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Dong, C., Liu, Y., Zhang, Y., Shi, P., Shao, X., Ma, C..  2018.  Abnormal Bus Data Detection of Intelligent and Connected Vehicle Based on Neural Network. 2018 IEEE International Conference on Computational Science and Engineering (CSE). :171–176.
In the paper, our research of abnormal bus data analysis of intelligent and connected vehicle aims to detect the abnormal data rapidly and accurately generated by the hackers who send malicious commands to attack vehicles through three patterns, including remote non-contact, short-range non-contact and contact. The research routine is as follows: Take the bus data of 10 different brands of intelligent and connected vehicles through the real vehicle experiments as the research foundation, set up the optimized neural network, collect 1000 sets of the normal bus data of 15 kinds of driving scenarios and the other 300 groups covering the abnormal bus data generated by attacking the three systems which are most common in the intelligent and connected vehicles as the training set. In the end after repeated amendments, with 0.5 seconds per detection, the intrusion detection system has been attained in which for the controlling system the abnormal bus data is detected at the accuracy rate of 96% and the normal data is detected at the accuracy rate of 90%, for the body system the abnormal one is 87% and the normal one is 80%, for the entertainment system the abnormal one is 80% and the normal one is 65%.
Ma, C., Wang, L., Gai, C., Yang, D., Zhang, P., Zhang, H., Li, C..  2020.  Frequency Security Assessment for Receiving-end System Based on Deep Learning Method. 2020 IEEE/IAS Industrial and Commercial Power System Asia (I CPS Asia). :831–836.
For hours-ahead assessment of power systems with a high penetration level of renewable generation, a large number of uncertain scenarios should be checked to ensure the frequency security of the system after the severe power disturbance following HVDC blocking. In this situation, the full time-domain simulation is unsuitable as a result of the heavy calculation burden. To fulfill the quick assessment of the frequency security, the online frequency security assessment framework based on deep learning is proposed in this paper. The Deep Belief Network (DBN) method is used to establish the framework. The sample generation method is researched to generate representative samples for the purposed of higher assessment accuracy. A large-scale AC-DC interconnected power grid is adopted to verify the validity of the proposed assessment method.
Ma, C., Guo, Y., Su, J..  2017.  A Multiple Paths Scheme with Labels for Key Distribution on Quantum Key Distribution Network. 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). :2513–2517.

This paper establishes a probability model of multiple paths scheme of quantum key distribution with public nodes among a set of paths which are used to transmit the key between the source node and the destination node. Then in order to be used in universal net topologies, combining with the key routing in the QKD network, the algorithm of the multiple paths scheme of key distribution we propose includes two major aspects: one is an approach which can confirm the number and the distance of the selection of paths, and the other is the strategy of stochastic paths with labels that can decrease the number of public nodes and avoid the phenomenon that the old scheme may produce loops and often get the nodes apart from the destination node father than current nodes. Finally, the paper demonstrates the rationality of the probability model and strategies about the algorithm.

Ma, C., Yang, X., Wang, H..  2018.  Randomized Online CP Decomposition. 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). :414-419.

CANDECOMP/PARAFAC (CP) decomposition has been widely used to deal with multi-way data. For real-time or large-scale tensors, based on the ideas of randomized-sampling CP decomposition algorithm and online CP decomposition algorithm, a novel CP decomposition algorithm called randomized online CP decomposition (ROCP) is proposed in this paper. The proposed algorithm can avoid forming full Khatri-Rao product, which leads to boost the speed largely and reduce memory usage. The experimental results on synthetic data and real-world data show the ROCP algorithm is able to cope with CP decomposition for large-scale tensors with arbitrary number of dimensions. In addition, ROCP can reduce the computing time and memory usage dramatically, especially for large-scale tensors.