Deep belief network (DBN) is an effective deep learning model, which can learn the complex data by extracting features hierarchically. However, the successful application of DBN depends on the suitable size of the structure (the number of hidden neurons), which is still an open problem. Currently, the network structure size is basically determined by experience with a time-consuming process. In this article, a complexity-based structural optimization (CBSO) algorithm, based on multiobjective ordinal optimization (MOO), is developed for designing the DBN structure. First, the problem formulation of structural optimization of DBN is given, where the multiple objectives are to minimize the fitting error and complexity. Second, the lower bound for alignment probability in optimizing DBN structure is developed according to MOO. Finally, an effective method to maximize the probability of correct select is given to pursue the good tradeoff between the complexity and the performance. The performance of proposed CBSO algorithm is demonstrated via predicting and controlling water quality of wastewater treatment process (WWTP) using the CBSO-DBN-based model predictive control (MPC) strategy. The simulation results show that the resulting CBSO-DBN can find the better structure design by using CBSO algorithm with smaller fitting error and limited computational complexity, and thereby achieve the better performance in WWTP than its peers. Especially, the CBSO-DBN-MPC improves the control accuracy by 76.16% and computational complexity by 50.45%, respectively.

With the continuing penetration of sensor devices and the development of wireless communication techniques, increasing number of applications involving context awareness in ubiquitous computing have been used in daily life. How to collect data from mobile devices at a low energy cost and to mine contextual habits of users remains a key challenge for ubiquitous computing. We proposed an efficient context- awareness computing middleware system, Senz. By leveraging high-efficiency mobile data transmission method, using high-performance context recognition algorithms, and combining mobile data with online third-party data, this middleware system can recognize various user behavior patterns reliably, accurately and efficiently. Experiments show that the Senz recognition accuracy of context activity is above 83% on average and the energy cost is relatively low. By integrating Senz SDK and cloud computing engine, developers can build rich user experience apps with better understanding of users' behavior data and providing various personalized contextual services.

We are witnessing increasing interests in developing “smart cities” which helps improve the efficiency, reliability, and security of a traditional city. An important aspect of developing smart cities is to enable “smart transportation,” which improves the efficiency, safety, and environmental sustainability of city transportation means. Meanwhile, the increasing use of GPS devices has led to the emergence of big trajectory data that consists of large amounts of historical trajectories and real-time GPS data streams that reflect how the transportation networks are used or being used by moving objects, e.g., vehicles, cyclists, and pedestrians. Such big trajectory data provides a solid data foundation for developing various smart transportation applications, such as congestion avoidance, reducing greenhouse gas emissions, and effective traffic accident response, etc. Instead of proposing yet another specific smart transportation application, we propose the parallel-distributed network-constrained moving objects database (PD-NMOD), a general framework that manages big trajectory data in a scalable manner, which provides an infrastructure that is able to support a wide variety of smart transportation applications and thus benefiting the smart city vision as a whole. The PD-NMOD manages both transportation networks and trajectories in a distributed manner. In addition, the PD-NMOD is designed to support general SQL queries over moving objects and to efficiently process the SQL queries on big trajectory data in parallel. Such design facilitates smart transportation applications to retrieve relevant trajectory data and to conduct statistical analyses. Empirical studies on a large trajectory data set collected from 3,500 taxis in Beijing offer insight into the design properties of the PD-NMOD and offer evidence that the PD-NMOD is efficient and scalable.