This paper proposes a time-dependent visiting trip planning (TVTP) framework to find the most efficient visiting order and plan the fastest moving paths based on Internet of Things (IoT) localization. The proposed TVTP framework consists of a deep learning based crowd density prediction model and a time-dependent visiting trip planning algorithm. In the developed prediction model, densely connected convolutional networks are explored with spatiotemporal data fusion to further reduce prediction errors. In the designed planning algorithm, visitors are guided to multiple target places at feasible time points to minimize total moving time based on predicted future crowd densities. According to our review of relevant research, this is the first framework that integrates deep learning for crowd density prediction with time-dependent planning for a multi-target visiting trip, which can precisely estimate future moving times based on predicted crowd densities and efficiently plan the optimal visiting order and guiding paths with the shortest total moving time to visit all target places. The open crowd dataset is adopted to evaluate the performance of existing works and TVTP. Experimental results show that our framework outperforms existing methods and can accurately predict the future crowd density of indoor people as well as significantly reduce the total moving time in the planned multi-target visiting trip.

The fuzzy cross entropy of intuitionistic fuzzy sets, so-called intuitionistic cross entropy, is introduced by analogy with the cross entropy of probability distributions. This paper introduces the concepts of discrimination information and cross-entropy in the intuitionistic fuzzy sets. Based on this improved entropy measure, we add information of an intuitionistic fuzzy index and reveal an intuitive and mathematical connection between the notions of entropy for intuitionistic fuzzy sets in terms of fuzziness and intuitionism. Finally, a fault diagnosis example of the turbine has been illustrated. The example demonstrates that the proposed method cannot only diagnose the main fault types of the turbine, it can also detect useful information for future trends and multi-fault analysis.