Aiming at the problems of data sparsity and inaccuracy of recommendation results in the traditional collaborative filtering algorithms, a Probability Matrix Factorization recommendation model based on Latent Dirichlet Allocations (LDA) and Convolutional Neural Network (CNN) named LCPMF was proposed, which considers the topic information and deep semantic information of project review document comprehensively. Firstly, the LDA topic model and the text CNN were used to model the project review document respectively. Then, the significant potential low-dimensional topic information and the global deep semantic information of project review document were obtained in order to capture the multi-level feature representation of the project document. Finally, the obtained features of users and multi-level projects were integrated into the Probability Matrix Factorization (PMF) model to generate the prediction score for recommendation. LCPMF was compared with the classical PMF, Collaborative Deep Learning (CDL) and Convolutional Matrix Factorization (ConvMF) models on the real datasets Movielens 1M, Movielens 10M and Amazon. The experimental results show that, compared to PMF, CDL and ConvMF models, on the Movielens 1M dataset, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed recommender model LCPMF are reduced by 6. 03% and 5.38%, 5.12% and 4.03%, 1.46% and 2.00% respectively; on the Movielens 10M dataset, the RMSE and MAE of LCPMF are reduced by 5.35% and 5.67%, 2.50% and 3.64%, 1.75% and 1.74% respectively; while on the Amazon dataset, the RMSE and MAE of LCPMF are reduced by 17.71% and 23.63%, 14.92% and 17.47%, 3.51% and 4.87% respectively. The feasibility and effectiveness of the proposed model in the recommendation system are verified.

For the problems of low recommendation accuracy, data sparsity and malicious recommendation, a new recommendation model based on Probability Matrix Factorization (PMF) and fusing trust was proposed. Firstly, by establishing a Collaborative Filtering Model based on Trust Similarity (CFMTS), the improved trust mechanism was integrated into the collaborative filtering recommendation algorithm. The trust value was obtained through global trust and local trust calculation. The local trust was obtained by calculating the direct trust value and the indirect trust value of the user by the trust propagation mechanism, the global trust was calculated by the trust directed graph. Then, the trust value was combined with the score similarity to solve the problems of data sparsity and malicious recommendation. At the same time, CFMTS was integrated into the PMF model to establish a new recommendation model-Model based on Probability Matrix Factorization and Fusing Trust (MPMFFT). The user feature vectors and the project feature vectors were calculated by the gradient descent algorithm to generate the predicted scores, further improving the accuracy of the recommender system. Through experiments, the proposed MPMFFT was compared with the classical models such as PMF, Social Matrix Factorization (SocialMF), Social Recommendation (SoRec) and Recommendations with Social Trust Ensemble (RSTE). The proposed model has the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) decreased by 2.9% and 1.5% respectively compared with the optimal model RSTE on the open real dataset Epinions, and has the MAE and RMSE decreased by 1.1% and 1.8% respectively compared with the optimal SocialMF model on open real dataset Ciao, verifying that the proposed model is significantly improved on the above indicators. The results confirme that the propose model can resolve the problem of data sparseness and malicious recommendation to some extent, and effectively improved the recommendation quality.

On the purpose of integrated optimization of production scheduling and preventive maintenance for multi-product tasks which in producing workshops, an integrated optimization model of production scheduling and preventive maintenance was established comprehensively, in which processing sequence, batch quantity, preventive maintenance measures and other factors were taken into account consequently, on the premise that there are sufficient orders, as well as the joint optimization objective to minimize overall manufacturing costs and processing time. In view of the characteristics of the model, based on the non-dominated sorting genetic algorithm, a single-parent genetic algorithm with variable-length genome was put forward as the resolving method for the model based on the catastrophe mechanism and glory space, which keeps in combination with introducing interruption and splice operators. Besides, under different parameter conditions and various scales of problems, simulative experiments were conducted to verify the efficiency of the proposed algorithm to resolve complex integrated optimization problems of production scheduling and preventive maintenance.

Aiming at the problem of mutual interference between attitude algorithm and external force acceleration estimation in inertial navigation system, a new method based on quaternion and extended Kalman filter was proposed. Firstly, the acceleration data of the sensor was corrected by using the estimated external force acceleration data to obtain the accurate reverse gravity acceleration, combined with geomagnetic field vector and calculated by the gradient descent algorithm, the rotate quaternions were obtained. Secondly, the extended Kalman filter model was constructed to update the rotate quaternions and external force acceleration, the prediction value of rotate quaternions and the external force were obtained. Finally, the measured values of rotate quaternions and the acceleration data were corrected by Kalman filtering method, the accurate rotate quaternions and the external force acceleration of the three axis directions in reference coordinate system were obtained. The experimental results show that the method for the synchronization estimation of attitude and external force acceleration by extended Calman filter can quickly converge and accurately get the information of the attitude and the external force acceleration, its Euler angle error is ±1.95° and acceleration error is ±0.12 m/s ². The method can effectively restrain the influence of the external force acceleration on the attitude algorithm, and accurately estimate the external force.

