Considering the problem of out-of-stock and inventory overstock caused by dual-channel supply chain inventory system, three dynamic optimization models of three modes: single control, centralized control and cross-replenishment control of dual-channel inventory were established under the condition that both online and offline channels are facing stochastic demand. Firstly, based on the dynamic differential equation of inventory, guided by the control theory creatively, and by means of Taylor expansion and Laplace transformation, the feedback transfer function of dual-channel inventory system was obtained. Secondly, considering the periodic interactions, upstream and downstream interactions and inter-channel interactions in the process of cross-replenishment’s purchase-sale-stock, delay control, feedback control and Proportion-Integral-Derivative (PID) control were used to construct a complex interactive system with two inputs and two outputs, so as to explore the dynamic balance between supply and demand of the dual-channel inventory system itself and among channels, optimize the dual-channel inventory holdings, reduce the out-of-stock times and amount and keep them to a dynamic equilibrium. Finally, through numerical simulation experiments, three dual-channel inventory control strategies were compared. The simulation results show that when on online and offline channels were facing different distributions of stochastic demand, the residual stock of cross-replenishment control decreased by 4.9% compared with that of single control, and the out-of-stock rate of cross-replenishment control decreased by 66.7% and 60% respectively compared those of single control and centralized control. The experimental results show that when online and offline channels are facing different distributions of stochastic demand, the use of cross-replenishment strategy can effectively reduce inventory holdings, reduce the times and amount of out-of-stock, and thus save the inventory costs.

To solve the problem of classification of unbalanced data sets and the problem that the general cost-sensitive learning algorithm can not be applied to multi-classification condition, an integration method of cost-sensitive algorithm based on average distance of K-Nearest Neighbor (KNN) samples was proposed. Firstly, according to the idea of maximizing the minimum interval, a resampling method for reducing the density of decision boundary samples was proposed. Then, the average distance of each type of samples was used as the basis of judgment of classification results, and a learning algorithm based on Bayesian decision-making theory was proposed, which made the improved algorithm cost sensitive. Finally, the improved cost-sensitive algorithm was integrated according to the K value. The weight of each base learner was adjusted according to the principle of minimum cost, obtaining the cost-sensitive AdaBoost algorithm aiming at the minimum total misclassification cost. The experimental results show that compared with traditional KNN algorithm, the improved algorithm reduces the average misclassification cost by 31.4 percentage points and has better cost sensitivity.

Concerning the problem that too large visual dictionary may increase the time cost of image classification in the Bag-Of-Visual words (BOV) model, a Weighted-Maximal Relevance-Minimal Semantic similarity (W-MR-MS) criterion was proposed to optimize visual dictionary. Firstly, the Scale Invariant Feature Transform (SIFT) features of images were extracted, and the K-Means algorithm was used to generate an original visual dictionary. Secondly, the correlation between visual words and image categories and semantic similarity among visual words were calculated, and a weighted parameter was introduced to measure the importance of the correlation and the semantic similarity in image classification. Finally, based on the weighing result, the visual word which correlation with image categories was weak and semantic similarity among visual words was high was removed, which achieved the purpose of optimizing the visual dictionary. The experimental results show that the classification precision of the proposed method is 5.30% higher than that of the traditional K-Means algorithm under the same visual dictionary scale; the time cost of the proposed method is reduced by 32.18% compared with the traditional K-Means algorithm under the same classification precision. Therefore, the proposed method has high classification efficiency and it is suitable for image classification.

In view of the problem that the fault tolerance capability is low and strong constraint conditions need to be satisfied in the construction of most array erasure codes at present, a new type of array erasure codes based on coding chains was proposed. In the new array erasure codes, coding chains with different slopes were used to organize the relationship among data elements and check elements, so as to achieve infinite fault tolerance capability in theory; the strong constraint conditions like the prime number limitation was avoided in construction, which is easy to be practical and extensible. Simulation results show that, compared with Reed-Solomon codes (RS codes), the efficiency of the proposed array erasure codes based on coding chains is more than 2 orders of magnitude; under the condition of fixed fault tolerance, its storage efficiency can be improved with the increase of the strip size. In addition, the update penalty and repair cost of the array codes is a fixed constant, which will not increase with the expansion of the storage system scale or the increase of fault tolerance capability.

