Transformer Network Intelligent Flight Situation Awareness Assessment Based on Pilot Visual Gaze and Operation Behavior Data

Jiang, Guangyi; Chen, Hua; Wang, Changyuan; Xue, Pengxiang

doi:10.1142/s0218001422590157

Cited by 11 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this kind of approach, each type of condition data is firstly converted to a score according to the relevant standards (e.g., IEEE/IEC or CIGRE). Then, a weight indicating its significance to the health condition of the entire transformer is assigned on the basis of experts' experience or the relevance to standard codes, or a combination of both [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Finally, a summation of all of these weighted scores is performed, which provides the health index of the transformer of interest.…”

Section: Type-i Health Indexmentioning

confidence: 99%

“…Recent efforts by Kinectrics [16][17][18], US and Thai utilities [19], Hydro-Québec [20,21], Wuhan University [22], University of Cambria [23], Ann University [24], as well as other utilities and research institutes can be classified as belonging to this type of health index [25][26][27][28][29]. A schematic for realizing the so-called Type-I health index is presented in Figure 2.…”

Section: Type-i Health Indexmentioning

confidence: 99%

See 1 more Smart Citation

Review of Transformer Health Index from the Perspective of Survivability and Condition Assessment

Cui

et al. 2023

Electronics

View full text Add to dashboard Cite

As a critical indicator for assessing the survivability and condition of transformers in a fleet, the transformer health index has attracted attention from both asset owners and international organizations like CIGRE and IEEE DEIS/PES. To provide a systematic and comprehensive review for further study or to guide transformer asset management, this paper summarizes the state-of-the-art of the transformer health index, from the early proposed weighted-score-sum approaches to the more recently proposed artificial intelligence algorithm-based methods. Firstly, different methods for determining the transformer health index are reviewed. Each of these is specified as belonging to a certain type on the basis of its formulation and composition schematic. Subsequently, the steps to determine each type of health index are summarized, and examples derived from literature are provided for further illustration. Comparisons are finally carried out in order to better understand the pros and cons of different types of transformer health index, and the future development trends for transformer health indexes are also discussed. This work can serve as a valuable reference for the survivability and condition assessment of transformers in the power industry.

show abstract

Section: Type-i Health Indexmentioning

confidence: 99%

Section: Type-i Health Indexmentioning

confidence: 99%

Review of Transformer Health Index from the Perspective of Survivability and Condition Assessment

Cui

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Recently, a robust feature extractor based on the self-attention mechanism, known as the Transformer model, has garnered significant attention in the computer vision domain [28], [29], [30]. Such models, adept at modeling long-range dependencies and correlations among different parts of images, prove particularly effective for tasks like image deblurring.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Patch Aggregation Transformer For Motion Deblurring

Wu,

Lei,

Ling

et al. 2023

Preprint

View full text Add to dashboard Cite

The paradigm of harnessing encoder-decoder frameworks, underpinned by Transformer constituents, has emerged as an exemplar in the realm of image deblurring architectural designs. In this investigation, we critically reexamine this approach. Our analysis reveals that many current architectures focus heavily on limited local regions during the feature extraction phase. This narrow focus compromises the richness and diversity of features channeled to the encoder-decoder framework, resulting in an information bottleneck. Furthermore, these designs tend to rely excessively on global features, which can lead to the neglect of crucial local details in specific areas, adversely affecting image deblurring efficacy. To address these issues, we present the a novel hierarchical patch aggregation Transformer(HPAT) architecture. In the initial feature extraction phase, we incorporate cross-axis spatial Transformer blocks that exhibit linear complexity, complemented by an adaptive hierarchical attention fusion mechanism. These enhancements enable the model to adeptly capture spatial interrelationships among features and integrate insights from multiple hierarchical layers. Subsequently, we optimize the feedforward network within the Transformer blocks of the encoder-decoder framework, leading to the development of the Fusion Feedforward Network (F3N). This innovation streamlines the aggregation of token information, bolstering the model's ability to capture and retain local details. Our comprehensive experimental assessments, conducted across a variety of publicly available datasets, confirm the effectiveness of the HPAT model. Empirical results decisively prove that our HPAT model establishes a new benchmark in image deblurring tasks.

show abstract