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f-CaO is a key factor affecting the quality of cement in production. In this paper, the cement clink production process is introduced and discussed in detail. The time delay between the variables leads to an inaccurate matching relationship with each other and defects the performance of traditional soft sensors. To this end, a semi-supervised spatial-information-based soft sensor for f-CaO content is proposed. First, we analyzed the relationship between process variables and quality variable and then reconstruct the input of samples into data matrix by stitching unlabeled process data together. The semi-supervised structure helps retain process information in the data. Then, an end-to-end soft sensor based on CNN is established: convolution and pooling operations are used to extract the features of two-dimensional data containing spatial information; a multi-layer perceptron models the extracted features regressively. Further, in order to solve the defect of insufficient generalization ability of the CNN-based model, a framework for spatial feature extracting and transferring is proposed. Compared with the multilayer perceptron, strong regression models with spatial features get better prediction accuracy. An actual cement production case is used to verify the effectiveness of the proposed method.

Aiming at the problem that Mark XIIA Mode 5 burst communication system has low recognition rate under interference environment, this paper constructs a strategybased anti-interference intelligent decision engine, which is applied in this system to form an adaptive burst communication system. The paper gives the structure of adaptive burst communication system, designs anti-jamming strategy and effectiveness evaluation objective function, and studies the strategy-based interrogation/response process decision algorithm. The simulation results show that compared with the traditional Mark XIIA Mode 5 system, the adaptive burst communication system using the strategy-based decision can not only avoid or suppress interferences, but also effectively improve the system's power efficiency and transmission reliability in different interference environments.

Mark XIIA identification foe or friend (IFF) system is the main battle equipment that used by the U.S. and NATO forces. Mode 5 is the latest development mode of the system with a stronger anti-interference ability than the previous mode. However, it is still limited and difficult to transmit reliably in the interference environment of linear frequency modulation (LFM). Therefore, this paper proposes a LFM interference cancellation algorithm based on multiple discrete polynomial phase transform and weighted combination (MDPT-WC) for Mark XIIA Mode 5 system, which can reconstruct LFM interference and eliminate it effectively. The simulation results demonstrate that the LFM cancellation algorithm proposed in this paper can improve the transmission accuracy of Mode 5 system in a wide range of interference to signal ratio (ISR), so the proposed algorithm is valuable in application.

Fault diagnosis of rolling bearings is a key issue in the field of engineering. To solve the problem that the accuracy of the current fault diagnosis of rolling bearings is not high and the model construction time is long, This paper proposed a new fault diagnosis method for rolling bearings based on invariant moments of three-dimensional vibration spectrogram. The pseudo-Wigner-Ville distribution time-frequency analysis method was adopted to generate vibration spectrum images of the rolling bearings by means of signal processing. This method extracts the point cloud three-dimensional invariant moments of the vibration spectrogram as the characteristics of the failure mode, and realizes the bearing fault identification with the BP neural network. The experimental results show that the proposed method not only has better recognition rate than the feature extraction method of the two-dimensional Hu invariant moment, but also can effectively identify and classify faults such as inner ring and outer ring, which has strong application value in the fault diagnosis of bearings and other rotating machinery.

This paper studies the problem of the parameter estimation for a class of closed-loop nonlinear systems. Based on the closed-loop identification scheme, a filtering based multi-innovation stochastic gradient algorithm is presented for estimating parameters of closed-loop Hammerstein nonlinear systems. In order to reduce the computation complexity, a hierarchical estimation approach is proposed by means of the decomposition technique. The simulation results show that the proposed algorithm is effective.

Base on the characteristics and application needs of the distribution system in China smart grid, the distribution automation technology and its development strategy are studied with a reality applications engineering. First, this paper makes a particular analysis of the main problems occurred on distribution automation engineering developing now in China. And then some key technology strategies are discussed, including the correlative technology with the concentrated type distribution automation system, the special application needs of the distribution in China smart grid, master station and remote terminal unit of distribution automation system, information integration, of distribution automation system, and so on. Finally, some application examples are given about the above key technology strategies to the several practical projects in China.