A5088577145- Gene Regulatory Network Analysis
- Microbial Metabolic Engineering and Bioproduction
- Receptor Mechanisms and Signaling
- Advanced Control Systems Optimization
- Bioinformatics and Genomic Networks
- Fault Detection and Control Systems
- Mitochondrial Function and Pathology
- Molecular Communication and Nanonetworks
- Bacterial Genetics and Biotechnology
- stochastic dynamics and bifurcation
- Microtubule and mitosis dynamics
- Computational Drug Discovery Methods
- Anomaly Detection Techniques and Applications
- Viral Infectious Diseases and Gene Expression in Insects
- Extremum Seeking Control Systems
- Reliability and Maintenance Optimization
- Remote Sensing in Agriculture
- Generative Adversarial Networks and Image Synthesis
- Advanced Statistical Process Monitoring
- Machine Fault Diagnosis Techniques
- Image and Signal Denoising Methods
- Advanced Optical Network Technologies
- Horticultural and Viticultural Research
- Lysosomal Storage Disorders Research
- Origins and Evolution of Life
Zhejiang Normal University
2022-2025
University of Sannio
2018-2022
In this letter, we study the control of probabilistic Boolean networks (PBCNs) by leveraging a model-free reinforcement learning (RL) technique. particular, propose Q-learning (QL) based approach to address feedback stabilization problem PBCNs, and design optimal state controllers such that PBCN is stabilized at given equilibrium point. The are designed for both finite-time stability asymptotic PBCNs. order verify convergence proposed QL algorithm, obtained policy compared with solutions...
In this letter, the output tracking control design of switched Boolean networks (SBCNs) is investigated via state feedback and control. The algebraic state-space representation method which resorts to semi-tensor product (STP) matrices utilized; necessary sufficient conditions for solvability problem are presented. A constructive procedure given obtain all possible switching signal-dependent controllers under arbitrary signals such that SBCNs tracks a time-varying reference trajectory....
In this letter, a model-free co-design scheme of triggering-driven controller is proposed for probabilistic Boolean control networks (PBCNs) in order to achieve feedback stabilization with minimum efforts. Specifically, Q-learning (QL) algorithm exploited devise self-triggered strategy wherein the update time computed advance by using current state information. A new QL (STQL) presented and rendering closed-loop system stable at given equilibrium point. Finally, some examples are demonstrate...
The disturbance decoupling problem (DDP) whereby the system outputs become insensitive to exogenous signals or disturbances plays a vital role in systems engineering and biological systems. Notably, many signalling with multiple are usually susceptible external environmental changes. authors investigate DDP for Boolean control networks (BCNs) present novel technique based on algebra solve DDP. In particular, results solvability derived by transforming dynamics simplified form called...
In this article, a reinforcement learning (RL)-based scalable technique is presented to control the probabilistic Boolean networks (PBCNs). particular, double deep-Q network (DDQN) approach firstly proposed address output tracking problem of PBCNs, and optimal state feedback controllers are obtained such that PBCNs tracks constant as well time-varying reference signal. The method model-free offers scalability, thereby provides an efficient way large-scale natural choice model gene regulatory...
In this article, the state-flipped control technique is explored to investigate stabilization of probabilistic Boolean networks (PBNs). Changing values many nodes from 0 1 (or 0) called control. The concepts fixed point, reachable sets, and finite-time global PBNs under are proposed. Several necessary sufficient conditions for also derived based on sets a given state. Furthermore, model-free reinforcement learning algorithm, namely, <inline-formula...
Probabilistic Boolean control network (PBCN) is a discrete-time dynamical system comprised of collection networks (BCNs) and switching among them in stochastic manner. In this paper, the output tracking PBCNs investigated via state feedback control. By resorting to algebraic state-space representation BCNs, necessary sufficient conditions for solvability problem are presented. A constructive procedure given obtain all possible controllers such that tracks constant reference signal. Finally,...
