Sheng Wu Gong Cheng Xue Bao
September 2024
In recent years, artificial intelligence has been employed to empower synthetic biology, demonstrating great potential in the simulation and prediction of protein structures as well as the design and optimization of regulatory elements and metabolic networks. Integrating artificial intelligence into the teaching of Synthetic Biology is in line with the development trends of synthetic biology and can promote the cultivation of interdisciplinary high-level talents and collaborative innovation. This paper expounds the idea of integrating artificial intelligence into the teaching of Synthetic Biology from establishing interdisciplinary course contents and teaching methods, simultaneously considering the fundamentals and application of artificial intelligence in synthetic biology, cultivating independent learning and innovative practice abilities, and enhancing the ethics education related to artificial intelligence.
View Article and Find Full Text PDFA supervised probabilistic dynamic-controlled latent-variable (SPDCLV) model is proposed for online prediction, as well as real-time optimisation of process quality indicators. Compared to existing probabilistic latent-variable models, the key advantage of the proposed method lies in explicitly modelling the dynamic causality from the manipulated inputs to the quality pattern. This is achieved using a well-designed, dynamic-controlled Bayesian network.
View Article and Find Full Text PDFTranscription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabolic regulatory systems based on TFs. Although various deep-learning algorithms have been developed for predicting TFBSs, the prediction performance needs to be improved.
View Article and Find Full Text PDFThis paper introduces a novel data-driven self-triggered control approach based on a hierarchical reinforcement learning framework in networked motor control systems. This approach divides the self-triggered control policy into higher and lower layers, with the higher-level policy guiding the lower-level policy in decision-making, thereby reducing the exploration space of the lower-level policy and improving the efficiency of the learning process. The data-driven framework integrates with the dual-actor critic algorithm, using two interconnected neural networks to approximate the hierarchical policies.
View Article and Find Full Text PDFSensors (Basel)
June 2023
While system identification methods have developed rapidly, modeling the process of batch polymerization reactors still poses challenges. Therefore, designing an intelligent modeling approach for these reactors is important. This paper focuses on identifying actual models for batch polymerization reactors, proposing a novel recursive approach based on the expectation-maximization algorithm.
View Article and Find Full Text PDFBackground: Psychological workplace violence (WPV) is the primary form of workplace violence suffered by nursing interns. Psychological WPV not only damages the physical and mental health of nursing interns, but also has a negative impact on their work quality and career choice.
Aim: To investigate the characteristics and types of psychological WPV suffered by nursing interns in China, analyze the influencing factors of psychological WPV among nursing interns, and explore the influence of psychological WPV on the professional commitment of nursing interns.
Sensors (Basel)
October 2022
As the core link of the "Internet + Recycling" process, the value identification of the sorting center is a great challenge due to its small and imbalanced data set. This paper utilizes transfer fuzzy c-means to improve the value assessment accuracy of the sorting center by transferring the knowledge of customers clustering. To ensure the transfer effect, an inter-class balanced data selection method is proposed to select a balanced and more qualified subset of the source domain.
View Article and Find Full Text PDFA novel completely mode-free integral reinforcement learning (CMFIRL)-based iteration algorithm is proposed in this article to compute the two-player zero-sum games and the Nash equilibrium problems, that is, the optimal control policy pairs, for tidal turbine system based on continuous-time Markov jump linear model with exact transition probability and completely unknown dynamics. First, the tidal turbine system is modeled into Markov jump linear systems, followed by a designed subsystem transformation technique to decouple the jumping modes. Then, a completely mode-free reinforcement learning algorithm is employed to address the game-coupled algebraic Riccati equations without using the information of the system dynamics, in order to reach the Nash equilibrium.
View Article and Find Full Text PDFIEEE Trans Cybern
July 2023
This article proposes a robust Bayesian inference approach for linear state-space models with nonstationary and heavy-tailed noise for robust state estimation. The predicted distribution is modeled as the hierarchical Student- t distribution, while the likelihood function is modified to the Student- t mixture distribution. By learning the corresponding parameters online, informative components of the Student- t mixture distribution are adapted to approximate the statistics of potential uncertainties.
View Article and Find Full Text PDFThis article examines the distributed filtering problem for a general class of filtering systems consisting of distributed time-delayed plant and filtering networks with semi-Markov-type topology switching (SMTTS). The SMTTS implies the topology sojourn time can be a hybrid function of different types of probabilistic distributions, typically, binomial distribution used to model unreliable communication links between the filtering nodes and Weibull distribution employed to depict the cumulative abrasion failure. First, by properly constructing a sojourn-time-dependent Lyapunov-Krasovski function (STDLKF), both time-varying topology-dependent filter and topology-dependent filter are designed.
View Article and Find Full Text PDFhas been classically used to treat diarrhea and diarrhea-related diseases. However, in the past two decades, fungal infections caused by have been increasing among immunocompromised patients, and it takes too long to isolate from blood to diagnose it in time. In this paper, a new method for the isolation and selection of from red blood cells (RBC) is proposed by designing a microfluidic chip with an optically-induced dielectrophoresis (ODEP) system.
