Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating du...Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.展开更多
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h...Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.展开更多
Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel perf...Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.展开更多
In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult...In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration.展开更多
1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf h...1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.展开更多
Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overloo...Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overlooking instancewise complexity variations.Moreover,owing to pruning or decomposition techniques,the upper bound of network representation capabilities might be permanently diminished.In this work,an input-dependent multiscale dynamic inference method(MSDI)is developed to strike a better balance between model performance and inference acceleration.Specifically,we modify the main body of a convolutional network to obtain a series of parameter-sharing subnetworks with varying levels of complexity.A side branch structure is then introduced to assign an input instance to a suitable subnetwork as its inference route,and we expect to accelerate the inference by assigning the easy input to the subnetwork with low capacity.We further propose multiscale distillation training to optimize the training of the modified subnetworks.Additionally,we compare the entropy-based and learning-based grading approaches,aiming to obtain a more suitable route assignment method.Experiments show that MSDI can accelerate most existing convolutional models,achieving up to 74.7%computation savings across diverse datasets.展开更多
Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed ...Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.展开更多
Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’...Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s edge.However,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models challenging.Additionally,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors.These factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these topics.Consequently,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using TinyML.It identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software frameworks.In the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning strategies.In the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major sectors.As a result,this article provides valuable insights for researchers,practitioners,and developers.It assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.展开更多
Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operatio...Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operations,owing to their malleability and versatility.However,the computation-intensive and latency-sensitive natures of CNNs present a formidable obstacle to their deployment on resource-constrained UAVs.Some early studies have explored a hybrid approach that dynamically switches between lightweight and complex models to balance accuracy and latency.However,they often overlook scenarios involving multiple concurrent CNN streams,where competition for resources between streams can substantially impact latency and overall system performance.In this paper,we first investigate the deployment of both lightweight and complex models for multiple CNN streams in UAV swarm.Specifically,we formulate an optimization problem to minimize the total latency across multiple CNN streams,under the constraints on UAV memory and the accuracy requirement of each stream.To address this problem,we propose an algorithm called Adaptive Model Switching of collaborative inference for MultiCNN streams(AMSM)to identify the inference strategy with a low latency.Simulation results demonstrate that the proposed AMSM algorithm consistently achieves the lowest latency while meeting the accuracy requirements compared to benchmark algorithms.展开更多
In order to solve the problems of high experimental cost of ammunition,lack of field test data,and the difficulty in applying the ammunition hit probability estimation method in classical statistics,this paper assumes...In order to solve the problems of high experimental cost of ammunition,lack of field test data,and the difficulty in applying the ammunition hit probability estimation method in classical statistics,this paper assumes that the projectile dispersion of ammunition is a two-dimensional joint normal distribution,and proposes a new Bayesian inference method of ammunition hit probability based on normal-inverse Wishart distribution.Firstly,the conjugate joint prior distribution of the projectile dispersion characteristic parameters is determined to be a normal inverse Wishart distribution,and the hyperparameters in the prior distribution are estimated by simulation experimental data and historical measured data.Secondly,the field test data is integrated with the Bayesian formula to obtain the joint posterior distribution of the projectile dispersion characteristic parameters,and then the hit probability of the ammunition is estimated.Finally,compared with the binomial distribution method,the method in this paper can consider the dispersion information of ammunition projectiles,and the hit probability information is more fully utilized.The hit probability results are closer to the field shooting test samples.This method has strong applicability and is conducive to obtaining more accurate hit probability estimation results.展开更多
