Set Membership Research Articles

Data-driven control of echo state-based recurrent neural networks with robust stability guarantees

In this work we propose a new data-based approach for robust controller design for a rather general class of recurrent neural networks affected by bounded measurement noise. We first identify the model set compatible with available data in a selected model class via set membership (SM). Then, incremental input-to-state stability and desired performances for the closed loop system are enforced robustly to all models in the identified model set via a linear matrix inequality (LMI) optimization problem. Numerical results show the effectiveness of the comprehensive method.

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

Abstract 4138373: Machine learning identifies clinically distinct phenotypes in patients with aortic regurgitation.

Background: Aortic regurgitation (AR) is a prevalent valve disease with a long latent period to symptoms. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation. Method: We sought to evaluate the role of unsupervised cluster analyses in identifying different clinical clusters, including clinical status, and a large number of echocardiographic variables including left ventricular (LV) volumes, and their association with mortality. Patients with ≥moderate-severe chronic AR identified using echocardiography at Mayo Clinic, Rochester were retrospectively analyzed. Primary outcome was all-cause mortality censored at aortic valve surgery/last follow-up. Uniform Manifold Approximation and Projection (UMAP) with K -means algorithm was used to cluster patients using clinical and, echocardiographic variables at the time of presentation. Missing data were imputed with the Multiple Imputation by Chained Equations (MICE) method. A supervised approach trained on the training set was used to find cluster membership in a hold-out validation set. Log-rank tests were used to assess differences in mortality rates between the clusters, both in the training and validation sets. Results: Three distinct clusters were identified among 1100 patients (log-rank P for survival <0.001). Cluster 1 (n=337), which included younger males with severe AR but fewer symptoms, showed the best survival, 75.6% (69.5, 82.3). Cluster 2 (n=235), older and more females with elevated filling pressures, showed intermediate survival of 64.2 % (56.8, 72.5). Cluster 3 (n=253), characterized by severe symptomatic AR, demonstrated the lowest survival of 45.3 % (34.4, 59.8) at 5 years. Similar clusters were formed in the internal validation cohort. Conclusion: Distinct clusters with variable echocardiographic features and mortality differences exist within patients with chronic ≥moderate-severe AR. Recognizing these clusters can refine individual risk stratification and clinical decision-making after verification in future prospective studies.

Values as Master Identities within Identity Structures

A long-standing sociological puzzle deals with how societies shape individuals and their identities. Sociologists have theorized that identities are organized around core elements, termed “master identities,” that influence the adoption and expression of other identities. In this study, I examine the position of values as master identities within identity frameworks. I investigate the link between conservation and self-transcendence values and holding a diverse set of voluntary memberships. Additionally, I explore whether values mediate the relationship between age, gender, and memberships in voluntary organizations. Using multilevel structural equation models and data from the European Social Survey 2002, I find that conservation and self-transcendence are both directly associated with holding memberships across domains and mediate the relationship between both age and gender and voluntary memberships.

Evolutionary dynamics of stochastic games in set-structured populations.

In structured populations, the ecology of games may vary over neighborhoods. The effect of the ecological variations on population dynamics remains largely unknown. We here incorporate the ecological variations into the set-structured populations to explore the coevolutionary dynamics of the ecology and cooperation. Individuals of a population are distributed over sets. Interactions occur in the form of evolutionary games. When two individuals share more common sets, they play the weak prisoner's dilemma. Otherwise, they play the strong prisoner's dilemma. Both the set memberships and the strategy update in the evolutionary process. Changes in set memberships hold sway over the games to be played, which, in turn, influences the performance of strategies. Combining evolutionary set theory and random walks on graphs, we derived the conditions for cooperation to be selected under the weak selection limit. We find that a denser set-structured population increases the probability of individuals participating in a weak prisoner's dilemma, and thereby promoting the spread of cooperation. Properly modulating the population structure and the payoff feedback can further lower the critical benefit-cost ratio required for cooperation to be selected. Our results may help better understand the effects of ecological variations in enhancing cooperative behavior in set-structured populations.

