This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H-/H∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR).
In this article, the quasi-consensus control problem is investigated for a class of stochastic nonlinear time-varying multiagent systems (MASs). The innovation points of this research can be highlighted as follows: first of all, the dynamics of the plant are stochastic, nonlinear, and time varying, which resembles the natural systems in practice closely. Meanwhile, an energy harvesting protocol is put forward to collect adequate energy from the external environment. Second, as a generalization of the existing result, the ultimate control objective is quasi-consensus in a probabilistic sense, that is, designing a distributed control protocol in order that the probability of centering the allowable region for the states of each agent is larger than some predetermined values. Third, the MASs are subject to false data-injection (FDI) attacks, and a more general multimodal FDI model is proposed. On the basis of the probabilistic-constrained analysis technique and the recursive linear matrix inequalities (RLMIs), sufficient conditions are provided to guarantee the probabilistic quasi-consensus property. To derive the controller gains, an optimal probabilistic-constrained algorithm is designed by solving a convex optimization problem. Finally, two examples are provided to substantiate the validity of the proposed framework.
This article studies an event-based two-step transmission mechanism (TSTM) in the control design for networked T-S fuzzy systems. The transmission task is achieved in two steps. Consecutive triggering packets are relabeled in the first step by applying a traditional event-triggered mechanism (ETM). Then a probabilistic approach is employed to determine which packet is a real release packet (RRP) in the second step. This event-based TSTM is particularly suitable for scenarios in which traditional ETMs are unable to determine which packets are redundant. By discarding most of the unnecessary data packets, especially when the system is tending toward stability, the burden on the network bandwidth is reduced. To establish a control strategy for T-S fuzzy-based nonlinear systems with random uncertainties, a new timing analysis technique is proposed. Additionally, the necessary conditions for a nonlinear system's mean-square asymptotic stability (MSAS) are derived. Finally, two practical applications demonstrate the effectiveness of the suggested transmission mechanism in networked T-S fuzzy systems.
PURPOSE: To comprehensively analyze the impact of surgical compliance on the survival of patients with glioma and to explore the factors that influence surgical compliance. METHODS: Clinical data of patients with glioma between 2004 and 2018 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier curves and Cox regression were used to analyze the effect of surgical compliance on overall survival (OS) and disease-specific survival (DSS). Multivariate Cox regression was used to select the prediction variables and construct the nomograms. The predictive power of these models was assessed using Harell's consistency index (C-index), decision curve analysis (DCA), receiver operating characteristic (ROC) curves, and calibration curves. Multivariate logistic regression was performed to analyze the related variables of surgical compliance, and 1:1 propensity score matching (PSM) was applied to evaluate the validity of the results of patients with favorable and poor surgical compliance. RESULTS: Among the 47,573 eligible glioma patients recommended for surgery, 46,380 (97.5%) were in the surgical compliance group, while 1193 (2.5%) were in the noncompliance group. Surgical compliance was an independent prognostic factor for glioma patients, as indicated by multivariate Cox regression analysis that patients with surgical compliance had worse OS (hazard ratio [HR] 1.924; 95% confidence interval [CI] 1.800-2.056, p < 0.001) and DSS (HR 1.718; 95% CI 1.592-1.853, p < 0.001) in comparison to those without surgical compliance. A nomogram was developed and internally validated to be able to predict glioma prognosis. The nomogram can well predict patients' OS (C-index: 0.745) and DSS (C-index: 0.744). ROC curve, DCA curve, and calibration curve were applied to further assess the accuracy of the nomogram. Poor surgical compliance was found to be related to older age, female gender, tumor diameter, grade II or higher, poor grading, tumor location in the cerebellum and brainstem, and low household income. CONCLUSION: Surgical compliance is an independent prognostic factor for predicting the OS and DSS of patients with glioma, and good surgical compliance was significantly related to good survival.
Glioma , Humans , Female , Propensity Score , Glioma/surgery , Risk Factors , Calibration , Databases, Factual , Nomograms , Prognosis , SEER Program
This paper addresses the memory-based event-triggered consensus control issue for multi-UAV systems subject to deception attacks. In order to alleviate network bandwidth burden and reduce unnecessary data transmission, a memory-based event-triggered scheme (METS) is proposed by applying historic data information (HDI). Meanwhile, the average mechanism (AM) is introduced to replace the input of conventional event triggering scheme, which eliminates adverse event-triggering caused by instantaneous random jitter and deception attacks. Through this method, data mutation, peak/trough information loss, and energy consumption issues of UAV can be effectively addressed. With the aid of attack observer, a consensus control strategy is devised for each UAV to achieve control objective and compensate for the impact of attacks on multi-UAV system. Then, sufficient conditions are constructed to co-design the parameters and guarantee the attacked multi-UAV system can achieve consensus. The simulation results are provided to demonstrate the validity and practicality of the proposed strategy.
