To solve the problems of fine-grained image classification methods, such as highly complex methods and difficulty of using deeper models, a Deep Model Transfer (DMT) method was proposed. Firstly, the deep model was pre-trained on the coarse-grained image dataset. Secondly, the pre-trained deep model classification layer was trained based on inexact supervised learning by using fine-grained image dataset and transferred to the feature distribution direction of the novel dataset. Finally, the trained model was exported and tested on the corresponding test sets. The experimental results show that the classification accuracy rates on the STANFORD DOGS, CUB-200-2011 and OXFORD FLOWER-102 fine-grained image datasets are 72.23%, 73.33%, and 96.27%, respectively, which proves the effectiveness of DMT method on fine-grained image classification.

A cloud model is not suitable for non-normal distribution. In order to solve the problem, a new one-dimensional backward cloud algorithm based on uniform distribution was proposed and applied to the credibility evaluation system of simulation system. Firstly, the importance of simulation credibility was expounded, and the credibility evaluation index of the evaluation results for a type of equipment concerning anti-jamming capability was established based on the actual project background. Secondly, the system was evaluated by using the evaluation method for simulation credibility based on cloud model, and the evaluation method was improved. Finally, in order to improve the evaluation method, a one-dimensional backward cloud algorithm based on uniform distribution was derived, and the experiment was designed for verifying the validity of the algorithm. The simulation experimental results show that, the average absolute error of the proposed backward cloud algorithm is less than 5% for large data, which has high applicability and provides a way of thinking for the perfection of cloud model theory. In addition, the simulation credibility evaluation results show that, the proposed method has high accuracy and contains the data information of dispersion and agglomeration, which can provides more comprehensive evaluation and the prediction of error data.

Focusing on the issue that the recommender systems with explicit feedback drastically degrade the accuracy, the recommender technique using probabilistic matrix factorization with explicit and implicit feedback was proposed. So the explicit and implicit feedback was taken into account in this method. Firstly, user trust relationship matrix and user-item matrix were factorized using probabilistic matrix factorization to mix the feedback of user rating records. Then the model was trained to provide users with accurate prediction. The experimental results show that this technique can obtain user preferences effectively and produce large amounts of highly accurate recommendations.

As surgeons do not have effective prediction on the the implant surgery for vocal cord paralysis, resulting in high rate of failure, the finite element method was used for preoperative simulation. Through Computed Tomography (CT) data of larynx, the 3D geometric model of vocal cords and glottis trachea was extracted by Mimics, and then imported into ANSYS-Fluent to simulate the vocal vibration mode and airflow dynamic coupling characteristics before and after implanted surgery. The experimental data and clinical statistics data were compared to prove the feasibility of the finite element analysis techniques for implant surgery simulation of vocal cord paralysis. The experimental result can provide support for the design of surgery program.

IETF put forward a new version of IKE, IKEv2.Different from the old IKE,IKEv2 combines and redefines key exchange process. This paper introduced IKEv2, and analysed the security of key negotiation mechanism of IKEv2.Aiming at some security problems in EAP exchange, such as authentication with digital certificate and reauthentication to avoid accessing VPN tunnel with unauthorized identity, some improvement advice and solutions were given.