Visible to the public Biblio

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C
Lin, Y., Qi, Z., Wu, H., Yang, Z., Zhang, J., Wenyin, L..  2018.  CoderChain: A BlockChain Community for Coders. 2018 1st IEEE International Conference on Hot Information-Centric Networking (HotICN). :246–247.
An online community based on blockchain is proposed for software developers to share, assess, and learn codes and other codes or software related knowledge. It involves three modules or roles, namely: developer (or coder, or more generally, knowledge contributor), code (or knowledge contribution), and jury (or assessor, who is usually a developer with advanced skills), in addition to the blockchain based database. Each full node of the blockchain hosts a copy of all activities of developers in such community, including uploading contributions, assessing others' contributions, and conducting transactions. Smart contracts are applicable to automate transactions after code assessment or other related activities. The system aims to assess and improve the value of codes accurately, stimulate the creativity of the developers, and improve software development efficiency, so as to establish a virtuous cycle of a software development community.
D
Wang, M., Li, Z., Lin, Y..  2017.  A Distributed Intrusion Detection System for Cognitive Radio Networks Based on Evidence Theory. 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). :226–232.

Reliable detection of intrusion is the basis of safety in cognitive radio networks (CRNs). So far, few scholars applied intrusion detection systems (IDSs) to combat intrusion against CRNs. In order to improve the performance of intrusion detection in CRNs, a distributed intrusion detection scheme has been proposed. In this paper, a method base on Dempster-Shafer's (D-S) evidence theory to detect intrusion in CRNs is put forward, in which the detection data and credibility of different local IDS Agent is combined by D-S in the cooperative detection center, so that different local detection decisions are taken into consideration in the final decision. The effectiveness of the proposed scheme is verified by simulation, and the results reflect a noticeable performance improvement between the proposed scheme and the traditional method.

I
Lin, Y., Abur, A..  2017.  Identifying security vulnerabilities of weakly detectable network parameter errors. 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton). :295–301.
This paper is concerned about the security vulnerabilities in the implementation of the Congestion Revenue Rights (CRR) markets. Such problems may be due to the weakly detectable network model parameter errors which are commonly found in power systems. CRRs are financial tools for hedging the risk of congestion charges in power markets. The reimbursements received by CRR holders are determined by the congestion patterns and Locational Marginal Prices (LMPs) in the day-ahead markets, which heavily rely on the parameters in the network model. It is recently shown that detection of errors in certain network model parameters may be very difficult. This paper's primary goal is to illustrate the lack of market security due to such vulnerabilities, i.e. CRR market calculations can be manipulated by injecting parameter errors which are not likely to be detected. A case study using the IEEE 14-bus system will illustrate the feasibility of such undetectable manipulations. Several suggestions for preventing such cyber security issues are provided at the end of the paper.
Zhang, H., Lin, Y., Xiao, J..  2017.  An innovative analying method for the scale of distribution system security region. 2017 IEEE Power Energy Society General Meeting. :1–5.

Distribution system security region (DSSR) has been widely used to analyze the distribution system operation security. This paper innovatively defines the scale of DSSR, namely the number of boundary constraints and variables of all operational constraints, analyzes and puts forward the corresponding evaluation method. Firstly, the influence of the number of security boundary constraints and variables on the scale of DSSR is analyzed. The factors that mainly influence the scale are found, such as the number of transformers, feeders, as well as sectionalizing switches, and feeder contacts modes between transformers. Secondly, a matrix representing the relations among transformers in distribution system is defined to reflect the characteristics of network's structure, while an algorithm of the scale of DSSR based on transformers connection relationship matrix is proposed, which avoids the trouble of listing security region constraints. Finally, the proposed method is applied in a test system to confirm the effectiveness of the concepts and methods. It provides the necessary foundation for DSSR theory as well as safety analysis.

R
Wang, C., Xie, H., Bie, Z., Yan, C., Lin, Y..  2017.  Reliability evaluation of AC/DC hybrid power grid considering transient security constraints. 2017 13th IEEE Conference on Automation Science and Engineering (CASE). :1237–1242.

With the rapid development of DC transmission technology and High Voltage Direct Current (HVDC) programs, the reliability of AC/DC hybrid power grid draws more and more attentions. The paper takes both the system static and dynamic characteristics into account, and proposes a novel AC/DC hybrid system reliability evaluation method considering transient security constraints based on Monte-Carlo method and transient stability analytical method. The interaction of AC system and DC system after fault is considered in evaluation process. The transient stability analysis is performed firstly when fault occurs in the system and BPA software is applied to the analysis to improve the computational accuracy and speed. Then the new system state is generated according to the transient analysis results. Then a minimum load shedding model of AC/DC hybrid system with HVDC is proposed. And then adequacy analysis is taken to the new state. The proposed method can evaluate the reliability of AC/DC hybrid grid more comprehensively and reduce the complexity of problem which is tested by IEEE-RTS 96 system and an actual large-scale system.

T
Lin, Y., Liu, H., Xie, G., Zhang, Y..  2018.  Time Series Forecasting by Evolving Deep Belief Network with Negative Correlation Search. 2018 Chinese Automation Congress (CAC). :3839-3843.

The recently developed deep belief network (DBN) has been shown to be an effective methodology for solving time series forecasting problems. However, the performance of DBN is seriously depended on the reasonable setting of hyperparameters. At present, random search, grid search and Bayesian optimization are the most common methods of hyperparameters optimization. As an alternative, a state-of-the-art derivative-free optimizer-negative correlation search (NCS) is adopted in this paper to decide the sizes of DBN and learning rates during the training processes. A comparative analysis is performed between the proposed method and other popular techniques in the time series forecasting experiment based on two types of time series datasets. Experiment results statistically affirm the efficiency of the proposed model to obtain better prediction results compared with conventional neural network models.