264 research outputs found
Segmentation Method of Magnetic Tile Surface Defects Based on Deep Learning
Magnet tile is an essential part of various industrial motors, and its performance significantly affects the use of the motor. Various defects such as blowholes, break, cracks, fray, uneven, etc., may appear on the surface of the magnet tile. At present, most of these defects rely on manual visual inspection. To solve the problems of slow speed and low accuracy of segmentation of different defects on the magnetic tile surface, in this paper, we propose a segmentation method of the weighted YOLACT model. The proposed model uses the resnet101 network as the backbone, obtains multi-scale features through the weighted feature pyramid network, and performs two parallel subtasks simultaneously: generating a set of prototype masks and predicting the mask coefficients of each target. In the prediction mask coefficient branch, the residual structure and weights are introduced. Then, masks are generated by linearly combining the prototypes and the mask coefficients to complete the final target segmentation. The experimental results show that the proposed method achieves 43.44/53.44 mask and box mAP on the magnetic tile surface defect dataset, and the segmentation speed reaches 24.40 fps, achieving good segmentation results
The impact of corporate governance on executive compensation and pay-performance sensitivity
Executives are responsible for the company's daily operation management and major strategy decisions, which is directly related to the company performance. In the situation of increasingly fierce competition, how to establish an effective compensation incentive system for executives and how to solve the principal-agent problem has become a matter of concern. In this context, this study examines the impact of corporate governance on executive compensation and the pay-performance sensitivity using a comprehensive sample from more than 1000 non-financial Chinese listed companies during the period 2017-2019. This study takes executive compensation as the dependent variable, and for the independent variables, ROA is selected as an indicator to measure company performance, and four corporate governance elements including ownership concentration, CEO duality, board size and board independence are selected from the aspects of ownership structure and board structure. In order to study the impact of corporate governance on pay-performance sensitivity, the cross multiplicative term of ROA and corporate governance variables is added into the original model.
The empirical results show that there is a positive relationship between executive compensation and firm performance. CEO duality will positively affect executive compensation in Chinese listed companies, while ownership concentration, board size, and board independence have a negative impact on executive compensation. As for the pay-performance sensitivity, the results also show that CEO duality can weaken the pay-performance link, larger board are more likely to link executive compensation to firm performance, while ownership concentration and board independence have no significant moderating effect in Chinese listed companies. On the basis of these findings, this research puts forward some useful suggestions and made some contributions to enrich the relevant literature.
Keywords: Executive Compensation, Corporate Governance, Pay-performance Sensitivit
Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method
Graph Representation Learning (GRL) is an influential methodology, enabling a
more profound understanding of graph-structured data and aiding graph
clustering, a critical task across various domains. The recent incursion of
attention mechanisms, originally an artifact of Natural Language Processing
(NLP), into the realm of graph learning has spearheaded a notable shift in
research trends. Consequently, Graph Attention Networks (GATs) and Graph
Attention Auto-Encoders have emerged as preferred tools for graph clustering
tasks. Yet, these methods primarily employ a local attention mechanism, thereby
curbing their capacity to apprehend the intricate global dependencies between
nodes within graphs. Addressing these impediments, this study introduces an
innovative method known as the Graph Transformer Auto-Encoder for Graph
Clustering (GTAGC). By melding the Graph Auto-Encoder with the Graph
Transformer, GTAGC is adept at capturing global dependencies between nodes.
This integration amplifies the graph representation and surmounts the
constraints posed by the local attention mechanism. The architecture of GTAGC
encompasses graph embedding, integration of the Graph Transformer within the
autoencoder structure, and a clustering component. It strategically alternates
between graph embedding and clustering, thereby tailoring the Graph Transformer
for clustering tasks, whilst preserving the graph's global structural
information. Through extensive experimentation on diverse benchmark datasets,
GTAGC has exhibited superior performance against existing state-of-the-art
graph clustering methodologies
A Real-time Non-contact Localization Method for Faulty Electric Energy Storage Components using Highly Sensitive Magnetometers
With the wide application of electric energy storage component arrays, such
as battery arrays, capacitor arrays, inductor arrays, their potential safety
risks have gradually drawn the public attention. However, existing technologies
cannot meet the needs of non-contact and real-time diagnosis for faulty
components inside these massive arrays. To solve this problem, this paper
proposes a new method based on the beamforming spatial filtering algorithm to
precisely locate the faulty components within the arrays in real-time. The
method uses highly sensitive magnetometers to collect the magnetic signals from
energy storage component arrays, without damaging or even contacting any
component. The experimental results demonstrate the potential of the proposed
method in securing energy storage component arrays. Within an imaging area of
80 mm 80 mm, the one faulty component out of nine total components can
be localized with an accuracy of 0.72 mm for capacitor arrays and 1.60 mm for
battery arrays
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark
In this paper, we introduce a large Multi-Attribute and Language Search
dataset for text-based person retrieval, called MALS, and explore the
feasibility of performing pre-training on both attribute recognition and
image-text matching tasks in one stone. In particular, MALS contains 1,510,330
image-text pairs, which is about 37.5 times larger than prevailing CUHK-PEDES,
and all images are annotated with 27 attributes. Considering the privacy
concerns and annotation costs, we leverage the off-the-shelf diffusion models
to generate the dataset. To verify the feasibility of learning from the
generated data, we develop a new joint Attribute Prompt Learning and Text
Matching Learning (APTM) framework, considering the shared knowledge between
attribute and text. As the name implies, APTM contains an attribute prompt
learning stream and a text matching learning stream. (1) The attribute prompt
learning leverages the attribute prompts for image-attribute alignment, which
enhances the text matching learning. (2) The text matching learning facilitates
the representation learning on fine-grained details, and in turn, boosts the
attribute prompt learning. Extensive experiments validate the effectiveness of
the pre-training on MALS, achieving state-of-the-art retrieval performance via
APTM on three challenging real-world benchmarks. In particular, APTM achieves a
consistent improvement of +6.96%, +7.68%, and +16.95% Recall@1 accuracy on
CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets by a clear margin, respectively
An Improved Local Descriptor based Object Recognition in Cluttered 3D Point Clouds
Object recognition in three-dimensional point clouds is a new research topic in the field of computer vision. Numerous nuisances, such as noise, a varying density, and occlusion greatly increase the difficulty of 3D object recognition. An improved local feature descriptor is proposed to address these problems in this paper. At each feature point, a local reference frame is established by calculating a scatter matrix based on the geometric center and the weighted point-cloud density of its neighborhood, and an improved normal vector estimation method is used to generate a new signature of histograms of orientations (SHOT) local-feature descriptor. The geometric consistency and iterative closest point method realize 3D model recognition in the point-cloud scenes. The experimental results show that the proposed SHOT feature-extraction algorithm has high robustness and descriptiveness in the object recognition of 3D local descriptors in cluttered point-cloud scenes
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