Trigger Parameters Research Articles

Cointegration Analysis of Crop Yields and Extreme Weather Factors Using Actuaries Climate Index with Application of Bonus–Malus System

This article analyzes the long-term temporal co-movement of the extreme weather variables in the Actuaries Climate Index (ACI) and crop yields, modeling their relationship using an error correction model (ECM). The analysis suggests that significant weather variables can serve as trigger parameters in the pricing framework of weather index crop insurance. To address the challenge of weather index crop insurance while preserving the advantages of a bonus–malus system (BMS), we propose a transition rule that distinguishes between damages caused by severe weather and those resulting from the policyholder’s decisions. Subsequently, we also explore the challenges of implementing such a new hybrid BMS for crop insurance where extreme weather outcomes are integrated into the classical BMS.

A spatiotemporal chaos based deep learning model watermarking scheme

With deep learning techniques achieving great results in modern industry, the intellectual property (IP) protection for deep learning models has attracted the attention of academics and engineers. However, training a commercially viable deep learning model usually needs professional resources and time. Once a malicious user clones, illegally distributes and uses the model, it can infringe on the model owner's IP and even steal its market share. Among the existing IP protection methods, scholars prefer the black-box watermarking approaches, of which the content of the trigger set and the label are the key part of the watermarking technique. However, most schemes do not consider the security and invisibility of the trigger set, which allows attackers to easily trigger the model by creating a fake trigger set, thereby committing a fraudulent ownership claim attack and claiming the ownership belongs to themselves. To overcome these drawbacks, we proposed a spatiotemporal chaotic data annotation method. Firstly, the unpredictability and acyclicity of chaos make the model resistant to fraudulent ownership claim attacks, statistical inference and other common machine learning attacks; Secondly, the trigger set and parameters are independent of each other, guaranteeing the security of the key; Thirdly, the spatiotemporal chaotic system provides a large key space, which meets the commercialization needs of deep learning models. Theoretical analysis and experimental results show that our scheme has security, practicality and robustness. To further validate the superiority of the proposed method, we also compare it with the Logistic chaotic annotation watermarking-based method, and the results show that our method performs better in terms of robustness, effectiveness, completeness, fidelity, security and practicality.

Simultaneous attitude fault detection and control of the six-rotor UAV based on event trigger mechanism

This paper analyzes the problem of simultaneous fault detection and control of the six-rotor unmanned aerial vehicle control system, considering external disturbance, measurement disturbance, and actuator fault. The integral event trigger mechanism is introduced in the control side and sensor side of the system. Based on Lyapunov stability and H∞ control theory, the sufficient conditions to make the fault system asymptotically stable and have certain performance indexes are given by means of linear matrix inequalities; at the same time, the design criteria of event trigger parameters are also given. The linear matrix inequality is decoupled, and the calculation method of gain matrix of simultaneous fault detection and control module is given. The effectiveness of the proposed method is verified by simulation experiments.

Universal adversarial backdoor attacks to fool vertical federated learning

Vertical federated learning (VFL) is a privacy-preserving distribution learning paradigm that enables participants, owning different features of the same sample space to train a machine learning model collaboratively while retaining their data locally. This paradigm facilitates improved efficiency and security for participants such as financial or medical fields, making VFL an essential component of data-driven Artificial Intelligence systems. Nevertheless, the partitioned structure of VFL can be exploited by adversaries to inject a backdoor, enabling them to manipulate the VFL predictions. In this paper, we aim to investigate the vulnerability of VFL in the context of binary classification tasks. To this end, we define a threat model for backdoor attacks in VFL and introduce a universal adversarial backdoor (UAB) attack to poison the predictions of VFL. The UAB attack, consisting of universal trigger generation and clean-label backdoor injection, is incorporated during the VFL training at specific iterations. This is achieved by alternately optimizing VFL sub-problems' universal trigger and model parameters. Our work distinguishes itself from existing studies on designing backdoor attacks for VFL, as those require the knowledge of auxiliary information that is not accessible within the split VFL architecture. In contrast, our approach does not require additional data to execute the attack. On the real-world datasets, our approach surpasses existing state-of-the-art methods, achieving up to 100% backdoor task performance while maintaining the main task performance. Our results in this paper make a major advance in revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure VFL. Our results in this paper make a major advance in revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure VFL applications such as finance.

