyuan Yang

и другие.

Measurement, Год журнала: 2025, Номер unknown, С. 116678 - 116678

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing DOI
Sang-Woong Lee, Amir Haider, Amir Masoud Rahmani

и другие.

Computer Science Review, Год журнала: 2025, Номер 57, С. 100740 - 100740

Опубликована: Март 3, 2025

Язык: Английский

Процитировано

1
A multi-objective optimization method for enclosed-space lighting design based on MOPSO DOI
Xian Zhang,

Jingluan Wang,

Yao Zhou

и другие.

Building and Environment, Год журнала: 2024, Номер 250, С. 111185 - 111185

Опубликована: Янв. 10, 2024

Язык: Английский

Процитировано

7
Forecasting solar power generation using evolutionary mating algorithm-deep neural networks DOI Creative Commons
Mohd Herwan Sulaiman, Zuriani Mustaffa

Energy and AI, Год журнала: 2024, Номер 16, С. 100371 - 100371

Опубликована: Апрель 17, 2024

This paper proposes an integration of recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) in optimizing the weights and biases deep neural networks (DNN) for forecasting solar power generation. The study employs a Feed Forward Neural Network (FFNN) to forecast AC output using real plant measurements spanning 34-day period, recorded at 15-minute intervals. intricate nonlinear relationship between irradiation, ambient temperature, module temperature is captured accurate prediction. Additionally, conducts comprehensive comparison with established algorithms, including Differential Evolution (DE-DNN), Barnacles Optimizer (BMO-DNN), Particle Swarm Optimization (PSO-DNN), Harmony Search (HSA-DNN), DNN Adaptive Moment Estimation optimizer (ADAM) Nonlinear AutoRegressive eXogenous inputs (NARX). experimental results distinctly highlight exceptional performance EMA-DNN by attaining lowest Root Mean Squared Error (RMSE) during testing. contribution not only advances methodologies but also underscores potential merging algorithms contemporary improved accuracy reliability.

Язык: Английский

Процитировано

7
A review of the influencing factors of building energy consumption and the prediction and optimization of energy consumption DOI Creative Commons

Zhongjiao Ma,

Z. Yan,

M. He

и другие.

AIMS energy, Год журнала: 2025, Номер 13(1), С. 35 - 85

Опубликована: Янв. 1, 2025

<p>Concomitant with the expeditious growth of construction industry, challenge building energy consumption has become increasingly pronounced. A multitude factors influence operations, thereby underscoring paramount importance monitoring and predicting such consumption. The advent big data engendered a diversification in methodologies employed to predict Against backdrop influencing operation consumption, we reviewed advancements research pertaining supervision prediction deliberated on more energy-efficient low-carbon strategies for buildings within dual-carbon context, synthesized relevant progress across four dimensions: contemporary state supervision, determinants optimization Building upon investigation three predictive were examined: (ⅰ) Physical methods, (ⅱ) data-driven (ⅲ) mixed methods. An analysis accuracy these revealed that methods exhibited superior precision actual Furthermore, predicated this foundation identified determinants, also explored prediction. Through an in-depth examination prediction, distilled pertinent accurate forecasting offering insights guidance pursuit conservation emission reduction.</p>

Язык: Английский

Процитировано

0
The application performance of individualized radiant cooling and heating systems, a review DOI
Dongkai Zhang, Cui Li, Zhengrong Li

и другие.

Building and Environment, Год журнала: 2024, Номер 256, С. 111488 - 111488

Опубликована: Апрель 5, 2024

Язык: Английский

Процитировано

3
Study on the degradation models based on the experiments considering the coupling effect of freeze-thaw and carbonation DOI

Qianting Yang,

Ming Liu, Jiaxu Li

и другие.

Structures, Год журнала: 2024, Номер 64, С. 106659 - 106659

Опубликована: Май 31, 2024

Язык: Английский

Процитировано

3
Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine DOI Creative Commons
Marzia Ahmed, Mohd Herwan Sulaiman, Md. Maruf Hassan

и другие.

Results in Control and Optimization, Год журнала: 2025, Номер unknown, С. 100518 - 100518

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Swarm Intelligence-Empowered Bug Prediction Strategy for Decision Support in Software Defect Prediction DOI

D. R. Medhunhashini,

Jeen Marseline K. S.

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 18 - 28

Опубликована: Апрель 15, 2024

Swarm intelligence is inherent in many living things and inspiring new ways of thinking among computer scientists. Scientists from all walks life including software corporates are interested it because its ties to collective behaviour. Bugs an expensive quality killer development. The development DP models was driven by the critical need predict defects early on. Classifying modules as either defect-prone or non-defect-prone relies heavily on machine learning algorithms. Improving defect prediction. SI improves accuracy efficacy bug predictions modelling their actions after social group behaviour insect colonies. objective this chapter outline swarm intelligence-based prediction order assist engineers QA teams with increased accuracy.

Язык: Английский

Процитировано

0