Remote sensing image interpretation plays an important role in the acquisition of Land Use and Land Cover (LULC) information, and automatic classification serves as the key to improve the efficiency of LULC information acquisition. The actual scenes have a great mount of inaccurate prior knowledge. Extracting and integrating the available knowledge in the prior knowledge can help to further improve the accuracy, automation rate and scale application ability of image classification methods. Based on the above situation, a new deep learning classification method of Landsat 8 OLI images based on inaccurate prior knowledge was proposed. For the proposed method, inaccurate units in prior knowledge were avoided automatically, realizing automatic region selection and feature extraction of classified samples and obtaining high confidence knowledge in the constraint space of patches. Then, the deep residual network was trained by using these classified samples, and the accurate classification of large-area images was achieved. In the experiment, Xinbei district of Changzhou city was taken as the example, the data of 2009 land use status of this district was selected as the prior data, and the 2014 Landsat 8 OLI image of this district was selected as the to-be-classified image. The experimental results show that the proposed method has advantages such as the integration of inaccurate prior knowledge and the accurate classification of large-area contiguous LULC information. Besides, it can obtain the accurate boundary of main land use patches, and has the accuracy for patch classification in the whole image of 88.7% and the Kappa coefficient of 0.842.The proposed method can cooperate with deep learning method to achieve high precision Landsat 8 OLI remote sensing image classification.

The network of Integrated Modular Avionics (IMA) platform has complex communication structure. When fault injection testing, it is difficult to select a appropriate test path, and there are much equivalent and invalid fault injection. According to the characteristics of the network communication structure of the IMA platform, a new fault injection strategy was proposed. Firstly, according to the requirements of real-time and certainty in the network of IMA platform, a test path optimization algorithm based on communication links was proposed, optimal test paths were generated to achieve orderly coverage of IMA platform network test tasks. Secondly, after determining the test path, a test case automatic generation model was constructed by using Colored Petri Net (CPN) modeling method, the equivalent and invalid faults were eliminated, and test cases required for each test task in the path were streamlined. The simulation results show that the proposed method is less than the traditional fault injection strategy in terms of test times and test time, so it can overcome the shortcomings of disorder and blindness in the traditional strategy and reduce the time cost of the test.

Aiming at the problem of inter-zone interference in co-frequency deployment of ultra-dense network cells, a non-uniform pricing power control scheme based on Stackelberg game was proposed. Firstly, a non-uniform pricing power control model based on Stackelberg game was established. Then the optimal price based transmission power was obtained by this model, which is a function of interference price. Secondly, the Lagrange function was introduced to solve the problem of the optimal interference price of the corresponding base station, and the controller sent the interference price to the corresponding base station, and the base station adjusted its own transmission power value to weaken the interference to the current users. The simulation results show that compared with the unified pricing power control scheme based on Stackelberg game, the proposed scheme reduces the average outage probability of the system by an average of 3 percentage points. Compared with the power control scheme based on the weight of the base station, when the number of base stations is less than 105, the proposed scheme increases the average outage probability by an average of 1.4 percentage points; when the number of base stations exceeds 105, the proposed scheme reduces the average outage probability by an average of 1.6 percentage points. In addition, compared with the two scheme mentioned above, the proposed scheme increases the average throughput of the system by 12 percentage points and 10.5 percentage points respectively, the average spectrum efficiency of the system is increased by 9 percentage points and 8.5 percentage points respectively, and the average power efficiency of the system is improved by 13 percentage points and 12 percentage points respectively. The experimental results show that the proposed solution can better improve the performance of the cellular system when more base stations are deployed.

Otsu algorithm is widely used in classic image segmentation, while the application of the two-dimensional Otsu thresholding algorithm based on the Otsu algorithm has been restricted for the complex computation. Concerning this, this paper proposed an improved two-dimensional Otsu thresholding algorithm. The authors first divided the two-dimensional histogram into regions, and took each region as a point to form a new two-dimensional histogram, to which 2D Otsu thresholding algorithm and the fast recursive algorithm were applied, getting the region number of the threshold. Then the two algorithms were applied again on the region and finally the threshold for the original image was obtained. The experimental results show that the proposed algorithm greatly reduces the running time and the storage space, and gets basically the same results as the original algorithm.

To solve the multi-spot inventory sharing problem in an emergency system, emergency transportation strategy was studied in a system with random fuzzy demand in this paper through a multi-product and three-echelon emergency supply system. When the stockout happened, the nearest emergency lateral transshipment principle and partial inventory sharing strategy among the spots were permitted to satisfy the demand, and the model for the total cost expectation of random fuzzy demand was developed according to it, taking account of the service time constraints and the spots' storage space limitation. An advanced computing method combining Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm, called PSO-SA algorithm, was proposed to calculate the model, and the effects on the partial transshipment with the variation of the transshipment trigger inventory level, the per-item transshipment time and the inventory storage space were analyzed through a numerical example. The availability of the proposed algorithm and the model applicability were verified at last.

In order to improve spectrum usage in Cognitive Radio System (CRS), and reduce channel switching frequency, a new prediction mechanism was designed, which was used chaotic neural network to analyze and predict the last time of channel status. Simulation results show that the prediction accuracy can reach 90%, thus the effectivess of this new prediction mechanism was proved.

This paper analyzed the latest spectrum allocation algorithms of graph coloring theory. Concerning the shortage of the cognitive users' requirement not being satisfied, the user-satisfaction was proposed. Based on it, the authors set the spectrum allocation priority function. And the users, whose need was not well satisfied, were preferentially assigned. A spectrum allocation algorithm of graph coloring based on the requirement was obtained. The simulation results show that the proposed algorithm can enhance the system's channel efficiency, meet the needs of multiple users' bandwidth requirements better, and improve the spectrum efficiency.