In order to improve the detection level of bridge cracks,and solve the time-consuming and laborious problem in manual detection and the parameters to be set manually in traditional image processing methods,an improved bridge crack detection algorithm was proposed based on GoogLeNet. Firstly,a large-scale bridge crack Retinex-Laplace-Histogram equalization(RLH)dataset was constructed for model training and testing. Secondly,based on the original GoogLeNet model,the inception module was improved by using the normalized convolution kernel,three improved schemes were used to modify the beginning of the network,the seventh and later inception layers were removed,and a bridge crack feature image classification system was established. Finally,the sliding window was used to accurately locate the cracks and the lengths and widths of the cracks were calculated by the skeleton extraction algorithm. The experimental results show that compared with the original GoogLeNet network,the improve-GoogLeNet network increased the recognition accuracy by 3. 13%, and decreased the training time to the 64. 6% of the original one. In addition,the skeleton extraction algorithm can consider the trend of the crack,calculate the width more accurately,and the maximum width and the average width can be calculated. In summary,the classification and measurement method proposed in this paper have the characteristics of high accuracy,fast speed,accurate positioning and accurate measurement.

The scalar neurons of convolutional neural networks cannot express the feature location information,and have poor adaptability to the complex vehicle driving environment,resulting in low traffic sign recognition rate. Therefore,an intelligent traffic sign recognition method based on capsule network was proposed. Firstly,the very deep convolutional neural network was used to improve the feature extraction part. Then,a pooling layer was introduced in the main capsule layer. Finally,the movement index average method was used for improving the dynamic routing algorithm. The test results on the GTSRB dataset show that the improved capsule network method improves the recognition accuracy in special scenes by 10. 02 percentage points. Compared with the traditional convolutional neural network,the proposed method has the recognition time for single image decreased by 2. 09 ms. Experimental results show that the improved capsule network method can meet the requirement of accurate and real-time traffic sign recognition.

To improve the performance of vehicle classification by making full use of image information from traffic surveillance, Histogram of Oriented Gradient Convolutional (HOG-C) features extraction method was added on the capsule network, and a Capsule Network model fusing with HOG-C features (HOG-C CapsNet) was proposed. Firstly, the gradient data in the images were calculated by the gradient statistical feature extraction layer, and then the Histogram of Oriented Gradient (HOG) feature map was plotted. Secondly, the color information of the image was extracted by the convolutional layer, and then the HOG-C feature map was plotted with the extracted color information and HOG feature map. Finally, the HOG feature map was input into to the convolutional layer extract its abstract features, and the abstract features were encapsulated through a capsule network into capsules with the three-dimensional spatial feature representation, so as to realize the vehicle classification by dynamic routing algorithm. Compared with other related models on the BIT-Vehicle dataset, the proposed model has the accuracy of 98.17%, the Mean Average Precision (MAP) of 97.98%, the Mean Average Recall (MAR) of 98.42% and the comprehensive evaluation index of 98.20%. Experimental results show that the vehicle classification in traffic surveillance scenarios can be achieved with better performance by using HOG-C CapsNet.

Aiming at the problem that the deep learning network model applied to medical image target detection only has a fixed receptive field and cannot effectively detect the cervical lymph nodes with obvious morphological and scale differences, a new recognition algorithm based on adaptive receptive field mechanism was proposed, applying deep learning to the automatic recognition of cervical lymph nodes in complete three-dimensional medical images at the first time. Firstly, the semi-random sampling method was used to crop the medical sequence images to generate the grid-based local image blocks and the corresponding truth labels. Then, the DeepNode network based on the adaptive receptive field mechanism was constructed and trained through the local image blocks and labels. Finally, the trained DeepNode network model was used for prediction. By inputting the whole sequence images, the cervical lymph node recognition results corresponding to the whole sequence was obtained end-to-end and quickly. On the cervical lymph node dataset, the cervical lymph node recognition using the DeepNode network has the recall rate of 98.13%, the precision of 97.38%, and the number of false positives per scan is only 29, and the time consumption is relatively shorter. The analysis of the experimental results shows that compared with current algorithms such as the combination of two-dimensional and three-dimensional convolutional neural networks, the general three-dimensional object detection and the weak supervised location based recognition, the proposed algorithm can realize the automatic recognition of cervical lymph nodes and obtain the best recognition results. The algorithm is end-to-end, simple and efficient, easy to be extended to three-dimensional target detection tasks for other medical images and can be applied to clinical diagnosis and treatment.

To solve the difficulty of attribute correlation in the process of simulating table data, an H model was proposed for describing not-temporal attribute correlation in table data. Firstly, the key attributes of the evaluation subject and the evaluated subject were extracted from the data set, by the twofold frequency statistics, four relationships of the key attributes were obtained. Then, the Maximum Information Coefficient (MIC) of each relationship was calculated to evaluate the correlation of each relationship, and each relationship was fitted by the Stretched Exponential (SE) distribution. Finally, the data scales of the evaluation subject and the evaluated subject were set. According to the result of fitting, the activity of the evaluation subject was calculated, and the popularity of the evaluated subject was calculated. H model was obtained through the association that was established by equal sum of activity and popularity. The experimental results show that H model can effectively describe the correlation characteristics of the non-temporal attributes in real data sets.

