Aiming at the problem of low detection rate of depth feature based object detection method Faster R-CNN (Faster Region-based Convolutional Neural Network) in flame detection tasks, a color-guided anchoring strategy was proposed. In this strategy, a flame color model was designed to limit the generation of anchors, which means the flame color was used to limit the generation locations of the anchors, thereby reducing the number of initial anchors and improving the computational efficiency. To further improve the computational efficiency of the network, the masked convolution was used to replace the original convolution layer in the region proposal network. Experiments were conducted on BoWFire and Corsician datasets to verify the detection performance of the proposed method. The experimental results show that the proposed method improves detection speed by 10.1% compared to the original Faster R-CNN, has the F-measure of flame detection of 0.87 on BoWFire, and has the accuracy reached 99.33% on Corsician.The proposed method can improve the efficiency of flame detection and can accurately detect flames in images.
Conventional approaches for Common Weights (CW) generation in Data Envelopment Analysis (DEA) are either non-linear or scale-relevant. To solve this problem, according to the demand of military training performance evaluation, a new method was proposed to generate CW in DEA. The new method took DEA efficient units as the basis of calculation. Firstly, training data were normalized, and then multi-objective programing was employed for CW generation, which can lead to a fairer and more reasonable ranking of performances. The proposed method is not only linear, but also scale-irrelevant. Lastly, a military application illustrates that the proposed method is scientific and effective.
Aiming at the problem that the classification accuracy in malware behavior analysis system was low,a malware classification method based on Support Vector Machine (SVM) was proposed. First, the risk behavior library which used software behavior results as characteristics was established manually. Then all of the software behaviors were captured and matched with the risk behavior library, and the matching results were converted to data suitable for SVM training through the conversion algorithm. In the selection of the SVM model, kernel function and parameters (C,g), a method combining the grid search and Genetic Algorithm (GA) was used to search optimization after theoretical analysis. A malware behavior assessment system based on SVM classification model was designed to verify the effectiveness of the proposed malware classification method. The experiments show that the false positive rate and false negative rate of the system were 5.52% and 3.04% respectively. It means that the proposed method outperforms K-Nearest Neighbor (KNN) and Naive Bayes (NB); its performance is at the same level with the BP neural network, however, it has a higer efficiency in training and classification.
For the users' privacy security in electronic transactions, an electronic transaction scheme was proposed to protect the users' privacy. The scheme combined the oblivious transfer and ElGamal signature, achieved both traders privacy security in electronic transactions. A user used a serial number to choose digital goods and paid the bank anonymously and correctly. After that, the bank sent a digital signature of the digital goods to the user, then the user interacted with the merchant obliviously through the digital signature that he had paid. The user got the key though the number of exponentiation encryption, the merchant could not distinguish the digital goods ordered. The serial number was concealed and restricted, so the user could not open the message with the unselected serial number, they could and only could get the digital goods they paid. Correctness proof and security analysis shows that the proposed scheme can protect both traders mutual information in electronic transactions and prevent merchant's malicious fraud. The scheme has short signature, small amount of calculation and dynamic changed keys, its security is strong.
In cognitive Orthogonal Frequency Division Multiplexing (OFDM) systems, to avoid interference to Primary Users (PU), the transmission power of Cognitive Users (CU) need to be controlled and allocated. Since the transmission power can not be allocated legitimately and the data transmission rate can not be improved effectively, a power allocation algorithm of double factor binary search optimization was proposed on the basis of traditional water-filling power allocation algorithm. In the presented algorithm, the interference temperature limit on the cognitive user channel was taken into account. Firstly, a surplus function was introduced under the total power constraints. Secondly, because of the monotonicity of the surplus function, the accurate values of Lagrangian multipliers could be attained through the double binary search iteration method. Finally, the power allocation of the sub-channels was conducted through the values of Lagrangian multipliers. The simulation results show that the proposed algorithm can effectively use the spectrum hole between primary users. The data transmission rate of the cognitive users can be maximized under both total power constraints and Interference Temperature (IT) constraints. The data transmission rate is approaching to the traditional water-filling algorithm. Compared with the total power average control algorithm and the interference temperature average control algorithm, the data transmission rate of the presented algorithm is obvious higher, which exceeds about 4×105b/s under the same circumstance. Moreover, the algorithm has less processing time and reflects a good robustness.
The architecture of software and hardware of the embedded CAN-Ethernet gateway were introduced, and the principle, the designing methods and technoloques of the CAN Device Driver in uClinux were described. According to the features of the CAN protocol, data package was classified into four groups with different real-time request; the structure of multi-frame was proposed to satisfy the request of sending mass data; the data structure and the method of management for the buffer of the CAN Device Driver were designed to improve the capability of communication.