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A novel near lossless image compression method is proposed in this paper. In the proposed method, a redundant representation of the image is firstly obtained by pixel level shifting and then partitioned into image blocks with the same size. Next, the image blocks are classified into smooth image blocks and non-smooth image blocks. For the smooth image blocks, improved block coding which includes mean residual error is utilized for pixel rectangles with the same pixel value in each to remove the redundancy. Lastly, the whole image is scanned by efficient image scanning that only one pixel is scanned for each pixel rectangle and then compressed by Huffman coding. Simulation results indicate that the proposed method has good performance compared with traditional lossless/near lossless image compression methods.

In order to meet the needs of data processing, information communication, etc., many modern electronic devices are equipped with PCBs inside, which has an impact on the shielding effectiveness of the metal cavity with holes. Based on the extended transmission line method, the PCB is equivalent to a dielectric slab of the same thickness and the calculation formula of the shielding effectiveness is deduced. The shielding effectiveness of the cavity is compared in the presence or absence of the PCB. The influence on shielding effectiveness of the thickness, mounting position, permittivity, conductivity and other factors of the PCB are analyzed. The effectiveness of the established model is verified by comparing the simulation results with the electromagnetic simulation software CST. The research results can provide reference for electromagnetic pulse effect analysis and electromagnetic safety protection design of electronic devices.

Sufficient training data for time series classification algorithms is critical. However, training data in many realworld applications exists a large imbalance between the majority class and the minority class inevitably to result in reducing the time series classification accuracy. Although a lot of existing solutions attempt to address the imbalanced learning problem, the synthetic samples based on the oversampling methods are short of diversity or variation to fail to accurately reveal the real minority class distribution. In order to create diverse artificial samples, a new oversampling method for imbalanced time series classification based on generative adversarial network (O-GAN) is proposed in this paper. An O-GAN application to imbalanced discharge voltage time series classification of lithium-ion cells is implemented. In addition, we selected 61177 cells with capacities between 2.06 Ah and 2.10 Ah as experimental samples. The samples are first preprocessed to obtain a labeled train set. For 1063 minority class samples, we use four oversampling methods to generate 1000 synthetic samples, respectively. Finally, the fully convolutional network (FCN) is considered as a discharge voltage time series classification algorithm to evaluate the oversampling methods. The results show that the proposed O-GAN method can effectively address the imbalanced time series classification problem.

An approach is proposed to analyze the conducted coupling effect of the electromagnetic pulse based on integral equation equivalent circuit and electromagnetic topology. Based on the integral equation equivalent circuit method, an equivalent source model of the external electromagnetic interference is established. The output of the equivalent source model is taken as the input of the internal circuit. The electromagnetic pulse conduction coupling effect is calculated based on the electromagnetic topology analysis and Baum-Liu-Tesche(BLT) equation. The method can be used for the analysis of the electromagnetic pulse effect of electronic devices containing complex transmission line networks and integrated circuit plug-ins, and is closer to the reality of electronic devices. The influence of the cable amount on the conducted coupling effect is analyzed. Experimental results are compared with the electromagnetic simulation software CST to verify the effectiveness of the proposed method.

In this paper, we propose a method for fusion of low-resolution multispectral (LRM) image and high-resolution panchromatic (HRP) image to obtain high-resolution multispectral (HRM) image based on distributed compressed sensing (DCS). In the proposed method, HRP image is firstly used to obtain approximation and detail dictionary. Then, joint-sparsity-model-1 (JSM-1) is applied directly to both LRM bands and HRM bands. Each band in LRM image is decomposed into common component and innovation component which can be sparsely represented over the approximation dictionary. Based on Orthogonal Matching Pursuit (OMP) algorithm, the sparse coefficients are calculated from JSM-1 of the LRM image. Lastly, each band in HRM image is modeled as the fusion of the corresponding LRM band and detail band over the detail dictionary. Two datasets are used in the experiments to validate the proposed method and the results show that the proposed method has better performance than the traditional methods.

To assess the multi-state of a rolling bearing more effectively and simultaneously, a unified assessment method is proposed based on chaos fruit fly optimization algorithm hyper-sphere support vector machine (CFOA-HSVM) two measures combination. Aiming to the blindness of parameters selection for HSVM, multiple parameters of HSVM can be searched the optimal values using chaos theory combined with fruit fly optimization algorithm (CFOA), and HSVM model based on CFOA can be constructed. On above basis, the minimum of difference coefficient, that is generalized minimum distance, can be extracted and regarded as geometric assessment distance measure that is sensitive to absolute value. Meanwhile, angle cosine distance is introduced to calculate angle cosine distance between classification state and normal state, and is regarded as assessment distance measure which is sensitive to the direction. Then the assessment measure of generalized minimum distance is compensated and the unified assessment index of multi-state of the rolling bearing is constructed based on two measures combination, further the assessment model can be constructed and the assessment curve can be obtained. Experiments show that the proposed method can assess the different fault location and different failure degree of the rolling bearing more effectively and simultaneously.

In enterprises and schools, information systems are heterogeneous customarily in operating system, architecture and program language because created time and requirement is different. The self-governed identity authentication and user management lead to differ identity, redundancy information, self-governed system and bad security. This paper integrates independent user management module and authentication module into uniform platform with uniform data exchange standard and interface standard. Using identity management mechanism based on certificate, the uniform identity authentication system based on ESB can make the best use of existing resource to avoid redundant data and reduce program logic effectively. The uniform access control mechanism can enhance security greatly.

Considering the factors that affect the karst water level, the improved Neural Network Model has been applied to construct the random model that analogs the dynamic change of karst water. The accuracy of our analog has been greatly improved compared with that of multi-line recurrence model; moreover, BP model has strong functions of study, fault tolerance and association. In a word, BP model is an effective tool to predict the dynamic change of karst water. In addition, the spewing tendency of springs in Jinan is analyzed based on our prediction results in this paper.