This study is devoted to event-triggered fuzzy load frequency control (LFC) for wind power systems (WPSs) with measurement outliers and transmission delays. Due to the integration of wind turbine (WT) with nonlinearity, the T-S fuzzy model of WPS is established for stability analysis and controller design. To mitigate the network burden, a new sampled memory-event-triggered mechanism (SMETM) related to historical system information is presented. It has the following two merits: 1) the utilization of continuous memory outputs over a given interval is useful to reduce the information loss in the period of samples and the redundant triggering events induced by disturbances and noises and 2) an extra upper constraint is added in the triggering condition to generate a new event only when the error signal belongs to a bounded range, thus, the false events caused by measurement outliers can be differentiated out and then be dropped. By representing the memory signal with transmission delay as a time-varying distributed delay term, a T-S fuzzy time-varying distributed delay system is built up to model the H∞ LFC WPS. With the help of the Lyapunov method and the integral inequality relying on distributed delay, some criteria are derived to solve the triggering matrix and fuzzy controllers. Finally, the merits of the proposed SMETM are tested by simulation results.
This paper focuses on the problem of formation control for multiple unmanned aerial vehicles (UAV) subject to cyber attacks by a novel event-triggered communication scheme. An average method is introduced to design the triggering condition of this communication scheme, by which the amount of wrong triggering events caused by the sudden change of system states is greatly decreased, thereby saving a great deal of network bandwidth and reducing network congestion. Considering cyber attacks, a new event-based formation control strategy is developed for multi-UAV systems under directed topology by utilizing a control compensation approach. Sufficient conditions for the multi-UAV system to achieve the desired formation are acquired. Finally, a simulation example is undertaken to demonstrate the effectiveness of the theoretical results.
In this study, the event-triggered problem of networked control systems (NCSs) is investigated, and a novel information transmission scheme is established. Under this scheme, the segment-weighted information (SWI) in a sliding historical window (SHW) is calculated and then sampled. Compared with the traditional direct sampling method, in this approach, the control input includes historical information in the SHW, thereby leading to less information loss due to sampling. This study also emphasizes on designing an SWI-based event-triggered mechanism (ETM) for scheduling network transmission. Different from most of the existing ETMs, the proposed SWI-based ETM leverages historical information to determine which data are necessary for the whole control system. Our approach can greatly reduce the number of unexpected triggering events of a control system with stochastic disturbances owing to the introduction of the SWI in the ETM. Moreover, Zeno phenomena are prevented thanks to periodic sampling. Sufficient conditions are derived based on the Lyapunov functional approach, and a numerical simulation example is provided to demonstrate the effectiveness of the proposed method.
In this article, a networked fault detection (FD) problem is investigated for interval type-2 T-S fuzzy systems. A novel adaptive memory-event-triggered mechanism (METM) is proposed by introducing historical information of the measured output in a prescribed sliding window. The current measured output in the traditional event-triggered mechanism is replaced by a weighting function-based historical information. As a result, the data releasing rate can be effectively reduced and maltriggering events aroused by unknown abrupt disturbance or measurement noise can be avoided as well. Meanwhile, an adaptive threshold depending on the historical information is utilized to further adjust the data releasing rate. The FD filter is designed and derived in terms of linear matrix inequalities to guarantee the H∞ performance of fault detected systems. Finally, a hardware-in-loop simulation experiment platform is built to manifest the effectiveness of the proposed METM-based FD method.
In this technical correspondence, the resilient fuzzy stabilization is enhanced in the direction of elevating the feasible stabilization region as large as possible while the same alert threshold is chosen as the recent one. To do this, the switching-type fuzzy state-feedback controller is designed with a set of switch modes so that more groups of gain matrices can be introduced to enhance the degree of freedom. What is far more important is that a novel augmented time-variant matrix approach is proposed in order to collect the proprietary features of normalized fuzzy weighting functions with regard to each switch mode. Then, all the obtained augmented time-variant matrices are split into a set of positive/negative matrices, which can be elaborately assigned into different monomials of our designing conditions under the framework of homogeneous polynomials. Therefore, less conservative results of resilient fuzzy stabilization are obtained even if some higher alert thresholds are chosen for probably ensuring the establishment of the involved precondition. Finally, the superiority of our approach is validated by giving some detailed comparisons on the benchmark example.
