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Overhead crane is regarded as a kind of complex nonlinear dynamic system in which the under-actuated characteristic makes the controller hardly control the space of load swing effectively. In addition, the system internal and external uncertain factors also give rise to the adverse effect for control performance. Therefore, aiming at these problems, a type-2 fuzzy theory-based sliding mode controller (type-2 FSMC) is designed for damping the oscillation of the payload in which the proposed type-2 FSMC inherits the benefits of type-2 fuzzy logic theory and sliding mode control theory for improving anti-swing control performance. Furthermore, in order to configure the type-2 FSMC parameter efficiently, an adaptive differential evolutionary algorithm-based parameter tuning mechanism is presented for acquiring the optimal control performance. Through computer simulation and comparisons under different operation conditions, the optimal type-2 FSMC demonstrates the effectiveness in eliminating the payload residual vibration.

The diversity and complexity of network data bring great challenges to data classification technology. Feature selection has always been an important and difficult problem in classification technology. To improve the classification performance of the classifier, an improved feature selection algorithm, FACO, is proposed by combining the ant colony optimization algorithm and feature selection. A fitness function is designed, and the pheromone updating rule is optimized to effectively eliminate redundant features and prevent feature selection from falling into a local optimum. The experimental results show that the classification accuracy of the classifier can be significantly improved by selecting the data features using the FACO algorithm, which is of practical significance.

As a new type of network structure, the Software Defined Network (SDN) provides a new solution for network flow management and optimization, which has made the accurate detection of anomaly SDN flows a hot research topic. This paper presents an SDN-based flow detection method, builds structures for detecting anomaly SDN flows and performs classification detection on the flows using the double P-value of transductive confidence machines for K-nearest neighbors algorithm. The experimental results show that the algorithm proposed achieves a lower false positive rate, higher precision, and better adaptation to the SDN environment than do other algorithms of the same type.

The overhead crane systems is a kind of complex dynamic nonlinear system, and the inherent under-actuated characteristics (the dimension of control variables are less than the degree of freedom dimension of the system) bring great difficulty to the payload control. In order to effectively control the movement of the load and eliminate the residual oscillations of the payload, a fuzzy PID control method is proposed in this paper. Firstly, according to the input and output characteristics of the overhead crane, the corresponding PID controller and the parameters configuration are established. Moreover, according to the actual operation situation, the experience of the operator is combined with the PID controller, and the fuzzy inferencing method is applied to achieve the optimal adjustment of PID parameters. Finally, through theoretical analysis and Simulink simulation experiment, the proposed fuzzy PID control method can significantly damp the payload oscillation.

In the cloud computing environment, a growing number of users share their own data files through cloud storage. However, there will be some security and privacy problems due to the reason that the cloud is not completely trusted, so it needs to be resolved by access control. Attribute-based encryption (ABE) and searchable encryption (SE) can solve fine-grained access control. At present, researchers combine the two to propose an attribute-based searchable encryption scheme and achieved remarkable results. Nevertheless, most of existing attribute-based searchable encryption schemes cannot resist online/offline keyword guessing attack. To solve the problem, we present an attribute-based (CP-ABE) searchable encryption scheme that supports trapdoor updating (CSES-TU). In this scheme, the data owner can formulate an access strategy for the encrypted data. Only the attributes of the data user are matched with the strategy can the effective trapdoor be generated and the ciphertext be searched, and that this scheme will update trapdoors at the same time. Even if the keywords are the same, new trapdoors will be generated every time when the keyword is searched, thus minimizing the damage caused by online/offline keyword guessing attack. Finally, the performance of the scheme is analyzed, and the proof of correctness and security are given at the same time.

Personal health record (PHR) systems contain a large amount of private and sensitive data so that an access control paradigm is required to guarantee security and flexibility. Ciphertext-policy attribute-based encryption (CP-ABE) supports access control on encrypted data, which is a promising technique for PHR systems. However, current CP-ABE schemes consume much computing resources for encryption, decryption, and revocation. Furthermore, since there is much value behind mass health records it is often to see unauthorized access to these data by private keys obtained from illegal trade, which is called key abuse problem. Worse still, existing CP-ABE schemes are short of an effective mechanism to keep away from key abuse. In this paper, we propose an accountable, revocable, and pairing-free ciphertext-policy attribute-based encryption (ARP-CP-ABE). Each private key is inserted by a unique identity so that key abuse can be detected in decryption. Meanwhile, the traitor can also be immediately locked. Then we construct a fine-grained and immediate revocation mechanism based on attribute group and optimize its computational efficiency. In addition, no pairing computation is needed in ARP-CP-ABE, which reduces computation overhead and make it more suitable for mobile applications of PHR systems.

Software Defined Network (SDN) separates control plane from data plane, and uses multiple controllers to resolve the extensibility and security of SDN. However, most research concentrates on the control layer architecture, ignoring the controllers placement problem. In this paper, we first define the total flow request cost that considers switch weights, switch-to-controller routing costs, and inter-controller routing costs. Next, we propose controller-based load balance factor, with a known number of controllers, the position of the specific controllers is derived by minimizing the linear function of the load balance factor and the total flow request cost. Finally, through simulation, the controller layout scheme reduces the flow request cost of the SDN and achieves load balancing among the controllers.