We propose a functional time series method to obtain accurate multi-step-ahead forecasts for age-specific mortality rates. The dynamic functional principal component analysis method is used to decompose the mortality curves into dynamic functional pr...
We study the recursive core introduced in Huang and Sjöström [8]. In general partition function form games, the recursive core coalition structure may be either coarser or finer than the one that maximizes the social surplus. Moreover, the recursive...
Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between t...
Accurately predicting the changes in turbine vibration trends is a key part of the operational condition maintenance of hydropower units, which is of great significance for improving both the operational condition and operational efficiency of hydrop...
Predicting electrical demand in distribution systems is a fundamental problem for the efficient operation of smart grids, especially under scenarios of high temporal variability. This study compares two multi-step forecasting strategies for 24-h hori...
Circular RNAs (circRNAs) are a novel class of non-coding RNA that, unlike linear RNAs, form a covalently closed loop without the 5′ and 3′ ends. Growing evidence shows that circular RNAs play important roles in life processes and have gre...
The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-sta...
Diffusion models have been widely used in image generation and structured trajectory generation. DPM++ samplers improve few-step inference by recursively propagating current and historical denoised states, but their behavior in the low-noise regime r...
The solid oxide fuel cell (SOFC) system is complicated because the characteristics of gas, heat, and electricity are intricately coupled. During the operation of the system, problems such as frequent failures and a decrease in the stack’s perfo...
To address the limited candidate voltage vectors, insufficient voltage-tracking capability, and parameter sensitivity of conventional model predictive voltage control (MPVC) for permanent magnet synchronous motor (PMSM) drives, this paper proposes an...
This paper presents a new method of batch-to-batch optimization control for a fed-batch fermentation process. A recursively updated extreme learning machine (ELM) neural network model is used to model a fed-batch fermentation process. ELM models have...
In this paper, a trajectory tracking control strategy is investigated for unmanned vehicles operating on structured urban roads under multiple constraints. A hierarchical control framework is developed to coordinate lateral steering and longitudinal...
Multi-resolution image fusion has been studied for years to solve the trade-off between temporal and spatial resolution in remote sensing instruments and has been widely applied to detect and monitor natural phenomena like floods. Despite the conside...
Nonlinear model predictive control (NMPC) delivers high tracking accuracy for industrial processes but requires solving a nonlinear program at each sampling instant, limiting its applicability under tight real-time constraints. The Koopman operator p...
To strengthen major transportation infrastructure projects’ (MTIPs’) organizational resilience and fortify their capacity for crisis management and project risk prevention. In this paper, based on the resilience theory development process...
Tethered space net robots (TSNRs) offer a flexible and promising configuration for active space debris removal. This study investigates the formation tracking control of TSNR for debris capture in the presence of unknown bounded disturbances. To enha...
Chaotic time series have been involved in many fields of production and life, so their prediction has a very important practical value. However, due to the characteristics of chaotic time series, such as internal randomness, nonlinearity, and long-te...
Accurate reservoir inflow forecasting is vital for effective water resource management. Reliable forecasts enable operators to optimize storage and release strategies to meet competing sectoral demands—such as water supply, irrigation, and hydr...
To remove parameter dependence in existing sensorless control strategies, a parameter-free model predictive current control is proposed for permanent magnet synchronous motor without any position sensor. First, the current variation during one sampli...
This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When an intelligent vehicle drives at high speed on roads with different friction coefficients, the difficulty of its trajectory tracking control...
With the increasing demand for high speed and high-precision motion control in CNC machine tools, permanent magnet linear synchronous motors (PMLSMs) have been widely adopted in feed drive systems due to their excellent dynamic performance and positi...
In the operation process of a power battery pack, the inconsistency among lithium-ion cells may seriously restrict the pack’s capacity, power capability and lifetime, which may bring hidden danger to the use of electric vehicles. Equalization managem...
The present study utilized a recursive feature elimination approach in conjunction with a random forest algorithm to assess the efficacy of various features in predicting cognitive strategy usage in Raven’s Advanced Progressive Matrices. In add...
In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final produ...
A variety of accurate information inputs are of great importance for automotive control. In this paper, a novel joint soft-sensing strategy is proposed to obtain multi-information under diverse vehicle driving scenarios. This strategy is realized by...
Background/Objective: Predicting pharmacological response in cancer remains a key challenge in precision oncology due to intertumoral heterogeneity and the complexity of drug–gene interactions. While machine learning models using multi-omics da...