QoS-Dependent Event-Triggered Control for UAVs on Cognitive Radio Networks Subject to Deception Attacks

Based on cognitive radio (CR) networks, this paper considers event-triggered control for unmanned aerial vehicle (UAV) systems subject to deception attacks. Two states (ON and OFF) are switched in CR networks. For the switched model, a new Quality of Service (QoS)-dependent event-triggered scheme (ETS) is proposed over CR networks under deception attacks. For a non-ideal QoS, more sensitive trigger parameters are required against deception attacks. For an ideal QoS, less sensitive trigger parameters are adjusted to reduce data transmissions. Based on the QoS-dependent ETS, a new switched system is modeled subject to deception attacks. By constructing the Lyapunov functional, the criteria are obtained to guarantee the exponential mean-square stability of UAV systems and the desired <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{\infty }$</tex-math></inline-formula> performance. Moreover, an algorithm is developed to adjust the parameters of QoS-dependent ETS according to the QoS of CR network. Finally, an UAV system is utilized to show the effectiveness of the proposed approach.

Quality Control Patient Trigger Ventilator using Negative Air Pressure

This research aims to develop a quality control tool for patient trigger ventilators at PT Stechoq Robotika Indonesia. The increasing demand for ventilators has driven PT Stechoq Robotika Indonesia to create high-end ventilators. However, the production process faces challenges, particularly in the manual quality control of patient trigger alarms. Therefore, an automated quality control tool is needed to replace the manual mechanism. The research utilizes the Research and Development method. Trials are conducted on SIMV and CPAP modes with variations in pressure trigger and flow trigger parameters to evaluate the performance of patient trigger alarms on ventilators. The results of implementing the patient trigger quality control tool show an average accuracy rate of 70% for pressure and flow triggers in SIMV and CPAP modes. This tool is expected to reduce time and effort in the quality control process while improving the accuracy of triggering patient alarms on ventilators. Overall, the patient trigger ventilator quality control tool has significant potential in the ventilator industry, with positive implications for product quality and production efficiency at PT Stechoq Robotika Indonesia.

Reinforcement learning-based hybrid differential evolution for global optimization of interplanetary trajectory design

The global optimization and design of interplanetary trajectories is one of the most important tasks in deep space exploration. Its search space is often characterized by multi-constraints, extreme non-linearity and sensitivity to initial conditions. To cope with these difficulties, an improved reinforcement learning-based hybrid differential evolution (DE) named RL_HDE is proposed in this paper. In RL_HDE, a novel multi-mutation strategy LSHADE based on an adaptive Q-Learning framework is proposed for global exploration. To further balance the global exploration and local exploitation of RL_HDE, a new parameter adaptive strategy based on Q-Learning is designed to control two trigger parameters. The performance of RL_HDE is verified by the well-known Global Trajectory Optimization Problems (GTOP), which are developed by Advanced Concepts Team of European Space Agency (ESA-ACT). A comparison of RL_HDE with three sets of algorithms, including ten state-of-the-art hybrid evolutionary algorithms, four interplanetary trajectory design algorithms, and seven reinforcement learning-based hybridizations. Experimental statistical results demonstrate that RL_HDE outperforms other competitors in terms of convergence efficiency and accuracy. RL_HDE has better performance for solving complex interplanetary trajectory design problems such as Cassini2 and Messenger-full.

Disturbance rejection‐based cluster synchronization of nonlinear complex dynamical networks with hybrid‐driven mechanism

AbstractThe issue of cluster synchronization and disturbance rejection of multi‐weighted complex dynamical networks subject to nonlinearities and external disturbances are studied in this article. In particular, a hybrid‐triggering control scheme is implemented to mitigate the burden of the network transmission according to the prespecified triggering condition, under which each node will transmit its signal independently to the controller. To be more specific, a Bernoulli distributed stochastic variable is introduced to describe the switching law of the communication scheme. Also, the disturbance rejection performance is carried out with the aid of an improved equivalent input disturbance estimator approach. The sufficient criterion in the form of linear matrix inequalities is derived by constructing a suitable Lyapunov–Krasovskii functional and employing stochastic theory. Based on these settings, the feedback gain matrices and the trigger parameters affirming the desired cluster synchronization performance of the considered networks are established. Eventually, two illustrative examples including Lur'e networks with simulations are presented, which evidently proves the effectiveness of the obtained results.

