A data center undertakes increasing background services of various applications, and the data flows transmitted between the nodes in data center networks (DCNs) are consequently increased. At the same time, the traffic of each link in a DCN changes dynamically over time. Flow scheduling algorithms can improve the distribution of data flows among the network links so as to improve the balance of link loads in a DCN. However, most current load balancing works achieve flow scheduling decisions to the current links on the basis of past link flow conditions. This situation impedes the existing link scheduling methods from implementing optimal decisions for scheduling data flows among the network links in a DCN. This paper proposes a predictive link load balance routing algorithm for a DCN based on residual networks (ResNet), i.e., the link load balance route (LLBR) algorithm. The LLBR algorithm predicts the occupancy of the network links in the next duty cycle, according to the ResNet architecture, and then the optimal traffic route is selected according to the predictive network environment. The LLBR algorithm, round-robin scheduling (RRS), and weighted round-robin scheduling (WRRS) are used in the same experimental environment. Experimental results show that compared with the WRRS and RRS, the LLBR algorithm can reduce the transmission time by approximately 50%, reduce the packet loss rate from 0.05% to 0.02%, and improve the bandwidth utilization by 30%.

Recognition of Traditional Chinese Medicine (TCM) entities from different types of literature is challenging research, which is the foundation for extracting a large amount of TCM knowledge existing in unstructured texts into structured formats. The lack of large-scale annotated data makes unsatisfactory application of conventional deep learning models in TCM text knowledge extraction. Some other unsupervised methods rely on other auxiliary data, such as domain dictionaries. We propose a multigranularity text-driven NER model based on Conditional Generation Adversarial Network (MT-CGAN) to implement TCM NER with small-scale annotated corpus. In the model, a multigranularity text features encoder (MTFE) is designed to extract rich semantic and grammatical information from multiple dimensions of TCM texts. By differentiating the conditional constraints of the generator and discriminator of MT-CGAN, the synchronization between the generated tag labs and the named entities is guaranteed. Furthermore, seeds of different TCM text types are introduced into our model to improve the precision of NER. We compare our method with other baseline methods to illustrate the effectiveness of our method on 4 kinds of gold-standard datasets. The experiment results show that the standard precision, recall, and F1 score of our method are higher than the state-of-the-art methods by 0.24∼8.97%, 0.89∼12.74%, and 0.01∼10.84%. MT-CGAN is able to extract entities from different types of TCM literature effectively. Our experimental results indicate that the proposed approach has a clear advantage in processing TCM texts with more entity types, higher sparsity, less regular features, and a small-scale corpus.

With the development of traffic systems, some issues such as traffic jams become more and more serious. Efficient traffic flow theory is needed to guide the overall controlling, organizing and management of traffic systems. On the basis of the cellular automata model and the traffic flow model with look-ahead potential, a new cellular automata traffic flow model with negative exponential weighted look-ahead potential is presented in this paper. By introducing the negative exponential weighting coefficient into the look-ahead potential and endowing the potential of vehicles closer to the driver with a greater coefficient, the modeling process is more suitable for the driver's random decision-making process which is based on the traffic environment that the driver is facing. The fundamental diagrams for different weighting parameters are obtained by using numerical simulations which show that the negative exponential weighting coefficient has an obvious effect on high density traffic flux. The complex high density non-linear traffic behavior is also reproduced by numerical simulations.

In this paper, based on the nonlinear dynamical coupled model of string of vehicles, by the design method of terminal sliding mode control, the longitudinal following control of string of vehicles in automated highway system is studied, the control law for each vehicle in the platoon is designed. Simulation results showed that the convergence rate of spacing errors of the vehicle platoon is fast.