The study develops alternatives of the classical Lee-Carter stochastic mortality model in assessment of uncertainty of mortality rates forecasts. We use the Lee-Carter model expressed as linear Gaussian state-space model or state-space model with Mar...
This paper simplifies indirect resonant switched-capacitor (ReSC) converters using the state-space average model method. The operation principles of the 4:1 and 5:1 ReSC converters derived from the Dickson (4:1) circuit are analyzed, and the correspo...
In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of param...
Underwater images often suffer from strong color casts, low contrast, and blurred textures. It is observed that low resolution can provide globally correct color, so low-resolution priors can guide high-resolution correction. While many recent method...
Internal waves (IWs) play a crucial role in the transport of energy and matter within the ocean while also posing significant risks to marine engineering, navigation, and underwater communication systems. Consequently, effective segmentation methods...
Modelling and estimating spatio-temporal dynamic field are common challenges in much applied research. Most existing spatio-temporal interpolation methods require massive prior calculations and consistent observational data, resulting in low interpol...
Infrared and visible image fusion plays a critical role in multimodal perception systems, particularly under challenging conditions such as low illumination, occlusion, or complex backgrounds. However, existing approaches often struggle with global f...
It is necessary to develop a health monitoring system (HMS) for complex systems to improve safety and reliability and prevent potential failures. Time-series signals are collected from multiple sensors installed on the equipment that can reflect the...
Residual stress is closely related to the evolution process of the component fatigue state, but it can be affected by various sources. Conventional fatigue evaluation either focuses on the physical process, which is limited by the complexity of the p...
Over-track buildings above metro depots have become common in megacities due to urban land shortages. The transmission of vibrations into the over-track buildings during routine train operations has the potential to adversely impact the occupants in...
The stability of Modular multilevel converter (MMC) itself is the premise of analyzing the stability of MMC cascading with other cells, so this paper addressed the small-signal stability of MMC with dc voltage control mode which is the common operati...
Every month, teachers face the dilemma of what exercises their students should practice, and what their consequences are regarding long-term learning. Since teachers prefer to pose their own exercises, this generates a large number of questions, each...
Power electronic converters are important elements of many modern devices. Therefore, there is a need for a thorough analysis of their behavior and the ability to properly control them. Typically, the converter’s dynamics are investigated using...
In the field of intelligent inspection, high-definition video data collected by quadruped robot dogs face severe transmission and storage constraints. Although existing advanced lossy video coding standards can significantly improve compression effic...
Small-signal models of DC-DC converters are often based on a state-space averaging approach, from which both control-oriented and other frequency-domain characteristics, such as input or output impedance, can be derived. Updating these models when ex...
This paper develops a bias compensation-based parameter and state estimation algorithm for the observability canonical state-space system corrupted by colored noise. The state-space system is transformed into a linear regressive model by eliminating...
Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the lit...
Sequential recommendation systems face challenges in integrating local sequential patterns with global collaborative information. While Transformers capture long-term dependencies through self-attention, they suffer from quadratic complexity. State-s...
Understanding temporal variation in population productivity is critical for effective assessment and management of pelagic fish stocks under a changing climate. In this study, we applied a stochastic surplus production model in continuous time (SPiCT...
Remote sensing images often degrade during acquisition due to various environmental factors, leading to noise contamination and loss of texture details. Existing methods based on convolutional neural networks (CNNs) are limited by their local recepti...
We develop novel multivariate state-space models wherein the latent states evolve on the Stiefel manifold and follow a conditional matrix Langevin distribution. The latent states correspond to time-varying reduced rank parameter matrices, like the lo...
Small-signal models of dc-dc converters are often designed using a state-space averaging approach. This design can help discuss and derive the control-oriented and other frequency-domain attributes, such as input or output impedance parameters. This...
Due to the high permeability characteristics of power electronic devices connected to the distribution grid, the potential harmonic coupling problem cannot be ignored. The Vienna rectifier is widely utilized in electric vehicle charging stations and...
Deep learning for precipitation forecasting remains constrained by complex meteorological factors affecting accuracy. To address this issue, this paper proposes TransMambaCNN, which is a spatiotemporal transformer network fusing state-space models an...
This paper proposes an innovative algorithm for forecasting the motion response of floating offshore wind turbines by employing force-to-motion transfer functions and state-space models. Traditional numerical integration techniques, such as the Newma...
Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a...