Associations of per-and polyfluoroalkyl substances(PFAS)on lipid metabolism have been documented but research remains scarce regarding effect of PFAS on lipid variability.To deeply understand their relationship,a step...Associations of per-and polyfluoroalkyl substances(PFAS)on lipid metabolism have been documented but research remains scarce regarding effect of PFAS on lipid variability.To deeply understand their relationship,a step-forward in causal inference is expected.To address these,we conducted a longitudinal study with three repeated measurements involving 201 participants in Beijing,among which 100 eligible participants were included for the present study.Twenty-three PFAS and four lipid indicators were assessed at each visit.We used linear mixed models and quantile g-computation models to investigate associations between PFAS and blood lipid levels.A latent class growth model described PFAS serum exposure patterns,and a generalized linear model demonstrated associations between these patterns and lipid variability.Our study found that PFDA was associated with increased TC(β=0.083,95%CI:0.011,0.155)and HDL-C(β=0.106,95%CI:0.034,0.178).The PFAS mixture also showed a positive relationship with TC(β=0.06,95%CI:0.02,0.10),with PFDA contributing most positively.Compared to the low trajectory group,the middle trajectory group for PFDA was associated with VIM of TC(β=0.756,95%CI:0.153,1.359).Furthermore,PFDA showed biological gradientswith lipid metabolism.This is the first repeated-measures study to identify the impact of PFAS serum exposure pattern on the lipid metabolism and the first to estimate the association between PFAS and blood lipid levels in middle-aged and elderly Chinese and reinforce the evidence of their causal relationship through epidemiological studies.展开更多
In recent years,the world has seen an exponential increase in energy demand,prompting scientists to look for innovative ways to exploit the power sun’s power.Solar energy technologies use the sun’s energy and light ...In recent years,the world has seen an exponential increase in energy demand,prompting scientists to look for innovative ways to exploit the power sun’s power.Solar energy technologies use the sun’s energy and light to provide heating,lighting,hot water,electricity and even cooling for homes,businesses,and industries.Therefore,ground-level solar radiation data is important for these applications.Thus,our work aims to use a mathematical modeling tool to predict solar irradiation.For this purpose,we are interested in the application of the Adaptive Neuro Fuzzy Inference System.Through this type of artificial neural system,10 models were developed,based on meteorological data such as the Day number(Nj),Ambient temperature(T),Relative Humidity(Hr),Wind speed(WS),Wind direction(WD),Declination(δ),Irradiation outside the atmosphere(Goh),Maximum temperature(Tmax),Minimum temperature(Tmin).These models have been tested by different static indicators to choose the most suitable one for the estimation of the daily global solar radiation.This study led us to choose the M8 model,which takes Nj,T,Hr,δ,Ws,Wd,G0,and S0 as input variables because it presents the best performance either in the learning phase(R^(2)=0.981,RMSE=0.107 kW/m^(2),MAE=0.089 kW/m2)or in the validation phase(R^(2)=0.979,RMSE=0.117 kW/m^(2),MAE=0.101 kW/m^(2)).展开更多
The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,direc...The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.展开更多
Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult f...Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.展开更多
Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-len...Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-length fields,and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance.Hence,this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty.Specifically,we design four related constructions between entropy changes and protocol field segmentation,introduce random variables,and construct joint probability distributions with traffic sample observations.Probabilistic inference is then performed to identify the possible protocol segmentation points.Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.展开更多
Chronic diseases are major causes of global death and disability,significantly impacting individual health and imposing economic burdens.This study aims to investigate the causal relationship between physical activity...Chronic diseases are major causes of global death and disability,significantly impacting individual health and imposing economic burdens.This study aims to investigate the causal relationship between physical activity(PA)and the development of chronic diseases.Using a Mendelian randomization(MR)approach,we incorporated average PA and its subtypes(more than 450000 participants)as exposure measures and eight chronic diseases as outcome measures.Data were obtained from the European Genome-Wide Association Study(GWAS).The primary causal analysis technique employed was the inverse variance weighting(IVW)method,with MR-Egger,weighted median,weighted mode,and simple mode used to validate the results.Sensitivity analyses were performed to assess heterogeneity and pleiotropy.The IVW approach results show that vigorous physical activity(VPA)is associated with a modest reduction in the risk of major coronary heart disease(OR=0.95,95%CI:0.91–0.99,p=0.01).The causal directions of the other four MR methods are consistent with this result and validated by sensitivity analysis.No substantial associations were found between other levels of PA and chronic disease.Our findings underscore the importance of VPA in preventive cardiology and suggest its potential role in public health initiatives.Further research should explore the impact of PA on different demographic groups and the doseresponse relationship of VPA on heart health.展开更多
Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that ...Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.展开更多
Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and so...Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack (PIA) against FedGNNs. Compared with prior works on centralized GNNs, in PIAFGNN, the attacker can only obtain the global embedding gradient distributed by the central server. The attacker converts the task of stealing the target user’s local embeddings into a regression problem, using a regression model to generate the target graph node embeddings. By training shadow models and property classifiers, the attacker can infer the basic property information within the target graph that is of interest. Experiments on three benchmark graph datasets demonstrate that PIAFGNN achieves attack accuracy of over 70% in most cases, even approaching the attack accuracy of inference attacks against centralized GNNs in some instances, which is much higher than the attack accuracy of the random guessing method. Furthermore, we observe that common defense mechanisms cannot mitigate our attack without affecting the model’s performance on mainly classification tasks.展开更多
Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken us...Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken using component failure rate data to predict PTC for a full stroke test and a partial stroke test.Given the subjective and uncertain aspects of the FMEDA approach,specifically the selection of component failure rates and the determination of the probability of detecting failure modes,a Fuzzy Inference System(FIS)was proposed to manage the data,addressing the inherent uncertainties.Fuzzy inference systems have been used previously for various FMEA type assessments,but this is the first time an FIS has been employed for use with FMEDA.ESDV PTC values were generated from both the standard FMEDA and the fuzzy-FMEDA approaches using data provided by FMEDA experts.This work demonstrates that fuzzy inference systems can address the subjectivity inherent in FMEDA data,enabling reliable estimates of ESDV proof test coverage for both full and partial stroke tests.This facilitates optimized maintenance planning while ensuring safety is not compromised.展开更多
Ensuring the safety,resilience,and reliability of overhead low-voltage(LV)and medium-voltage(MV)power cables in aging infrastructure is vital for maintaining essential services and mitigating risks in electrical power...Ensuring the safety,resilience,and reliability of overhead low-voltage(LV)and medium-voltage(MV)power cables in aging infrastructure is vital for maintaining essential services and mitigating risks in electrical power distribution systems.This study introduces a novel framework that integrates fault tree analysis(FTA)/failure mode and effect analysis(FMEA)and a fuzzy inference system(FIS)to systematically address performance variability and enhance the operational life of these critical assets while ensuring safety and resilience.This structured framework employs risk-informed decision-making as well as reliability-based safety and resilience assurance,incorporating international standards and best practices.Under this methodology,the FIS models uncertainties in asset performance,using expert-defined rules and membership functions to categorize risk levels and assess failure severity.A case study is performed on overhead MV power cables,specifically the"Racoon"allaluminum alloy conductor(AAAC),demonstrating its practical implementation.The results reveal that for typical environmental conditions and design parameters,the yearly reliability of the conductor is nearly 1,indicating compliance with IEC standards.Furthermore,a risk matrix is developed using current carrying capacity(CCC),a failure mode obtained from the FTA as the probability of occurrence,and power shutdown duration(PSD)as the severity indicator.The risk matrix estimates the potential risk(PR)level based on CCC and PSD inputs.For example,for CCC=260 A and PSD=50 min,the resulting PR is 12.9,indicating a moderate risk exists.These risk levels guide appropriate resilience actions,ensuring proactive power cable management by identifying critical risks,prioritizing mitigation measures,and ensuring adaptability and compliance.This approach not only addresses the challenges of aging infrastructure but also contributes to long-term system integrity and operational reliability.展开更多
文摘Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00518960)in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00563192).
文摘Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment.
文摘Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
基金supported by the National Natural Science Foundation of China(No.62201079)the Beijing Natural Science Foundation(No.L232051).
文摘In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration.
基金supported by the National Key Research and Development Program of China(2022YFB2703503)the National Natural Science Foundation of China(62293501,62525210,and 62293502)the China Scholarship Council(202306280318).
