Results 131 to 140 of about 27,643 (149)

Research on optimization of temperature sensitive points of machine tool thermal error based on independent variable selection criteria. [PDF]

Sci Rep
Li H, Liu J, Li Y.
europepmc   +1 more source

Technology and Application of Multi‐Energy System: An Engineering Study in China

Energy Internet, EarlyView.
ABSTRACT Multi‐energy system (MES) is crucial for the development of smart cities. This paper summarises the research progress and achievements from an engineering case study in China which aims at enhancing MES energy efficiency. Key theories and technologies were tested at an MES demonstration site in Hunan, China, using both software and hardware ...
Yong Li, Jinting Li, Yixiu Guo, Yahui Wang, Xusheng Yang, Yong Xu   +5 more
wiley   +1 more source

A CNN-LSTM-attention based seepage pressure prediction method for Earth and rock dams. [PDF]

Sci Rep
Chen H, Wang K, Zhao M, Chen Y, He Y.
europepmc   +1 more source

LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks. [PDF]

Sci Rep
Sorour SE, Aljaafari M, Shaker AM, Amin AE.   +3 more
europepmc   +1 more source

A novel transformer health state direct prediction method based on knowledge and data fusion‐driven model

High Voltage, EarlyView.
Abstract Predicting the future health state of a transformer can offer early warning of latent defects and faults within the transformer, thereby facilitating the formulation of power outage maintenance plans and power dispatch strategies. However, existing prediction methods based on the structure of ‘splicing prediction and diagnosis method’ suffer ...
Peng Zhang, Guoliang Zhang, Fei Zhou, Xiaoyu Fan, Yi Zhang, Zexu Du   +5 more
wiley   +1 more source

Elevator fault precursor prediction based on improved LSTM-AE algorithm and TSO-VMD denoising technique. [PDF]

PLoS One
Cao H, Du X.
europepmc   +1 more source

Variance‐driven security optimisation in industrial IoT sensors

IET Networks, Volume 14, Issue 1, January/December 2025.
The methodology employed in this study adopts a systematic approach to deploying machine learning algorithms for the detection of anomalies in industrial sensor data. Data preprocessing, the first step in the process, ensures that the industrial sensor data is cleaned and prepared for subsequent model training.
Hardik Gupta, Sunil K. Singh, Sudhakar Kumar, Karan Sharma, Hardeep Saini, Brij B. Gupta, Varsha Arya, Kwok Tai Chui   +7 more
wiley   +1 more source

Energy efficient and robust node localization in WSNs using LSTM optimized DV hop framework to mitigate multihop localization errors. [PDF]

Sci Rep
Rehman A, Mahmood T, Alahmadi TJ, Almasoud AS, Saba T.   +4 more
europepmc   +1 more source

Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials

IET Power Electronics, EarlyView.
This paper outlines the methodology for predicting power loss in magnetic materials. A neural network based method is introduced, which adopts a long short‐term memory network, expressing the core loss as a function of magnetic flux density in the frequency domain, temperature, frequency, and classification of the waveforms.
Dixant Bikal Sapkota, Puskar Neupane, Mecon Joshi, Shahabuddin Khan   +3 more
wiley   +1 more source

LSTM-conformal forecasting-based bitcoin forecasting method for enhancing reliability. [PDF]

PLoS One
Zhang X, Kang Y, Li C, Wang W, Wang K.
europepmc   +1 more source