The open-shop scheduling problem is a typical NP-hard problem. Most of the existing research assumes that the processing time of a procedure is fixed. However， in real-world production scenarios， the processing time can be controlled by adjusting the processing power. At the same time， optimizing the two conflicting objectives of completion time and energy consumption is significant for the high-efficiency and energy-saving open-shop production. Therefore， the Multi-objective Open-shop Scheduling Problem with Controllable Processing Time （MOOSPCPT） was studied， a mixed-integer programming model was constructed with the objectives of minimizing makespan and total extra energy consumption， and a Multi-objective Hybrid Evolutionary Algorithm （MOHEA） was proposed to solve MOOSPCPT. Several strategies were developed in the MOHEA： 1） the migration strategy and mutation strategy in the biogeographic-based optimization algorithm were improved for global search， which facilitated the diversity of the population effectively； 2） a self-adjusting variable neighborhood search strategy was designed based on the critical path， which enhanced the local search performance of the algorithm； 3） a processing time resetting operator was designed， which improved the search efficiency of the algorithm significantly. Simulation results show that the proposed strategies are effective in improving algorithm performance； MOHEA solves MOOSPCPT more effectively compared with Non-dominated Sorting Genetic Algorithm Ⅱ （NSGA-Ⅱ）， Non-dominated Sorting Genetic Algorithm Ⅲ （NSGA-Ⅲ） and Strength Pareto Evolutionary Algorithm 2 （SPEA2）.

Aiming at the problem of advanced persistent threats in power information networks, a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) intrusion detection model was proposed, by which current network states were classified according to various statistical characteristics of network traffic. Firstly, pre-processing works such as feature encoding and normalization were performed on the network traffic obtained from log files. Secondly, spatial correlation features between different hosts' intrusion traffic were extracted by using deformable convolution kernels in CNN. Finally, the processed data containing spatial correlation features were staggered in time, and the temporal correlation features of the intrusion traffic were mined by RNN. The experimental results showed that the Area Under Curve (AUC) of the model was increased by 7.5% to 14.0% compared to traditional machine learning models, and the false positive rate was reduced by 83.7% to 52.7%. It indicates that the proposed model can accurately identify the type of network traffic and significantly reduce the false positive rate.

A spatial co-location pattern represents a subset of spatial features whose instances are frequently located together in spatial neighborhoods. The existing interesting metrics for spatial co-location pattern mining do not take account of the difference between features and the diversity between instances belonging to the same feature. In addition, using the traditional data-driven spatial co-location pattern mining method, the mining results often contain a lot of useless or uninteresting patterns. In view of the above problems, firstly, a more general study object-spatial instance with utility value was proposed, and the Utility Participation Index (UPI) was defined as the new interesting metric of the spatial high utility co-location patterns. Secondly, the domain knowledge was formalized into three kinds of semantic rules and applied to the mining process, and a new domain-driven iterative mining framework was put forward. Finally, by the extensive experiments, the differences between mined results with different interesting metrics were compared in two aspects of utility ratio and frequency, as well as the changes of the mining results after taking the domain knowledge into account. Experimental results show that the proposed UPI metric is a more reasonable measure in consideration of both frequency and utility, and the domain-driven mining method can effectively find the co-location patterns that users are really interested in.

Through the analysis of the LTE system in FPGA-based spatial multiplexing coding problem, and propose a implementation of a codebook-based precoding. According to the parameters of the upper informed carries on the table look-up in the mathematical table and the addition and subtraction relations table, the data of layer mapping first carries on coefficient multiplication, then add and subtract, thus this has replaced the complex matrix multiplication operation. Therefore this method can greatly reduce the complex matrix multiplication operation in the precoding procedure, and reduce the complexity of encoding process, improve the speed of the encoding operation. The experimental results indicate that the proposed algorithm can achieve a good system function.

In this paper, considering the Web service composition model described by DAG (Directed Acrylic Graph), a new Quality of Service (QoS) computing method for the composition service based on topological sequence reduction (QCMTSR) was proposed. Based on the basic structures and their QoS computing formulas of iterative reduction method two kinds of basic structures (i.e. serial reduction structure and parallel reduction structure) were defined in graph DAG, and their QoS calculation formulas were also given. During accessing each node step by step in the topology sequence for DAG. Repeating this process until the last node in this queue, then the QoS measure results of the last node were the computing results of the composition service. It has been proved that the algorithm can be applied to all the composition services described by DAG, and the experimental results show that the algorithm QCMTSR is more accurate in the measurement of reliability and availability.

According to the mechanism of speech signal, a super resolution pitch detection algorithm, which combined Linear Predictive Coding (LPC) with Average Magnitude Difference Function (AMDF), was proposed. Firstly, residual of LPC was extracted by linear predictive analysis. Then, cumulative mean normalized difference function and difference signal revision were used to make pitch valley sharper. At last, parabolic interpolation and pitch multiple check were taken to select real pitch period. The experimental results indicate that the pitch detection effect of the algorithm is superior to that of the conventional algorithms. The proposed algorithm conquers half frequency errors, and has good accuracy and robustness under the condition of high Signal-to-Noise Ratio (SNR).