Focusing on the failure of intrusion detection resulted from low captured image width of traditional Wireless Visual Sensor Network （WVSN） target-barrier， a Wireless visual sensor network β Quality of Monitoring （β-QoM） Target-Barrier coverage Construction （WβTBC） algorithm was proposed to ensure that the captured image width is not less than β. Firstly， the geometric model of the visual sensor β-QoM region was established， and it was proven that the width of intruder image captured by the target-barrier of intersection of all adjacent visual sensor β-QoM regions must be greater than or equal to β. Then， based on the linear programming modeling for optimal β-QoM target-barrier coverage of WVSN， it was proven that this coverage problem is NP-hard. Finally， in order to obtain suboptimal solution of the problem， a heuristic algorithm WβTBC was proposed. In this algorithm， the directed graph of WVSN was constructed according to the counterclockwise β neighbor relationship between sensors， and Dijkstra algorithm was used to search β-QoM target-barriers in WVSN. Experimental results show that WβTBC algorithm can construct β-QoM target-barriers effectively， and save about 23.3%， 10.8% and 14.8% sensor nodes compared with Spiral Periphery Outer Coverage （SPOC）， Spiral Periphery Inner Coverage （SPIC） and Target-Barrier Construction （TBC） algorithms， respectively. In addition， under the condition of meeting the requirements of intrusion detection， with the use of WβTBC algorithm， the smaller β is， the higher success rate of building β-QoM target-barrier will be， the fewer nodes will be needed in forming the barrier， and the longer working period of WVSN for β-QoM intrusion detection will be.

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.

Using vector method for color image denoising, the complexity of the algorithm is high and cannot achieve real-time performance. A method for high fidelity color image denoising was proposed based on Gaussian weighting and adaptive manifold. Firstly, it used the non-local means algorithm to get high-dimensional data, and used the improved Gaussian kernel to calculate the weight of color image. Secondly, splatting method was used to deal with the high-dimensional data, and a Gaussian distance-weighted projection of the colors of all pixels was performed onto each adaptive manifold. Thirdly, smooth dimensionality reduction was done on convection shape, and iterative method was used for image smoothing. Finally, the final filter response was computed for each pixel by interpolating blurred values gathered from all adaptive manifolds. The experimental results show that the algorithm has a superior denoising performance than the original one, and it also can improve real-time performance. By using this algorithm, the details can be preserved well. Peak Signal-to-Noise Ratio (PSNR) can be improved nearly 2.0dB, and Structural Similarity Index Measurement (SSIM) can be improved more than 1%.

It is difficult for the object tracking algorithm based on single feature, to track the object in complex cases. Therefore, this paper proposed an algorithm fusing multiple features for object tracking based on adaptive background information. The algorithm was based on the use of color feature and gray level co-occurrence matrix texture feature to represent the object. Under the frame of particle filter, it analyzed the particle space distribution, particle value distribution and ability to distinguish the background information with different feature. Then it presented an efficient fusion coefficient calculation. According to the object's appearance of changes in the process of tracking, it updated the object template adaptively. The experimental results in different settings show that this algorithm greatly improves the resistance to background interference, under the premise of not reducing the real-time. In all sorts of situations, it has good stability and robustness.

This paper presented an improved sequential heuristic algorithm to solve the classical One-Dimensional Cutting Stock Problem (1D-CSP). The prices of the items assigned to the current pattern were adjusted so as to make them more reasonable. The pattern, whose unit value was the maximum among those of the patterns generated from solving a bounded knapsack problem, was added to the cutting plan. Several solutions were constructed iteratively and at last the best one was selected. The heuristic algorithm can generate cutting plans of higher material utilization level, and at the same time, can consider multiple objectives such as pattern reduction and residual length increment. The computational results show the effectiveness of the presented algorithm.

Focused on the requirements of low cost and low power in Wireless Sensor Network (WSN), this paper proposed a range-free localization algorithm based on Support Vector Machine (SVM) classification regions. First, SVM constructed a binary decision tree classifier via learning of the training data. Then the classifier determined the certain classification region where the unknown nodes were located. Finally, the study used the region's center point as the estimated position of the unknown node. The proposed algorithm required mere connectivity information (i.e., hop counts only), so as to reduce the network cost and communication load. The simulation results show that this algorithm alleviates the coverage holes and border problem significantly while certain location accuracy is assured.