文摘1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.
文摘Modern compression and acceleration methods for exploring efficient deep neural networks render real-world applications more feasible.Existing approaches uniformly apply the same procedure to every input image,overlooking instancewise complexity variations.Moreover,owing to pruning or decomposition techniques,the upper bound of network representation capabilities might be permanently diminished.In this work,an input-dependent multiscale dynamic inference method(MSDI)is developed to strike a better balance between model performance and inference acceleration.Specifically,we modify the main body of a convolutional network to obtain a series of parameter-sharing subnetworks with varying levels of complexity.A side branch structure is then introduced to assign an input instance to a suitable subnetwork as its inference route,and we expect to accelerate the inference by assigning the easy input to the subnetwork with low capacity.We further propose multiscale distillation training to optimize the training of the modified subnetworks.Additionally,we compare the entropy-based and learning-based grading approaches,aiming to obtain a more suitable route assignment method.Experiments show that MSDI can accelerate most existing convolutional models,achieving up to 74.7%computation savings across diverse datasets.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82204396,82304491,and 82400511).
文摘Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.
文摘Edge Machine Learning(EdgeML)and Tiny Machine Learning(TinyML)are fast-growing fields that bring machine learning to resource-constrained devices,allowing real-time data processing and decision-making at the network’s edge.However,the complexity of model conversion techniques,diverse inference mechanisms,and varied learning strategies make designing and deploying these models challenging.Additionally,deploying TinyML models on resource-constrained hardware with specific software frameworks has broadened EdgeML’s applications across various sectors.These factors underscore the necessity for a comprehensive literature review,as current reviews do not systematically encompass the most recent findings on these topics.Consequently,it provides a comprehensive overview of state-of-the-art techniques in model conversion,inference mechanisms,learning strategies within EdgeML,and deploying these models on resource-constrained edge devices using TinyML.It identifies 90 research articles published between 2018 and 2025,categorizing them into two main areas:(1)model conversion,inference,and learning strategies in EdgeML and(2)deploying TinyML models on resource-constrained hardware using specific software frameworks.In the first category,the synthesis of selected research articles compares and critically reviews various model conversion techniques,inference mechanisms,and learning strategies.In the second category,the synthesis identifies and elaborates on major development boards,software frameworks,sensors,and algorithms used in various applications across six major sectors.As a result,this article provides valuable insights for researchers,practitioners,and developers.It assists them in choosing suitable model conversion techniques,inference mechanisms,learning strategies,hardware development boards,software frameworks,sensors,and algorithms tailored to their specific needs and applications across various sectors.
基金supported by the National Natural Science Foundation of China(No.61931011)the Jiangsu Provincial Key Research and Development Program,China(No.BE2021013-4)the Fundamental Research Project in University Characteristic Disciplines,China(No.ILF240071A24)。
文摘Unmanned Aerial Vehicles(UAVs)coupled with deep learning such as Convolutional Neural Networks(CNNs)have been widely applied across numerous domains,including agriculture,smart city monitoring,and fire rescue operations,owing to their malleability and versatility.However,the computation-intensive and latency-sensitive natures of CNNs present a formidable obstacle to their deployment on resource-constrained UAVs.Some early studies have explored a hybrid approach that dynamically switches between lightweight and complex models to balance accuracy and latency.However,they often overlook scenarios involving multiple concurrent CNN streams,where competition for resources between streams can substantially impact latency and overall system performance.In this paper,we first investigate the deployment of both lightweight and complex models for multiple CNN streams in UAV swarm.Specifically,we formulate an optimization problem to minimize the total latency across multiple CNN streams,under the constraints on UAV memory and the accuracy requirement of each stream.To address this problem,we propose an algorithm called Adaptive Model Switching of collaborative inference for MultiCNN streams(AMSM)to identify the inference strategy with a low latency.Simulation results demonstrate that the proposed AMSM algorithm consistently achieves the lowest latency while meeting the accuracy requirements compared to benchmark algorithms.
