Multivariate time series anomaly detection with missing data is one of the most pending issues for industrial monitoring. Due to scarcity of labeled anomalies, most advanced data-driven anomaly detection approaches fa...
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Multivariate time series anomaly detection with missing data is one of the most pending issues for industrial monitoring. Due to scarcity of labeled anomalies, most advanced data-driven anomaly detection approaches fall in the unsupervised learning paradigm. As a premise in the presence of missing data, one needs to improve the data quality through data imputation with a separate model. Our concern lies in the consistency between data imputation and unsupervised learning for robust anomaly detection, regarding accurately discovering the spatiotemporal dependence among multiple variables over time. However, the existing practice tends to overlook this consistency and decouple the training process for these two closely linked tasks. This article novelly proposes a probabilistic multivariate time series anomaly detection framework that unifies data imputation and unsupervised learning. A deep probabilistic graphical model abbreviated SCNF is first devised for unsupervised density estimation. A tailored expectation maximization-based optimization scheme is then developed to achieve the joint training of data imputation and unsupervised learning with missing data. The efficacy is experimentally corroborated in several industrial applications, including chemical process, water treatment and network traffic. Briefly, the joint training framework enhances the AUROC of SCNF by averagely 6.34% for three applications under 50% data missing rate.
Recent research has demonstrated that the ultra-scale computation by self-assembly DNA tiles can be implemented in the laboratory. One of the significant applications is the DNA-based cryptography systems. In this pap...
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Splicing P systems are a class of distributed parallel computing models in the framework of membrane computing, which are inspired by the recombination of DNA molecules under the influence of restriction enzymes. In t...
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Splicing P systems are a class of distributed parallel computing models in the framework of membrane computing, which are inspired by the recombination of DNA molecules under the influence of restriction enzymes. In this work, a variant of P machine, called splicing P machine, is presented, which can provide an automata approach to investigate splicing P systems. It is shown that such a device can do what the splicing P system does. Several examples are given to illustrate that splicing P machine can provide the same results as the corresponding splicing P system. This work provides an answer to an open problem formulated by G. Ciobaun and M. Gontineac.
In this paper, the Pneumatic Muscle (PM) as actuator is investigated. The PM model is established by using a phenomenological model consisting of a contractile element, a spring element, and a damping element in paral...
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Intensive task-oriented repetitive physical therapies need be provided by individualized interaction between the patients and the rehabilitation specialists to improve hand motor performance for those survived from st...
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Pneumatic muscle (PM) has strong time varying characteristic. The complex nonlinear dynamics of PM system poses problems in achieving accurate modeling and control. To solve these challenges, we propose an echo state ...
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Nowadays, mechanical fault detection techniques based on signals from motor drive are becoming popular. However, in electromechanical system with flexible drive-train, mechanical fault signature reflected in motor dri...
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Nowadays, mechanical fault detection techniques based on signals from motor drive are becoming popular. However, in electromechanical system with flexible drive-train, mechanical fault signature reflected in motor drive is suppressed by the transmission path. This signal attenuation reduces the effectiveness of fault detection. Therefore, research on the law of fault signature transmission from load to motor is necessary. In this article, a two-mass electromechanical system with a long shaft is concerned. First, the transmission path from load torque to electromagnetic torque is analyzed, and the concept of torque transmission efficiency is proposed. Then, the frequency characteristic method and transfer function method are presented to establish the signal transmission model. Finally, the model-based signal enhancement method is proposed to compensate the attenuation. Simulation and experimental results show that the proposed method can enhance the quality of fault signature transmission and improve the performance of fault classification.
In recent years, the field of person re-identification has made significant advances riding on the wave of deep learning. However, owing to the fact that there are much more easy examples than those meaningful hard ex...
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In recent years, the field of person re-identification has made significant advances riding on the wave of deep learning. However, owing to the fact that there are much more easy examples than those meaningful hard examples in a dataset, the training tends to stagnate quickly and the model may suffer from over-fitting, which leads to some error matching of models especially for some hard samples during the test process. Therefore, the hard sample mining method is fateful to optimize the model and improve the learning efficiency. In this paper, an Adaptive Hard Sample Mining algorithm is proposed for training a robust person re-identification model. No need for hand-picking the images in the batch or designing the loss function for both positive and negative pairs, we can briefly calculate the hard level by comparing the prediction result with the true label of the sample. Meanwhile, taking into account the change in the number of samples required for the model during training process, an adaptive threshold of hard level can make the algorithm not only stay in step with training process harmoniously but also alleviate the under-fitting and over-fitting problem simultaneously. Besides, the designed network to implement the approach is very efficient and has good generalization performance that can be combined with various existing models readily. Experimental results on Market-1501, DukeMTMC-reID and CUHK03 datasets clearly demonstrate the effectiveness of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.
The home energy system today involves multiple renewable energy sources and battery energy storage systems, which can be considered as a microgrid. The battery energy storage system is a key component in the home ener...
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The home energy system today involves multiple renewable energy sources and battery energy storage systems, which can be considered as a microgrid. The battery energy storage system is a key component in the home energy system for the sake of filling the gap between the user demand and volatile energy supplies to maximize the techno and economic performances. However, the battery scheduling must suffer the stochastic nature of renewable energy resources and loads, which results in an intractable multi-period stochastic optimization problem with security constraints. An improved actor-critic-based reinforcement learning is proposed for this issue, where a distributional critic net is applied for faster and more accurate reward estimation under uncertainties, and a policy net incorporating protective secondary control is adopted to satisfy security constraints, preventing the unsafe state of batteries during the trial-and-error process. Numerical tests show that the proposed approach outperforms conventional reinforcement learning algorithms, as well as the rule-based battery scheduling approach while guaranteeing safe operation. The robustness and adaptability of the proposed method are also verified in case studies with different optimization tasks.
A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain ...
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A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject;2) LA can be used as a preprocessing step before different feature extraction and classification algorithms;and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.
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