An Unknown Input Multi-Observer Approach for Estimation and Control under Adversarial Attacks

We address the problem of state estimation, attack isolation, and control of discrete-time linear time-invariant systems under (potentially unbounded) actuator and sensor false data injection attacks. Using a bank of unknown input observers, each observer leading to an exponentially stable estimation error (in the attack-free case), we propose an observer-based estimator that provides exponential estimates of the system state in spite of actuator and sensor attacks. Exploiting sensor and actuator redundancy, the estimation scheme is guaranteed to work if a sufficiently small subset of sensors and actuators are under attack. Using the proposed estimator, we provide tools for reconstructing and isolating actuator and sensor attacks; and a control scheme capable of stabilizing the closed-loop dynamics by switching off isolated actuators. Simulation results are presented to illustrate the performance of our tools.Comment: arXiv admin note: substantial text overlap with arXiv:1811.1015

A Multi-Observer Based Estimation Framework for Nonlinear Systems under Sensor Attacks

We address the problem of state estimation and attack isolation for general discrete-time nonlinear systems when sensors are corrupted by (potentially unbounded) attack signals. For a large class of nonlinear plants and observers, we provide a general estimation scheme, built around the idea of sensor redundancy and multi-observer, capable of reconstructing the system state in spite of sensor attacks and noise. This scheme has been proposed by others for linear systems/observers and here we propose a unifying framework for a much larger class of nonlinear systems/observers. Using the proposed estimator, we provide an isolation algorithm to pinpoint attacks on sensors during sliding time windows. Simulation results are presented to illustrate the performance of our tools.Comment: arXiv admin note: text overlap with arXiv:1806.0648

A Robust CACC Scheme Against Cyberattacks Via Multiple Vehicle-to-Vehicle Networks

Cooperative Adaptive Cruise Control (CACC) is a vehicular technology that allows groups of vehicles on the highway to form in closely-coupled automated platoons to increase highway capacity and safety, and decrease fuel consumption and CO2 emissions. The underlying mechanism behind CACC is the use of Vehicle-to-Vehicle (V2V) wireless communication networks to transmit acceleration commands to adjacent vehicles in the platoon. However, the use of V2V networks leads to increased vulnerabilities against faults and cyberattacks at the communication channels. Communication networks serve as new access points for malicious agents trying to deteriorate the platooning performance or even cause crashes. Here, we address the problem of increasing robustness of CACC schemes against cyberattacks by the use of multiple V2V networks and a data fusion algorithm. The idea is to transmit acceleration commands multiple times through different communication networks (channels) to create redundancy at the receiver side. We exploit this redundancy to obtain attack-free estimates of acceleration commands. To accomplish this, we propose a data-fusion algorithm that takes data from all channels, returns an estimate of the true acceleration command, and isolates compromised channels. Note, however, that using estimated data for control introduces uncertainty into the loop and thus decreases performance. To minimize performance degradation, we propose a robust $H_{\infty}$ controller that reduces the joint effect of estimation errors and sensor/channel noise in the platooning performance (tracking performance and string stability). We present simulation results to illustrate the performance of our approach

A Multi-Observer Approach for Attack Detection and Isolation of Discrete-Time Nonlinear Systems

We address the problem of attack detection and isolation for a class of discrete-time nonlinear systems under (potentially unbounded) sensor attacks and measurement noise. We consider the case when a subset of sensors is subject to additive false data injection attacks. Using a bank of observers, each observer leading to an Input-to-State Stable (ISS) estimation error, we propose two algorithms for detecting and isolating sensor attacks. These algorithms make use of the ISS property of the observers to check whether the trajectories of observers are `consistent' with the attack-free trajectories of the system. Simulations results are presented to illustrate the performance of the proposed algorithms.Comment: arXiv admin note: text overlap with arXiv:1805.0424

On Joint Reconstruction of State and Input-Output Injection Attacks for Nonlinear Systems

We address the problem of robust state reconstruction for discrete-time nonlinear systems when the actuators and sensors are injected with (potentially unbounded) attack signals. Exploiting redundancy in sensors and actuators and using a bank of unknown input observers (UIOs), we propose an observer-based estimator capable of providing asymptotic estimates of the system state and attack signals under the condition that the numbers of sensors and actuators under attack are sufficiently small. Using the proposed estimator, we provide methods for isolating the compromised actuators and sensors. Numerical examples are provided to demonstrate the effectiveness of our methods.Comment: arXiv admin note: text overlap with arXiv:1904.0423