Machine Learning Identifies Clinically Distinct Phenotypes in Patients with Aortic Regurgitation.

Aortic regurgitation (AR) is a prevalent valve disease with a long latent period to symptoms. Recent data has suggested the role of novel markers of myocardial overload in assessing onset of decompensation. We sought to evaluate the role of unsupervised cluster analyses in identifying different clinical clusters, including clinical status, and a large number of echocardiographic variables including left ventricular (LV) volumes, and their association with mortality. Patients with ≥moderate-severe chronic AR identified using echocardiography at Mayo Clinic, Rochester were retrospectively analyzed. Primary outcome was all-cause mortality censored at aortic valve surgery/last follow-up. Uniform Manifold Approximation and Projection (UMAP) with K-means algorithm was used to cluster patients using clinical and, echocardiographic variables at the time of presentation. Missing data were imputed with the Multiple Imputation by Chained Equations (MICE) method. A supervised approach trained on the training set was used to find cluster membership in a hold-out validation set. Log-rank tests were used to assess differences in mortality rates between the clusters, both in the training and validation sets. Three distinct clusters were identified among 1100 patients (log-rank P for survival <0.001). Cluster 1 (n=337), which included younger males with severe AR but fewer symptoms, showed the best survival, 75.6% (69.5, 82.3). Cluster 2 (n=235), older and more females with elevated filling pressures, showed intermediate survival of 64.2 % (56.8, 72.5). Cluster 3 (n=253), characterized by severe symptomatic AR, demonstrated the lowest survival of 45.3 % (34.4, 59.8) at 5 years. Similar clusters were formed in the internal validation cohort. Distinct clusters with variable echocardiographic features and mortality differences exist within patients with chronic ≥moderate-severe AR. Recognizing these clusters can refine individual risk stratification and clinical decision-making after verification in future prospective studies.

Single-Beacon Localization for Mobile Robot: A Set Membership Filtering Approach

Single-Beacon Localization for Mobile Robot: A Set Membership Filtering Approach

Research on Navigation Risk Assessment of Unmanned Ship Under Complex Navigation Conditions

With the research on unmanned ships conducted by the International Maritime Organization, the European Union and China, the navigation risk of unmanned ships has become a hot topic around the world. Based on the research and development history and current situation of unmanned ships at home and abroad, focusing on the three key elements of “Unmanned Ship, shore-based control personnel, external navigation environment”, this study establishes a navigation risk index system for unmanned ships under complex navigation conditions, considering the particularity of the unmanned ship, its perception ability, environmental understanding, situation judgment, communication ability and other indicators. The analytic hierarchy process is used to solve the weight of all kinds of navigation risk factors, and a consistency test of the established index system is carried out. Through expert investigation, the fuzzy membership set of risk indexes is established, and a fuzzy comprehensive evaluation model is established to evaluate the navigation risk of unmanned ships under complex navigation conditions. To avoid the situation of single-factor information being inundated, this study adopts the method of fuzzy comprehensive evaluation from a low level to a high level, and it verifies the algorithm through cases, which proves the validity and rationality of the proposed risk assessment method.

Decision-making techniques based on aggregation operators and similarity measures under q-rung orthopair hesitant fuzzy connection numbers and their application

The concept of q-rung orthopair hesitant fuzzy set represents an advancement and extension of hesitant fuzzy sets, encompassing both fuzzy sets and q-rung orthopair fuzzy sets. q-rung orthopair hesitant fuzzy set characterizes a set of membership and non-membership grades within the interval [0, 1], which enhances its adaptability compared to existing methods. This flexibility proves invaluable in providing more insightful data about various objects. The primary objective of this research is to introduce a decision-making technique in the context of q-RHF using the theory of set pair analysis (SPA). q-RHFS effectively handles ambiguous data by incorporating membership and non-membership grades, while the connection number (CN) based on SPA theory manages the intricacies of uncertainty and certainty structures by relying on "identity", "discrepancy" and "contrary" grades. Building on the relationship between q-RHFS and the connection number of set pair analysis, a comprehensive framework known as q-rung hesitant fuzzy connection number set (qHCNs) is developed. This model not only addresses uncertainty, but also offers valuable insights. Furthermore, this research introduces similarity measures derived from qHCN and examines their advantages through illustrative examples. Additionally, a novel approach to decision modeling utilizing these measures applied to medical diagnosis is also introduced. The application of this established model contributes to an effective approach and demonstrated its soundness and efficiency. In addition, a detailed comparative study is conducted with the existing models and advantages of proposed model. The research concludes with a summary of the authors' findings, highlighting the consistency and effectiveness of their work.

