Computer simulation of investment efficiency function model based on GMM method and artificial intelligence

2021 ◽  
Author(s):  
Shuai Liu ◽  
Shan Wu ◽  
Fei Xie ◽  
Guanghui Yuan
Keyword(s):  
Artificial Intelligence ◽  
Computer Simulation ◽  
Investment Efficiency ◽  
Function Model ◽  
Efficiency Function ◽  
Model Based

A new underwater positioning model based on average sound speed

2021 ◽  
pp. 1-17
Author(s):  
Yixu Liu ◽  
Xiushan Lu ◽  
Shuqiang Xue ◽  
Shengli Wang
Keyword(s):  
Efficient Algorithm ◽  
Sound Speed ◽  
Incident Angle ◽  
Function Model ◽  
Positioning Accuracy ◽  
Correction Model ◽  
Model Based ◽  
Underwater Positioning ◽  
Speed Function ◽  
The Relationship

Abstract The layout of seafloor datum points is the key to constructing the seafloor geodetic datum network, and a reliable underwater positioning model is the prerequisite for achieving precise deployment of the datum points. The traditional average sound speed positioning model is generally adopted in underwater positioning due to its simple and efficient algorithm, but it is sensitive to incident angle related errors, which lead to unreliable positioning results. Based on the relationship between incident angle and sound speed, the sound speed function model considering the incident angle has been established. Results show that the accuracy of positioning is easily affected by errors related to the incident angle; the new average sound speed correction model based on the incident angle proposed in this paper is used to significantly improve the underwater positioning accuracy.

Recognition of teaching method effects based on grid model simplification and artificial intelligence

2020 ◽  
pp. 1-11
Author(s):  
Guo Yunfeng ◽  
Li Jing
Keyword(s):  
Artificial Intelligence ◽  
Teaching Method ◽  
Evaluation Model ◽  
Geometric Constraints ◽  
Sorting Algorithm ◽  
Grid Model ◽  
Model Based ◽  
Refinement Method ◽  
Method Effects ◽  
Structure Simplification

In order to improve the effect of the teaching method evaluation model, based on the grid model, this paper constructs an artificial intelligence model based on the grid model. Moreover, this paper proposes a hexahedral grid structure simplification method based on weighted sorting, which comprehensively sorts the elimination order of candidate base complexes in the grid with three sets of sorting items of width, deformation and price improvement. At the same time, for the elimination order of basic complex strings, this paper also proposes a corresponding priority sorting algorithm. In addition, this paper proposes a smoothing regularization method based on the local parameterization method of the improved SLIM algorithm, which uses the regularized unit as the reference unit in the local mapping in the SLIM algorithm. Furthermore, this paper proposes an adaptive refinement method that maintains the uniformity of the grid and reduces the surface error, which can better slow down the occurrence of geometric constraints caused by insufficient number of elements in the process of grid simplification. Finally, this paper designs experiments to study the performance of the model. The research results show that the model constructed in this paper is effective.

scholarly journals Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

2021 ◽  
Vol 13 (11) ◽  
pp. 6038
Author(s):  
Sergio Alonso ◽  
Rosana Montes ◽  
Daniel Molina ◽  
Iván Palomares ◽  
Eugenio Martínez-Cámara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Sustainable Development Goals ◽  
Modern Society ◽  
Agenda 2030 ◽  
Model Based ◽  
Development Goals ◽  
Sustainability Challenges ◽  
New Research ◽  
Decision Making Model

The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Christopher-John L. Farrell
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Computer Simulation ◽  
Clinical Chemistry ◽  
Human Interaction ◽  
Final Decision ◽  
P Values ◽  
Laboratory Staff

Abstract Objectives Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors. Methods De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%. Laboratory staff volunteers reviewed the AI model’s predictions, and the EUC results on which they were based, before making a final decision regarding the presence or absence of a WBIT error. The performance of this approach was compared to the performance of the AI model operating without human supervision. Results Laboratory staff supervised the classification of 510 sets of EUC results. This workflow identified WBIT errors with an accuracy of 81.2%, sensitivity of 73.7% and specificity of 88.6%. However, the AI model classifying these samples autonomously was superior on all metrics (p-values<0.05), including accuracy (92.5%), sensitivity (90.6%) and specificity (94.5%). Conclusions Human interaction with AI models can significantly alter their performance. For computationally complex tasks such as WBIT error identification, best performance may be achieved by autonomously functioning AI models.

Cardiovascular risk and mortality prediction in patients suspected of sleep apnea: a model based on an artificial intelligence system

2021 ◽  
Author(s):  
Margaux Blanchard ◽  
Mathieu Feuilloy ◽  
Chloé Gervès-Pinquié ◽  
Wojciech Trzepizur ◽  
Nicole Meslier ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Risk ◽  
Sleep Apnea ◽  
Mortality Prediction ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Model Based

scholarly journals Analysis of the Evacuation Capacity of Parallel Double Running Stairs in Different Merging Form Based on Simulation

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xia-zhong Zheng ◽  
Xue-ling Xie ◽  
Dan Tian ◽  
Jian-lan Zhou ◽  
Ming Zhang
Keyword(s):  
Computer Simulation ◽  
Evaluation Model ◽  
Pedestrian Flow ◽  
Agent Based Model ◽  
Evacuation Time ◽  
Agent Based ◽  
Positive Correlation ◽  
Model Based ◽  
Evacuation Model ◽  
Merging Behavior

In order to analyze the evacuation capacity of parallel double running stairs, a dozen stairs merging forms are set by investigation and statistics, and the improved agent-based evacuation model that considers the merging behavior is used to simulate the process of merging and evacuation in the stairs. The stairs evacuation capacity is related to the evacuation time and the robustness of stairs, and the evacuation time can be calculated by using the improved agent-based model based on computer simulation. The robustness of each merging form can be obtained according to the fluctuation degree of evacuation time under the different pedestrian flow. The evaluation model of stairs evacuation capacity is established by fusing the evacuation time and the robustness of stairs. Combined with the specific example to calculate the evacuation capacity of each stairs form, it is found that every merging form has different evacuation time and different robustness, and the evacuation time has not positive correlation with the robustness for the same form stairs. Meanwhile, the evacuation capacity of stairs is not related to the number of the floor entrances. Finally, the results show that the evacuation capacity of stairs is optimal when the floor entrances are close to out stairs in parallel double running stairs and suitable to the case where pedestrian flow and the change of pedestrian flow are large.

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