Results 81 to 90 of about 11,734,216 (296)

An estimation method for the state-of-charge of lithium-ion battery based on PSO-LSTM

AIP Advances, 2023
The accuracy of state-of-charge (SOC) estimation will affect the performance of the battery management system. The higher the accuracy the better the performance. To improve the accuracy of SOC estimation, a particle swarm optimization (PSO) based method
Meng Dang, Chuanwei Zhang, Zhi Yang, Jianlong Wang, Yikun Li, Jing Huang   +5 more
doaj   +1 more source

Bitcoin Price Prediction Using Long Short Term Memory (LSTM): Bitcoin Price Prediction Using Long Short Term Memory (LSTM)

, 2020
Price fluctuation is a necessity in every business, including the price of Bitcoin. Therefore humans need a device or application that can assist in making predictions quickly and accurately. Deep Learning can be used to make predictions which include setting the parameters used during training, choosing the best parameters for prediction, and choosing
Ulumuddin, Ihyak, Sunardi, Sunardi, Fadlil, Abdul   +2 more
openaire   +1 more source

Long Short-Term Memory (LSTM) Deep Neural Networks in Energy Appliances Prediction

, 2019
The application of Long Short-Term Memory (LSTM) Deep Neural Networks has been increased the last years. This paper proposes a novel methodology based on a hybrid model using the Long Short-Term Memory (LSTM) Networks and the Particle Swarm Optimization (
Kouziokas G.N., Georgios N. Kouziokas
core   +1 more source

Improved Skills for Predicting Sub‐Seasonal to Seasonal Global Sea Surface Temperature

Advanced Science, EarlyView.
Addressing the critical Sub‐seasonal to Seasonal predictability gap, this study presents CAS‐Canglong, a novel deep learning framework. By leveraging 3D Swin‐Transformers to capture long‐range ocean interactions, the model demonstrates superior skill over traditional GCMs.
Longhao Wang, Xuanze Zhang, Ruby L. Leung, Francis H. S. Chiew, Amir AghaKouchak, Kairan Ying, Yongqiang Zhang   +6 more
wiley   +1 more source

The analysis of bidirectional long short-term memory network model for construction of cultural gene map and information extraction

Scientific Reports
To create a cultural gene map and extract information, this paper introduces a two-way long and short-term memory network (LSTM) model and verifies it using Jinxiu Yao headwear as an example.
Xing Ding, Jing Wang
doaj   +1 more source

Deep Learning-based Air Quality Forecasting System for Urban India Using Temporal Neural Networks [PDF]

ITM Web of Conferences
Air pollution poses a serious hazard to public health in Cities of India; Among all sources, those resulting from Energy Consumption dominate the deaths caused by air pollution.
Tang Zhijun
doaj   +1 more source

Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems

Advanced Electronic Materials, EarlyView.
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang, Weipeng Wang, Xuan Cong, Shiyu Wang, Gao Li, Sen Gong, Lin Huang, Hongxin Zeng, Yaxin Zhang, Ziqiang Yang   +9 more
wiley   +1 more source

Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model

Applied Sciences
Cultural heritage digitization is of great significance for the protection, restoration, and rejuvenation of cultural relics. In particular, the investigation of fading mechanisms is essential for virtual restoration to accurately recreate the original ...
Zhen Liu, An-Ran Zhao, Si-Lu Liu
doaj   +1 more source

Enhanced High Dimensionality and the Information Processing Capacity in Interfered Spin Wave‐Based Reservoir Computing, Achieved With Eight Detectors

Advanced Electronic Materials, EarlyView.
Physical reservoir computing (PRC) based on spin wave interference has demonstrated high computational performance, yet room for improvement remains. In this study, we fabricated this concept PRC with eight detectors and evaluated the impact of the number of detectors using a chaotic time series prediction task.
Sota Hikasa, Wataru Namiki, Daiki Nishioka, Maki Nishimura, Ryo Iguchi, Kazuya Terabe, Takashi Tsuchiya   +6 more
wiley   +1 more source

Predicting Bitcoin and Ethereum Prices Using the Long Short- Term Memory (LSTM) Model

Journal of Information Systems and Informatics
Cryptocurrency is a highly volatile digital asset, necessitating accurate and adaptive forecasting methods. This study implements a Long Short-Term Memory (LSTM) model to predict the daily closing prices of two leading cryptocurrencies Bitcoin (BTC) and
M Aswadi, Usman Ependi
doaj   +1 more source