The absolute positioning accuracy of industrial manipulators is frequently bottlenecked by the interplay of geometric tolerances and complex, unmodeled non-geometric parameter drifts. Traditional static kinematic models, predicated on rigid-body assu...
Reliable energy models are needed to determine building energy performance. Relatively detailed energy models can be auto-generated based on 3D shape representations of existing buildings. However, parameters describing thermal performance of the bui...
Power grids are currently undergoing a significant transition to enhance operational resilience and elevate power quality issues, aiming to achieve universal access to electricity. In the last few decades, the energy sector has witnessed substantial...
This study presents an error flow research method for simulation models of hydraulic systems in construction machinery based on the state-space approach, aiming to ensure the reliable application of digital twin models. Initially, a comprehensive ana...
Accurate classification of three-dimensional (3D) point clouds in real-world environments is often impeded by sensor noise, occlusions, and incomplete data. To overcome these challenges, we propose SMCNet, a robust multimodal framework for 3D point c...
Distributed power converters represent a technical solution to improve the performance of large or utility-scale photovoltaic (PV) plants. Unfortunately, evaluation of the yield obtained in large PV fields by using distributed converters is a difficu...
In this study we develop a multi-factor extension of the family of Lee-Carter stochastic mortality models. We build upon the time, period and cohort stochastic model structure to extend it to include exogenous observable demographic features that can...
30 August 2024
The emergence of the COVID-19 pandemic in 2020 led to the implementation of legal restrictions on individual activities, significantly impacting traffic and air pollution levels in urban areas. This study employs a state-space intervention method to...
A longitudinal study for 847 bladder cancer patients for a period of fifteen years is presented. After the first surgery, the patients undergo successive ones (recurrences). A state-model is selected for analyzing the evolution of the cancer, based o...
Deep learning-based super-resolution (SR) techniques play a crucial role in enhancing the spatial resolution of images. However, remote sensing images present substantial challenges due to their diverse features, complex structures, and significant s...
In contemporary visual decoding models, traditional neural network-based methods have made some advancements; however, their performance in addressing complex visual tasks remains constrained. This limitation is primarily due to the restrictions of l...
Government may need to launch policies to stabilize real estate prices being away from unusual rise at an unexpected pace through short-term regulations of sales and purchases. Short-term control policies are often not effective immediately after wit...
This study introduces an efficient fundus image enhancement framework based on an improved Mamba model and the Denoising Diffusion Probabilistic Model (DDPM). By integrating wavelet transform for local feature extraction and applying a reverse diffus...
In this work, a new approach for the generation of a generalized state-space aeroservoelastic model based on tangential interpolation is presented. The resulting system of differential algebraic equations (DAE) is reduced to a set of ordinary differe...
Precise identification of cancer subtypes from whole slide images (WSIs) is pivotal in tailoring patient-specific therapies. Under the weakly supervised multiple instance learning (MIL) paradigm, existing techniques frequently fall short in simultane...
Sleep occupies about one-third of human life and is crucial for health, but traditional sleep staging relies on experts manually performing polysomnography (PSG), a process that is time-consuming, labor-intensive, and susceptible to subjective differ...
The water supply pipeline system is responsible for providing clean drinking water to residents, but pipeline leaks can lead to water resource wastage, increased operational costs, and safety hazards. To effectively detect the leakage level in the wa...
Many algorithms and numerical methods, such as implicit and explicit finite differences and the method of characteristics, have been applied for transient flow in gas pipelines. From a computational point of view, the state space model is an effectiv...
A nonlinear state-space model for Schottky diode rectifiers is presented that incorporates junction dynamics, layout parasitic effects, and electromagnetic coupling effects. Unlike prior approaches, the model resolves conduction intervals under harmo...
Recent technological advances have provided new settings to enhance individual-based data collection and computerized-tracking data have became common in many behavioral and social research. By adopting instantaneous tracking devices such as computer...
State-space models have been successfully employed for model order reduction and control purposes in acoustics in the past. However, due to the cubic complexity of the singular value decomposition, which makes up the core of many subspace system iden...
This paper aims to address a finite-horizon model predictive control (MPC) for non-linear drum-type boiler-turbine system using a system-identification method. Considering that the strong state coupling of a non-linear mechanism model, the subspace i...
A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilisti...
Hysteresis is a fundamental characteristic of magnetic materials. The Jiles–Atherton (J-A) hysteresis model, which is known for its few parameters and clear physical interpretations, has been widely employed in simulating hysteresis characteris...
of 95