A novel controller for finding the best communication point is proposed for collecting data from a seabed platform by a single unmanned surface vehicle (USV) using underwater acoustic communication (UAC). As far as we know, extremum seeking based on climbing control is usually implemented by multiple vehicles or agents because of the large range of measurement and easy acquisition of gradient estimation. A single vehicle cannot rapidly estimate the field because of the limited extent for measurement; therefore, it is difficult for a single vehicle to seek the extremum point in a field. In this study, an oscillation motion (OM) is designed for a single USV to acquire UAC’s link strength data between the seabed platform and the USV. The field for UAC’s link strength is updated using new measurement from an OM of the USV based on a multi-variable weight linear iteration method. A controller for seeking the best UAC’s point of the USV is designed using gradient climbing and artificial potential considering iterative estimation of an unknown field and an OM operation, and the stability is proved. The reliability and efficiency are shown in simulation results.
Controller area network (CAN) is a widely used fieldbus protocol in various industrial applications. To understand the network behavior under errors for the optimal design of networked control systems, the message response time of the CAN network needs to be analyzed. In this study, a novel delay time distribution analysis method for the response messages is proposed when considering errors. In this method the complex message queues are decomposed into typical message patterns and cases. First, a stochastic fault model is developed, and the probability factor is defined to calculate the error distribution. Then the message delay time distribution for the single slave node configuration is analyzed based on the error distribution. Next, based on the delay time distribution analysis of typical patterns and cases, an analysis framework of message delay time distribution for the master/slave configuration is developed. The testbed is constructed and case studies are conducted to demonstrate the proposed methodology under different network configurations. Experimental results show that the delay time distributions calculated by the proposed method agree well with the actual observations.
Attribute-based encryption (ABE) has been a preferred encryption technology to solve the problems of data protection and access control, especially when the cloud storage is provided by third-party service providers. ABE can put data access under control at each data item level. However, ABE schemes have practical limitations on dynamic attribute revocation. We propose a generic attribute revocation system for ABE with user privacy protection. The attribute revocation ABE (AR-ABE) system can work with any type of ABE scheme to dynamically revoke any number of attributes.
With the development of cloud computing technology, data can be outsourced to the cloud and conveniently shared among users. However, in many circumstances, users may have concerns about the reliability and integrity of their data. It is crucial to provide data sharing services that satisfy these security requirements. We introduce a reliable and secure data sharing scheme, using the threshold secret sharing technique and the Chaum-Pedersen zero-knowledge proof. The proposed scheme is not only effective and flexible, but also able to achieve the semantic security property. Moreover, our scheme is capable of ensuring accountability of users’ decryption keys as well as cheater identification if some users behave dishonestly. The efficiency analysis shows that the proposed scheme has a better performance in terms of computational cost, compared with the related work. It is particularly suitable for application to protect users’ medical insurance data over the cloud.
Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface (API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.
The identification of important nodes in a power grid has considerable benefits for safety. Power networks vary in many aspects, such as scale and structure. An index system can hardly cover all the information in various situations. Therefore, the efficiency of traditional methods using an index system is case-dependent and not universal. To solve this problem, an artificial intelligence based method is proposed for evaluating power grid node importance. First, using a network embedding approach, a feature extraction method is designed for power grid nodes, considering their structural and electrical information. Then, for a specific power network, steady-state and node fault transient simulations under various operation modes are performed to establish the sample set. The sample set can reflect the relationship between the node features and the corresponding importance. Finally, a support vector regression model is trained based on the optimized sample set for the later online use of importance evaluation. A case study demonstrates that the proposed method can effectively evaluate node importance for a power grid based on the information learned from the samples. Compared with traditional methods using an index system, the proposed method can avoid some possible bias. In addition, a particular sample set for each specific power network can be established under this artificial intelligence based framework, meeting the demand of universality.
To overcome the shortcomings of the traditional measurement error calibration methods for spaceflight telemetry, tracking and command (TT&C) systems, an online error calibration method based on low Earth orbit satellite-to-ground doubledifferential GPS (LEO-ground DDGPS) is proposed in this study. A fixed-interval smoother combined with a pair of forward and backward adaptive robust Kalman filters (ARKFs) is adopted to solve the LEO-ground baseline, and the ant colony optimization (ACO) algorithm is used to deal with the ambiguity resolution problem. The precise baseline solution of DDGPS is then used as a comparative reference to calibrate the systematic errors in the TT&C measurements, in which the parameters of the range error model are solved by a batch least squares algorithm. To validate the performance of the new online error calibration method, a hardware-in-the-loop simulation platform is constructed with independently developed spaceborne dual-frequency GPS receivers and a Spirent GPS signal generator. The simulation results show that with the fixed-interval smoother, a baseline estimation accuracy (RMS, single axis) of better than 10 cm is achieved. Using this DDGPS solution as the reference, the systematic error of the TT&C ranging system is effectively calibrated, and the residual systematic error is less than 5 cm.
We propose a Taylor expansion multiple signal classification (TE MUSIC) method for joint direction of departure (DOD) and direction of arrival (DOA) estimation in a bistatic multiple-input multiple-output (MIMO) array. First, using a Taylor expansion of the steering vector, a two-dimensional (2D) search in the conventional MUSIC method for MIMO arrays is reduced to a two-step one-dimensional (1D) search in the proposed TE MUSIC method. Second, DOAs of the targets can be achieved via Lagrange multiplier by a 1D search. Finally, substituting the DOA estimates into the 2D MUSIC spectrum function, DODs of the targets are obtained by another 1D search. Thus, the DOD and DOA estimates can be automatically paired. The performance of the proposed method is better than that of the MIMO ESPRIT method, and is similar to that of the 2D MUSIC method. Furthermore, due to the 1D search, the TE MUSIC method avoids the high computational complexity of the 2D MUSIC method. Simulation results are presented to show the effectiveness of the proposed method.
The segmentation of moving and non-moving regions in an image within the field of crowd analysis is a crucial process in terms of understanding crowd behavior. In many studies, similar movements were segmented according to the location, adjacency to each other, direction, and average speed. However, these segments may not in turn indicate the same types of behavior in each region. The purpose of this study is to better understand crowd behavior by locally measuring the degree of interaction/complexity within the segment. For this purpose, the flow of motion in the image is primarily represented as a series of trajectories. The image is divided into hexagonal cells and the finite time braid entropy (FTBE) values are calculated according to the different projection angles of each cell. These values depend on the complexity of the spiral structure that the trajectories generated throughout the movement and show the degree of interaction among pedestrians. In this study, behaviors of different complexities determined in segments are pictured as similar movements on the whole. This study has been tested on 49 different video sequences from the UCF and CUHK databases.
Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively.
By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template (FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.