While the theory of Bayesian system identification provides a probabilistic means for reliably and robustly inferring models of a dynamic system and their parameters based on measured dynamic ...response, exploiting sparseness during online tracking of changing model parameters is not well understood. The focus in this study is to implement the dual Kalman filter for real-time Bayesian sequential state and parameter identification based on noisy sensor signals while incorporating sparse Bayesian learning to impose sparse model parameter changes from their initial reference values. We also want out model to be able to capture the evolution of sparse model parameter changes between two successive time instants. To this end, we present a hierarchical Bayesian model for tracking the joint posterior distribution of the state and model parameter vectors for a monitored dynamical system, where the two afore-mentioned sparseness constraints (sparse changes from reference values and with time) are also effectively incorporated for each time. We show our stochastic model of the structural dynamical system can be represented as a coupled conditionally-linear Gaussian state space model for the state and model parameter vectors, leading to some interesting analytical properties of the method that allow quantities of interest to be calculated in real time by using Kalman filtering equations. The parameters for the measurement and state prediction errors are learned solely from the available data up to the current time and so our method resolves the well-known instability problem in Kalman filtering due to arbitrary assignment of the error-distribution parameters. Finally, two illustrative applications are presented, one for identification of stiffness degradation and the other for input time–history identification where both are based on noisy dynamic response measurements from a structure.
•A dual Kalman filter method is proposed for real-time Bayesian state and parameter estimation.•Soft (probabilistic) sparseness constraints for both model parameter changes from reference values and with time are imposed by a hierarchical Bayesian model.•Coupled analytical Kalman filtering equations are derived for the state and model parameter vectors.•The noise parameters are learned solely from the data, which resolves the instability problem in Kalman filtering.•Load time-history estimation and identification of stiffness degradation during some time interval are illustrated.
System identification is a mature research area with well established paradigms, mostly based on classical statistical methods. Recently, there has been considerable interest in so called ...kernel-based regularisation methods applied to system identification problem. The recent literature on this is extensive and at times difficult to digest. The purpose of this contribution is to provide an accessible account of the main ideas and results of kernel-based regularisation methods for system identification. The focus is to assess the impact of these new techniques on the field and traditional paradigms.
•First review paper specially focused on OMA problems to the authors’ knowledge.•Broad classification scheme provided for the relevant literature.•Discussed the context and the techniques to solve a ...wide range of challenges specific to OMA.•Key contributions and limitations of a majority of methods are summarized.•Future areas of research and open problems are summarized.
Output-only modal identification has seen significant activity in recent years, especially in large-scale structures where controlled input force generation is often difficult to achieve. This has led to the development of new system identification methods which do not require controlled input. They often work satisfactorily if they satisfy some general assumptions – not overly restrictive – regarding the stochasticity of the input. Hundreds of papers covering a wide range of applications appear every year related to the extraction of modal properties from output measurement data in more than two dozen mechanical, aerospace and civil engineering journals. In little more than a decade, concepts of blind source separation (BSS) from the field of acoustic signal processing have been adopted by several researchers and shown that they can be attractive tools to undertake output-only modal identification. Originally intended to separate distinct audio sources from a mixture of recordings, mathematical equivalence to problems in linear structural dynamics have since been firmly established. This has enabled many of the developments in the field of BSS to be modified and applied to output-only modal identification problems. This paper reviews over hundred articles related to the application of BSS and their variants to output-only modal identification. The main contribution of the paper is to present a literature review of the papers which have appeared on the subject. While a brief treatment of the basic ideas are presented where relevant, a comprehensive and critical explanation of their contents is not attempted. Specific issues related to output-only modal identification and the relative advantages and limitations of BSS methods both from theoretical and application standpoints are discussed. Gap areas requiring additional work are also summarized and the paper concludes with possible future trends in this area.
