Symbolic music generation is still an unsolved problem in the field of artificial intelligence and faces many challenges. It has been found that the existing methods for generating polyphonic music fail to meet the marke requirements in terms of melody， rhythm and harmony， and most of the generated music does not conform to basic music theory knowledge. In order to solve the above problems， a new Transformer-based multi-track music Generative Adversarial Network （Transformer-GAN） was proposed to generate music with high musicality under the guidance of music rules. Firstly， the decoding part of Transformer and the Cross-Track Transformer （CT-Transformer） adapted on the basis of Transformer were used to learn the information within a single track and between multiple tracks respectively. Then， a combination of music rules and cross-entropy loss was employed to guide the training of the generative network， and the well-designed objective loss function was optimized while training the discriminative network. Finally， multi-track music works with melody， rhythm and harmony were generated. Experimental results show that compared with other multi-instrument music generation models， for piano， guitar and bass tracks， Transformer-GAN improves Prediction Accuracy （PA） by a minimum of 12%， 11% and 22%， improves Sequence Similarity （SS） by a minimum of 13%， 6% and 10%， and improves the rest index by a minimum of 8%， 4% and 17%. It can be seen that Transformer -GAN can effectively improve the indicators including PA and SS of music after adding CT-Transformer and music rule reward module， which leads to a relatively high overall improvement of the generated music.

Aiming at the problems of low detection efficiency and accuracy in the health management process of industrial robot axis， a new Health Index （HI） construction method based on action cycle degradation similarity measurement under the background of mechanical axis operation monitoring big data was proposed， and the robot Remaining Useful Life （RUL） prediction was carried out by combining Long Short-Term Memory （LSTM） network. Firstly， MPdist was used to focus on the similarity features of sub-cycle sequences between different action cycles of mechanical axis， and the deviation distance between normal cycle data and degradation cycle data was calculated， so that the HI was constructed. Then， the LSTM network model was trained by HI set， and the mapping relationship between HI and RUL was established. Finally， the MPdist-LSTM hybrid model was used to automatically calculate the RUL and give early warning in time. The six-axis industrial robot of a company was used to carry the experiments， and about 15 million pieces of data were collected. The monotonicity， robustness and trend of HI and Mean Absolute Error （MAE）， Root Mean Square Error （RMSE）， R-Square （ R^{\mathrm{2}} ）， Error Range （ER）， Early Prediction （EP） and Late Prediction （LP） of RUL were tested. The proposed method were compared with the methods such as Dynamic Time Warping （DTW）， Euclidean Distance （ED）， Time Domain Eigenvalue （TDE） combined with LSTM， MPdist combined with RNN and LSTM. The experimental results show that， compared with other comparison methods， the proposed method has the HI monotonicity and trend higher by at least 0.07 and 0.13 respectively， the higher RUL prediction accuracy， and the smaller ER， which verifies the effectiveness of the proposed method.

To solve the problem in most of conventional multi-task learning algorithms which evaluate risk independently for single task and lack uniform constraint across all tasks, a new hyper-spherical multi-task learning algorithm with adaptive grouping was proposed in this paper. Based on Extreme Learning Machine (ELM) as basic framework, this algorithm introduced hyper-spherical loss function to evaluate the risks of all tasks uniformly, and got decision model via iterative reweighted least squares solution. Furthermore, considering the existence of relatedness between tasks, this paper also constructed regularizer with grouping structure based on the assumption that related tasks had more similar weight vector, which would make the tasks in same group be trained independently. Finally, the optimization object was transformed into a mixed 0-1 programming problem, and a multi-objective method was utilized to identify optimal grouping structure and get model parameters. The simulation results on toy data and cylindrical vibration signal data show that the proposed algorithm outperforms state-of-the-art methods in terms of generalization performance and the ability of identifying inner structure in tasks.

In order to improve the security of secure communication, a new Generalized Hybrid Dislocated Function Projective Synchronization (GHDFPS) based on generalized hybrid dislocated projective synchronization and function projective synchronization was researched by Lyapunov stability theory and adaptive active control method. At the same time, the control methods of GHDFPS between two different-order chaotic systems with uncertain parameter and parameter identification were presented, and the application of the novel synchronization on secure communication was analyzed. By strict mathematical proof and numerical simulation, the GHDFPS between two different-order chaotic systems with uncertain parameter were achieved, the uncertain parameter was identified. Because of the variety of function scaling factor matrix, the security of secure communication has been increased by GHDFPS. Moreover, this synchronization form and method of control were applied to secure communication via chaotic masking modulation. Many information signals can be recovered and validated.