For dealing with the problem of being unable to sample beat signals with equal interval that is inherent in the Pseudo random Noise (PN) code modulated Doppler laser radar, a new Discrete Fourier Transform (DFT) method which applies to non-uniform sampling data was proposed. Firstly, system model of Doppler laser radar for simultaneously measuring range and speed was provided, and the reason of being unable to sample beat signals with equal interval was pointed out. Then, by theoretical deducing, a new spectrum analysis method was proposed for processing non-uniform sampling signals. Finally, simulations were performed to verify the feasibility of applying the proposed method to non-uniform sampling data. As a result, within Doppler frequency range which is created by moving targets in roads, the method can obtain the frequency of non-uniform sampling Doppler signals efficiently even when Signal-to-Noise Ratio (SNR) is low to 0 dB.

To solve the problems of complex planning method, too many man-made specified parameters and huge computation in the existing gait dynamic model, the gait generation approach of humanoid robot based on the data collected by Kinect to learn human gait was proposed. Firstly, the skeleton information was collected by Kinect device, human joint local coordinate system was built by the least square fitting method. Next, the dynamic model of human body mapping robot was built, and robot joint angle trajectory was generated according to mapping relation between main joints, the studies of walking posture from human was realized. Then, Robot's ankle joint was optimized and controlled by gradient descent on the basis of Zero-Moment Point (ZMP) stability principle. Finally, on the gait stability analysis, safety factor was proposed to evaluate the stability of robot walk. The experimental results show that the safety factor of walking keeps in 0 to 0.85, experctation is 0.4825 and ZMP closes to stable regional centres, the robot realizes walking imitating human posture and gait stability, which proves the validity of the method.

Image sharing is an attractive research subject in computer image information security field. Seeking for Perfect and Ideal image threshold secret sharing scheme (i.e. the complete image sharing scheme) is one of the unresolved challenging problems. By introducing into the methods of pixel matrix secret sharing over pixel value field GF(2m) and algebraic-geometry coding, a complete scheme of image sharing with a (t, n) threshold structure was achieved in this paper. The scheme could encode secret images into n shadow images in such a way that all the shadow images were in a Perfect and Ideal (t, n) threshold structure, while each shadow image had its own visual content assigned at random. This approach to image sharing was able to be applied to the new information carrier technology, e.g. network multipathed transmission of secret image in high security level, distributed storage control of secret image, bar-code in k dimension and Popcode. This paper also presented a method to cut down a great deal of computational time for image sharing based on a pixel field GF(2m), called "partition and paralleling of m-bit pixel".

By analyzing the structure of Low-Density Parity-Check (LDPC) code check matrix in LTE-Advanced standard, this paper proposed a low-cost encoder with high input packet throughput on Quasi Cyclic-LDPC (QC-LDPC) code. With exploiting the number of null matrices in the mother parity check matrix, the whole parity check matrix could be partitioned into an array of block matrices, where each block matrix was either a null sub-matrix or a cyclic shift of an identity sub-matrix, and then it encoded serially. The experimental results show that the proposed encoder's coding time is the same as 32% of the ideal time and the resources consumption is the same as 33% of the ideal situation within the analogous methods. This result achieves the balance between coding time and resources consumption, which means the designed encoder meets the LTE-Advanced standard: low cost with high transmission. In addition, by changing the parameters in the ROM which saves the check matrix, the proposed encoder is flexible to implement the encoding of LDPC code with different code length or rate.

Concerning the slow convergence speed of unlabeled samples classification while using the traditional Active Learning (AL) method to deal with the large-scale data, a Hierarchical Clustering Active Learning (HC_AL) algorithm was proposed. During operation in the algorithm, the majority of the unlabeled data were clustered hierarchically and the center of each cluster was labeled to replace the category label of this hierarchy. Then the wrong labeled data were added into the training data sets. The experimental results at the data sets show that the proposed algorithm improves the generalization ability and the convergence speed. Moreover, it can greatly improve the active learning convergence speed and obtain relatively satisfactory learning ability by using the method of hierarchical refinement and stepwise refinement.