Grid-connected inverters are known to become unstable when the grid impedance is high. Existing approaches to analyzing such instability are based on inverter control models that account for the grid ...impedance and the coupling with other grid-connected inverters. A new method to determine inverter-grid system stability using only the inverter output impedance and the grid impedance is developed in this paper. It will be shown that a grid-connected inverter will remain stable if the ratio between the grid impedance and the inverter output impedance satisfies the Nyquist stability criterion. This new impedance-based stability criterion is a generalization to the existing stability criterion for voltage-source systems, and can be applied to all current-source systems. A single-phase solar inverter is studied to demonstrate the application of the proposed method.
•Propose a novel deep convolutional neural network-based method for remaining useful life predictions.•No prior expertise on prognostics and signal processing is required, that facilitates the ...application of the proposed method.•Effects of the key factors on the prognostic performance are widely investigated and the model parameters are optimized.•Experiments on a popular aero-engine degradation dataset (C-MAPSS) and comparisons with the related state-of-the-art results validate the effectiveness and superiority of the proposed method.
Traditionally, system prognostics and health management (PHM) depends on sufficient prior knowledge of critical components degradation process in order to predict the remaining useful life (RUL). However, the accurate physical or expert models are not available in most cases. This paper proposes a new data-driven approach for prognostics using deep convolution neural networks (DCNN). Time window approach is employed for sample preparation in order for better feature extraction by DCNN. Raw collected data with normalization are directly used as inputs to the proposed network, and no prior expertise on prognostics and signal processing is required, that facilitates the application of the proposed method. In order to show the effectiveness of the proposed approach, experiments on the popular C-MAPSS dataset for aero-engine unit prognostics are carried out. High prognostic accuracy on the RUL estimation is achieved. The superiority of the proposed method is demonstrated by comparisons with other popular approaches and the state-of-the-art results on the same dataset. The results of this study suggest that the proposed data-driven prognostic method offers a new and promising approach.
Intelligent machinery fault diagnosis system has been receiving increasing attention recently due to the potential large benefits of maintenance cost reduction, enhanced operation safety and ...reliability. This paper proposes a novel deep learning method for rotating machinery fault diagnosis. Since accurately labeled data are usually difficult to obtain in real industries, data augmentation techniques are proposed to artificially create additional valid samples for model training, and the proposed method manages to achieve high diagnosis accuracy with small original training dataset. Two augmentation methods are investigated including sample-based and dataset-based methods, and five augmentation techniques are considered in general, i.e. additional Gaussian noise, masking noise, signal translation, amplitude shifting and time stretching. The effectiveness of the proposed method is validated by carrying out experiments on two popular rolling bearing datasets. Fairly high diagnosis accuracy up to 99.9% can be obtained using limited training data. By comparing with the latest advanced researches on the same datasets, the superiority of the proposed method is demonstrated. Furthermore, the diagnostic performance of the deep neural network is extensively evaluated with respect to data augmentation strength, network depth and so forth. The results of this study suggest that the proposed intelligent fault diagnosis method offers a new and promising approach.
AC distributed power systems (DPS) can be found in several new and emerging applications. Similar to dc distributed power systems, an ac DPS relies on power electronics and control to realize its ...functions and achieve the required performance. System stability and power quality are important issues in both types of systems due to the complex system behavior resulted from active control at both the source and the load side. Traditional small-signal analysis methods cannot be directly applied to an ac DPS because of the periodically time-varying system operation trajectory. Possible solutions to this problem include transformation into a rotating ( dq ) reference frame, modeling using dynamic phasors, reduced-order modeling, and harmonic linearization. This paper reviews these small-signal methods and discusses their utilities as well as limitations. Compatibility of each type of models with state-space and impedance-based system analysis approaches will also be discussed. Problems related to the linearization of phasor-based models and their use in impedance-based system analysis are highlighted in particular.
There is a general consensus to develop renewable energy storage and conversion technologies to replace fossil fuel energy for sustainable development. Currently, the development of high performance ...energy storage and conversion devices is an important step on the road to alternative energy technologies. Among the newly developed materials, porous carbons and related functional materials including metal/metal oxide nanoparticles and carbon-metal/metal oxide hybrids show potential for applications in such devices. Recently, the newly emerging open framework materials, including metal-organic frameworks (MOFs) and porous organic frameworks (POFs), act as outstanding templates and/or precursors to fabricate porous carbons and related nanostructured functional materials based on their high surface areas, controllable structures and abundant metal/organic species in their scaffolds. Here, recent significant advances in the development of open frameworks for preparation of porous carbons and related nanostructured functional materials are reviewed with special emphases on the applications in the energy and environmental areas.