We investigate the linear system identification problem in the so-called fixed budget and fixed confidence settings. In the fixed budget setting, the learner aims at estimating the state transition ...matrix <inline-formula><tex-math notation="LaTeX">A</tex-math></inline-formula> from a random system trajectory of fixed length, whereas in the fixed confidence setting, the learner also controls the length of the observed trajectory - she can stop when she believes that enough information has been gathered. For both settings, we analyze the sample complexity in the PAC framework defined as the length of the observed trajectory required to identify the system parameters with prescribed accuracy and confidence levels <inline-formula><tex-math notation="LaTeX">(\varepsilon,\delta)</tex-math></inline-formula>. In the fixed budget setting, we first establish problem-specific sample complexity lower bounds. By problem-specific, we mean that the lower bound explicitly depends on <inline-formula><tex-math notation="LaTeX">A</tex-math></inline-formula>, and hence really captures the identification hardness specific to the system. We then present a finite-time analysis of the estimation error of the Least Squares Estimator (LSE) for stable linear time-invariant systems. For such systems, we show that in the high accuracy regime (for small <inline-formula><tex-math notation="LaTeX">\varepsilon</tex-math></inline-formula>), the sample complexity of the LSE matches our problem specific lower bounds. More precisely, up to universal multiplicative factor, it exhibits the same dependence in <inline-formula><tex-math notation="LaTeX">A</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">\varepsilon</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">\delta</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">d</tex-math></inline-formula>, the dimension of the system, as our lower bound. This paper hence establishes in a certain way the optimality of the LSE for stable systems. Our analysis of the performance of the LSE is simpler, sharper, and easier to interpret than existing analyses, and relies on novel concentration results for the covariates matrix. In the fixed confidence setting, in addition to the estimation objective, the learner also has to decide when to stop the collection of observations. The sample complexity then corresponds to the expected stopping time. For this setting, we also provide problem specific sample complexity lower bounds. We also propose a stopping rule which combined to the LSE enjoys a sample complexity that matches our lower bounds in the high accuracy and high confidence regime.
We present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To ...encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in decoupled update steps. Because in the uncertain-input setting neither the marginal likelihood nor the posterior distribution of the unknowns is tractable, we develop an approximation approach based on variational Bayes. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as Hammerstein models, blind system identification, and cascaded linear systems. This connection allows us to build a systematic procedure that applies effectively to all the aforementioned problems, as shown in the numerical simulations presented in the paper.
Summary
It would be beneficial to bridge design and maintenance if bridge damages and vehicle parameters could be identified. A simultaneous identification method for identifying bridge damage and ...vehicle parameters is proposed, considering more than one vehicle based on the output bridge responses. Bridge damage is localized in advance to decrease the bridge damage parameters. System parameters are identified by minimizing the error between the measured and identified responses. The proposed method is then verified with a numerical simulation, and the effects of environmental noise, damage degree, road surface roughness, and multiple damages on the identification accuracy are investigated, demonstrating the efficiency of the proposed method.
The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT devices has exposed the ...IoT to untold risks with severe consequences. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. Conventional approaches use cryptographic mechanisms to authenticate and verify legitimate devices' identities. However, cryptographic protocols are not available in many systems. Meanwhile, these methods are less effective when legitimate devices can be exploited or encryption keys are disclosed. Therefore, noncryptographic IoT-device identification and rogue device detection become efficient solutions to secure existing systems and will provide additional protection to systems with cryptographic protocols. Noncryptographic approaches require more effort and are not yet adequately investigated. In this article, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators. We classify the IoT-device identification and detection into four categories: 1) device-specific pattern recognition; 2) deep learning-enabled device identification; 3) unsupervised device identification; and 4) abnormal device detection. Meanwhile, we discuss various ML-related enabling technologies for this purpose. These enabling technologies include learning algorithms, feature engineering on network traffic traces and wireless signals, incremental learning, and abnormality detection.
Patient verification by unique identification is an important procedure in health care settings. Risks to patient safety occur throughout health care settings by failure to correctly identify ...patients, resulting in the incorrect patient, incorrect site procedure, incorrect medication, and other errors. To avoid medical malpractice, radio-frequency identification (RFID), fingerprint scanners, iris scanners, and other technologies have been implemented in care settings. The drawbacks of these technologies include the possibility to lose the RFID bracelet, infection transmission, and impracticality when the patient is unconscious.
The purpose of this study was to develop a mobile health app for patient identification to overcome the limitations of current patient identification alternatives. The development of this app is expected to provide an easy-to-use alternative method for patient identification.
We have developed a facial recognition mobile app for improved patient verification. As an evaluation purpose, a total of 62 pediatric patients, including both outpatient and inpatient, were registered for the facial recognition test and tracked throughout the facilities for patient verification purpose.
The app was developed to contain 5 main parts: registration, medical records, examinations, prescriptions, and appointments. Among 62 patients, 30 were outpatients visiting plastic surgery department and 32 were inpatients reserved for surgery. Whether patients were under anesthesia or unconscious, facial recognition verified all patients with 99% accuracy even after a surgery.
It is possible to correctly identify both outpatients and inpatients and also reduce the unnecessary cost of patient verification by using the mobile facial recognition app with great accuracy. Our mobile app can provide valuable aid to patient verification, including when the patient is unconscious, as an alternative identification method.
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