Intuitionistic fuzzy least squares MLTSVM for noisy label data using label-specific features and local label correlation

The multilabel twin support vector machine (MLTSVM) has been widely applied to multilabel classification fields because of its excellent classification performance, but it has the following three disadvantages. (a) In practical classification tasks, due to human negligence or some objective factors, there are many data with noise labels. Noisy label data have a negative effect on generating classification hyperplanes. However, MLTSVM cannot effectively reduce the negative effects on noisy label data. (b) Each label has its own specific features. However, for each label, MLTSVM can only use the same feature representation for classification. Therefore, it cannot obtain the optimal classification results. (c) In multilabel classification problems, there is a general correlation among the labels. However, when MLTSVM constructs a classification hyperplane for each label, it ignores the label correlation information. To address the above disadvantages, in this paper, a novel algorithm is proposed the called intuitionistic fuzzy least squares MLTSVM for noisy label data using label-specific features and local label correlation (SFLLC-IFLSMLTSVM). First, SFLLC-IFLSMLTSVM constructs an intuitionistic fuzzy set for each label; second, SFLLC-IFLSMLTSVM exploits the membership and nonmembership degrees in the intuitionistic fuzzy set to filter out the samples with noise labels and then performs clustering analysis to extract the label-specific features and the structure information of samples; finally, SFLLC-IFLSMLTSVM mines the local label correlation information in the process of constructing classification hyperplanes and exploits the membership and nonmembership degrees to suppress the negative effects of noise labels. Extensive comparative experiments on multilabel datasets show that SFLLC-IFLSMLTSVM can efficiently handle the multilabel classification problems with noise labels.

Sistem Pendukung Keputusan Pembelian Rumah Menggunakan Metode Fuzzy dan Metode Simple Multi Attribute Rating Technique (Smart)

Home is one of the primary needs of society. Various considerations of a person in buying a house, including considerations in terms of accessibility, environmental safety, road infrastructure and drainage, price, size and layout, building structure and model, availability of internet connection and signal layout, building structure and model, availability of internet connection and cell phone signal, availability of water and electricity, legal availability of water and electricity, legality, distance to workplace, and public facilities.public facilities. The fuzzy method is able to be used to classify the set of membership criteria and the SMART method can be used to rank the value of the criteria used criteria used. Calculations that have been carried out starts from using a fuzzy method that is useful for finding the set of membership criteria, followed by calculating the membership set of criteria, followed by calculating the matrix and ranking with the SMART method. ranking with the SMART method. Ranking with the highest value is house 2 with a value of 87.16 followed by house 3 with a value of 85.87 followed by house 1 with a value of 84.92 and the last house 4 with a value of 72.13

Shadowed set approximations of L-fuzzy sets

Pedrycz shadowed sets are three-way approximations of fuzzy sets by transforming the infinite levels of fuzzy set membership grades in the unit interval [0,1] into three levels. The three levels represent qualitatively the sets of the white, grey, and black members of a shadowed set. In this paper, we generalize the notion of shadowed sets to the case of L-fuzzy sets by making three new contributions. First, we consider two representations of a shadowed set. One is a three-valued L-fuzzy set and the other is three pairwise disjoint sets. Second, we introduce two methods for constructing a shadowed set. One divides a finite lattice based on the notion of a pair of a set of designated core membership grades and a set of designated null membership grades. The other uses a pair of threshold sets, which generalizes the method that uses a pair of thresholds. We study formal properties of the two methods and show that they are equivalent. Finally, based on a distance function on a lattice, we present a simple method to build the sets of designated core and null membership grades.