This review discusses the open frameworks as templates and/or precursors for the synthesis of porous carbons and other nanostructured functional materials including metal/metal oxide nanoparticles and carbon-metal/metal oxide hybrids. A survey of the research progress in the applications of these materials for gas sorbents, fuel cells, Li(S/O
2
)-ion rechargeable batteries, supercapacitors and catalysts is given.
Massive emissions of CO2 have caused environmental problems all over the world. The fixation of CO2 into cyclic carbonates is regarded as an effective way of capturing and utilizing CO2. Ionic liquid ...catalysts (ILCs) have received great attention and been employed for catalyzing the above reaction in recent years due to their unique properties. However, there are still a few problems requiring solutions in order to finally find the “ideal catalyst”. Herein, we reviewed a number of recent related literature. The progresses of both homogeneous and heterogeneous ILCs were discussed to find out where we are and which directions to work on. The effects of cations and anions of ionic liquids (ILs), functional groups, reaction phase states, structures of supports, preparation methods of supported ionic liquids (SILs), and interactions between the ILs and supports were investigated systematically. Accordingly, basic principles of designing ILCs for the title reaction are summarized and directions of future investigations are highlighted.
Designing ILs: Homogeneous and heterogeneous ionic liquid catalysts for the cycloaddition reaction of CO2 and epoxides to synthesize cyclic carbonates were reviewed. The effects of cations and anions of ionic liquids, functional groups, reaction phase states, structures of supports, preparation methods of supported ionic liquids, and interactions between the ILs and supports were investigated. Accordingly, basic principles of designing ionic liquid catalysts for the reaction were summarized.
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet 1 and Fast R-CNN 2 have reduced the running time of these ...detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network(RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model 3, our detection system has a frame rate of 5 fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.
In recent years, there has been a growing interest in scholars and practitioners to explore the factors that lead to an improvement in Students' psychological wellbeing. Due to the tough challenges ...faced by students during their academic life, severe issues of stress, anxiety, and other mental health issues emerge, which affect their academic performance and have a long-lasting impact on their future careers. The pandemic accelerates the stress levels, anxiety, and mental issues of students. The main purpose of this study was to explore how music education impacts on Students' psychological wellbeing and academic performance. This study also investigates the mediating effect of self-esteem and self-efficacy. To the best of our knowledge, there has been little to no study exploring the relationship of music education on the psychological wellbeing and performance of students, especially from the perspective of Asian countries. This study was conducted in undergraduate and graduate institutions of China. This study was quantitative in nature and data were collected from 319 respondents. The structural equation modeling (SEM) technique was employed for data analysis. Results reveal that music education has a significant positive impact on psychological wellbeing, which improves Students' academic performance. Moreover, psychological wellbeing also has a significant and positive impact on Students' academic performance. Self-efficacy and self-esteem significantly mediate the relationship between music education and psychological wellbeing. The findings of this study open new avenues for future research in music education and psychological wellbeing. This study suggests that the policymakers and practitioners should make such policies that encourage educational institutes to adopt music education to improve the psychological wellbeing of students.
The demand for economical and environmentally benign catalysts for important chemical transformations has recently initiated great efforts on nonprecious metal-catalyzed hydrosilylation reactions. ...The special chemical properties of cobalt enable the development of diverse cobalt complex-based catalysts for hydrosilylation reactions. This paper reviews the significant advances of cobalt complex-catalyzed hydrosilylation of alkenes and alkynes from the early studies in the 1960s until now, with the objective of providing readers with the status of the field and the underlying late 3d metal chemistry that is meaningful for new nonprecious metal catalyst design. Progress, problems, and perspectives in this vibrant field are discussed.
Identifying and understanding factors that influence the demand of ridesourcing market is essential for online hailing systems to improve the quality of service. This paper proposes a two-level ...growth model (GM) to identify the potential multi-level factors that may affect online ride-hailing service demand. By using the massive datasets from Didi Chuxing, Inc., including both Didi Express and Didi Taxi services, the order number fluctuations at different urban circle zones after the implementation of restrictions on ridesourcing in Shanghai, 2016 were analyzed, to assess the competition and mutual complementarities between Express and Taxi, the two major services provided by Didi Chuxing. The relative market share of Express was estimated to reveal the possible related spatial and temporal factors, which further demonstrates significant positive associations between ridesourcing demand and built environment factors, such as commercial/residential land use, public transport accessibility, as well as weather conditions. Metro service availability and rainy weather were found correlated with a relatively higher market share of Express service. Additionally, compared to the regular road transit service, the metro system was found to have a stronger correlation with the ridesourcing demand. Findings of this study may provide guidelines for urban planning and traffic operations, which in turn assists to achieve high-quality ridesourcing service for travellers.
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