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Since Professor Greziter first proposed a theoretical model to predict the dynamic behavior of a compression system in 1976, the contribution of the volume effect to compressor flow instabilities has been widely studied, but the role of volume effect on the compressor performance during acceleration and deceleration has not received much attention. Therefore, starting from the Greitzer lumped parameter approach and integrating with real engine simulated components to improve the Greitzer’s model, an engine transient simulation model accounting for the compressor volume effect (referred to as the unsteady model) is developed in this paper. Based on a real turbofan engine, transient examination comprising acceleration and deceleration has been conducted for the validation of the unsteady model. The simulation results show better agreement with the experimental data compared with the transient simulation without considering the compressor volume effect (referred to as the quasi-steady model), which confirms the importance of introducing the compressor volume effect into the engine transient modeling. And the cause of the deviation between unsteady model and quasi-steady model are further explained by the compressor inlet and outlet mass flow curve. The results may further contribute to the development of engine transient model.

Knowledge distillation(KD) aims to improve the performance of a student network by mimicing the knowledge from a powerful teacher network. Existing methods focus on studying what knowledge should be transferred and treat all samples equally during training. This paper introduces the adaptive sample weighting to KD. We discover that previous effective hard mining methods are not appropriate for distillation. Furthermore, we propose Prime-Aware Adaptive Distillation (PAD) by the incorporation of uncertainty learning. PAD perceives the prime samples in distillation and then emphasizes their effect adaptively. PAD is fundamentally different from and would refine existing methods with the innovative view of unequal training. For this reason, PAD is versatile and has been applied in various tasks including classification, metric learning, and object detection. With ten teacher-student combinations on six datasets, PAD promotes the performance of existing distillation methods and outperforms recent state-of-the-art methods., Accepted by ECCV 2020

In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition. It incorporates one text attention module during feature extraction which enforces the model to focus on text regions and the whole framework is supervised by two branches. One supervision branch comes from context-level modelling and another comes from one extra supervision enhancement branch which aims at tackling inexplicit semantic information at character level. These two supervisions can benefit each other and yield better performance. The proposed approach can recognize text in arbitrary length and does not need any predefined lexicon. Our method outperforms the current state-of-the-art methods on three text recognition benchmarks: IIIT5K, ICDAR2013 and SVT reaching accuracy 88.6%, 92.3% and 84.1% respectively which suggests the effectiveness of the proposed method.

Ultra high voltage direct current (UHVDC) hierarchical connection to AC grid mode has the advantages of leading flow reasonable distribution and improving voltage support capability of receiving-end system. But the DC transmission system based on line-commutated inverter has the inherent voltage stability problem. This paper focuses on the voltage stability of AC-DC system under UHVDC hierarchical connection mode, deduced the computing method of voltage stability factor under hierarchical connection mode, analyzed the voltage stability characteristic of AC-DC system while adopting different control mode, and verified the algorithm rationality by simulating in actual small system. Analysis shows that adopting constant current or power on the rectifier side and constant voltage on the inverter side control mode is most beneficial for voltage stability of AC-DC system.

Content providers build serving stacks to deliver content to users. An important goal of a content provider is to ensure good user experience, since user experience has an impact on revenue. In this paper, we describe a system at Yahoo called YTrace that diagnoses bad user experience in near real time. We present the different components of YTrace for end-to-end multi-layer diagnosis (instrumentation, methods and backend system), and the system architecture for delivering diagnosis in near real time across all user sessions at Yahoo. YTrace diagnoses problems across service and network layers in the end-to-end path spanning user host, Internet, CDN and the datacenters, and has three diagnosis goals: detection, localization and root cause analysis (including cascading problems) of performance problems in user sessions with the cloud. The key component of the methods in YTrace is capturing and discovering causality, which we design based on a mix of instrumentation API, domain knowledge and blackbox methods. We show three case studies from production that span a large-scale distributed storage system, a datacenter-wide network, and an end-to-end video serving stack at Yahoo. We end by listing a number of open directions for performance diagnosis in cloud and content providers.

With the development of smart demand response (DR) technology, it provide new ideas to utilize DR technologies to participate in spinning reserve of power system. Based on smart DR technologies, this paper presents a dispatching framework with DR participating in power system's reserve in various time scales, and then a coordinated optimization model is proposed to determine how much capacity the DR should provide with considering of the cost of reserve. In this optimization model, the system cost includes both deterministic cost and risk cost, and the optimal total cost of DR participating in spinning reserve is the goal of the proposed coordinating optimization scheme. Finally, the feasibility and effectiveness of the proposed scheme is verified by numerical cases and simulation results.