基金supported by the National Natural Science Foundation of China(No.71501183).
文摘In order to solve the problems of high experimental cost of ammunition,lack of field test data,and the difficulty in applying the ammunition hit probability estimation method in classical statistics,this paper assumes that the projectile dispersion of ammunition is a two-dimensional joint normal distribution,and proposes a new Bayesian inference method of ammunition hit probability based on normal-inverse Wishart distribution.Firstly,the conjugate joint prior distribution of the projectile dispersion characteristic parameters is determined to be a normal inverse Wishart distribution,and the hyperparameters in the prior distribution are estimated by simulation experimental data and historical measured data.Secondly,the field test data is integrated with the Bayesian formula to obtain the joint posterior distribution of the projectile dispersion characteristic parameters,and then the hit probability of the ammunition is estimated.Finally,compared with the binomial distribution method,the method in this paper can consider the dispersion information of ammunition projectiles,and the hit probability information is more fully utilized.The hit probability results are closer to the field shooting test samples.This method has strong applicability and is conducive to obtaining more accurate hit probability estimation results.
基金supported by the National Natural Science Foundation of China(No.82404365)the Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0513200)+7 种基金China Medical Board(No.15-230)China Postdoctoral Science Foundation(Nos.2023M730317and 2023T160066)the Fundamental Research Funds for the Central Universities(No.3332023042)the Open Project of Hebei Key Laboratory of Environment and Human Health(No.202301)the National Key Research and Development Program of China(No.2022YFC3703000)the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2022-JKCS-11)the CAMS Innovation Fund for Medical Sciences(No.2022-I2M-JB-003)the Programs of the National Natural Science Foundation of China(No.21976050).
文摘Associations of per-and polyfluoroalkyl substances(PFAS)on lipid metabolism have been documented but research remains scarce regarding effect of PFAS on lipid variability.To deeply understand their relationship,a step-forward in causal inference is expected.To address these,we conducted a longitudinal study with three repeated measurements involving 201 participants in Beijing,among which 100 eligible participants were included for the present study.Twenty-three PFAS and four lipid indicators were assessed at each visit.We used linear mixed models and quantile g-computation models to investigate associations between PFAS and blood lipid levels.A latent class growth model described PFAS serum exposure patterns,and a generalized linear model demonstrated associations between these patterns and lipid variability.Our study found that PFDA was associated with increased TC(β=0.083,95%CI:0.011,0.155)and HDL-C(β=0.106,95%CI:0.034,0.178).The PFAS mixture also showed a positive relationship with TC(β=0.06,95%CI:0.02,0.10),with PFDA contributing most positively.Compared to the low trajectory group,the middle trajectory group for PFDA was associated with VIM of TC(β=0.756,95%CI:0.153,1.359).Furthermore,PFDA showed biological gradientswith lipid metabolism.This is the first repeated-measures study to identify the impact of PFAS serum exposure pattern on the lipid metabolism and the first to estimate the association between PFAS and blood lipid levels in middle-aged and elderly Chinese and reinforce the evidence of their causal relationship through epidemiological studies.
文摘In recent years,the world has seen an exponential increase in energy demand,prompting scientists to look for innovative ways to exploit the power sun’s power.Solar energy technologies use the sun’s energy and light to provide heating,lighting,hot water,electricity and even cooling for homes,businesses,and industries.Therefore,ground-level solar radiation data is important for these applications.Thus,our work aims to use a mathematical modeling tool to predict solar irradiation.For this purpose,we are interested in the application of the Adaptive Neuro Fuzzy Inference System.Through this type of artificial neural system,10 models were developed,based on meteorological data such as the Day number(Nj),Ambient temperature(T),Relative Humidity(Hr),Wind speed(WS),Wind direction(WD),Declination(δ),Irradiation outside the atmosphere(Goh),Maximum temperature(Tmax),Minimum temperature(Tmin).These models have been tested by different static indicators to choose the most suitable one for the estimation of the daily global solar radiation.This study led us to choose the M8 model,which takes Nj,T,Hr,δ,Ws,Wd,G0,and S0 as input variables because it presents the best performance either in the learning phase(R^(2)=0.981,RMSE=0.107 kW/m^(2),MAE=0.089 kW/m2)or in the validation phase(R^(2)=0.979,RMSE=0.117 kW/m^(2),MAE=0.101 kW/m^(2)).