To address the issues of excessive current ripple and poor dynamic response in conventional angle position control (APC) for high-speed switched reluctance generator (SRG), this paper proposes an online parameter identification-based model-free predi...
This paper attempts to develop a unified model predictive control (MPC) method for integrated path planning and trajectory tracking of autonomous underwater vehicles (AUVs). To deal with the computational burden of online path planning, an event-trig...
Versatile Video Coding (VVC) achieves impressive coding gain improvement (about 40%+) over the preceding High-Efficiency Video Coding (HEVC) technology at the cost of extremely high computational complexity. Such an extremely high complexity increase...
Accurate forecasts of stock indexes can not only provide reference information for investors to formulate relevant strategies but also provide effective channels for the government to regulate the market. However, due to its volatility and complexity...
Game spaces in which an organism must repeatedly compete with an opponent for mutually exclusive outcomes are critical methodologies for understanding decision-making under pressure. In the non-transitive game rock, paper, scissors (RPS), the only te...
To improve the performance of the interior permanent magnet synchronous motor control system, a Markov decision model predictive current control strategy based on a lumped disturbances sliding mode disturbance observer is proposed in this paper. A fa...
Optimal operation and control, which can result in the global optimal operation performance of industrial processes, has been a hot topic in recent control strategy designs. However, existing control strategies, such as real-time optimization (RTO),...
Cervical cancer remains a significant global public health challenge, particularly in low- and middle-income countries where invasive diagnostic methods are underutilized due to limited medical resources. Machine learning has provided a new pathway t...
Surface deformation induced by underground coal mining is characterized by strong nonlinearity and spatial heterogeneity, which complicates early warning and ecological assessment. While InSAR-driven data assimilation models and remote sensing-based...
Wheel loaders in mines and construction sites repeatedly load soil from a pile to load receivers. Automating this task presents a challenging planning problem since each loading’s performance depends on the pile state, which depends on previous...
Most failures in steel materials are due to fatigue damage, so it is of great significance to analyze the key features of fatigue strength (FS) in order to improve fatigue performance. This study collected data on the fatigue strength of steel materi...
This study develops a comprehensive two-stage hybrid framework to forecast food prices in Türkiye, addressing inflation prediction challenges in volatile emerging markets where sample sizes are limited. In the first stage, systematic relationshi...
The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can...
As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new contr...
The active suspension system of a vehicle manipulated using electro-hydraulic actuators is a challenging nonlinear control problem. In this research work, a novel Linear Parameter Varying (LPV) State-Space (SS) model with a fictional input is propose...
Accurate remaining useful life (RUL) prediction of rolling bearings is important for condition-based maintenance. This study proposes an interpretable rolling RUL prediction framework integrating adaptive multidomain degradation representation, progn...
This paper proposes a mismatch-index-driven coordinated flexible-step terminal-free distributed model predictive control (DMPC) scheme with an adaptive prediction horizon for asynchronous multi-agent systems (MASs) subject to bounded disturbances. Th...
Background: Hospital readmission among patients with diabetes remains a healthcare challenge, contributing to adverse clinical outcomes, increased healthcare expenditures, and reduced quality of care. This study presents a leakage-resistant and clini...
Conclusive evidence has demonstrated the critical importance of adaptive cruise control (ACC) in relieving traffic congestion. To improve the performance of the ACC system, this paper proposes a novel ACC strategy for electric vehicles based on a hie...
Silicone structural glazing (SSG) sealants are crucial sealing materials in modern building curtain walls, whose performance degradation may lead to functional and safety issues, posing significant challenges to building safety maintenance. This stud...
Accurate forecasting of the CBOE Volatility Index (VIX) is an important problem in financial risk modeling and time-series prediction due to its role as a widely used indicator of market uncertainty. This study proposes a comparative forecasting fram...
The transition to environmentally sustainable maritime operations has gained urgency with the International Maritime Organization’s (IMO) 2023 GHG reduction strategy, aiming for net-zero emissions by 2050. While alternative fuels like LNG and m...
Dam deformation monitoring is essential for ensuring the safe operation of hydraulic structures, yet practical data are often compromised by missing values and noise, while spatial coupling among monitoring points further complicates prediction. To a...
High stromal tumor-infiltrating lymphocytes (sTILs) are associated with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC). We hypothesize that integrating high sTILs and additional clinicopathologic features associat...
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