24 papers · ranked by Valyu relevance
Miguel Fribourg, Diomedes E. Logothetis, Javier González-Maeso, Stuart C. Sealfon + 4 more
'Stuart C. Sealfon' 'Belén Galocha-Iragüen' 'Fernando Las-Heras Andrés' 'Vladimir Brezina' 'Thomas Voets'] Overall cellular responses to biologically-relevant stimuli are mediated by networks of simpler lower-level processes. Although information about some of these processes can now be obtained by visualizing and…
Nart Gashi, Panagiotis Kakosimos, Papafotiou, George
— Kolmogorov-Arnold Networks (KANs) are emerging as a powerful framework for interpretable and efficient system identification in dynamic systems. By leveraging the Kolmogorov-Arnold representation theorem, KANs enable function approximation through learnable activation functions, offering improved scalability…
Runzhe Han, Christian Bøhn, Georg Bauer
Blind identification is popular for modeling a system without the input information, such as in the research areas of structural health monitoring and audio signal processing. Existing blind identification methods have both advantages and disadvantages, in this paper, we briefly outline current methods and propose a…
Wenjie Mei, Muhammad Nadeem, MirSaleh Bahavarnia, Ahmad F. Taha
System identification through learning approaches is emerging as a promising strategy for understanding and simulating dynamical systems, which nevertheless faces considerable difficulty when confronted with power systems modeled by differential-algebraic equations (DAEs). This paper introduces a neural network (NN)…
Wiktor Jakowluk, Karol Godlewski
The main objective of the system identification is to deliver maximum information about the system dynamics, while still ensuring an acceptable cost of the identification experiment. The focus of such an idea is to design an appropriate experiment so that the departure from normal working conditions during the…
Takaho Tsuchiya, Masashi Fujii, Naoki Matsuda, Katsuyuki Kunida + 5 more
'Shinsuke Uda' 'Hiroyuki Kubota' 'Katsumi Konishi' 'Shinya Kuroda' 'Alexander Hoffmann'] Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system…
Eduardo Sontag
The recently proposed notion of dynamical compensation in biological circuits is reinterpreted in terms of two related notions in systems biology: system equivalence and parameter (un)identifiability. This recasting leads to effective tests for verifying the validity of the property.
Zhengbin Li, Lijun Ma, Yongqiang Wang, Qichun Zhang
The vast majority of reports mainly focus on the steady-state performance of parameter estimation. Few findings are reported for the instantaneous performance of parameter estimation because the instantaneous performance is difficult to quantify by using the design algorithm, for example, in the initial stage of…
Zheng Wenju, Hao Ye
In this work, a new two-stage identification method based on dynamic programming and sparsity inducing is proposed for switched linear systems. Our method achieves sparsity inducing in the identification of switched linear systems by the constrained switching mechanism, in contrast to previous optimization-based…
Arsalan Rahimabadi, Habib Benali
Parameterization and a priori identifiability analysis are two interconnected steps that should be carried out in advance of model calibration. In the first place, we propose a framework for parameterizing a recently introduced and analytically studied generalization of the celebrated…
Krishnan Srinivasarengan, José Ragot, Christophe Aubrun, Didier Maquin
'Didier Maquin'] In several model-based system maintenance problems, parameters are used to represent unknown characteristics of a component, equipment degradation, etc. This allows for modelling constant, slow-varying terms. The identifiability of these parameters is an important condition to estimate them. Linear…
Chao Huang, Hao Zhang, Zhuping Wang
Recently, a system identification (SID) method based on center manifold (CM) is proposed to identify polynomial nonlinear systems with uncontrollable linearization [1]. This note presents some simulation results of the method to show its effectiveness. To make the note self-contained, in Section II, we state the…
Maren Philipps, Nina Schmid, Jan Hasenauer
Universal Differential Equations (UDEs) combine mechanistic differential equations with data-driven artificial neural networks, forming a flexible framework for modelling complex biological systems. This hybrid approach leverages prior knowledge and data to uncover unknown processes and deliver accurate predictions.…
Jie Chen, Hengrui Zhang, Carolin Wahl, Wei Liu + 4 more
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected…
Bartosz Pawłowicz, Bartosz Trybus, Mateusz Salach, Piotr Jankowski-Mihułowicz
'Piotr Jankowski-Mihułowicz'] The paper covers the application of Radio Frequency IDentification (RFID) technology in road traffic management with regard to vehicle identification. Various infrastructure configurations for Automated Vehicle Identification (AVI) have been presented, including configurations that can be…
Torsten Gross, Nils Blüthgen
A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network’s response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimising the experimental design is essential to achieve a meaningful…
Yosuke Otani, Hitoshi Ogawa
Individual identification is an important technique in animal research that requires researcher training and specialized skillsets. Face recognition systems using artificial intelligence (AI) deep learning have been put into practical use to identify in humans and animals, but a large number of annotated learning…
Victoria Wang, John V. Tucker
Surveillance is a social phenomenon that is general and commonplace, employed by governments, companies and communities. Its ubiquity is due to technologies for gathering and processing data; its strong and obvious effects raise difficult social questions. We give a general definition of surveillance that captures the…
Authors not listed
Digital twins are virtual companions for the design, scale-up, and control of chemical processes. Equipping digital twins with mechanistic models of their mirrored unit operation expands their range of applicability compared to pure data-driven models. As constructing mechanistic models requires time, effort, and…
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…
Authors not listed
Diversity and properties of ring systems contained in small molecules are of high interest for applications such as drug discovery or material sciences. In the present work we extract, analyse and classify ring systems found in small molecule compounds of open access databases such as PubChem, ChEMBL, DrugCentral…
Ricardo Moreira Borges, Gabriela de Assis Ferreira, Mariana Martins Campos, Andrew Magno Teixeira + 3 more
Natural products and metabolomics are intrinsically linked by the efforts of analyzing complex mixtures for compound annotation. Although most of the studies that aims for compound identification in mixtures use MS as the main analysis technique, NMR has complementary advances that are worth exploring for enhanced…
Thi Thi Zin, Moe Zet Pwint, Pann Thinzar Seint, Shin Thant + 3 more
'Shuhei Misawa' 'Kosuke Sumi' 'Kyohiro Yoshida'] Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the farmers can track…
Rama El-khawaldeh, Mason Guy, Finn Bork, Nina Taherimakhsousi + 6 more
This work presents a generalizable computer vision (CV) and machine learning model that is used for automated real-time monitoring and control of a diverse array of workup processes. Our system simultaneously monitors multiple physical parameters (e.g., liquid level, homogeneity, turbidity, solid, residue, and color)…