基金National Key Research and Development Program of China,Grant/Award Number:2020YFB1711704。
文摘The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints.Numerous methods use a partial-to-complete framework,directly predicting missing components via global characteristics extracted from incomplete inputs.However,this makes detail re-covery challenging,as global characteristics fail to provide complete missing component specifics.A new point cloud completion method named Point-PC is proposed.A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion.Concretely,a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format.The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain,enabling retrieval of analogous shapes from incomplete inputs.In addition,the authors employ backdoor adjustment to eliminate confounders,which are shape prior components sharing identical semantic structures with incomplete inputs.Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches.The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.
基金the financial support by the National Natural Science Foundation of China(Grant No.U24B2029)the Key Projects of the National Natural Science Foundation of China(Grant No.52334001)+1 种基金the Strategic Cooperation Technology Projects of CNPC and CUPB(Grand No.ZLZX2020-02)the China University of Petroleum,Beijing(Grand No.ZX20230042)。
文摘Offshore drilling costs are high,and the downhole environment is even more complex.Improving the rate of penetration(ROP)can effectively shorten offshore drilling cycles and improve economic benefits.It is difficult for the current ROP models to guarantee the prediction accuracy and the robustness of the models at the same time.To address the current issues,a new ROP prediction model was developed in this study,which considers ROP as a time series signal(ROP signal).The model is based on the time convolutional network(TCN)framework and integrates ensemble empirical modal decomposition(EEMD)and Bayesian network causal inference(BN),the model is named EEMD-BN-TCN.Within the proposed model,the EEMD decomposes the original ROP signal into multiple sets of sub-signals.The BN determines the causal relationship between the sub-signals and the key physical parameters(weight on bit and revolutions per minute)and carries out preliminary reconstruction of the sub-signals based on the causal relationship.The TCN predicts signals reconstructed by BN.When applying this model to an actual production well,the average absolute percentage error of the EEMD-BN-TCN prediction decreased from 18.4%with TCN to 9.2%.In addition,compared with other models,the EEMD-BN-TCN can improve the decomposition signal of ROP by regulating weight on bit and revolutions per minute,ultimately enhancing ROP.
文摘Protocol Reverse Engineering(PRE)is of great practical importance in Internet security-related fields such as intrusion detection,vulnerability mining,and protocol fuzzing.For unknown binary protocols having fixed-length fields,and the accurate identification of field boundaries has a great impact on the subsequent analysis and final performance.Hence,this paper proposes a new protocol segmentation method based on Information-theoretic statistical analysis for binary protocols by formulating the field segmentation of unsupervised binary protocols as a probabilistic inference problem and modeling its uncertainty.Specifically,we design four related constructions between entropy changes and protocol field segmentation,introduce random variables,and construct joint probability distributions with traffic sample observations.Probabilistic inference is then performed to identify the possible protocol segmentation points.Extensive trials on nine common public and industrial control protocols show that the proposed method yields higher-quality protocol segmentation results.
文摘Chronic diseases are major causes of global death and disability,significantly impacting individual health and imposing economic burdens.This study aims to investigate the causal relationship between physical activity(PA)and the development of chronic diseases.Using a Mendelian randomization(MR)approach,we incorporated average PA and its subtypes(more than 450000 participants)as exposure measures and eight chronic diseases as outcome measures.Data were obtained from the European Genome-Wide Association Study(GWAS).The primary causal analysis technique employed was the inverse variance weighting(IVW)method,with MR-Egger,weighted median,weighted mode,and simple mode used to validate the results.Sensitivity analyses were performed to assess heterogeneity and pleiotropy.The IVW approach results show that vigorous physical activity(VPA)is associated with a modest reduction in the risk of major coronary heart disease(OR=0.95,95%CI:0.91–0.99,p=0.01).The causal directions of the other four MR methods are consistent with this result and validated by sensitivity analysis.No substantial associations were found between other levels of PA and chronic disease.Our findings underscore the importance of VPA in preventive cardiology and suggest its potential role in public health initiatives.Further research should explore the impact of PA on different demographic groups and the doseresponse relationship of VPA on heart health.