Finite-Time Control of High-Order Nonlinear Random Systems Using State Triggering Signals

In order to improve the efficiency of data transmission and save communication resources, the problems of double event-triggered control are investigated for a class of high-order nonlinear random systems. Under more general system conditions, in addition to overcoming the difficulty of recursive design caused by signal discontinuity, the effects of high-order nonlinearity and random disturbances also need to be addressed. Based on the adding power integral technique, a practical finite-time stable result is established for the nonlinear random systems under a double event-triggered mechanism (ETM) and proved that there is no Zeno phenomenon. Compared with the existing results, the update frequency of the signals is effectively reduced, and the upper bound of the stable error is independent of trigger parameters, thus can be made sufficiently small by tuning design parameters. Furthermore, the result is expanded to finite-time stabilization, state variables converge to the origin in a finite time. Finally, numerical simulations verify the effectiveness of the proposed algorithm.

Trigger motion and interface optimization of an eye-controlled human-computer interaction system based on voluntary eye blinks

ABSTRACT Eye-controlled human-computer interaction (ECHCI) attracts attention for its human-centered, natural and direct operation characteristics. The most common ECHCI trigger motion is “gazing,” which causes Midas Touch problems, lowering usability and operation experience. This paper innovatively proposed using motion combinations based on blinking as the trigger of interactive objects (IOs). Trigger motions and IO design parameters were explored on the platform consisting of Tobii eye-tracker and computer vision kits. In the motion experiment, it was concluded 2-blinks could bring a low task load and high success rate of 95%. In the IO size experiment, the larger the IO diameter, the shorter the users’ response time and the higher users’ interaction success rate, and 55.5 mm was the optimal setting. Based on this, it was found a larger spacing could bring higher operation efficiency, and 33.3 mm was taken as the recommended value. Based on the conclusion above, an eye-controlled multimedia player prototype was built for usability evaluation, which showed an equally high efficiency compared to mouse-controlled HCI. The findings in this paper overcame Midas Touch issues and avoided the interference of unavoidable involuntary blinking, which brought a significant increase in system robustness and has a wide range of potential applications.

Resilient event-triggered stabilization for uncertain Markovian jump delayed neural networks under time-window-based aperiodic DoS attacks

In this paper, the problem of robustly stable in mean square of uncertain Markovian jump neural networks (UMJNNs) with time-varying delays under time-window-based aperiodic denial-of-service (DoS) attacks is investigated. First, a new class of portrayal is proposed for DoS attacks, that is, fixed time-window-based non-periodic DoS attacks. In addition, a resilient event-triggered communication scheme (RETCS) is designed between sensors and controllers to reduce “unnecessary” waste of network resources under the proposed non-periodic DoS attack. Then, a new model of UMJNNs with time-varying delays considering non-periodic DoS attacks is developed on this basis. Second, a new single Lyapunov function is constructed in this paper for non-periodic DoS attacks. In addition, the stability criteria of UMJNNs with time-varying delays are obtained based on Lyapunov stability theory and the linear matrix inequality technique. Then, the criterion for co-designing the trigger parameters of RETCS and the gain matrix of the controller is proposed. Finally, the validity of the obtained result is illustrated by two examples.

Single Earthquake Bond Pricing Framework with Double Trigger Parameters Based on Multi Regional Seismic Information

The investor interest in multi-regional earthquake bonds may drop because high-risk locations are less appealing to investors than low-risk ones. Furthermore, a single parameter (earthquake magnitude) cannot accurately express the severity due to an earthquake. Therefore, the aim of this research is to propose valuing a framework for single earthquake bonds (SEB) using a double parameter trigger type, namely magnitude and depth of earthquakes, based on zone division according to seismic information. The zone division stage is divided into two stages. The first stage is to divide the covered area based on regional administrative boundaries and clustering based on the earthquake disaster risk index (EDRI), and the second stage involves clustering based on magnitude and depth of earthquakes and distance between earthquake events using the K-Means and K-Medoids algorithms. The distribution of double parameter triggers is modeled using the Archimedean copula. The result obtained is that the price of SEB based on the clustering result of EDRI categories and K-Means is higher than the price obtained by clustering EDRI categories and K-Medoids with maturities of less than 5 years. The result of this research is expected to assist the Special Purpose Vehicle in determining the price of SEB.

Transfusion trigger in the critically ill with sepsis or septic shock: A prospective study.

