Along with the Internet of Vehicles, some intelligent systems can help the medical vehicles transport medical supplies and patients. In terms of emergency issues like catastrophic natural disasters or serious accidents, safe and timely transportation for medical vehicles is particularly important. For assistance to medical vehicles on the road, models with position prediction can provide accurate position information of ambient vehicles in the next seconds. However, with the increasing number of vehicles on the road and the changing road environment, it is an important challenge to predict the location of vehicles in the road correctly. Current location prediction models for vehicles usually use the previous trajectory of vehicles, lacking the consideration of vehicle's state and real-time traffic information, which leads to relatively low accuracy and safety. Based on the deep belief nets (DBN) and long short-term memory (LSTM), this paper presents a location prediction model for assistance to medical vehicles (LPMVs), which fully considers vehicle's attributes, road information and driving environment as well as the relationship between the factors that influence vehicle driving behaviors and vehicle positions. By experiment, we prove that LPMVs can predict more accurately than current location prediction models and be a good choice for assistance to medical vehicles. INDEX TERMS Internet of Vehicles, medical vehicles, neural network, prediction model.

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Aiming at the fatigue life of bearing in oscillatory applications, through the analysis of the equivalent load of each part of bearing in oscillatory applications, the equivalent load of each part is calculated. Taking the 7005 angular contact ball bearing as the research object, the stress changes of each component are obtained through the transient dynamic analysis of the model, the fatigue of the bearing is analyzed based on the stress damage theory, and the damage and fatigue life of the bearing is obtained. The results show that the rolling element of the 7005 bearings is the weak part of failure under the given working condition, and it is more reasonable to design the service life of the shafting structure with an equivalent load of the rolling element, which provides reference measures for improving the service life of the oscillated bearing.

To further reduce the power of exciter in the common fatigue testing methods and increase the testing frequency to decrease the fatigue testing time, this paper proposes an improved fatigue testing method—Tug Fatigue Testing system (TFTs). The advantages of this new fatigue testing method are low power and high testing frequency of its exciter due to no exciter with moving masses attached to the blade. In TFTs, exciters mounted on the ground or fixed bracket can be used to excite the blade in the uniaxial or biaxial fatigue test. In this paper, the mechanical model of TFTs is established to compare the motor power required by exciters in TFTs and the inertial exciters and the shear load on the blades in both ways. Furthermore, a test of a 56.5 m blade will be performed to verify the feasibility of the new method. In addition, the bending moment distribution of an 80 m blade excited by TFTs was measured and compared with the bending moment distribution of the same blade excited by inertial resonance excitation to evaluate its excitation effect. The test results prove that this improved method needs lower power of exciter, produces smaller shear loads, and provides a higher test frequency in the flap-wise test direction than common inertial resonance excitation. Biaxial fatigue tests can also be conducted by this new method.