169 research outputs found
Supply Chain Joint Inventory Management and Cost Optimization Based on Ant Colony Algorithm and Fuzzy Model
With the advancement of the marketization process, inventory management has transformed from a single backup protection function to an essential function for enterprises, which helps to survive and develop. Inventory control in supply chain management is the important content of supply chain management. The new management mode makes inventory management present many new characteristics and problems compared with traditional inventory management. From the view of system theory and integration theory, it is imperative to re-examine the problem of inventory control, put forward new inventory management strategies adapted to integrated supply chain management, and improve the integration of the whole supply chain, which can enhance the agility and market response speed of enterprises. Based on the in-depth study of the joint inventory management model, this paper analyzed the current situation of the joint inventory management to optimize the inventory. In view of the achievements and shortcomings of the current research, a more systematic and improved optimization model of the supply chain inventory was proposed by using the basic ideas of ant colony algorithm and fuzzy model
Direct observation of magnon-phonon coupling in yttrium iron garnet
The magnetic insulator yttrium iron garnet (YIG) with a ferrimagnetic
transition temperature of 560 K has been widely used in microwave and
spintronic devices. Anomalous features in the spin Seeback effect (SSE)
voltages have been observed in Pt/YIG and attributed to the magnon-phonon
coupling. Here we use inelastic neutron scattering to map out low-energy spin
waves and acoustic phonons of YIG at 100 K as a function of increasing magnetic
field. By comparing the zero and 9.1 T data, we find that instead of splitting
and opening up gaps at the spin wave and acoustic phonon dispersion
intersecting points, magnon-phonon coupling in YIG enhances the hybridized
scattering intensity. These results are different from expectations of
conventional spin-lattice coupling, calling for new paradigms to understand the
scattering process of magnon-phonon interactions and the resulting
magnon-polarons.Comment: 5 pages, 4 figures, PRB in pres
AutoKary2022: A Large-Scale Densely Annotated Dateset for Chromosome Instance Segmentation
Automated chromosome instance segmentation from metaphase cell microscopic
images is critical for the diagnosis of chromosomal disorders (i.e., karyotype
analysis). However, it is still a challenging task due to lacking of densely
annotated datasets and the complicated morphologies of chromosomes, e.g., dense
distribution, arbitrary orientations, and wide range of lengths. To facilitate
the development of this area, we take a big step forward and manually construct
a large-scale densely annotated dataset named AutoKary2022, which contains over
27,000 chromosome instances in 612 microscopic images from 50 patients.
Specifically, each instance is annotated with a polygonal mask and a class
label to assist in precise chromosome detection and segmentation. On top of it,
we systematically investigate representative methods on this dataset and obtain
a number of interesting findings, which helps us have a deeper understanding of
the fundamental problems in chromosome instance segmentation. We hope this
dataset could advance research towards medical understanding. The dataset can
be available at:
https://github.com/wangjuncongyu/chromosome-instance-segmentation-dataset.Comment: Accepted by ICME 202
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining
Educational Data Mining (EDM) has emerged as a vital field of research, which
harnesses the power of computational techniques to analyze educational data.
With the increasing complexity and diversity of educational data, Deep Learning
techniques have shown significant advantages in addressing the challenges
associated with analyzing and modeling this data. This survey aims to
systematically review the state-of-the-art in EDM with Deep Learning. We begin
by providing a brief introduction to EDM and Deep Learning, highlighting their
relevance in the context of modern education. Next, we present a detailed
review of Deep Learning techniques applied in four typical educational
scenarios, including knowledge tracing, undesirable student detecting,
performance prediction, and personalized recommendation. Furthermore, a
comprehensive overview of public datasets and processing tools for EDM is
provided. Finally, we point out emerging trends and future directions in this
research area.Comment: 21 pages, 5 figure
ClusterFusion: Real-time Relative Positioning and Dense Reconstruction for UAV Cluster
As robotics technology advances, dense point cloud maps are increasingly in
demand. However, dense reconstruction using a single unmanned aerial vehicle
(UAV) suffers from limitations in flight speed and battery power, resulting in
slow reconstruction and low coverage. Cluster UAV systems offer greater
flexibility and wider coverage for map building. Existing methods of cluster
UAVs face challenges with accurate relative positioning, scale drift, and
high-speed dense point cloud map generation. To address these issues, we
propose a cluster framework for large-scale dense reconstruction and real-time
collaborative localization. The front-end of the framework is an improved
visual odometry which can effectively handle large-scale scenes. Collaborative
localization between UAVs is enabled through a two-stage joint optimization
algorithm and a relative pose optimization algorithm, effectively achieving
accurate relative positioning of UAVs and mitigating scale drift. Estimated
poses are used to achieve real-time dense reconstruction and fusion of point
cloud maps. To evaluate the performance of our proposed method, we conduct
qualitative and quantitative experiments on real-world data. The results
demonstrate that our framework can effectively suppress scale drift and
generate large-scale dense point cloud maps in real-time, with the
reconstruction speed increasing as more UAVs are added to the system
- …