To analyze and estimate the parameters of Frequency-Hopping (FH) signals effectively, an adaptive time-frequency analytical method was proposed according to the characteristics of FH signal spectrogram and Wigner-Ville Didtribution (WVD). The width of window function for spectrogram was selected based on entropy measure so as to obtain optimal spectrogram representation of FH signal. The theoretical analysis and simulation results show that the spectrum analytical method based on entropy measure could give accurate estimation of the hopping cycle of FH signal under the white Gaussian noise environment of greater than 0dB. Compared with Smoothed Pseudo WVD (SPWVD) and its adaptive method, the proposed method can effectively reduce the variance and improve the accuracy of parameter estimation at low Signal-to-Noise Ratio (SNR). While for long observation signal, it owns faster operation speed.

This paper proposed a Secure LZW (SLZW) coding algorithm, where encryption was embedded into the improved LZW coding process, and SLZW can fulfill compression and encryption in a single step. In SLZW algorithm, dynamic Huffman tree was utilized to code the dictionary of LZW, and the initialization and updating of Huffman tree were controlled by a sequence of keystream generated by Coupled Map Lattcie (CML). The code words were further XORed with the keystream to generate the ciphertext. The SLZW was applied to GIF image encryption. The experimental results and their analyses indicate that the proposed SLZW algorithm not only has good security, but can also improves the compression ratio by about 10%. Therefore, SLZW can find its wide applications in practice.

During the restoration of actual motion blurred images based on Wiener filtering, restoration results get affected by serious ringing effect and unsatisfactory local restoration. Its main reasons were found through theoretical analysis, experimental comparisons and the study of the characteristics of the actual motion blurring process. It was proposed that the artificial boundary compensation and block-restoration were used to restrain ringing effect and local unsatisfactory restoration. The relations of blur parameters, space positions and speeds, even the standard of blocking partition were given. The experimental results verify that the proposed method of the boundary compensation and the block-restoration can effectively reduce ringing effect and maintain the consistency of the overall image restoration effect.

Concerning the problems of low precision ratio and low recall ratio of function match in the research of workflow integration, the authors implemented the matching mechanism based on formal semantic of extract pre/post match pattern, and proposed matching principles on the basis of algebraic expressions in high level programming languages. The specific algorithm was raised up and also an example was given to analyze and illustrate the algorithm. The proposed algorithm is suitable for function matching in workflow integration and it is founded on strict formal method, so that it can be analyzed and verified conveniently with mathematical methods. The limitation is that it is based on elementary algebraic expression.

In order to improve the information processing capacity of chaotic neural networks, associative memory performance of a time-delay symmetric global coupled neural network was investigated by using a parameter modulated control method to control the coherent parameter. It can be observed that its output can be stabilized when only partial neurons enter the periodic orbits and the output sequence of the controlled network does not contain other patterns but the stored pattern corresponding to the initial input and its reverse pattern. The experimental results suggest that the network has good tolerance and excellent correct rate so that it is fit for information processing and pattern recognition.

Radio Frequency IDentification (RFID) is an automatic technology for identification of objects in the open system environment. The technology currently offers a wide range of application prospects in logistics, manufacturing, retail and many other areas. Thus how to design secure and efficient authentication protocols for RFID systems is a fundamental issue. In this paper, a novel RFID mutual authentication protocol that utilized a keyed hash function and synchronized information was proposed. We described the protocol in detail and discussed the related protocols. The results show our protocol has the advantages in security and efficiency.

The scheduling strategies and schedulability conditions of RM(Rate Monotonic) tasks allocation algorithms were analyzed, and the actions that how the tasks allocation algorithms allocate the task to processors were depicted in detail based on the model of preemptive period task in multiprocessor system. According to the theory of Liu & Layland, the theory of the minimum utilization bound for task allocation algorithm was presented. The results of simulation show that the utilization bound of allocation algorithm is the key principle for differently characterized task sets to select different algorithms to allocate tasks. Through the comparison between the total utilization of the task set and the utilization bound of the algorithm, it can be judged whether the algorithm will bring out feasible allocation of the task set.