Wireless vehicle-to-vehicle communication brings inevitable imperfections. This work simultaneously addresses the resource utilization and security issues for a vehicle platoon. Inspired by the modeling of denial of service attacks, a novel queuing model is constructed to depict false data injection attacks. A switched event-triggered mechanism is proposed to optimize network resources. The transmission rate can be actively reduced when the system is under malicious attacks. Then, a unified error system is established, where string stability is interpreted as global exponential stability. By using a proper Lyapunov function, a co-design approach is developed for closed-loop controllers and event-triggering parameters, with which the vehicle platoon can maintain a small safety distance and resist the negative impacts of cyber attacks. Finally, the proposed methods are verified by a virtual vehicle platoon.
Awareness , Excipients , Injections , Communication , Records
This article focuses on the problem of resilient H∞ filtering for Takagi-Sugeno fuzzy-model-based nonlinear networked systems with multisensors. A weighted fusion approach is adopted before information from multisensors is transmitted over the network. A novel event-triggered mechanism is proposed, which allows us not only to reduce the data-releasing rate but also to prevent abnormal data being potentially transmitted over the network due to sensor measurement or other practical factors. The problem of denial-of-service (DoS) attacks, which often occurs in a communication network, is also considered, where the DoS attack model is based on an assumption that the periodic attack includes active periods and sleeping periods. By employing the idea of the switching model for filtering error systems to deal with DoS attacks, sufficient conditions are derived to guarantee that the filtering error system is exponentially stable. Simulation results are given to demonstrate the effectiveness of the theoretical analysis and design method.
This article investigates the problem of event-based intermittent formation control for multi-UAV systems subject to deception attacks. Compared to the available research studies on multi-UAV systems with continuous control strategy, the proposed intermittent control strategy saves a large amount of computation resources. An average method is introduced in developing the event-triggered mechanism (ETM) such that the amount of unexpected triggering events induced by uncertain disturbances is greatly reduced. Moreover, such a mechanism can further decrease the average data-releasing rate, thereby alleviating the burden of network bandwidth. Sufficient conditions for multi-UAV systems with deception attacks to achieve the predefined formation are obtained with the aid of Lyapunov stability theory. Finally, the validity of the proposed theoretical results is demonstrated via a simulation example.
Members of the genus Bacillus are known to play an important role in promoting plant growth and protecting plants against phytopathogenic microorganisms. In this study, 21 isolates of Bacillus spp. were obtained from the root micro-ecosystem of Suaeda glauca. Analysis of the 16S rRNA genes indicated that the isolates belong to the species Bacillus amyloliquefaciens, Bacillus velezensis, Bacillus subtilis, Bacillus pumilus, Bacillus aryabhattai and Brevibacterium frigoritolerans. One of the interesting findings of this study is that the four strains B1, B5, B16 and B21 are dominant in rhizosphere soil. Based on gyrA, gyrB, and rpoB gene analyses, B1, B5, and B21 were identified as B. amyloliquefaciens and B16 was identified as B. velezensis. Estimation of antifungal activity showed that the isolate B1 had a significant inhibitory effect on Fusarium verticillioides, B5 and B16 on Colletotrichum capsici (syd.) Butl, and B21 on Rhizoctonia cerealis van der Hoeven. The four strains grew well in medium with 1-10% NaCl, a pH value of 5-8, and promoted the growth of Arabidopsis thaliana. Our results indicate that these strains may be promising agents for the biocontrol and promotion of plant growth and further study of the relevant bacteria will provide a useful reference for the development of microbial resources.
Antifungal Agents , Bacillus/physiology , Chenopodiaceae/microbiology , Plant Development , Rhizosphere , Alkalies/metabolism , Arabidopsis/growth & development , Bacillus/classification , Bacillus/genetics , Bacillus/isolation & purification , Biological Control Agents , Genes, Essential/genetics , Phylogeny , Plant Roots/microbiology , RNA, Ribosomal, 16S/genetics , Salt Tolerance
This article investigates the problem of memory-event-triggered H∞ output feedback control for neural networks with mixed delays (discrete and distributed delays). The probability density of the communication delay among neurons is modeled as the kernel of the distributed delay. To reduce network communication burden, a novel memory-event-triggered scheme (METS) using the historical system output is introduced to choose which data should be sent to the controller. Based on a constructed Lyapunov-Krasovskii functional (LKF) with the distributed delay kernel and a generalized integral inequality, new sufficient conditions are formed by linear matrix inequalities (LMIs) for designing an event-triggered H∞ controller. Finally, experiments based on a computer and a real wireless network are executed to confirm the validity of the developed method.
