HVAC systems are the major energy consumers in commercial buildings in the United States. These systems are operated to provide comfortable thermal conditions for building occupants. The common practice of defining operational settings for HVAC systems is to use fixed set points, which assume occupants have static comfort requirements. However, thermal comfort has been shown to vary from person to person and also change over time due to climatic variations or acclimation. In this paper, we introduce an online learning approach for modeling and quantifying personalized thermal comfort. In this approach, we fit a probability distribution to each comfort condition (i.e., uncomfortably warm, comfortable, and uncomfortably cool) data set and define the overall comfort of an individual through combing these distributions in a Bayesian network. In order to identify comfort variations over time, Kolmogorov-Smirnov test is used on the joint probability distributions. In order to identify comfortable environmental conditions, a Bayesian optimal classifier is trained using online learning. In order to validate the approach, we collected data from 33 subjects, and an average accuracy of 70.14% and specificity of 76.74% were achieved. In practice, this approach could transform the comfort objectives to constrain functions and prevents pareto optimality problems.

The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused; particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively.

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Mechanical signals are not only disturbed by Gaussian noise, but also by non-Gaussian noise. These Gaussian noise and non-Gaussian noise have gravely impeded detecting of rolling bearing defects using traditional methods. In this context, the paper develops a novel detection method for rolling bearing, which combines bispectrum analysis with an improved ensemble empirical mode decomposition (EEMD). To effectively eliminate Gaussian noise in the signal, bispectrum analysis is adopted. In order to effectively reduce non-Gaussian noise, a cloud model-improved EEMD is proposed, where the cloud model is introduced to restrain the mode mixing phenomenon. Then a rolling bearing defect detection plan based on the proposed method is put forward. From theoretical analysis and experimental verification, it is demonstrated that the proposed method has superior performance in reducing multiple background noise. Furthermore, compared with other three methods, the results show that the proposed method can detect the defect of rolling bearings more effectively. INDEX TERMS Bispectrum analysis, cloud model, defect detection, ensemble empirical mode decomposition, mode mixing, rolling bearing.

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Buildings account for approximately 32% of the total energy consumption globally and up to 40% in the developed countries, which makes buildings a prime target for energy conservation. Various energy conservation measures (ECMs) have been proposed to improve the energy efficiency in buildings, and these ECMs are usually designed and assessed using calibrated building energy models. However, there is empirical evidence that reveals noticeable discrepancies between simulated performances of ECMs reported in building energy models and their actual performances measured in buildings. This paper examines two possible causes of such discrepancies. Specifically, this paper tests the following two hypotheses: (1) using assumed occupancy data as opposed to actual occupancy data in building energy simulation reduces the reliability of estimated performance of demand-driven ECMs; and (2) using an energy model built for one ECM to cross estimate energy consumption of another ECM is statistically inaccurate. An educational building was used as a test bed. The results proved both hypotheses true, showing that estimations were more accurate and consistent for models calibrated using actual occupancy compared with those using assumed occupancy, and that cross-ECM estimation resulted in statistical inaccuracy. The findings indicated that current

Borate ore deposits occur predominantly in Phanerozoic evaporative sedimentary environments but are scarce in Precambrian strata. However, massive B-and Mg-rich borate deposits are abundant in the Paleoproterozoic strata of Northeast (NE) China. In addition, several of these borate deposits are dominated by Fe (e.g., >60% Fe2O3 content in the Wengquangou deposit). To constrain the origin of these unusual deposits, we obtained B, Fe, and Mg isotope data on the wall rocks and ores of the Mg-rich Houxianyu borate deposit and the Fe-rich Wengquangou borate deposit in NE China. The δ 11 B values of the borate deposits (10.66 ± 4.35‰, n = 15) are higher than most types of igneous andThe final publication is available at Elsevier via

The Home IoT Voice System (HIVS) such as Amazon Alexa or Apple Siri can provide voicebased interfaces for people to conduct the search tasks using their voice. However, how to protect privacy is a big challenge. This paper proposes a novel personalized search scheme of encrypting voice with privacypreserving by the granule computing technique. Firstly, Mel-Frequency Cepstrum Coefficients (MFCC) are used to extract voice features. These features are obfuscated by obfuscation function to protect them from being disclosed the server. Secondly, a series of definitions are presented, including fuzzy granule, fuzzy granule vector, ciphertext granule, operators and metrics. Thirdly, the AES method is used to encrypt voices. A scheme of searchable encrypted voice is designed by creating the fuzzy granule of obfuscation features of voices and the ciphertext granule of the voice. The experiments are conducted on corpus including English, Chinese and Arabic. The results show the feasibility and good performance of the proposed scheme.