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1,988 results on '"Yang Hongjun"'

1. Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models

Author

Xue, Siqiao, Qi, Danrui, Jiang, Caigao, Shi, Wenhui, Cheng, Fangyin, Chen, Keting, Yang, Hongjun, Zhang, Zhiping, He, Jianshan, Zhang, Hongyang, Wei, Ganglin, Zhao, Wang, Zhou, Fan, Yi, Hong, Liu, Shaodong, and Chen, Faqiang

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. The technologies of interacting with data particularly have an important entanglement with LLMs as efficient and intuitive data interactions are paramount. In this paper, we present DB-GPT, a revolutionary and product-ready Python library that integrates LLMs into traditional data interaction tasks to enhance user experience and accessibility. DB-GPT is designed to understand data interaction tasks described by natural language and provide context-aware responses powered by LLMs, making it an indispensable tool for users ranging from novice to expert. Its system design supports deployment across local, distributed, and cloud environments. Beyond handling basic data interaction tasks like Text-to-SQL with LLMs, it can handle complex tasks like generative data analysis through a Multi-Agents framework and the Agentic Workflow Expression Language (AWEL). The Service-oriented Multi-model Management Framework (SMMF) ensures data privacy and security, enabling users to employ DB-GPT with private LLMs. Additionally, DB-GPT offers a series of product-ready features designed to enable users to integrate DB-GPT within their product environments easily. The code of DB-GPT is available at Github(https://github.com/eosphoros-ai/DB-GPT) which already has over 10.7k stars. Please install DB-GPT for your own usage with the instructions(https://github.com/eosphoros-ai/DB-GPT#install) and watch a 5-minute introduction video on Youtube(https://youtu.be/n_8RI1ENyl4) to further investigate DB-GPT.

Published
2024

2. Calculation of the Dynamic Characteristics of Ship’s Aft Stern Tube Bearing Considering Journal Deflection

Author

Yang Hongjun, Li Jun, and Li Xiaolong

Subjects
journal deflection, aft stern tube bearing, dynamic characteristic, Naval architecture. Shipbuilding. Marine engineering, VM1-989
Abstract

Dynamic properties are vital for the working reliability of aft stern tube bearings. However, the determination of such properties currently involves several simplifications and assumptions. To obtain its dynamic characteristics accurately, the aft stern tube bearing was divided into several bearing segments. The oil film reaction force was considered in the calculation of shaft alignment, and the journal deflection and actual oil film thickness were obtained accordingly. Subsequently, the perturbed Reynolds equation was solved using the finite difference method when the dynamic characteristics of journal bearings with finite width were evaluated. Then, a calculation program was developed and verified by comparing with the results of other studies. Finally, the dynamic characteristics were calculated under different revolutions. The results showed that the stiffness at the vertical direction of the aft stern tube bearing was several times that of the horizontal direction and varied with the revolutions of the shafting system. These findings can provide the foundation for the precise calculation of the journal trajectory under dynamic conditions, as well as for the evaluation of the oil film thickness. Moreover, the results led to favorable conditions for the accurate calculation of the shafting whirling vibration.

Published
2020
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3. Crop yield prediction method based on an improved neural network

Author

Liu Peng, Zheng Yong, and Yang Hongjun

Subjects
crop yield prediction, long short-term memory, deep learning, recurrent neural network, climate factor, Electronics, TK7800-8360
Abstract

Crop yield forecasting plays a decisive role in the development of national economy planned by the government. It is of great significance to rationally plan planting strategies and reduce the waste of water and fertilizer. There are many factors affecting crop yield, and accurate prediction of crop growth means a lot. Climate is an important factor affecting crop yield. Based on climatic factors, this paper proposes a time series prediction method based on the long short-term memory(LSTM) for crop yield prediction. The method combines historical yields with climatic factors to forecast the crop yield of the next period by years. Experimental results show that compared with LSTM and support vector machine methods, the proposed method achieves higher accuracy in crop yield prediction.

Published
2019
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6. DB-GPT: Empowering Database Interactions with Private Large Language Models

Author

Xue, Siqiao, Jiang, Caigao, Shi, Wenhui, Cheng, Fangyin, Chen, Keting, Yang, Hongjun, Zhang, Zhiping, He, Jianshan, Zhang, Hongyang, Wei, Ganglin, Zhao, Wang, Zhou, Fan, Qi, Danrui, Yi, Hong, Liu, Shaodong, and Chen, Faqiang

Subjects
Computer Science - Databases
Abstract

The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. Database technologies particularly have an important entanglement with LLMs as efficient and intuitive database interactions are paramount. In this paper, we present DB-GPT, a revolutionary and production-ready project that integrates LLMs with traditional database systems to enhance user experience and accessibility. DB-GPT is designed to understand natural language queries, provide context-aware responses, and generate complex SQL queries with high accuracy, making it an indispensable tool for users ranging from novice to expert. The core innovation in DB-GPT lies in its private LLM technology, which is fine-tuned on domain-specific corpora to maintain user privacy and ensure data security while offering the benefits of state-of-the-art LLMs. We detail the architecture of DB-GPT, which includes a novel retrieval augmented generation (RAG) knowledge system, an adaptive learning mechanism to continuously improve performance based on user feedback and a service-oriented multi-model framework (SMMF) with powerful data-driven agents. Our extensive experiments and user studies confirm that DB-GPT represents a paradigm shift in database interactions, offering a more natural, efficient, and secure way to engage with data repositories. The paper concludes with a discussion of the implications of DB-GPT framework on the future of human-database interaction and outlines potential avenues for further enhancements and applications in the field. The project code is available at https://github.com/eosphoros-ai/DB-GPT. Experience DB-GPT for yourself by installing it with the instructions https://github.com/eosphoros-ai/DB-GPT#install and view a concise 10-minute video at https://www.youtube.com/watch?v=KYs4nTDzEhk.

Published
2023

39. Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

Author

Fan, Chen-Chen, Xie, Haiqun, Peng, Liang, Yang, Hongjun, Ni, Zhen-Liang, Wang, Guan'an, Zhou, Yan-Jie, Chen, Sheng, Fang, