The existing industry-university-research performance evaluation systems and methods have problems such as single coverage of evaluation indicators， insufficient expression of evaluation sample features， and self-optimization ability of evaluation models to be improved， the system and method of subjective and objective intelligent evaluation of industry-university-research comprehensive performance were proposed. Firstly， for the three-party cooperation subjects， the factors and the connections between these factors that affect performance in the process of industry-university-research cooperation were excavated， and the three-level subjective and objective performance evaluation system of industry-university-research was self-constructed. Secondly， the features expression of discrete samples was enhanced by mapping the collected discrete sequence evaluation samples to different high-dimensional spatial domains， such as polar coordinate space and Markov transfer matrix. Then， through the chaotic optimization strategy design based on elite reverse somersault foraging， the depth model redundancy compression and hyperparameter global optimization efficiency were improved， and the ParNet （Parallel Network） classification model with lightweight compression and high-dimensional superparameter Adaptive optimization （AParNet） was constructed. Finally， the model was applied to industry-university-research performance evaluation to achieve high-performance intelligent performance evaluation. The experimental results show that this method fits well with the applications of discrete sequence non-linear classification and improves the classification performance while reducing the computational load when an optimization strategy is added to the model. Specifically， compared to ParNet， AParNet reduces the number of parameters by 10.8%， effectively achieving model compression， and its classification accuracy in performance evaluation of industry-university-research cooperation can reach 98.6%. Therefore， in the applications of intelligent performance evaluation of industry-university-research cooperation， the proposed method improves the adaptive ability of evaluation model and achieves accurate and efficient industry-university-research performance evaluation.

Traditional bipedal robot walking is controlled by trajectory tracking， while human walking is in the passive state in most of the time. Aiming at the problem that the semi-passive bipedal robot with variable length telescopic legs starts to walk from a static condition， a starting and walking human-like control method was proposed. Firstly， a serial elasticity driven Bipedal Spring-Loaded Inverted Pendulum （B-SLIP） model was used. Then， the Lagrange method was used to establish the walking dynamics equation. With the self-stability of the proposed model， in the double support stage， the energy error Proportional-Integral （PI） feedback control and lazy control method were used to control the hind leg extension and contraction. In the single support stage， the swing-leg swing back method was used to control the height and forward speed of the robot. Simulation results show that the proposed control strategy can enable the bipedal robot to realize the starting and walking process on the horizontal plane， and the corresponding control system has anti-interference ability against external period disturbance force.

In the field of traffic safety， the road abandoned objects easily cause traffic accidents and become potential traffic safety hazards. Focusing on the problems of low recognition rate and poor detection effect for different abandoned objects of traditional road abandoned object detection methods， a road abandoned object detection algorithm based on the optimized instance segmentation model CenterMask was proposed. Firstly， the residual network ResNet50 optimized by dilated convolution was used as the backbone neural network to extract image features and carry out the multi-scale processing. Then， the Fully Convolutional One-Stage （FCOS） target detector optimized by Distance Intersection over Union （DIoU） function was used to realize the detection and classification of road abandoned objects. Finally， the spatial attention-guided mask was used as the mask segmentation branch to realize the object shape segmentation， and the model training was realized by the transfer learning method. Experimental results show that， the detection rate of the proposed algorithm for road abandoned objects is 94.82%， and compared with the common instance segmentation algorithm Mask Region-Convolutional Neural Network （Mask R-CNN）， the proposed road abandoned object detection algorithm has the Average Precision （AP） increased by 8.10 percentage points in bounding box detection.

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.

The effect data of actions show a significant randomness because of lots of uncertain elements in the course of action. In order to explore the rules of warfare hidden behind the data, the effectiveness evaluation was studied based on statistical analysis method. The basic concept of action and its effectiveness were analyzed. With the simulation data produced by enhanced irreducible semi-autonomous adaptive combat neural simulation toolkit (EINSTein), a single, a group and multi group experimental methods were used to study the statistical characteristics of offensive actions and find out that to a party who has a combat advantage, compared with increased number of personnel, the increased radius of firepower can achieve better operational results. On this basis, an evaluation method of action effectiveness was proposed and validated with simulation data. Therefore, a feasible resolution is provided to evaluate the action effectiveness based on actual combat data.

According to the non-rigid characteristics of the moving target, an algorithm based on Mantle ratio, which can effectively separate multi-target using Support Vector Machine (SVM) and automatically select the largest characteristic region of non-rigid was proposed. It canceled the special requirements of selected regions beforehand.