This article investigates the problem of event-triggered secure path tracking control of autonomous ground vehicles (AGVs) under deception attacks. To relieve the burden of the shareable vehicle communication network and to improve the tracking performance in the presence of deception attacks, a learning-based event-triggered mechanism (ETM) is proposed. Different from existing ETMs, the triggering threshold of the proposed mechanism can be dynamically adjusted with conditions of the latest vehicle state. Each vehicle in this study is deemed as an agent, under which a novel control strategy is developed for these autonomous agents with deception attacks. With the assistance of Lyapunov stability theory, sufficient conditions are obtained to guarantee the stability and stabilization of the overall system. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed theoretical results.
This paper is concerned with an event-triggered filter design for fuzzy-model-based cyber-physical systems with cyber-attacks. Spurious events may be triggered under the conventional event-triggered mechanism (ETM) when the sampling data has a rapid change arising from unpredicted external disturbance. To avoid spurious decisions on data releasing a new ETM is proposed. Furthermore, the communication network is vulnerable to attacks by malicious attackers. Under this scenario, a new resilient filter is designed to ensure the security. Sufficient conditions are established to make the filtering error system asymptotically stable. A numerical example is provided to show the effectiveness of the proposed results.
This paper focuses on the issue of designing an adaptive event-triggered scheme to the decentralized filtering for a class of networked nonlinear interconnected system. A novel adaptive event-triggered condition is proposed by constructing an adaptive law for the threshold. This new type of threshold mainly depends on the error between the states at the current sampling instant and the latest releasing instant, by which the data release rate is adapted to the variation of the system. The limitation of network bandwidth is alleviated on account of a large amount of "unnecessary" packets being dropped out before accessing the network. Sufficient conditions are derived such that the overall filtering error system under the proposed adaptive data-transmitting scheme is asymptotically stable with a prescribed disturbance attenuation level. An example is given to show the effectiveness of the proposed scheme.
Objective To study the structure and differences of bacterial communities in different soils, and to explore the effectiveness of 16S rRNA sequencing in identification of different soil. Methods Soil samples from 7 places in Shanghai were collected, then bacterial genomic DNA were extracted from them. The fragments of hypervariable region from 16S rRNA sequences were sequenced with high-throughput sequencing techniques. The results were quantified or visualized with bioinformatics software. The differences in diversity and abundance among the three kinds of bacterial communities in soil samples from grassland, forests and beaches were compared and analyzed. Results The statistical differences that existed among the alpha diversity indexes of bacterial communities in soil samples of grassland, forests and beaches had statistical significance. The relative abundance and diversity of bacterial communities in these three kinds of soil were significantly different. Grassland soil had higher Acidobacteria abundance, forest soil had higher Proteobacteria abundance, beach soil had higher Actinobacteria abundance. However, the differences in soil bacterial communities in artificial grasslands, natural grasslands and industrial district grasslands did not have statistical significance. Conclusion 16S rRNA sequencing can effectively distinguish different soils. This method may be able to provide clues for first crime scene inference in criminal cases.
Biodiversity , China , DNA, Bacterial/genetics , Forensic Genetics , High-Throughput Nucleotide Sequencing/methods , Phylogeny , RNA, Ribosomal, 16S/genetics , Soil Microbiology
Forensic microorganism is one of the hotspots of forensic science research. Due to its conservatism and specificity, the 16S rRNA gene is found to be an ideal marker for forensic identification. With the rapid development of high throughput sequencing technology, the research on microorganisms has been gradually applied to many fields such as environment and health care. In the field of forensic science, the results of forensic microbiology research, represented by 16S rRNA gene sequencing, are also gradually applied to forensic practice, such as biological samples identification, individual identification, postmortem interval estimation, and regional inference, which not only provide clues for the investigation of cases but also complement and assist traditional methods. This paper describes the research methods and related sequencing technologies of 16S rRNA gene sequencing, summarizes its research progress, and discusses the application value and potential of 16S rRNA in forensic science.
Forensic Sciences/trends , RNA, Ribosomal, 16S , Sequence Analysis, RNA