文摘Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.
基金supported by the National Natural Science Foundation of China(Nos.62176122 and 62061146002).
文摘Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack (PIA) against FedGNNs. Compared with prior works on centralized GNNs, in PIAFGNN, the attacker can only obtain the global embedding gradient distributed by the central server. The attacker converts the task of stealing the target user’s local embeddings into a regression problem, using a regression model to generate the target graph node embeddings. By training shadow models and property classifiers, the attacker can infer the basic property information within the target graph that is of interest. Experiments on three benchmark graph datasets demonstrate that PIAFGNN achieves attack accuracy of over 70% in most cases, even approaching the attack accuracy of inference attacks against centralized GNNs in some instances, which is much higher than the attack accuracy of the random guessing method. Furthermore, we observe that common defense mechanisms cannot mitigate our attack without affecting the model’s performance on mainly classification tasks.
文摘Published proof test coverage(PTC)estimates for emergency shutdown valves(ESDVs)show only moderate agreement and are predominantly opinion-based.A Failure Modes,Effects,and Diagnostics Analysis(FMEDA)was undertaken using component failure rate data to predict PTC for a full stroke test and a partial stroke test.Given the subjective and uncertain aspects of the FMEDA approach,specifically the selection of component failure rates and the determination of the probability of detecting failure modes,a Fuzzy Inference System(FIS)was proposed to manage the data,addressing the inherent uncertainties.Fuzzy inference systems have been used previously for various FMEA type assessments,but this is the first time an FIS has been employed for use with FMEDA.ESDV PTC values were generated from both the standard FMEDA and the fuzzy-FMEDA approaches using data provided by FMEDA experts.This work demonstrates that fuzzy inference systems can address the subjectivity inherent in FMEDA data,enabling reliable estimates of ESDV proof test coverage for both full and partial stroke tests.This facilitates optimized maintenance planning while ensuring safety is not compromised.
文摘Ensuring the safety,resilience,and reliability of overhead low-voltage(LV)and medium-voltage(MV)power cables in aging infrastructure is vital for maintaining essential services and mitigating risks in electrical power distribution systems.This study introduces a novel framework that integrates fault tree analysis(FTA)/failure mode and effect analysis(FMEA)and a fuzzy inference system(FIS)to systematically address performance variability and enhance the operational life of these critical assets while ensuring safety and resilience.This structured framework employs risk-informed decision-making as well as reliability-based safety and resilience assurance,incorporating international standards and best practices.Under this methodology,the FIS models uncertainties in asset performance,using expert-defined rules and membership functions to categorize risk levels and assess failure severity.A case study is performed on overhead MV power cables,specifically the"Racoon"allaluminum alloy conductor(AAAC),demonstrating its practical implementation.The results reveal that for typical environmental conditions and design parameters,the yearly reliability of the conductor is nearly 1,indicating compliance with IEC standards.Furthermore,a risk matrix is developed using current carrying capacity(CCC),a failure mode obtained from the FTA as the probability of occurrence,and power shutdown duration(PSD)as the severity indicator.The risk matrix estimates the potential risk(PR)level based on CCC and PSD inputs.For example,for CCC=260 A and PSD=50 min,the resulting PR is 12.9,indicating a moderate risk exists.These risk levels guide appropriate resilience actions,ensuring proactive power cable management by identifying critical risks,prioritizing mitigation measures,and ensuring adaptability and compliance.This approach not only addresses the challenges of aging infrastructure but also contributes to long-term system integrity and operational reliability.