During the process of software development, activities of programmers including copy and paste result in a lot of code clones. However, the inconsistent code changes are always harmful to the programs. To solve this problem, and find harmful code clones in programs effectively, a method was proposed to predict harmful code clones by using Bayesian network. First, referring to correlation research on software defects prediction and clone evolution, two software metrics including static metrics and evolution metrics were proposed to characterize the features of clone codes. Then the prediction model was constructed by using core algorithm of Bayesian network. Finally, the probability of harmful code clones occurrence was predicted. Five different types of open-source software system containing 99 versions written in C languages were tested to evaluate the prediction model. The experimental results show that the proposed method can predict harmfulness for clones with better applicability and higher accuracy, and further reduce the threat of harmful code clones while improving software quality.

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.

In order to solve the difficulty of selecting cluster centers and weakness of density analysis generalization, a novel clustering method was proposed. The method completed clustering by density and distance analysis based on genetic algorithm, which computed density with exponential method to reduce the impact of parameters and adopted genetic algorithm to search optimum threshold values. It introduced a penalty factor to overcome the excursion of search region for accelerating convergence. Numerical experiments on both artificial and UCI data sets show that compared with K-means, fast search clustering and Max_Min_SD, the proposed algorithm can achieve better or comparable performance on Rand index, accuracy, precision and recall.

Considering the demand of detecting Android malware and the redundancy of permission properties, a fast scheme was proposed to detect malware from the perspective of permission correlation. To eliminate the redundant permissions, Chi-square test was used to compute the influence of the permission on the classification results. Then some representative permissions were selected on the basis of permission clustering to further reduce redundancy. Finally an improved Naive Bayesian classification based on the weights of different permissions was proposed to classify the software. Results of the experiments conducted on 2000 software samples show that the miss rate of malware detection is 10.33% and the overall prediction accuracy is 88.98%. Experiments indicate that this scheme is capable of detecting malware on Android platform by using a few permission properties, which can provide a reference for further analysis and judgment.

The exiting model representation cannot meet the requirements of multistage modeling, so model share, reuse and management can hardly achieve in multistage modeling process. Therefore, a hierarchical model management framework based on universal relation model presentation was presented. Firstly, the requirements of model representation in model management and the limitations of exiting model representation were analyzed, then a model representation based on universal relation was investigated to set the mapping relation between layers of conceptual model and mathematical model, and the integrative model representation and the hierarchical model management framework including conceptual model, mathematical model and physical model were set up. At last, the logic of modelbase in management framework was designed and the physical model generation based on universal relation was investigated. The model of different modeling process could be managed uniformly based on an integrative model representation.

In order to forecast the number of equipment maintenance people more easily and validly, a common approach of selecting the features of input vector in Support Vector Machine (SVM) named Main Factor Method (MFM) was proposed. The relevant terms of "main factor", "driving factor", "voluntary action" and "actions' carrier" were defined, based on which the theoretical MFM was constructed. Firstly, the predicting vector's main factor of voluntary actions was setup by "infinitely related principle" and "action purpose" method. Then the driving factors which can be looked as the characteristics of SVM input vector were refined through the selected main factor and "selecting principles of driving factors". The experimental results and comparison with other congeneric methods show that the proposed method can select the more accurate prediction with the value of relative average error 0.0109.

In this paper, a fuzzy clustering algorithm based on w-mean distance was proposed to solve such defects of Fuzzy C-Means (FCM) algorithm as easily falling into local optimal value and being sensitive to clustering center and noise data. First, initial clustering centers were determined by making use of the idea of the mean distance according to the distribution of data set, and the regulating factor w was introduced to adjust the mean distance. Second, each sample in data set was assigned a weight, and the clustering center formula and target function formula were modified by the weight, so that the anti-noise performance was greatly improved for the algorithm. In the end, the experimental results validate that the proposed algorithm has good effects on selecting initial clustering centers, avoiding local convergence, and having higher performance of anti-noise and effectiveness.

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.

 Textile texture has much anomaly,because of yarn helix structure,different size of yarn and supple transform for textile itself. The methods for detecting the textile defects with feature and model to segment were low efficiency and not good enough in precision. A method of feature extraction of the textile defects by using Pulsed Coupled Neural Network(PCNN) was put forward to overcome these problems. The model and properties of PCNN was analyzed. According to different gray intensity between the field of textile defects and the field of normal textile, feature of textile defects were extracted for PCNN firing or not. After dilated, the textile defects’ edge were extracted with CANNY operator. Experiment shows that the method can much better get the feature of the textile defects and has a much better edge detection result of the textile defects.