Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

701
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
701
Multimachine Stability01:25

Multimachine Stability

218
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
218
Distributed Loads01:19

Distributed Loads

586
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
586
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

148
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
148
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

167
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
167
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

272
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
272

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An Entity Relationship Extraction Model Based on BERT-BLSTM-CRF for Food Safety Domain.

Computational intelligence and neuroscience·2022
Same author

An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain.

Computational intelligence and neuroscience·2022
Same author

Evaluation of the treatability of various odor compounds by powdered activated carbon.

Water research·2019
Same author

Nimotuzumab inhibits epithelial-mesenchymal transition in prostate cancer by targeting the Akt/YB-1/AR axis.

IUBMB life·2019
Same author

Nanomedical detection and downstream analysis of circulating tumor cells in head and neck patients.

Biological chemistry·2019
Same author

Calcium sulfate bone cements with nanoscaled silk fibroin as inducer.

Journal of biomedical materials research. Part B, Applied biomaterials·2019

Related Experiment Video

Updated: Aug 28, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

A Deep Machine Learning-Based Assistive Decision System for Intelligent Load Allocation under Unknown Credit Status.

Wenjing Yan1, Hong Wang1, Min Zuo1

  • 1National Engineering Research Centre for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.

Computational Intelligence and Neuroscience
|September 19, 2022
PubMed
Summary

This study introduces a two-stage loan allocation decision framework (TLAD-UC) for enterprises with unknown credit status. It uses deep machine learning and dynamic planning to optimize loan allocation, balancing bank profit and risk effectively.

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K

Related Experiment Videos

Last Updated: Aug 28, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K

Area of Science:

  • Financial risk management
  • Machine learning applications in finance
  • Enterprise credit assessment

Background:

  • Banks face increasing pressure in loan allocations due to rising enterprise applications and ambiguous financial risks.
  • Accurate credit status information is often unavailable for enterprises, necessitating inference from business data.

Purpose of the Study:

  • To develop an effective autonomous loan allocation decision scheme for banks.
  • To address the challenge of unknown enterprise credit status in loan allocation.

Main Methods:

  • A two-stage framework, TLAD-UC (Two-stage Loan Allocation Decision for unknown Credit status), is proposed.
  • Stage 1: Deep machine learning model to predict credit status for enterprises with unknown credit.
  • Stage 2: Dynamic planning model to optimize loan allocation considering profit and risk constraints.

Main Results:

  • The proposed framework successfully generates credit status predictions.
  • The dynamic planning model effectively describes bank profit and risk.
  • Computer simulations yield optimal loan allocation schemes.

Conclusions:

  • TLAD-UC provides a robust solution for loan allocation with unknown enterprise credit status.
  • The integration of deep learning and dynamic planning enhances decision-making for financial institutions.
  • This framework offers guidance for banks to navigate complex lending environments and mitigate risks.