Offensive speech on the internet seriously disrupts the normal network order and destroys the network environment for healthy communication. Existing detection technologies focus on the distinctive features in the text， and are difficult to discover more implicit attack methods. For the above problems， an offensive speech detection model BSWD （Bidirectional Encoder Representation from Transformers-based Sarcasm and Word Detection） incorporating irony mechanism was proposed. First， a model based on irony mechanism Sarcasm-BERT was proposed to detect semantic conflicts in speech. Secondly， a fine-grained word offensive feature extraction model WordsDetect was proposed to detect offensive words in speech. Finally， the model BSWD was obtained by fusing the above two models. The experimental results show that the accuracy， precision， recall， and F1 score indicators of the proposed model are generally improved by 2%， compared with the BERT（Bidirectional Encoder Representation from Transformers） and HateBERT methods. BSWD significantly improves the detection performance and can better detect implicit offensive speech. Compared with the SKS （Sentiment Knowledge Sharing） and BiCHAT （Bi-LSTM with deep CNN and Hierarchical ATtention） methods， BSWD has stronger generalization ability and robustness. The above results verify that BSWD can effectively detect the implicit offensive speech.

Considering the issue that the word alignment methods commonly used in the existing methods for aligned multimodal language sequence sentiment analysis lack interpretability， a Multi-Dynamic Aware Network （MultiDAN） for unaligned multimodal language sequence sentiment analysis was proposed. The core of MultiDAN was multi-layer and multi-angle extraction of dynamics. Firstly， Recurrent Neural Network （RNN） and attention mechanism were used to capture the dynamics within the modalities； secondly， intra- and inter-modal， long- and short-term dynamics were extracted at once using Graph Attention neTwork （GAT）； finally， the intra- and inter-modal dynamics of the nodes in the graph were extracted again using a special graph readout method to obtain a unique representation of the multimodal language sequence， and the sentiment score of the sequence was obtained by applying a MultiLayer Perceptron （MLP） classification. The experimental results on two commonly used publicly available datasets， CMU-MOSI and CMU-MOSEI， show that MultiDAN can fully extract the dynamics， and the F1 values of MultiDAN on the two unaligned datasets improve by 0.49 and 0.72 percentage points respectively， compared to the optimal Modal-Temporal Attention Graph （MTAG） in the comparison methods， which have high stability. MultiDAN can improve the performance of sentiment analysis for multimodal language sequences， and the Graph Neural Network （GNN） can effectively extract intra- and inter-modal dynamics.

Aiming at the problems of poor robustness， low precision and success rate in the existing tiny object tracking algorithms， a tiny object tracking algorithm， SiamTrans， was proposed on the basis of Siamese network and Transformer. Firstly， a similarity response map calculation module was designed based on the Transformer mechanism. In the module， several layers of feature encoding-decoding structures were superimposed， and multi-head self-attention and multi-head cross-attention mechanisms were used to query template feature map information in feature maps of different levels of search regions， which avoided falling into local optimal solutions and obtained a high-quality similarity response map. Secondly， a Prediction Module （PM） based on Transformer mechanism was designed in the prediction subnetwork， and the self-attention mechanism was used to process redundant feature information in the prediction branch feature maps to improve the prediction precisions of different prediction branches. Experimental results on Small90 dataset show that， compared to the TransT （Transformer Tracking） algorithm， the tracking precision and tracking success rate of the proposed algorithm are 8.0 and 9.5 percentage points higher， respectively. It can be seen that the proposed algorithm has better tracking performance for tiny objects.

The deviation of the aero-engine on-board adaptive system model could not be completely eliminated, which may result in serious estimation deviation and filtering divergence. A new Kalman estimation algorithm with fading factor was proposed. Adjusting the weight of innovation covariance and increasing the effect on realistic measurement data in state estimation, the accuracy of aero-engine parameters estimation was ensured. Compared with the conventional Kalman filtering, the simulation results shows that the method proposed can restrain filtering divergence and obtain the high accuracy of estimation and the short convergence time. The derivation of the new method is simple, the computation amount is little, and the engineering application value is high.

Considering the fact that there is a lack of Controller Area Network (CAN) bus application-layer driver in Android system, a method was developed to transplant the CAN bus application-layer driver from Linux to Android system. SAE J1939 protocol was selected to be the protocol of Android CAN bus application-layer protocol and the linux-can-j1939 project developed by Kurt Van Dijck and Pieter Beyens was ported to Android system. First, the authors analyzed the project structure and merged the corresponding files to Android kernel, then modified the header files and protocol implementation sources, and added the missing structures and functions in kernel. Finally, Android kernel was compiled after modifying the Makefile and Kbuild files. The experiment results show that the new kernel realizes the functions of SAE J1939 protocol such as address declaring, data packing and unpacking, and network management. Based on this porting, a variety of CAN-based Android applications can be developed with the help of Android application-layer interface.

According to the requirement of modern postal logistics, a kind of intelligent control system for post parcel servo was established. This system was structured by integrated OMRON Programmable Logic Controller (PLC), touch panel and IFIX supervisory control and data acquisition software. The function of variable-frequency driver for post parcel delivery was analyzed. The communication between PLC and variable-frequency driver was realized by Modbus Remote-Terminal-Unit (RTU) protocol. And the data exchange process was implemented by OLE for Process Control (OPC) and data services program. Application shows that automatic and intelligent control of post parcel delivery has been achieved with variable frequency technology, and the design of control system is reasonable and reliable.

Traveling by rail has become one of the important transportation modes of current residents. The core of Smart Rail Transportation (SRT) is to change the existing modes of railway transportation by a more intelligent way through modern information technology. Its aim is to bring more efficient, safe, comfortable intelligent transportation systems for human activities. This paper discussed four steps of a deeper wisdom in SRT, including wisdom data collection, wisdom data fusion, wisdom data mining and wisdom decision-making. These four steps form a spiral ascendance of intelligent information processing, and ultimately achieve a deeper wisdom in SRT.