Diffusion-based Time Series Data Imputation for Microsoft 365

Reliability is extremely important for large-scale cloud systems like Microsoft 365. Cloud failures such as disk failure, node failure, etc. threaten service reliability, resulting in online service interruptions and economic loss. Existing works focus on predicting cloud failures and proactively taking action before failures happen. However, they suffer from poor data quality like data missing in model training and prediction, which limits the performance. In this paper, we focus on enhancing data quality through data imputation by the proposed Diffusion+, a sample-efficient diffusion model, to impute the missing data efficiently based on the observed data. Our experiments and application practice show that our model contributes to improving the performance of the downstream failure prediction task

Anxiety, depression, psychological stress and coping style in medical postgraduates in southeastern China when restricted to commuting between the campus and hospital during the COVID-19 pandemic

BackgroundAs the COVID-19 epidemic was gradually brought under control, a new autumn semester began in 2020. How was the mental health of postgraduates as they experienced quarantine at home, only commuting between the school and hospital?MethodsThe research was conducted in a cross-sectional online survey in October 2020. The data were collected from 1,645 medical postgraduates (master’s and doctoral students) by using the demographic information questionnaire, the Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), the Questionnaire on Psychological Stressors of Postgraduates (QPSP), the Simplified Coping Style Questionnaire (SCSQ) and the Social Support Rate Scale (SSRS). One-way ANOVA and Pearson correlation were used to explore the relationships among anxiety, depression, psychological stressors, social support and coping style. Structural equation modeling (SEM) was conducted to assess the mediation model.ResultsAmong the total of 1,645 medical postgraduates, 21.6% (n = 356) had self-rated depression symptoms, and 9.4% (n = 155) had self-rated anxiety symptoms. The main disturbances they experienced were employment, academic and interpersonal pressure. The master of third grade students had the highest employment pressure, and the master of second grade students had the highest academic and interpersonal pressure. Negative coping played a negative mediating role and social support played a positive mediating role in the relationships between perceived stress and anxiety (β = 0.027, P < 0.01; β = 0.124, P < 0.01) and depression (β = 0.016, P < 0.01; β = 0.193, P < 0.01).ConclusionMedical postgraduates in China restricted to studies on campus and in the hospital experienced psychological distress. Our results suggest that providing employment and learning guidance, while strengthening social support and guiding positive coping may be effective at improving the mental health of the medical graduate students, mediating their perceived stress and negative emotions

A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients

ObjectiveTo develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images.MethodsA retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor’s region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC).ResultsThe auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction’s internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort.ConclusionThe ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses

The influence of summer hypoxia on sedimentary phosphorus biogeochemistry in a coastal scallop farming area, North Yellow Sea

In situ field investigations coupled with laboratory incubations were employed to explore the surface sedimentary phosphorus (P) cycle in a mariculture area adjacent to the Yangma Island suffering from summer hypoxia in the North Yellow Sea. Five forms of P were fractionated, namely exchangeable P (Ex-P), iron-bound P (Fe-P), authigenic apatite (Ca-P), detrital P (De-P) and organic P (OP). Total P (TP) varied from 13.42 to 23.88 mu mol g(-1) with the main form of inorganic P (IP). The benthic phosphate (DIP) fluxes were calculated based on incubation experiments. The results show that the sediment was an important source of P in summer with similar to 39% of the bioavailable P (Bio-P) recycled back into the water column. However, the sediment acted a sink of P in autumn. The benthic DIP fluxes were mainly controlled by the remobilizing of Fe-P, Ex-P and OP under contrasting redox conditions. In August (hypoxia season), similar to 0.92 mu mol g(-1) of Fe-P and similar to 0.52 mu mol g(-1) of OP could be transformed to DIP and released into water, while similar to 0.36 mu mol g(-1) of DIP was adsorbed to clay minerals. In November (non-hypoxia season), however, similar to 0.54 mu mol g(-1) of OP was converted into DIP, while similar to 0.55 mu mol g(-1) and similar to 0.28 mu mol g(-1) of DIP was adsorbed to clay minerals and bind to iron oxides. Furthermore, scallop farming activities also affected the P mobilization through biological deposition and reduced hydrodynamic conditions. The burial fluxes of P varied from 11.67 to 20.78 mu mol cm(-2) yr(-1) and its burial efficiency was 84.7-100%, which was consistent with that in most of the marginal seas worldwide. This study reveals that hypoxia and scallop farming activities can significantly promote sedimentary P mobility, thereby causing high benthic DIP flux in coastal waters. (C) 2020 Elsevier B.V. All rights reserved