Online education promotes the sharing of learning resources. Knowledge tracing (KT) is aimed at tracking the cognition function of students according to their performance on various exercises at ...different times and has attracted considerable attention. Existing KT models primarily use bisection representations for the performance and cognitive states of students, thus limiting the application scope of these models and the accuracy of the evaluation of student cognitive performance in learning processes. Therefore, fuzzy Bayesian KT models (namely, FBKT and T2FBKT) are proposed to address continuous score scenarios (e.g., subjective examinations) so that the applicability of KT models may be broadened. Moreover, fine-grained cognitive states can be discerned. In particular, referring to type-2 fuzzy theory, T2FBKT mitigates the model uncertainty of FBKT induced by uncertain parameters. Finally, extensive experiments demonstrate the effectiveness of the proposed fuzzy KT models.
Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be ...terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an m th-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors.
PALDi: Online Load Disaggregation via Particle Filtering Egarter, Dominik; Bhuvana, Venkata Pathuri; Elmenreich, Wilfried
IEEE transactions on instrumentation and measurement,
02/2015, Letnik:
64, Številka:
2
Journal Article
Recenzirano
Smart metering and fine-grained energy data are one of the major enablers for future smart grid and improved energy efficiency in smart homes. Using the information provided by smart meter power ...draw, valuable information can be extracted as disaggregated appliance power draws by non-intrusive load monitoring (NILM). NILM allows to identify appliances according to their power characteristics in the total power consumption of a household, measured by one sensor, the smart meter. In this paper, we present an NILM approach, where the appliance states are estimated by particle filtering (PF). PF is used for nonlinear and non-Gaussian disturbed problems and is suitable to estimate the appliance state. ON/OFF appliances, multistate appliances, or combinations of them are modeled by hidden Markov models, and their combinations result in a factorial hidden Markov model modeling the household power demand. We evaluate the PF-based NILM approach on synthetic and on real data from a well-known dataset to show that our approach achieves an accuracy of 90% on real household power draws.
In this article, the problem of the asynchronous fault detection (FD) observer design is discussed for 2-D Markov jump systems (MJSs) expressed by a Roesser model. In general, the FD observer cannot ...work synchronously with the system, that is, the mode of the observer varies with the mode of the system in line with some conditional transitional probabilities. For dealing with this difficult point, a hidden Markov model (HMM) is employed. Then, combining the <inline-formula> <tex-math notation="LaTeX">H_{\infty } </tex-math></inline-formula> attenuation index and <inline-formula> <tex-math notation="LaTeX">H_{\_{}} </tex-math></inline-formula> increscent index, a multiobjective solution to the FD problem is formed. In terms of linear matrix inequality technology, sufficient conditions are gained to guarantee the existence of the asynchronous FD. Simultaneously, an asynchronous FD algorithm is generated to acquire the optimal performance indices. Finally, a numerical example concerned with the Darboux equation is demonstrated to exhibit the soundness of the developed approach.
This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the ...nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance. Finally, two examples are provided to demonstrate the correctness and reliability of our developed approaches.
The expansion of residential demand side management programs and increased deployment of controllable loads require accurate appliance-level load modeling and forecasting. This paper proposes a ...conditional hidden semi-Markov model to describe the probabilistic nature of residential appliance demand. Model parameters are estimated directly from power consumption data using scalable statistical learning methods. We also propose an algorithm for short-term load forecasting as a key application for appliance-level load models. Case studies performed using granular sub-metered power measurements from various types of appliances demonstrate the effectiveness of the proposed load model for short-term prediction.
Driving style analysis plays a pivotal role in intelligent vehicle design. This paper presents a novel framework for driving style analysis based on primitive driving patterns. To this end, a ...Bayesian nonparametric approach based on a hidden semi-Markov model (HSMM) is introduced to extract the primitive driving patterns from muti-dimensional time-series driving data without prior knowledge of these driving patterns. For the Bayesian nonparametric approach, a hierarchical Dirichlet process (HDP) is applied to learn the unknown smooth dynamical modes in the HSMM, called primitive driving patterns. Two other types of Bayesian nonparametric approaches (HDP-HMM and sticky HDP-HMM) are developed as comparatives in order to show the advantages of the HDP-HSMM. The naturalistic car-following data of 18 drivers are collected from the University of Michigan Safety Pilot Model Deployment database. For each driver, 75 primitive driving patterns are semantically predefined according to their physical and psychological perception thresholds. The individual driving styles are then semantically analyzed based on the distribution over primitive driving patterns, and the similarity of driving styles among drivers is then evaluated. Experimental results demonstrate that the utilization of driving primitive pattern provides a semantically interpretable way to analyze driver's behavior and driving style.
This study focuses on the finite-time consensus tracking problem for multi-agent systems (MASs) subject to random abrupt changes via distributed asynchronous sliding mode control (SMC). In ...particular, Markov jump model is exploited to describe the random abrupt changes in MASs parameters caused by external factors. Moreover, considering the nonsynchronization between the controller mode and the system mode caused by the delayed measurements between agents, a continuous-time hidden Markov model is constructed to simulate this situation, in which the introduced mode-independent detector can directly observe the unmeasured information. Besides, by designing a novel distributed asynchronous SMC law, some sufficient conditions are provided to ensure fast consensus within a finite-time interval. Different from the existing SMC mechanisms, the proposed mechanism involves common sliding mode surface (SMS), which plays an important role in preventing the system trajectories from escaping from the SMS during the mode jump. Finally, numerical simulation results illustrate the feasibility of proposed control method.
Understanding how appliances in a house consume power is important when making intelligent and informed decisions about conserving energy. Appliances can turn ON and OFF either by the actions of ...occupants or by automatic sensing and actuation (e.g., thermostat). It is also difficult to understand how much a load consumes at any given operational state. Occupants could buy sensors that would help, but this comes at a high financial cost. Power utility companies around the world are now replacing old electro-mechanical meters with digital meters (smart meters) that have enhanced communication capabilities. These smart meters are essentially free sensors that offer an opportunity to use computation to infer what loads are running and how much each load is consuming (i.e., load disaggregation). We present a new load disaggregation algorithm that uses a super-state hidden Markov model and a new Viterbi algorithm variant which preserves dependencies between loads and can disaggregate multi-state loads, all while performing computationally efficient exact inference. Our sparse Viterbi algorithm can efficiently compute sparse matrices with a large number of super-states. Additionally, our disaggregator can run in real-time on an inexpensive embedded processor using low sampling rates.
Position-based services (PBSs) that deliver networked amenities based on roaming user's positions have become progressively popular with the propagation of smart mobile devices. Position is one of ...the important circumstances in PBSs. For effective PBSs, extraction and recognition of meaningful positions and estimating the subsequent position are fundamental procedures. Several researchers and practitioners have tried to recognize and predict positions using various techniques; however, only few deliberate the progress of position-based real-time applications considering significant tasks of PBSs. In this paper, a method for conserving position confidentiality of roaming PBSs users using machine learning techniques is proposed. We recommend a three-phase procedure for roaming PBS users. It identifies user position by merging decision trees and k-nearest neighbor and estimates user destination along with the position track sequence using hidden Markov models. Moreover, a mobile edge computing service policy is followed in the proposed paradigm, which will ensure the timely delivery of PBSs. The benefits of mobile edge service policy offer position confidentiality and low latency by means of networking and computing services at the vicinity of roaming users. Thorough experiments are conducted, and it is confirmed that the proposed method achieved above 90% of the position confidentiality in PBSs.
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