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

Background:The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals. Method:The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal. Result:The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108–142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis. Conclusion:The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.

Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study

Given the urgent need to simplify the end-of-line tuning of complex vehicle dynamics controllers, the Twin-in-the-Loop Control (TiL-C) approach was recently proposed in the automotive field. In TiL-C, a digital twin is run in real time on-board the vehicle to compute a nominal control action; an additional controller is used to compensate for the mismatch between the simulator and the actual vehicle. As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only. In this paper, we investigate the use of different black-box optimization techniques for the calibration of the compensator. More specifically, we compare the initially proposed Bayesian Optimization (BO) approach with Virtual Reference Feedback Tuning (VRFT), a one-shot direct data-driven design method, and with Set Membership Global Optimization (SMGO), a recently proposed black-box optimization method. The analysis will be carried out within a professional multibody simulation environment on a novel TiL-C application case study – the yaw-rate tracking problem – to further prove the TiL-C effectiveness on a challenging problem. Simulations will show that the VRFT approach is capable of providing a well-tuned controller after a single iteration, while 10 to 15 iterations are necessary for refining it with global optimizers. Also, SMGO is shown to reduce the computational effort required by BO significantly.

Dynamic State Estimation for Distribution Networks Based on Adaptive Set Membership Filter Under Unknown But Bounded Noise Environments

Dynamic State Estimation for Distribution Networks Based on Adaptive Set Membership Filter Under Unknown But Bounded Noise Environments

Measuring Employment and Unemployment from a Fuzzy Perspective. A Comparative Analysis Across European Countries

According to the standards set by the International Labour Office, people of working age who have performed some remunerated work during a specified short reference period are classified as employed, regardless of how many hours they have worked, while those who have not been able to work at all (despite wanting to) are classified as unemployed. As observed by many experts in the field, this rigid division between employed and unemployed can conceal labour markets with deeply different characteristics. In particular, the average number of hours worked and, more importantly, their distribution across employees may vary significantly across countries. The aim of this paper is to define fuzzy indicators of employment and unemployment by using the available information on the number of hours worked and the satisfaction or dissatisfaction of workers with this. In this approach, each person in the labour force is assigned a degree of employment between 0 and 1, where 0 means full non-membership in the fuzzy set of employed (and full membership in the fuzzy set of unemployed) and 1 means full membership in the fuzzy set of employed. To show the potentiality of the proposed method, we apply fuzzy measures to Labour Force Survey data from 29 European countries and compare the results with the official employment and unemployment statistics published by Eurostat.

Adaptive suboptimal control of some discrete-time plants with nonstochastic bounded disturbances

This paper deals with the adaptive suboptimal control of linear, discrete-time, time-invariant, minimum phase, scalar plants in the presence of nonstochastic bounded unmeasurable disturbances whose upper and lower bounds, which may be asymmetric, are assumed to be unknown a priori. Additional assumption is that an order of the difference equation describing the plant is known a priori. The distinguishing feature of the problem stated in this paper is that neither bounds on the unmeasured disturbances, nor bounds on an allowable region to which the unknown plant parameters belong are assumed to be known a priori. To solve this problem, adaptation procedures for the point and membership set estimation are utilized. The standard recursive procedure with adjustable dead zone is employed in order to derive the point estimates of unknown plant parameters together with the point estimate of time-invariant disturbance component. The size of this dead zone depends on the previous point estimate of the bounds on the time-varying disturbance component and also on a fixed suboptimality index chosen by the designer. The estimates generated by the point estimation procedure are directly exploited to derive the adaptive control law. The main idea advanced in this paper is that, instead of unknown a priori membership set of these parameters, their peculiar hypothetical a posteriori membership sets are designed via the use of the measured system’s signals together with the current point estimate of bounds on the time-varying disturbance component. Contrary to the usual membership set estimation approach, this set is updated if only it is discovered that the unknown parameter vector does not belong in reality to this set. To this end, a remarkable property of the point estimation procedure is utilized. Such an approach makes it possible to reconstruct this set and to update the previous estimate of the bounds on the time-varying disturbance component. The finite convergence of the adaptation procedures and also the ultimate boundedness of system’s signals are established. To demonstrate an efficiency of the adaptive controller and support the theoretical study, simulation results are presented.