View Article and Find Full Text PDFIn this article, the problem of the asynchronous fault detection (FD) observer design is discussed for 2-D Markov jump systems (MJSs) expressed by a Roesser model. In general, the FD observer cannot work synchronously with the system, that is, the mode of the observer varies with the mode of the system in line with some conditional transitional probabilities. For dealing with this difficult point, a hidden Markov model (HMM) is employed.
View Article and Find Full Text PDFObjective: The purpose of this study was to analyze the clinical efficacy of five therapeutic strategies in patients with CSP.
Materials And Methods: A total of 135 CSP patients were included and divided into five groups based on the treatment they received, including transvaginal resection (Group A), laparoscopic resection (Group B), uterine arterial embolization (UAE) combined with hysteroscopic curettage (Group C), UAE combined with uterine curettage (Group D), and hysteroscopic curettage (Group E). To investigate the clinical efficacy of these strategies, intraoperative bleeding, serum β-hCG levels and recovery time, menstruation recovery time, hormone levels at 1 month after treatment.
In this paper, a confidence set-membership state estimator is proposed for a class of polytopic linear parameter varying (LPV) systems with inexact scheduling variables. The set-bounded and Gaussian uncertainties are considered simultaneously in the process disturbances and measurement noises. The purpose of the proposed estimator is to achieve a confidence set of the state with given confidence level.
View Article and Find Full Text PDFThis article addresses the design issue of fuzzy asynchronous fault detection filter (FAFDF) for a class of nonlinear Markov jump systems by an event-triggered (ET) scheme. The ET scheme can be applied to cut down the transmission times from the system to FAFDF. It is assumed that the system modes cannot be obtained synchronously by the filter, and instead, there is a detector that can measure the estimated modes of the system.
View Article and Find Full Text PDFThe problem of sliding mode control (SMC) for a class of Markov jump systems (MJSs) is addressed in this paper based on a resource-aware triggering mechanism which realizes computational resources saving and disturbance attenuation simultaneously. By introducing the self-triggered policy, the next execution time is pre-computed for sampling, updating and executing by relying on the latest sampled information. Then, the switching surface and the related dynamics of the original MJSs are obtained by means of a self-triggered sampling scheme.
View Article and Find Full Text PDFTemperature variation will affect the prediction accuracy when using the near-infrared (NIR) analytical model to measure the viscosity of bisphenol-A. In order to correct the effect of temperature on the prediction performance of NIR model, a multilevel least-absolute shrinkage and selection operator (LASSO) is proposed in this paper. The multilevel LASSO algorithm combines LASSO with the multilevel simultaneous component analysis (MLSCA) to enhance the robustness of the model under temperature changes and external disturbances.
View Article and Find Full Text PDFThis article investigates the finite-time asynchronous control problem for continuous-time positive hidden Markov jump systems (HMJSs) by using the Takagi-Sugeno fuzzy model method. Different from the existing methods, the Markov jump systems under consideration are considered with the hidden Markov model in the continuous-time case, that is, the Markov model consists of the hidden state and the observed state. We aim to derive a suitable controller that depends on the observation mode which makes the closed-loop fuzzy HMJSs be stochastically finite-time bounded and positive, and fulfill the given L performance index.
View Article and Find Full Text PDFUnlike macroscopic process variables, near-infrared spectroscopy provides process information at the molecular level and can significantly improve the prediction of the components in industrial processes. The ability to record spectra for solid and liquid samples without any pretreatment is advantageous and the method is widely used. However, the disadvantages of analyzing high-dimensional near-infrared spectral data include information redundancy and multicollinearity of the spectral data.
View Article and Find Full Text PDFThis paper proposes a near-infrared (NIR) fault detection technology based on a process pattern via a potential function. Near-infrared spectroscopy is used to acquire process information at the molecular level. In this study, the process pattern concept is first introduced in the field of process control and a process pattern construction method based on elastic net-PCA is put forth.
View Article and Find Full Text PDFConsidering that temperature makes a difference to near-infrared spectrum, a probabilistic principle component regression (PPCR) based temperature compensation modeling strategy is investigated under the framework of maximum likelihood estimation. First, a PPCR model is established to extract the dynamic information of the spectra at designated experimental temperature. Then, by decomposing the temperature-induced spectral variation into the shift in horizontal direction and the drift in vertical direction, the quantitative expression between spectral variation and temperature change is derived.
View Article and Find Full Text PDFAppl Spectrosc
August 2018
The fault detection problem of the oil desalting process is investigated in this paper. Different from the traditional fault detection approaches based on measurable process variables, near-infrared (NIR) spectroscopy is applied to acquire the process fault information from the molecular vibrational signal. With the molecular spectra data, principal component analysis was explored to calculate the Hotelling T and squared prediction error, which act as fault indicators.
View Article and Find Full Text PDFThis article presents a new scheme to design full matrix controller for high dimensional multivariable processes based on equivalent transfer function (ETF). Differing from existing ETF method, the proposed ETF is derived directly by exploiting the relationship between the equivalent closed-loop transfer function and the inverse of open-loop transfer function. Based on the obtained ETF, the full matrix controller is designed utilizing the existing PI tuning rules.
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