Current practice around transfusion trigger in critically ill sepsis patients is not clear. Moreover, any association of haemoglobin trigger and other transfusion parameters such as age of red blood cells (RBCs) at transfusion and number of units of RBCs transfused with mortality and other adverse outcomes need further assessment. In this prospective study, patients aged 18-70 yr and admitted to intensive care with a diagnosis of sepsis were included (n=108). Baseline demographic, clinical and laboratory parameters were noted and various transfusion data, i.e., haemoglobin trigger, number of units of RBCs and the age of RBCs were recorded. Following outcome data were collected: 28 and 90 day mortality, duration of mechanical ventilation, vasopressor therapy, intensive care unit (ICU) and hospital stay and requirement of renal replacement therapy. Of the total 108 participants, 78 (72.2%) survived till 28 days and 66 (61.1%) survived till 90 days. Transfusion trigger was 6.9 (6.7-7.1) g/dl [median (interquartile range)]. On multivariable logistic regression analysis, acute physiology and chronic health evaluation (APACHE) II [adjusted odds ratio (aOR) (95% confidence interval {CI}): 0.86 (0.78, 0.96); P=0.005], cumulative fluid balance (CFB) [aOR (95% CI): 0.99 (0.99, 0.99); P=0.005] and admission platelet count [aOR (95% CI): 1.69 (1.01, 2.84); P=0.043] were the predictors of 28 day mortality [model area under the receiver operating characteristics (AUROC) 0.81]. APACHE II [aOR (95% CI): 0.88 (0.81, 0.97); P=0.013], CFB [a OR (95% CI): 0.99977 (0.99962, 0.99993); P=0.044] and transfusion trigger [aOR (95% CI): 3 (1.07, 8.34); P=0.035] were the predictors of 90 day mortality (model AUROC: 0.82). In sepsis, patients admitted to the ICU, current practice suggests transfusion trigger is below 7 g/dl and it does not affect any adverse outcome including 28 day mortality.

Dynamic event-triggered security control for networked T-S fuzzy system with non-uniform sampling

This paper is focused on the security control problem for the dynamic event-triggered networked T-S fuzzy control system with non-uniform sampling and stochastic network attacks. Different from existing research, the signals from sensor are sampled in a non-uniform period. To improve communication, a novel dynamic event-triggered method is proposed, which can adjust the trigger parameters dynamically according to the changes of external output. Then, a T-S fuzzy dynamic output feedback control model with event-triggered mechanism and network attacks is constructed. Furthermore, by introducing a set of slack matrix, the asynchronous premise variable problem caused by event-triggered method is solved, and some sufficient conditions that insure the asymptotic stability with an H∞ performance requirement of the established closed-loop system can be obtained by utilizing Lyapunov function method and stochastic analysis technique. Moreover, the gains of the fuzzy dynamic output feedback controllers are found by solving the linear matrix inequalities (LMIs). Finally, two practical examples are given to support the merits and effectiveness of the proposed method.

A forecasting tool for predicting asthma related emergency department visits and hospitalizations using heterogeneous data sources

New Zealand (NZ) is one of the countries with the highest prevalence and mortality rate due to asthma. Organisation for Economic Co-operation and Development (OECD) statistics [3] indicate NZ has one of the highest hospital admission rates for asthma of OECD countries. According to recent records [1], on average 77 New Zealanders each year lost their battle to asthma. Furthermore, the cost burden of asthma to the NZ economy has not improved over the years [2]. Not much work has been accomplished in the direction of population health forecasting for asthma in NZ. The aim of this study is to develop a prediction model for anticipating asthma-related hospitalisations in Auckland, NZ while exploiting diverse heterogeneous data sources. These sources accounts for triggers that exacerbate asthma conditions like meteorological and air quality factors plus the social media resource accessed through Google search trends. The research work is divided into two parts; in the first phase I analysed the relationship between trigger parameters and asthma using the Pearson Correlation Coefficient. Subsequently, in part two a comparative examination has been conducted based upon the prediction models developed using different machine learning techniques on the continuous data of 1097 days. The experimental evaluation shows that the best forecasting tool could predict asthma-related hospitalisation with an accuracy of 78.87% while the precision and recall for the model were 79.80% and 78.87%. The outcome achieved from implementing such a model would be beneficial for public health surveillance, thereby helping in more efficient and timely healthcare resource deployment.

Retrospective validation of the postnatal Growth and Retinopathy of Prematurity (G-ROP) criteria in a Swiss cohort

