Small Open Reading Frames （sORFs） in long non-coding RNA （lncRNA） can encode short peptides with length no more than 100 amino acids. Aiming at the problem that the features of sORFs in lncRNA are not distinct and the data with high reliability are not enough in short peptide prediction research， a Deep Forest （DF） model based on representation learning was proposed. Firstly， the conventional lncRNA feature extraction method was used to encode the sORFs. Secondly， the AutoEncoder （AE） was used to perform representation learning to obtain highly efficient representation of the input data. Finally， a DF model was trained to predict the short peptides encoded by lncRNA. Experimental results show that the accuracy of this model can achieve 92.08% on Arabidopsis thaliana dataset， which is higher than those of the traditional machine learning models ， deep learning models and combined models， and this model has better stability. In addition， the prediction accuracy of this method can reach 78.16% and 74.92% on Glycine max and Zea mays datasets respectively， verifying the good generalization ability of the proposed model.

To eliminate the most common problem of the flooding of Address Resolution Protocol (ARP) messages in Ethernet, a new ARP proxy mechanism, taking account of hybrid environment of Software-Defined Network (SDN) and traditional network was proposed environment. In this mechanism, the advantage of network-wide view of SDN paradigm was used to register hosts information once accessing into the network and update the records in real-time by keeping trace of hosts' dynamics and network failure. Thus, most ARP request messages could be responded directly by the controller. The evaluation results show that this proposed scheme reserves the auto-configuration characteristic, which is transparent to hosts, compatible with the existed hardware without any changes, reduces network traffic and allows redundant links existed in network, so as to improve the scalability of the Ethernet.

When building up the virtual battlefield system, to realize the three-dimensional (3D) detection range of radar in complex natural environment and complex environment of electronic interference, an improved support jamming model was proposed, according to the fundamental principle of Advanced Propagation Model (APM) and taking full consideration of the influence of electronic interference. This model mixed APM and electronic interference model together, and paid special attention to the refractive influence. Besides, this model could depict the double influence from complex natural and electronic interfering environments. Furthermore, a modified Marching Cube (MC) model, the triangles gained by MC being replaced by surface points and the interpolated points by middle points, was used to accelerate the process of visualization. According to the procedure of data gaining, data processing and data rendering, the 3D detection range of radar on specific electronic jamming environment was rendered via Visualization Toolkit (VTK).