With the development of the next generation of information technology, an increasing amount of attention is being paid to smart residential spaces, including smart cities, smart buildings, and smart homes. Building indoor safety intelligence is an important research topic. However, current indoor safety management methods cannot comprehensively analyse safety data, owing to a poor combination of safety management and building information. Additionally, the judgement of danger depends significantly on the experience of the safety management staff. In this study, digital twins (DTs) are introduced to building indoor safety management. A framework for an indoor safety management system based on DT is proposed which exploits the Internet of Things (IoT), building information modelling (BIM), the Internet, and support vector machines (SVMs) to improve the level of intelligence for building indoor safety management. A DT model (DTM) is developed using BIM integrated with operation information collected by IoT sensors. The trained SVM model is used to automatically obtain the types and levels of danger by processing the data in the DTM. The Internet is a medium for interactions between people and systems. A building in the bobsleigh and sled stadium for the Beijing Winter Olympics is considered as an example; the proposed system realises the functions of the scene display of the operation status, danger warning and positioning, danger classification and level assessment, and danger handling suggestions.

Facility emergence evacuation is often a complicated process under extreme conditions. Most of the buildings today use pre-installed signages to guide the emergence evacuation. However, these guidances are sometimes insufficient or misleading, particularly for evacuating from high-rise buildings or complex buildings, such as schools, hospitals, and stadiums. Following a planned route may lead the crowd to move towards dangers, such as smoke and fire. The future emergency guidance system should be more intelligent and be able to guide people to evacuate with a higher survival possibility. This study proposes a real-time building evacuation model with an improved cellular automata (CA) method. This algorithm combines cellular automata with the potential energy field (PEF) model in fluid dynamic theory (FDT) to choose safe paths for the crowd and reduce the possibility of stampedes. Custom-designed wireless sensors, artificial intelligence (A.I.) enhanced surveillance cameras, intelligent emergency signage systems, and edge computing servers are used to sample fire and crowd data, operate the intelligent evacuation algorithm, and guide the crowd with the signage system in real-time conditions. In addition, we performed the algorithm simulation on a two-dimensional plane generated based on the building structure of the Beijing Capital Airport Hospital. The evacuation drill simulations show that the average escape time is significantly shortened with optimal real-time guidance. In one case, a 72% reduction in evacuation time is achieved compared with evacuation using pre-installed signages. The results also demonstrated that the proposed model and system’s evacuation time reduction performance is particularly good in crowded buildings, such as schools or stadiums.

In semisupervised fuzzy clustering, this article extends the traditional pairwise constraint (i.e., must-link or cannot-link) to fuzzy pairwise constraint. The fuzzy pairwise constraint allows a supervisor to provide the grade of similarity or dissimilarity between the implicit fuzzy vectors of a pair of samples. This constraint can represent more complicated relationship between the pair of samples and avoid eliminating the fuzzy characteristics. Then, we propose a semisupervised fuzzy clustering with fuzzy pairwise constraints (SSFPC). The nonconvex optimization problem in our SSFPC is solved by a modified expectation-maximization algorithm, involving to solve several indefinite quadratic programming problems (IQPPs). Further, a diagonal block coordinate decent (DBCD) algorithm is proposed for these IQPPs, whose stationary points are guaranteed, and the global solutions can be obtained under certain conditions. To suit for different applications, the SSFPC is extended into various metric spaces, e.g., the reproducing kernel Hilbert space. Experimental results on several benchmark datasets and a facial expression database demonstrate the outperformance of our SSFPC compared with some state-of-the-art clustering models

Optimization of winding resistance for litz wire inductors is essential to reduce losses and improve energy transfer efficiency in electrical power systems. In this study, an approximate model for the optimization of winding resistance in litz wire inductors based on Dowell's equation is developed in three frequency ranges. The proximity and skin effects are analysed separately and subsequently combined to obtain an approximation with minimum theoretical error. Two error functions are defined to determine the frequency ranges for which the approximation is valid. Based on this Dowell's model approximation, it is found that, with the other parameters of the inductors fixed, both the porosity factor and conductivity have an optimal value that minimizes AC resistance for the litz wire windings. Thus, expressions for the optimum porosity factor and conductivity are derived, the optimization schemes under different practical engineering conditions are also given. The theoretical results are in good agreement with the experimental measurements, which indicates that the proposed model is valid for three frequency ranges, and the proposed optimization method is practical and suitable.

In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.