The intelligent security monitoring of buildings and their surroundings has become increasingly crucial as the number of high-rise buildings increases. Building structural health monitoring and early warning technology are key components of building safety, the implementation of which remains challenging, and the Internet of things approach provides a new technical measure for addressing this challenge. This article presents a novel integrated information system that combines Internet of things, building information management, early warning system, and cloud services. Specifically, the system involves an intelligent data box with enhanced connectivity and exchangeability for accessing and integrating the data obtained from distributed heterogeneous sensing devices. An extensible markup language (XML)-based uniform data parsing model is proposed to abstract the various message formats of heterogeneous devices to ensure data integration. The proposed Internet of things-based integrated information system structure was applied for monitoring an actual pit excavation engineering site. Three early warning levels were implemented according to rules based on the threshold value, which determined the specific safety personnel to be notified. The proposed Internet of things-based integrated information system is demonstrated to improve the effectiveness of monitoring processes and decision making in construction informatics applications. Our work highlights the crucial importance of a systematic approach toward integrated information systems for effective information collection and structural health monitoring.Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License

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We propose and experimentally demonstrate a novel cost-effective and distributed optical performance monitor by employing Gaussian process regression for OSNR monitoring and support vector machine for modulation format identification simultaneously in optical network link establishment.

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A simple flow regime detection method of gas-liquid two-phase flow is presented, used in mini-pipes. First, the capacitance signals are obtained, and some simple statistics eigenvalues, such as mean value, standard deviation, total energy, average amplitude, zero-crossing rate and probability density function, are analyzed and used to identify the flow regimes. The principal component analysis (PCA) method is used to reduce the dimensionality of data sets and eliminate multi-collinearity of variables. PCA of four pairs of capacitance sensor can also reduce the noise influence. Then support vector machine (SVM) method is used to detect flow regime. In experiment, bubble flow, stratified flow, slug flow and annular flow are observed in the pipes with inner diameter of 3.1mm. Four electrical capacitance sensors are installed. Based SVM theory, a set of binary classifier is constructed, and flow regime can be detected successfully. The results show that the presented method is effective, and can improve the accuracy of flow regime identification.