Traceable mixnets

We introduce the notion of traceable mixnets. In a traditional mixnet, multiple mix-servers jointly permute and decrypt a list of ciphertexts to produce a list of plaintexts, along with a proof of correctness, such that the association between individual ciphertexts and plaintexts remains completely hidden. However, in many applications, the privacy-utility tradeoff requires answering some specific queries about this association, without revealing any information beyond the query result. We consider queries of the following types: a) given a ciphertext in the mixnet input list, whether it encrypts one of a given subset of plaintexts in the output list, and b) given a plaintext in the mixnet output list, whether it is a decryption of one of a given subset of ciphertexts in the input list. Traceable mixnets allow the mix-servers to jointly prove answers to the above queries to a querier such that neither the querier nor a threshold number of mix-servers learn any information beyond the query result. Further, if the querier is not corrupted, the corrupted mix-servers do not even learn the query result. We first comprehensively formalise these security properties of traceable mixnets and then propose a construction of traceable mixnets using novel distributed zero-knowledge proofs (ZKPs) of set membership and of a statement we call reverse set membership. Although set membership has been studied in the single-prover setting, the main challenge in our distributed setting lies in making sure that none of the mix-servers learn the association between ciphertexts and plaintexts during the proof. We implement our distributed ZKPs and show that they are faster than state-of-the-art by at least one order of magnitude.

METHOD OF HIGH-PRECISION NEURO-FUZZY SYSTEMS DEFUZZIFIERS’ AUTOMATIC CONSTRUCTION

Increasing the precision of neuro-fuzzy approximation systems is considered. Modification of defuzzifier, which allows producing multiple types of approximating functions is presented. RMSE (Root Mean Square Error), maximal error, training time and models’s size are used to evaluate the efficiency of proposed method. Post-processing functions were also considered as a tool to increase approximation precision. Common solution to this problem is a modification of model’s fuzzy sets (membership functions and their number), which significantly increases model’s size and complexity. Given approach uses search algorithms to find the configuration of defuzzification layer, which produces the best approximation precision for a given function and fuzzy sets configuration, since all possible configurations are checked. It is possible to model classic approximation methods and combine them. Possible modifications of presented search algorithms application are described. Search depth can be limited in order to reduce the size of produced model. The computing experiment, aimed at finding best approximation precision was carried out. Presented method provided lowering of RMSE without significant increasing of model’s size. Using Fourier series components in resulting expression can be used for precise periodic functions approximation or for general precision improvement. Using post-processing functions provided significant improvement in precision for multiplication approximation, which proves, that it can be used as another tool to be used to reduce errors in some specific cases.

Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock.

Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. We sought to the determine reproducibility of the data-driven "persistent hypoxemia, encephalopathy, and shock" (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk strata. We retrained and validated a random forest classifier using organ dysfunction subscores in the 2012-2018 electronic health record (EHR) dataset used to derive the PHES phenotype. We used this classifier to assign phenotype membership in a test set consisting of prospectively (2003-2023) enrolled pediatric septic shock patients. We compared profiles of the PERSEVERE family of biomarkers among those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk strata. Twenty-five PICUs across the United States. EHR data from 15,246 critically ill patients with sepsis-associated MODS split into derivation and validation sets and 1,270 pediatric septic shock patients in the test set of whom 615 had complete biomarker data. None. The area under the receiver operator characteristic curve of the modified classifier to predict PHES phenotype membership was 0.91 (95% CI, 0.90-0.92) in the EHR validation set. In the test set, PHES phenotype membership was associated with both increased adjusted odds of complicated course (adjusted odds ratio [aOR] 4.1; 95% CI, 3.2-5.4) and 28-day mortality (aOR of 4.8; 95% CI, 3.11-7.25) after controlling for age, severity of illness, and immunocompromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and were more likely to be stratified as high risk based on PERSEVERE biomarkers predictive of death and persistent MODS. The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlapped with higher risk strata based on prospectively validated biomarker approaches.