Abstract-The modular multilevel converter (MMC) is distinguished by its modularity, that is the use of standardized submodules (SMs). To enhance reliability and avoid unscheduled maintenance, it is desired that MMC can remain operational without having to shut down despite some of its SMs are failed. Particularly, in this paper, a complete fault diagnosis and tolerant control solution, including the fault detection, fault tolerance, fault localization, and fault reconfiguration, has been proposed to ride through the IGBT open-circuit failures. The fault detection method detects the fault by means of state observers and the knowledge of fault behaviors of MMC, without using any additional sensors. Then MMC is controlled in a newly proposed tolerant mode until the specific faulty SM is located by the fault localization method, thus no overcurrent problems will happen during this time interval. After that, the located faulty SM will be bypassed while the remaining SMs are reconfigured to provide continuous operation. Throughout the fault periods, it allows the MMC to operate smoothly without obvious waveform distortion and power interruption. Finally, experimental results using a single-phase scaled-down MMC prototype with six SMs per arm show the validity and feasibility of the proposed methods. Index Terms-Fault diagnosis, fault tolerance, insulated gate bipolar transistor (IGBT), modular multilevel converter (MMC), open-circuit failure, redundancy, state observer, submodule (SM).

Periodic event-triggered control (PETC) is an appealing paradigm for the implementation of controllers on platforms with limited communication resources, a typical example being networked control systems. In PETC, transmissions over the communication channel are triggered by an event generator, which depends solely on the available plant and controller data, and is only evaluated at given sampling instants to enable its digital implementation. In this paper, we consider the general scenario where the controller communicates with the plant via multiple decoupled networks. Each network may contain multiple nodes, in which case a dedicated protocol is used to schedule transmissions among these nodes. The transmission instants over the networks are asynchronous and generated by local event generators. At given sampling instants, the local event generator evaluates a rule, which only involves the measurements and the control inputs available locally, to decide whether a transmission is needed over the considered network. Following the emulation approach, we show how to design the local triggering generators to ensure input-to-state stability and Lp-stability for the overall system based on a continuous-time output feedback controller that robustly stabilizes the network-free system. The method is applied to a class of Lipschitz nonlinear systems, for which we formulate the design conditions as linear matrix inequalities. The effectiveness of the scheme is illustrated via simulations of a nonlinear example.

Citation recommendation is an interesting and significant research area as it solves the information overload in academia by automatically suggesting relevant references for a research paper. Recently, with the rapid proliferation of information technology, research papers are rapidly published in various conferences and journals. This makes citation recommendation a highly important and challenging discipline. In this paper, we propose a novel citation recommendation method that uses only easily obtained citation relations as source data. The rationale underlying this method is that, if two citing papers are significantly co-occurring with the same citing paper(s), they should be similar to some extent. Based on the above rationale, an association mining technique is employed to obtain the paper representation of each citing paper from the citation context. Then, these paper representations are pairwise compared to compute similarities between the citing papers for collaborative filtering. We evaluate our proposed method through two relevant real-world data sets. Our experimental results demonstrate that the proposed method significantly outperforms the baseline method in terms of precision, recall, and F1, as well as mean average precision and mean reciprocal rank, which are metrics related to the rank information in the recommendation list.

In academia, scientific research achievements would be inconceivable without academic collaboration and cooperation among researchers. Previous studies have discovered that productive scholars tend to be more collaborative. However, it is often difficult and time-consuming for researchers to find the most valuable collaborators (MVCs) from a large volume of big scholarly data. In this paper, we present MVCWalker, an innovative method that stands on the shoulders of random walk with restart (RWR) for recommending collaborators to scholars. Three academic factors, i.e., coauthor order, latest collaboration time, and times of collaboration, are exploited to define link importance in academic social networks for the sake of recommendation quality. We conducted extensive experiments on DBLP data set in order to compare MVCWalker to the basic model of RWR and the common neighbor-based model friend of friends in various aspects, including, e.g., the impact of critical parameters and academic factors. Our experimental results show that incorporating the above factors into random walk model can improve the precision, recall rate, and coverage rate of academic collaboration recommendations.

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