This paper presents a novel inkjet-printed humidity sensor tag for passive radio-frequency identification (RFID) systems operating at ultrahigh frequencies (UHFs). During recent years, various ...humidity sensors have been developed by researchers around the world for HF and UHF RFID systems. However, to our best knowledge, the humidity sensor presented in this paper is one of the first passive UHF RFID humidity sensor tags fabricated using inkjet technology. This paper describes the structure and operation principle of the sensor tag as well as discusses the method of performing humidity measurements in practice. Furthermore, measurement results are presented, which include air humidity-sensitivity characterization and tag identification performance measurements.
Achieving carbon neutrality is widely recognized as the key measure to mitigate climate change. As the basis for achieving carbon neutrality, corporate carbon footprint (CCF) estimation is mainly ...based on the disclosed information of corporates to roughly estimate the direct carbon emission, but the estimation may not be comprehensive, timely, and accurate. In this article, the CCF estimation problem is formulated and a novel estimation methodology is proposed for the first time to estimate the direct and indirect carbon emissions of factories in real time. An appliance identification method based on the multihead self-attention mechanism and gated recurrent unit is proposed to identify the device states, and then, calculate the corresponding direct carbon emission. The indirect carbon emission is derived from the electricity consumption of the factory and the marginal carbon emission factor of the connected bus. A dataset containing load and device state data from six different industries is released and used to verify the effectiveness of the proposed method. Experiments show that the proposed appliance identification method is significantly superior to the benchmarks in the literature, and the proposed method can achieve a comprehensive and accurate estimation of the minute-level CCF.
In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of ...structural systems. By means of numerical simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.
Video-based person re-identification plays a central role in realistic security and video surveillance. In this paper, we propose a novel accumulative motion context (AMOC) network for addressing ...this important problem, which effectively exploits the long-range motion context for robustly identifying the same person under challenging conditions. Given a video sequence of the same or different persons, the proposed AMOC network jointly learns appearance representation and motion context from a collection of adjacent frames using a two-stream convolutional architecture. Then, AMOC accumulates clues from motion context by recurrent aggregation, allowing effective information flow among adjacent frames and capturing dynamic gist of the persons. The architecture of AMOC is end-to-end trainable, and thus, motion context can be adapted to complement appearance clues under unfavorable conditions ( e.g. , occlusions). Extensive experiments are conduced on three public benchmark data sets, i.e. , the iLIDS-VID, PRID-2011, and MARS data sets, to investigate the performance of AMOC. The experimental results demonstrate that the proposed AMOC network outperforms state-of-the-arts for video-based re-identification significantly and confirm the advantage of exploiting long-range motion context for video-based person re-identification, validating our motivation evidently.
We propose a methodology for the identification of nonlinear state–space models from input/output data using machine-learning techniques based on autoencoders and neural networks. Our framework ...simultaneously identifies the nonlinear output and state-update maps of the model. After formulating the approach and providing guidelines for tuning the related hyper-parameters (including the model order), we show its capability in fitting nonlinear models on different nonlinear system identification benchmarks. Performance is assessed in terms of open-loop prediction on test data and of controlling the system via nonlinear model predictive control (MPC) based on the identified nonlinear state–space model.
ABSTRACT
This paper reviews and synthesizes the behavioral literature on the various antecedents of auditor identities and explains, through social identity theory, how they influence audit outcomes. ...We discuss the four identities most relevant to auditors (client, firm, team, and profession), first reviewing the psychology literature to describe each of these identities and then reviewing the auditing literature to understand how these identities emerge and impact audit quality. Overall, we find that whereas all four auditor identities have been examined in the literature, much of the research focuses on client identification due to the risk to auditor independence and objectivity. Further, identities can impact audit quality positively or negatively depending on contextual factors. Also, we find few studies investigate whether multiple auditor identities interact to affect audit quality, which provides opportunities for future research with the hope that it can help the profession identify ways of improving audit outcomes.
Frequency-domain chipless RFID readers hold tremendous promise as a low-cost solution for mass production of this technology. However, the strong self-jamming signal leakage, from the transmitter to ...the receiver, significantly degrades the sensitivity of the reader. As a result, the maximum reading range of the system is diminished. To address this issue, we studied the sources of interference signals in the system and proposed a method to significantly reduce them. An ultrawideband compensator, over the wide frequency range of 4.3 to 7.3 GHz, was designed and implemented. This method realized an interference isolation better than 40, 55, and 55 dB in single-antenna, dual-polarized antenna, and two-antenna readers, respectively. A noticeable 25-dB excessive isolation to that of the conventional architecture which should help to bring this technology closer to commercial adoption.
Network traffic classification has become more important with the rapid growth of Internet and online applications. Numerous studies have been done on this topic which have led to many different ...approaches. Most of these approaches use predefined features extracted by an expert in order to classify network traffic. In contrast, in this study, we propose a
deep learning
-based approach which integrates both feature extraction and classification phases into one system. Our proposed scheme, called “Deep Packet,” can handle both
traffic characterization
in which the network traffic is categorized into major classes (e.g., FTP and P2P) and
application identification
in which identifying end-user applications (e.g., BitTorrent and Skype) is desired. Contrary to most of the current methods, Deep Packet can identify encrypted traffic and also distinguishes between VPN and non-VPN network traffic. The Deep Packet framework employs two deep neural network structures, namely stacked autoencoder (SAE) and convolution neural network (CNN) in order to classify network traffic. Our experiments show that the best result is achieved when Deep Packet uses CNN as its classification model where it achieves recall of 0.98 in application identification task and 0.94 in traffic categorization task. To the best of our knowledge, Deep Packet outperforms all of the proposed classification methods on UNB ISCX VPN-nonVPN dataset.
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