Network Security Situation Prediction Based on TCAN-BiGRU Optimized by SSA and IQPSO

The accuracy of historical situation values is required for traditional network security situation prediction(NSSP).There are discrepancies in the correlation and weighting of the various network security elements.To solve these problems,a combined prediction model based on the temporal convolution attention network(TCAN)and bi-directional gate recurrent unit(BiGRU)network is proposed,which is optimized by singular spectrum analysis(SSA)and improved quantum particle swarmoptimization algorithm(IQPSO).This model first decomposes and reconstructs network security situation data into a series of subsequences by SSA to remove the noise from the data.Furthermore,a prediction model of TCAN-BiGRU is established respectively for each subsequence.TCAN uses the TCN to extract features from the network security situation data and the improved channel attention mechanism(CAM)to extract important feature information from TCN.BiGRU learns the before-after status of situation data to extract more feature information from sequences for prediction.Besides,IQPSO is proposed to optimize the hyperparameters of BiGRU.Finally,the prediction results of the subsequence are superimposed to obtain the final predicted value.On the one hand,IQPSO compares with other optimization algorithms in the experiment,whose performance can find the optimum value of the benchmark function many times,showing that IQPSO performs better.On the other hand,the established prediction model compares with the traditional prediction methods through the simulation experiment,whose coefficient of determination is up to 0.999 on both sets,indicating that the combined prediction model established has higher prediction accuracy.

Role-based approaches for operational tasks modeling and flexible decomposition

In order to realize the fact that operational tasks can be decomposed flexibly with new tasks random adding, role-based approaches for operational tasks model and the flexibility decomposition are proposed. Firstly, aiming at the shortcomings of previous task decomposition methods, operational role concepts are introduced to design a computing framework of task flexibility decomposition. Within the framework, task structure matrixes are constructed for tasks decomposition with the mapping relations of information interaction and action process among tasks. On basis of this, via giving operational decomposition granularity, a flexibility approach of operational task decomposition is put forward with three parts about task decomposition granularity adjustment, tasks module partition with different operational role patterns and task decomposition scheme evaluation. Finally, as an application case shown, it is validated that the proposed approaches for tasks modeling and decomposition are available. By comparing with previous decomposition approaches, the proposed approach, within three types of operational roles, has strong environmental adaptability and scheme diversity, which can restrain calculation complexity increment for realizing task flexible decomposition effectively. © 2016 Beijing Institute of Aerospace Information.

Role-based approaches for operational task-resource flexible matching model and algorithm

In order to solve the problem that previous relation researches in operational task-resource matching are hard to flexible computing for generating a new matching scheme from the existing scheme under the condition of operational tasks or resources adjusted in uncertainty cases, role-based approaches to operational task-resource flexible matching model and algorithm are proposed in the paper for flexible matching operational tasks with resources. Firstly, through introducing the concept of roles, the role-based calculation framework of operational task-resource flexible matching is constructed. Secondly, two aspect indexes about resource utilization ratio (RUR) and time utilization ratio (TUR) are given to reflect operational task-resource matching quality (TRMQ), which can be regarded as the objective function for modeling role-based operational task-resource flexible matching mathematical formulation. On the basis of this, the role-based artificial bee colony (RABC) algorithm, including five specific calculating operators, is put forward for solving the flexible matching problem with double encoding on operational tasks and resources by roles. Finally, via comparing with the previous methods in application cases, it can be validated that role-based model and proposed algorithm are more effective, which can be used to obtain a new incremental scheme from the existing matching scheme for adopting the operational tasks or resources adjustment. Moreover, there are flexible computation advantages of the approaches proposed in this paper to solve the problem about operational task-resource matching in large-scale. © 1990-2011 Beijing Institute of Aerospace Information.

Comparisons of STRETCH meat grinders based on two different topologies of ICCOS

Inductive energy storage systems （IES）require opening switches which are technically much more difficult to realize than closing switches. Institute of Advanced Technology （ IAT ） puts forward a meat grinder with an inductive pulse power topology called slow transfer of energy through capacitive hybrid （STRETCH）. In this topology, an integrated gate-commutated thyristor （IGCT） is capable of breaking the charging currents on the order of 4 kA, and it may encounter difficulties in expanding to higher energy systems. German-French Institute of Saint Louis （ISL） has developed a high-power opening switch based on standard high-power thyristors by using their so-called inverse current commutation with semiconductor devices （ICCOS） countercurrent commutation principle. Two topologies of the STRETCH meat grinder with ICCOS are analyzed. And they are compared based on the voltage across the main switch, the current multiplication factor and other aspects by u- sing Matlab simulations. The advantages and disadvantages of the two topologies are shown through the comparisons. Therefore a more suitable topology can be chosen for realistic applications accord- ing to their performances.

LPI radar signal detection based on radial integration of Choi-Williams time-frequency image

This paper mainly revolves the time-frequency image of low probability of intercept(LPI) radar signals and carries out research work on image features selection and extraction and recognition. Since Choi-Williams distribution(CWD) uses the exponential kernel of bilinear generalized class of time-frequency distribution, it has an excellent time-frequency aggregation. And it is suitable for detecting LPI radar signals in a low signal-to-noise ratio(SNR) condition. A radial integration method based on the integral rotating factor is proposed to detect LPI radar signals when the signals' time-frequency image is obtained. First, the digital image processing method is used to preprocess the LPI radar signals' time-frequency images after CWD transformation; then, the radial integration method based on the integral rotating factor is used to detect LPI radar signals in the binary images. The analytic results of real data show that the method has a good performance on detecting LPI radar signals in a low SNR condition. Additionally,the method is simple and takes less logic resources and has the potential of real-time detection of LPI radar signals.

New progress in geometric computing for image and video processing

In recent years, geometry-based image and video processing methods have aroused significant interest. This paper considers progress from four aspects： geometric characteristics and shape, geometric transformations, embedded geometric structure, and differential geometry methods. Current research trends are also pointed out.

New self-calibration schemes for accelerometers in platform INS

To ensure success of precise navigation, it is necessary to carry out in-field calibration for the accelerometers in platform inertial navigation system(PINS) before a mission is launched.Traditional continuous self-calibration methods are not fit for fast calibration of accelerometers because the platform misalignments have to be estimated precisely and the nonlinear coupling terms will affect accuracy. The multi-position methods with a "shape of motion" algorithm also have some existing disadvantages: High precision calibration results cannot be obtained when the accelerometer's output data are used directly and it is difficult to optimize the calibration scheme. Focusing on this field, this paper proposes new fast self-calibration methods for the accelerometers of PINS. A data compression filter is employed to improve the accuracy of parameter estimation because it is impossible to obtain non-biased estimation for accelerometer parameters when using the "shape of motion" algorithm. Besides, continuous calibration schemes are designed and optimized by the genetic algorithm(GA) to improve the observability of parameters. Simulations prove that the proposed methods can estimate the accelerometer parameter more precisely than traditional continuous methods and multi-position methods, and they are more practical to deal with urgent situations than multi-position methods.