Due to using a single point in time for the processing unit, current network alerts information fusion methods cannot adapt to the network attacks with high concealment and long duration. Aiming at this problem, a network alerts depth information fusion method based on time confrontation was proposed. In view of multi-source heterogeneous alerts data flow, firstly, the alerts were collected and saved in a long time window. Then the alerts were clustered using a clustering algorithm based on sliding window. Finally, the alerts were fused by introducing window attenuation factor. The experimental results on real data set show that, compared with Basic-DS and EWDS (Exponential Weight DS), the proposed method has higher True Positive Rate (TPR) and False Positive Rate (FPR) as well as lower Data to Information Rate (DIR) because of longer time window. Actual test and theoretical analysis show that the proposed method is more effective on detecting network attacks, and can satisfy real-time processing with less time delay.

In order to alleviate the problem of inaccurate noise identifying and blurred restoration in image edges and details, a novel algorithm based on weighted Spatial Local Outlier Measure (SLOM) was proposed for removing random-valued impulse noise, namely WSLOM-EPR. Based on optimized spatial distance difference, the mean and standard deviation of neighborhood were introduced to set up a noise detection method for reflecting local characters in image edges, which could improve the precision of noise identification in edges. According to the precision detection results, the Edge-Preserving Regularization (EPR) function was optimized to improve the computation efficiency and preserving capability of edges and details. The simulation results showed that, with 40% to 60% noisy level, the overall performance in noise points detection was better than that of the contrast detection algorithms, which can maintain a good balance in false detection and miss detection of noise. The Peak Signal-to-Noise Ratios (PSNR) of WSLOM-EPR was better than that of the most of the contrast algorithms, and the restoring image had clear and continuous edges. Experimental results show that WSLOM-EPR can improve detection precision and preserve more edges and details information.

By analyzing and comparing the existing network security situation evaluation methods, it is found that they can not accurately reflect the features of large-scale, coordination, multi-stage gradually shown by network attack behaviors. Therefore, a network security situation evaluation method based on attack pattern recognition was proposed. Firstly, the causal analysis of alarm data in the network was made, and the attack intention and the current attack phase were recognized. Secondly, the situation evaluation based on the attack phase was realized. Lastly the State Transition Diagram (STG) of attack phase was created to realize the forecast of network security situation by combining with vulnerability and configuration information of host. A simulation experiment for the proposed network security situation evaluation model was performed by network examples. With the deepening of the attack phase, the value of network security situation would increase. The experimental results show that the proposed method is more accurate in reflecting the truth of attack, and the method does not need training on the historical sequence, so the method is more effective in situation forecasting.

To solve the problems in configuration network including compatibility of lower computer, data sharing and simplicity of presentation at client, a data acquisition model with king view supported by middleware was proposed. Based on configuration software with strong network connection and second development characteristic, taking general king view software as an example, the model was deeply analyzed with the theory of configuration software, the communication middleware was combined with the communication protocol, the device information and variable information of king view were defined, the display interface of king view was drawn, and the availability for the model was verified at last. The verification results show that, compared to the traditional configuration network model, the proposed model promotes the expandability and compatibility of configuration network data acquisition model, and it can be used for data sharing and variety show at client, and further accelerates the fusion of configuration and Internet of Things (IoT) technology.

Traditional hypernetwork model is biased towards the majority class, which leads to much higher accuracy on majority class than the minority when being tackled on imbalanced data classification problem. In this paper, a Boosting ensemble of cost-sensitive hypernetworks was proposed. Firstly, the cost-sensitive learning was introduced to hypernetwork model, to propose cost-sensitive hyperenetwork model. Meanwhile, to make the algorithm adapt to the cost of misclassification on positive class, cost-sensitive hypernetworks were integrated by Boosting. The proposed model revised the bias towards the majority class when traditional hypernetwork model was tackled on imbalanced data classification, and improved the classification accuracy on minority class. The experimental results show that the proposed scheme has advantages in imbalanced data classification.