RISK PRIORITY EVALUATION OF POWER TRANSFORMER PARTS BASED ON HYBRID FMEA FRAMEWORK UNDER HESITANT FUZZY ENVIRONMENT

The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority evaluation of the power transformer parts. Failure mode and effects analysis (FMEA) is a methodology for analyzing the potential failure modes (FMs) within a system in various industrial devices. This study puts forward a hybrid FMEA framework integrating novel hesitant fuzzy aggregation tools and CRITIC (Criteria Importance Through Inter-criteria Correlation) method. In this framework, the hesitant fuzzy sets (HFSs) are used to depict the uncertainty in risk evaluation. Then, an improved HFWA (hesitant fuzzy weighted averaging) operator is adopted to fuse risk evaluation for FMEA experts. This aggregation manner can consider different lengths of HFSs and the support degrees among the FMEA experts. Next, the novel HFWGA (hesitant fuzzy weighted geometric averaging) operator with CRITIC weights is developed to determine the risk priority of each FM. This method can satisfy the multiplicative characteristic of the RPN (risk priority number) method of the conventional FMEA model and reflect the correlations between risk indicators. Finally, a real example of the risk priority evaluation of power transformer parts is given to show the applicability and feasibility of the proposed hybrid FMEA framework. Comparison and sensitivity studies are also offered to verify the effectiveness of the improved risk assessment approach

Chemical Composition of Zanthoxylum avicennae Essential Oil and its Larvicidal Activity on Aedes albopictus Skuse

Purpose: To determine the larvicidal activity of the essential oil derived from Zanthoxylum avicennae (Lam.) DC. (Rutaceae) leaves and stems against the larvae of Aedes albopictus Skuse．Methods: Essential oil of Z. avicennae leaves and stems were obtained by hydrodistillation and analyzed by gas chromatography (GC) and gas chromaotography-mas spectrometry (GC-MS). The activity of the essential oil was evaluated, using World Health Organization (WHO) procedures, against the fourth larvae of A. albopictus for 24 h and larval mortality recorded at various essential oil concentrations ranging from 12.5 - 200 μg/mL.Results: A total of 31 components of the essential oil of Z. avicennae were identified. The essential oil had higher content of monoterpenoids (65.70 %) than sesquiterpenoids (33.45 %). The principal compounds of the essential oil were 1,8-cineol (53.05 %), β-elemene (6.13 %), α-caryophyllene (5.96%), β-caryophyllene (5.09 %) and caryophyllene oxide (4.59 %). The essential oil exhibited larvicidal activity against A. albopictus with a median lethal concentration (LC50) value of 48.79 μg/mL.Conclusion: The findings obtained indicate that the essential oil of Z. avicennae has potentials for use in the control of A. albopictus larvae and could be useful in the search for newer, safer and more effective natural compounds as larvicides.Keywords: Aedes albopictus, Essential oil, Larvicidal activity, Mosquito, Zanthoxylum avicenna

Developing normalization schemes for data isolated distributed deep learning

Distributed deep learning is an important and indispensable direction in the field of deep learning research. Earlier research has proposed many algorithms or techniques on accelerating distributed neural network training. This study discusses a new distributed training scenario, namely data isolated distributed deep learning. Specifically, each node has its own local data and cannot be shared for some reasons. However, in order to ensure the generalization of the model, the goal is to train a global model that required learning all the data, not just based on data from a local node. At this time, distributed training with data isolation is needed. An obvious challenge for distributed deep learning in this scenario is that the distribution of training data used by each node could be highly imbalanced because of data isolation. This brings difficulty to the normalization process in neural network training, because the traditional batch normalization (BN) method will fail under this kind of data imbalanced scenario. At this time, distributed training with data isolation is needed. Aiming at such data isolation scenarios, this study proposes a comprehensive data isolation deep learning scheme. Specifically, synchronous stochastic gradient descent algorithm is used for data exchange during training, and provides several normalization approaches to the problem of BN failure caused by data imbalance. Experimental results show the efficiency and accuracy of the proposed data isolated distributed deep learning scheme