21 papers · ranked by Valyu relevance
Hao Li
Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance…
Kevin Course, Prasanth B. Nair
State estimation is concerned with reconciling noisy observations of a physical system with the mathematical model believed to predict its behaviour for the purpose of inferring unmeasurable states and denoising measurable ones1,2. Traditional state-estimation techniques rely on strong assumptions about the form of…
Xue-Bo Jin, Ruben Jonhson Robert Jeremiah, Ting-Li Su, Yu-Ting Bai + 2 more
'Jian-Lei Kong' 'Geoff Merrett'] State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals…
Marcus Krogh Nielsen, Tobias Ritschel, Ib Jarle Christensen, Jess Dragheim + 3 more
'Jess Dragheim' 'Jakob Kjøbsted Huusom' 'Krist V. Gernaey' 'John Bagterp Jørgensen'] State estimation incorporates the feedback in optimization based advanced process control systems and is very important for the performance of model predictive control. We describe the extended Kalman filter, the unscented Kalman…
Guido Cavraro, Emiliano Dall’Anese, Joshua Comden, Andrey Bernstein
—The paper investigates the problem of estimating the state of a time-varying system with a linear measurement model; in particular, the paper considers the case where the number of measurements available can be smaller than the number of states. In lieu of a batch linear least-squares (LS) approach – well-suited for…
S. Andrew Gadsden, Mohammad Al‐Shabi, Saeid Habibi
This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is…
Wasi Ullah, Irshad Hussain, Iram Shehzadi, Zahid Rahman + 1 more
'Peerapong Uthansakul'] Faults and failures are familiar case studies in centralized and decentralized tracking systems. The processing of sensor data becomes more severe in the presence of faults/failures and/or noise. Effective schemes have been presented for decentralized systems, in the presence of faults only. In…
Muhammad Adeel Akram, Peilin Liu, Muhammad Owais Tahir, Waqas Ali + 1 more
'Yuze Wang'] Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model…
Najmeh Daroogheh, Nader Meskin, K. Khorasani
In this paper, a dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the Particle Filtering (PF) scheme. Our developed methodology is based on a concurrent implementation of state and parameter estimation filters as opposed to using a single…
Huazhen Fang, Ning Tian, Yebin Wang, MengChu Zhou + 1 more
—This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date, one of the most promising and popular approaches is to view and address…
Julian Ruggaber, Jonathan Brembeck, Radu Danescu
In Kalman filter design, the filter algorithm and prediction model design are the most discussed topics in research. Another fundamental but less investigated issue is the careful selection of measurands and their contribution to the estimation problem. This is often done purely on the basis of empirical values or by…
Lorenzo Ponticelli, Mario Barbaro, Geraldino Mandragora, Gianluca Pagano + 2 more
'Gianluca Pagano' 'Gonçalo Sousa Torres' 'Arturo de la Escalera Hueso'] Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring…
Authors not listed
Acoustic measurements of batteries are known to be correlated to their state-of-charge, creating opportunities for state estimation that do not rely on electrical signals. State estimators are typically parametric models fitted from data, often from the broad toolbox of machine learning. Such models can be easily…
Joshua A Cullen, Caroline L Poli, Robert J Fletcher, Denis Valle
Understanding animal movement often relies upon telemetry and biologging devices. These data are frequently used to estimate latent behavioral states to help understand why animals move across the landscape (or seascape). While there are a variety of methods that make behavioral inference from biotelemetry data, some…
Felipe O. Silva, Elder M. Hemerly, Waldemar C. Leite Filho, Jörg F. Wagner
'Jörg F. Wagner'] This paper presents the second part of a study aiming at the error state selection in Kalman filters applied to the stationary self-alignment and calibration (SSAC) problem of strapdown inertial navigation systems (SINS). The observability properties of the system are systematically investigated, and…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Elena D’Ambrosio, Zhou Fang, Ankit Gupta, Sant Kumar + 1 more
Time-lapse microscopy has become increasingly prevalent in biological experimentation, as it provides single-cell trajectories that unveil valuable insights into underlying networks and their stochastic dynamics. However, the limited availability of fluorescent reporters typically constrains tracking to only a few…
Benjamin Smart, Lucia Marucci, Ludovic Renson
Cybergenetics is an advancing field seeking to implement control theory within biological systems. When applying feedback control for the regulation of gene expression or cell proliferation, model-based control strategies can be applied; in this context, online adaptive mathematical models can be used to keep models in…
Devin S. Johnson, Jeff L. Laake, Sharon R. Melin, Robert L. DeLong
State-based Cormack-Jolly-Seber (CJS) models have become an often used method for assessing states or conditions of free ranging animals through time. Although originally envisioned to account for differences in survival and observation processes when animals are moving though various geographical strata, it has…
Authors not listed
Deriving versatile and robust mechanistic models from experimental data is a key challenge in engineering and natural sciences. This is especially true in chemical reaction engineering, where reactor manufacturers and operators increasingly pursue the development and maintenance of digital twins that rely on frequent…
Maria Harris Rasmussen, Jan H. Jensen
We present a method for the automatic determination of transition states (TSs) that is based on Grimme’s RMSD-PP semiempirical tight binding reaction path method ( J. Chem. Theory Comput. 2019, 15, 2847-2862), where the maximum energy structure along the path serves as an initial guess for DFT TS searches. The method…