Aiming at the security problem of remote control brought by illegal device access in Internet of Things (IoT), a contextual authentication method based on device fingerprint was proposed. Firstly, the fingerprint of IoT device was extracted by a proposed single byte analysis method in the interaction traffic. Secondly, the process framework of the authentication was proposed, and the identity authentication was performed according to six contextual factors including device fingerprint. Finally, in the experiments on IoT devices, relevant device fingerprint features were extracted and decision tree classification algorithms were combined to verify the feasibility of contextual authentication method. Experimental results show that the classification accuracy of the proposed method is 90%, and the 10% false negative situations are special cases but also meet the certification requirements. The results show that the contextual authentication method based on the fingerprint of IoT devices can ensure that only trusted IoT terminal equipment access the network.

Human Activity Recognition (HAR) has a wide range of applications in medical care, safety, and entertainment. With the development of sensor industry, sensors that can accurately collect human activity data have been widely used on wearable equipments such as wristband, watch and mobile phones. Compared with the behavior recognition method based on video images, sensor-based behavior recognition has the characteristics of low cost, flexibility and portability. Therefore, human activity recognition research based on wearable sensors has become an important research field. Data collection, feature extraction, feature selection and classification methods of HAR were described in detail, and the techniques commonly used in each process were analyzed. Finally, the main problems of HAR and the development directions were pointed out.

In order to investigate the cascading invulnerability attack strategy of complex network via community detection, the initial load of the node was defined by the betweenness of the node and its neighbors, this defining method comprehensively considered the information of the nodes, and the load on the broken nodes were redistributed to its neighbors according to the local preferential probability. When the network being intentionally attacked based on community detection, the couple strength, the invulnerability of Watts-Strogatz (WS) network, Barabási-Albert (BA) network, Erds-Rényi (ER) network and World-Local (WL) network, as well as network with overlapping and non-overlapping community under differet attack strategies were studied. The results show that the network's cascading invulnerability is negatively related with couple strength; as to different types of networks, under the premise that fast division algorithm correctly detects community structure, the networks invulnerability is lowest when the node with largest betweenness was attacked; after detecting overlapping community using the Clique Percolation Method (CPM), the network invulnerability is lowest when the overlapping node with largest betweenness was attacked. It comes to conclusion that the network will be largest destoryed when using the attack strategy of complex network via community detection.

A deterministic algorithm for two-stage cutting layout with same-shape block was proposed to solve the large-scale two-dimensional cutting problems. The algorithm used dynamic programming to generate the optimal same-shape block, solved the knapsack problem to determine the optimal layout of the same-shape block in the same-shape section and the same-shape section in the same-shape segment, and optimally selected the two segments to compose the optimal same-shape block layout. The algorithm was tested through 43 benchmark problems, and compared with the classic two-stage and the three-block algorithms. The experimental results indicate that the algorithm is superior in both computation time and material usage rate, it not only meets the requirements of shearing and punching process, but also achieves good results within reasonable time.

In order to investigate the effects of community structure on cascading invulnerability, in the frame of a community structure network, the initial load of the node was defined by its betweenness, and the load on the broken node was redistributed to its neighboring nodes according to the preferential probability. When the node with the largest load being intentionally attacked in the network, the relation of load exponent, coupling-strength in a community, coupling-strength between communities, modularity function and the network's invulnerability were studied. The results show that the network's cascading invulnerability is positively related with coupling-strength in a community, coupling-strength between communities and modularity function, negatively related with load exponent. With comparison to BA (Barabási-Albert) scale-free network and WS (Watts-Strogatz) small-world networks, the result indicates that community structure lowers the network's cascading invulnerability, thus the more homogeneous betweenness distribution is, the stronger network's cascading invulnerability is.

In order to deal with the problems of inefficient searching and low accuracy of Artificial Fish Swarm Algorithm （AFSA） for multimodal function optimization, an improved AFSA for multimodal function optimization was proposed. In the algorithm, the strategy of the survival of the fitter suppression was adopted, eliminating artificial fish which was situated in food with low concentration of similar artificial fish to select elite artificial fish swarm. Optimization for swarming behavior and following behavior contributed to artificial fish careful search in a new optimization trajectory to enhance its local search capacity. Modifying for preying behavior, artificial fish was avoided sinking flat position. In combination with Pattern Search Method (PSM), its local accuracy search capacity was enhanced. The simulation results indicate that the proposed algorithm has stronger global optimization and local optimization capabilities, and the search for each optimal solution accuracy has reached the ideal value, and it is able to be used for complex multimodal function optimization.

Scientific and reasonable maintenance activity is an important way to improve equipment efficiency and combating force of troops. Complex equipment demands higher reliability, maintainability and supportability, which brings new problems to maintenance activity. When to maintain and how to maintain play an important role in exerting the equipment's efficiency. This paper put forward a strategy model for maintenance activity from Equipment Health Management (EHM) point of view. Based on the analysis of health status, failure mode effect and failure mode probability, Fuzzy C-Means (FCM) clustering algorithm was used to research maintenance activity. Maintenance opportunity was differentiated and a proper maintenance strategy was proposed. Thus appropriate maintenance was made at appropriate opportunity. This could optimize maintenance activity as well as realize EHM goal. The simulation results with Matlab show that the proposed method is feasible and reasonable, which can support EHM decision making.

In this paper, a 3D reconstruction system was developed with VTK and MC. The problem of time and memory is the key to real-time system. The methods of merging vertexes and hashing map can improve speed and reduce memory. This system provides a basis for development and implementation of a complete medical visualization system.