Construction of Mental Health Knowledge Service Model Based on Online Medical Community

Xu, Jingxiu; Li, Xueguang; Wang, Xianju; Li, Bifeng; Hu, Zhihua; Wang, Rui; Liu, Xiaoyan

doi:10.1155/2022/1907074

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…New energy vehicles are developing rapidly in the world [1,2], China and Europe are vigorously promoting new energy vehicles. The main reasons are the shortage of fossil fuels and worsening air pollution [3]. What's more, the carbon dioxide emitted by cars has caused the global temperature to rise and extreme weather to occur frequently.…”

Section: Introductionmentioning

confidence: 99%

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

Li,

Dumlao

2023

FCIS

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New energy vehicles are developing rapidly in the world, China and Europe are vigorously promoting new energy vehicles. The State of Charge (SOC) is circumscribed as the remaining charge of the lithium battery (Li-ion), that indicates the driving range of a pure electric vehicle. Additionally, it is the basis for SOH and fault state prediction. Nevertheless, the SOC is incapable of measuring directly. In this paper, an LSTM-Attention-R network framework is proposed. The LSTM algorithm is accustomed to present the timing information and past state information of the lithium battery data. The Attention algorithm is used to extract the global information of features and solve the problem of long-term dependency. To ensure the diversity of feature extraction, the Attention algorithm in this paper uses multi-headed self-attentiveness. The CACLE dataset from the University of Maryland is used in this paper. Through the training of the model and the comparison, it is concluded that the LSTM-Attention-R algorithm networks proposed in this article can predict the value of SOC well. Meanwhile, this paper compares the LSTM-Attention-R algorithm with the LSTM algorithm, and also compares the LSTM-Attention-R algorithm with the Attention algorithm. Finally, it is concluded that the accomplishment of the network framework contrived is superior to the performance of these two algorithms alone. Finally, the algorithm has good engineering practice implications. The algorithm proposed provides a better research direction for future parameter prediction in the field of lithium batteries. It has a better theoretical significance.

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Section: Introductionmentioning

confidence: 99%

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

Li,

Dumlao

2023

FCIS

Self Cite

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Emotional Visualization Analysis Based on Online Book User Comments

Xu,

Vinluan

2024

Lecture Notes in Electrical Engineering

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An integrated framework for community medical and health services evaluation with fuzzy number intuitionistic fuzzy sets

Zhang

Tian

2023

IFS

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With the deepening reform of the medical and health system, China’s community health services are also continuously improving. As the “gatekeeper” of community residents’ health, community medical and health services provide basic health protection for community residents. In the final analysis, community medical and health service is a kind of service. In today’s era where everyone pursues experience, improving service experience has become an important goal of modern health services. The community medical and health services evaluation is a multi-attribute group decision making (MAGDM) issue. The fuzzy number intuitionistic fuzzy sets (FNIFSs) are used as a tool for characterizing uncertain information during the community medical and health services evaluation. In this paper, a novel MAGDM is built on given CoCoSo method under FNIFSs for community medical and health services evaluation. First of all, this paper extends the CoCoSo to FNIFSs environment to build the fuzzy number intuitionistic fuzzy CoCoSo (FNIF-CoCoSo) method. Secondly, a new MAGDM model for community medical and health services evaluation based on CoCoSo algorithm is built. Finally, the practical example for community medical and health services evaluation to show the practicability and some comparisons are supplied to prove the effectiveness of the decision algorithm.

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Construction of Mental Health Knowledge Service Model Based on Online Medical Community

Cited by 4 publications

References 7 publications

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

SOC Prediction for Lithium Battery Via LSTM-Attention-R Algorithm

Emotional Visualization Analysis Based on Online Book User Comments

An integrated framework for community medical and health services evaluation with fuzzy number intuitionistic fuzzy sets

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