BackgroundCurrently used screening criteria for retinopathy of prematurity (ROP) show high sensitivity for predicting treatment-requiring ROP but low specificity; over 90% of examined infants do not develop ROP that requires treatment (type 1 ROP). A novel weight gain-based prediction model was developed by the G-ROP study group to increase the specificity of the screening criteria and keep the number of ophthalmic examinations as low as possible. This retrospective cohort study aimed to externally validate the G-ROP screening criteria in a Swiss cohort.MethodsData from 645 preterm infants in ROP screening at Inselspital Bern between January 2015 and December 2019 were retrospectively retrieved from the screening log and analysed. The G-ROP screening criteria, consisting of 6 trigger parameters, were applied in infants with complete data. To determine the performance of the G-ROP prediction model for treatment-requiring ROP, sensitivity and specificity were calculated.ResultsComplete data were available for 322 infants who were included in the analysis. None of the excluded infants had developed type 1 ROP. By applying the 6 criteria in the G-ROP model, 214 infants were flagged to undergo screening: among these, 14 developed type 1 ROP, 9 developed type 2 ROP, and 43 developed milder stages of ROP. The sensitivity for predicting treatment-requiring ROP was 100% (CI, 0.79–1.00), and the specificity was 41% (CI, 0.35 –0.47). Implementing the novel G-ROP screening criteria would reduce the number of infants entering ROP screening by approximately one third.ConclusionsThe overall prevalence of treatment-requiring ROP was low (2.15%). Previously published performance parameters for the G-ROP algorithm were reproducible in this Swiss cohort. Importantly, all treatment-requiring infants were correctly identified. By using these novel criteria, the burden of screening examinations could be significantly reduced.

Observer‐based adaptive event‐triggered PID control for networked systems under aperiodic DoS attacks

AbstractIn the present article, the observer‐based proportional integral derivative (PID) security control problem is investigated for a networked system subject to aperiodic denial‐of‐service (DoS) attacks. First, by carefully modeling cyberattacks as aperiodic DoS attacks, a suitable switching observer is constructed to solve the state reconstruction problem. Second, to improve the utilization of network resources while resisting the impact of attacks, an adaptive event‐triggered communication protocol is introduced, whose trigger parameters can be dynamically adjusted according to the set learning rules. Third, based on the above analysis, an error switching system model is established, which describes the impact of the adaptive triggered protocol and aperiodic attacks in a unified framework. Then, by employing the switched system analysis method, sufficient conditions are obtained to ensure the error switched system being asymptotically stable. Furthermore, by means of orthogonal decomposition technique, a codesign method of the observer gains, triggered parameter, and PID controller gains is given. Finally, a simulation example is provided to illustrate the efficiency and applicability of the obtained results.

Observer-Based Leader-Following Consensus of General Linear Multiagent Systems Based on Novel Event Trigger Mechanism with Input Time Delay under Directed Graphs

This paper considers the tracking and containment consensus for the general linear systems with input time delays under directed communication networks. The distributed observer-based algorithm on the basis of event-triggering mechanism will be designed by using only neighboring agents information. In this way, we can save network resource effectively. The event-based protocol with input time delays will be proposed for the leader-follower systems. Appropriate feedback gain matrices and trigger parameters can be designed by using Lyapunov stability theory. Based on the designed control algorithm, if the feedback gain matrices and the event trigger are designed appropriately, the leader-follower general linear system can eventually reach tracking and containment consensus. Then, two simulation results are provided to demonstrate the practicability of the theoretical analysis.

Dynamic event‐triggered H∞ load frequency control for multi‐area power systems with communication delays

AbstractThis paper investigates dynamic event‐triggered H∞ load frequency control for multi‐area power systems with communication delays. A novel co‐design method of trigger parameters and H∞ controller based on output feedback is proposed. Unlike routine multiplication operations, this method eliminates rank constraints on the input and output matrices, and avoids the processing of nonlinear terms related to the trigger parameters. Furthermore, under the presented dynamic event‐triggered mechanism, the threshold parameters are dynamically regulated according to the dynamic variation of the system. Therefore, the average data releasing rate can be adjusted in real time to better coordinate the use of network bandwidth resources and requirements of system dynamic performance. Specially, the usage of the infinite‐series‐based inequality and reciprocally convex combination inequality effectively reduces conservatism of the criteria. Finally, some simulations and related comparisons are carried out to demonstrate superiorities of the proposed method.

Event-triggered sliding mode control of networked control systems with Markovian jump parameters

This paper deals with sliding mode control for networked Markovian jump systems with partially-known transition probabilities via an event-triggered scheme to reduce network bandwidth usage and save network resources. First, based on the analysis of event-triggered scheme and sliding mode control, a corresponding time-delay system model is constructed by investigating the effect of the network transmission delay. Then, the less-conservative mean-square asymptotic stability of the overall closed-loop system is established. Sufficient conditions are obtained to co-design both the switching function and trigger parameters. Simultaneously, a novel control scheme is used to handle the Markovian jump parameters. Cases of completely known or unknown probabilities transition probabilities are also presented. Furthermore, the reachability of the sliding surface under the event-triggered sliding mode controller is analyzed using the Lyapunov stability theory. Finally, simulation results are provided to verify the effectiveness of the proposed design schemes.