In this paper, the set stabilization of switched Boolean control networks (SBCNs) under sampled-data feedback is addressed. Here, input switching signal-dependent, and SBCNs can switch only at sampling instants. First, sampled point invariant subset (SPCIS) defined, an algorithm provided to obtain largest SPCIS arbitrary signal. Based on SPCIS, some necessary sufficient conditions are presented for by signal-dependent (SSDSD) state control. Furthermore, a constructive procedure given design...
On the verge of technology, manufacturing industries revolutionize into smart industries, which create a large amount multivariate time-series data. However, due to sensors' failure, extreme environment, etc., collected data are incomplete and have missing values at several instances that result in an erroneous analysis The key resolving this problem is imputation, i.e., replacing with synthetic values. In paper, we introduce generative adversarial network (GAN) framework generate pertaining...
Control of infectious diseases using bacteriophage therapy is regaining interests in modern medicine and systems biology as an alternative treatment for antibiotic-resistant bacteria. A key issue to control the phage's replication process: indeed, phage may switch either towards lytic state or lysogeny during its reproduction due some conditions. However, only relevant bacteria elimination. In this paper, we model switching dynamics λ phage, i.e., a class bacteriophage, switched Boolean...
Successful control of gene regulatory networks (GRNs) can help obtain potential treatments by making therapeutic interventions. In this work, GRNs are modeled as probabilistic Boolean (PBCNs) and strategies to devise an optimal feedback discussed. Model-free reinforcement learning (RL) based is proposed in order minimize model design efforts regulate with high complexities. We make use a deep Q-learning protocol stabilize PBCNs aperiodic framework. Finally, main results validated using...
The efficient handling of nitrogen has become a critical issue in modern agriculture, from financial standpoint, as well regard to reducing the environmental impacts using an excessive amount fertilizer. Manure compost is useful for maintaining or raising soil chemical levels without NO3- accumulation; however, best grain yield, it should be combined with N Via this study, we aimed develop optimal decision support system that indicates when initiate fertilization based on nitrogen-limited...
In this paper we investigate the reachability and controllability of delayed switched Boolean control networks (DSBCNs). By resorting to algebraic state space representation method built using semi-tensor product (STP) matrices, provide several necessary sufficient conditions for these properties hold which are based on input-state incidence matrix carrying entire network dynamics information. Also, realize DSBCNs in shortest time, an algorithm is presented finds switching sequences forcing...
Abstract One of the major issues in systems biology is developing control theory for gene regulatory networks (GRNs). Particularly, an important objective to develop therapeutic intervention strategies alter dynamics GRNs avoid undesired or diseased states. Several optimal have been developed find small (or medium) sized modeled as probabilistic Boolean (PBCNs). However, due humongous nature GRNs, we require strategy that scales large without posing any constraints on network dynamics. In...
Cascading is characterized by a sequence of line trips and load sheds which may lead to major system collapse or even blackout resulting in huge economic losses. In the absence on-site measurement, it has always remained challenge form an accurate model capture this cascading phenomena considering complexity interconnected network. An alarm prediction advance will save from complete motivated propose dynamic replicating propagation using real-time data. Branching Process one such captures...
Based on binary logic, this study presents a new framework to analyze the dynamics of Boolean control networks (BCNs) and investigates basic problem state-space approach without using semi-tensor product (STP). The logical form BCNs is transformed into discrete-time bilinear system by resorting Khatri-Rao minterms. This certainly reduces computational efforts compared STP based method. Subspace regular subspace are defined for BCNs. Necessary sufficient conditions existence presented....
With the increasing interconnectivity of cyber-physical systems (CPSs) in various fields, such as manufacturing plants, power and smart networked systems, large amounts multivariate data are generated through sensors actuators, also other sources measurements images. This paper focuses on anomaly detection (AD) problem, known fault or outlier detection, depending type dataset, which involves identifying anomalous values dataset using analytical methods. However, datasets often contain...