In order to achieve the stable and precise control of industrial processes with non-linearity， hysteresis， and strong coupling， a new control method based on Local Policy Interaction Exploration-based Deep Deterministic Policy Gradient （LPIE-DDPG） was proposed for the continuous control of deep reinforcement learning. Firstly， the Deep Deterministic Policy Gradient （DDPG） algorithm was used as the control strategy to greatly reduce the phenomena of overshoot and oscillation in the control process. At the same time， the control strategy of original controller was used as the local strategy for searching， and interactive exploration was used as the rule for learning， thereby improving the learning efficiency and stability. Finally， a penicillin fermentation process simulation platform was built under the framework of Gym and the experiments were carried out. Simulation results show that， compared with DDPG， the proposed LPIE-DDPG improves the convergence efficiency by 27.3%； compared with Proportion-Integration-Differentiation （PID）， the proposed LPIE-DDPG has fewer overshoot and oscillation phenomena on temperature control effect， and has the penicillin concentration increased by 3.8% in yield. In conclusion， the proposed method can effectively improve the training efficiency and improve the stability of industrial process control.

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.

In the non-overlapping filed of multi-camera system, the single-shot person identification methods cannot well deal with appearance and viewpoint changes. Based on the multiple frames acquired from surveillance cameras, a new technique which combined Hidden Markov Model (HMM) with appearance-based feature was proposed. First, considering the structural constraint of human body, the whole-body appearance of each individual was equally vertically divided into sub-images. Then multi-level threshold method was used to extract Segment Representative Color (SRC) and Segment Standard Variation (SSV) feature. The feature dataset acquired from multiple frames was applied to train continuous density HMM,and the final recognition was realized by these well-trained model. Extensive experiments on two public datasets show that the proposed method achieves high recognition rate, improves robustness against viewpoint changes and low resolution, and it is simple and easy to realize.

Along with the development of intelligent transportation information system, intelligent public transportation system is gradually popularized. Such information system is designed to provide all kinds of real-time information to transit passengers on the conditions of the network, and hence affect passengers’ travel choice behavior and improve passenger travel convenience and flexibility, so as to improve the social benefit and service level of the public transit system. Concerning the particularity of the transit network, with electronic bus stop information of Chengdu as an example, a questionnaire was designed to investigate passengers’ route choice behavior and travel intention. Qualitative and quantitative analysis and random utility theory were adopted，based on Logit model and mixed Logit model, route choice models were established, using characteristic variables of various options and passengers’ personal socio-economic attributes as explanatory variables. The method of Monte Carlo simulation and maximum likelihood were used to estimate parameters. The results indicate that the differences of route choice behavior resulting from individual preferences can be reasonably interpreted by mixed Logit model, which helps us better understand the complexity of transit behavior, so as to guide the application.

In order to overcome the drawbacks of Particle Swarm Optimization (PSO) that converges slowly at the last stage and easily falls into local minima, this paper proposed a new PSO algorithm with convergence acceleration and adaptive escape (FAPSO) inspired by the Artificial Bee Colony (ABC) algorithm. For each particle, FAPSO conducted two search operations. One was global search and the other was local search. When a particle got stuck, the adaptive escape operator was used to search the particle again. Experiments were conducted on eight classical benchmark functions. The simulation results demonstrate that the proposed approach improves the convergence rate and solution accuracy, when compared with some recently proposed PSO versions, such as CLPSO. Besides, the results of t-test show clear superiority.

Concerning the special nature of the transit network, the generalized path concept that maybe easily describe passenger route choice behavior was put forward. The key cost of each path was considered. Based on the analytical framework of cumulative prospect theory and passengers' perception, a stochastic user equilibrium assignment model was developed. A simple example revealed that the limitations of the traditional method can be effectively improved by this proposed method. The basic assumption of complete rationality in traditional model was improved. It helped us enhance our understanding of the complexity of urban public transportation behavior and the rule of decision-making. The facility layout and planning of the public transportation can be determined with this result, as well as the evaluation of the level of service. In addition, it can also be used as valid data support for traffic guidance.

Based on non-contact three-dimensional measuring principle of computer vision and light refraction principle, a new theoretical method of visual measurement in multi-medium was proposed. It used two teams of binocular cameras from different perspectives to observe space target in water, measured the various landmarks of the underwater target by making use of binocular stereo vision technology and the mathematical model of two light refractions, and then obtained the three-dimensional coordinates of landmarks, by plane fitting and coordinate transformation, eventually got six-dimensional position and attitude coordinates of underwater target. The results show that the method is suitable for multi-medium visual measurement with high accuracy and good stability.

The problem of birth defects is one of the most important public health problems in the world, and the application of data mining method to improve the diagnostic accuracy for birth defects is a hot medical research issue. The authors proposed two emerging patterns for birth defects feature extraction: the defection contrast to normal and the normal contrast to defection. The Birth Defects Detection based on Emerging Patterns (BDD-EP) algorithm was implemented through combining the proposed patterns with C4.5 decision tree. The extensive experimental results show that the detection accuracy of BDD-EP is as high as 90.1%, the F-measure of normal samples is 93.9%, and the F-measure of defect samples is 741%. Compared with other famous classical classification algorithms, BDD-EP algorithm can get better results.

In order to discover the special behaviors of Sub Complex System (SCS) under intervention, the authors proposed the concept of contrast inequality, proposed and implemented the algorithm for mining the segmentwise intervention; by imposing perturbance intervention on SCS, the authors proposed and implemented the causality discovery algorithm. The experiments on the real data show that segmentwise intervention algorithm discovers new intervention rules, and the causality discovery algorithm discovers the causality relations in the air pollution data set, and both are difficultly discovered by traditional methods.