Results 11 to 20 of about 4,842 (263)

EEG-SSM: Leveraging State-Space Model for Dementia Detection

CoRR
State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shorter intervals for model training and inference. Traditionally, SSMs capture only the temporal dynamics of time series data, omitting the equally critical spectral features.
Xuan-The Tran, Linh Le, Quoc Toan Nguyen, Thomas Tien-Thong Do, Chin-Teng Lin   +4 more
openaire   +3 more sources

MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Nonlinear state-space models are ubiquitous in modeling real-world dynamical systems. Sequential Monte Carlo (SMC) techniques, also known as particle methods, are a well-known class of parameter estimation methods for this general class of state-space models.
Mahdi Imani, Seyede Fatemeh Ghoreishi, Douglas L. Allaire, Ulisses M. Braga-Neto   +3 more
openaire   +3 more sources

A state-space model to derive motorboat noise effects on fish movement from acoustic tracking data [PDF]

Scientific Reports, 2021
Motorboat noise is recognized as a major source of marine pollution, however little is known about its ecological consequences on coastal systems. We developed a State Space Model (SSM) that incorporates an explicit dependency on motorboat noise to ...
Margarida Barcelo-Serra, Sebastià Cabanellas, Miquel Palmer, Marta Bolgan, Josep Alós   +4 more
doaj   +2 more sources

Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting

CoRR
State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years. These models approximate continuous systems using a set of basis functions and discretize them to handle input data, making them well-suited for modeling time series data collected at specific frequencies from continuous systems.
Jiaxi Hu, Disen Lan, Ziyu Zhou 0003, Qingsong Wen, Yuxuan Liang 0002   +4 more
openaire   +3 more sources

SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning

CoRR
Spiking neural networks (SNNs) provide an energy-efficient solution by utilizing the spike-based and sparse nature of biological systems. Since the advent of Transformers, SNNs have struggled to compete with artificial networks on long sequential tasks, until the recent emergence of state space models (SSMs), which offer superior computational ...
Yan Zhong 0001, Ruoyu Zhao, Chao Wang, Qinghai Guo, Jianguo Zhang 0001, Zhichao Lu, Luziwei Leng   +6 more
openaire   +3 more sources

SSM-CloudNet: state space model-based cloud and cloud shadow detection network for Sentinel-2 imagery

Journal of Physics: Conference Series
Abstract Remote sensing images acquired by satellite sensors are inevitably obscured by clouds and cloud shadows, which obscure true ground reflectance information in those areas. Therefore, to ensure the accuracy and reliability of downstream analyses, detecting clouds and their shadows in satellite images is crucial.
Lirong He, Pengyu Pu, Ying Zhang, Meng Xu   +3 more
openaire   +3 more sources

State space model detection of driving fatigue considering individual differences and time cumulative effect

International Journal of Transportation Science and Technology
Fatigue is an important cause of traffic crashes, and effective fatigue detection models can reduce these crashes. Research has found large differences in fatigued driving performance from driver to driver, as well as a significant cumulative effect of ...
Xuesong Wang, Mengjiao Wu, Chuan Xu, Xiaohan Yang, Bowen Cai   +4 more
doaj   +2 more sources

Performance comparison of filtering methods on modelling and forecasting the total precipitation amount: a case study for Muğla in Turkey

Journal of Water and Climate Change, 2021
Condensed water vapor in the atmosphere is observed as precipitation whenever moist air rises sufficiently enough to produce saturation, condensation, and the growth of precipitation particles.
Serdar Neslihanoglu, Ecem Ünal, Ceylan Yozgatlıgil   +2 more
doaj   +1 more source

Estimating the Competitive Storage Model with Stochastic Trends in Commodity Prices

Econometrics, 2021
We propose a State-Space Model (SSM) for commodity prices that combines the competitive storage model with a stochastic trend. This approach fits into the economic rationality of storage decisions and adds to previous deterministic trend specifications ...
Kjartan Kloster Osmundsen, Tore Selland Kleppe, Roman Liesenfeld, Atle Oglend   +3 more
doaj   +1 more source

Black Box-Based Incremental Reduced-Order Modeling Framework of Inverter-Based Power Systems

IEEE Open Journal of the Industrial Electronics Society, 2023
Due to the capability to perform participation factor analysis and oscillation origin location, the state–space model (SSM)-based eigenvalue method has been widely used for stability assessment of inverter-penetrated power systems.
Weihua Zhou, Jef Beerten
doaj   +1 more source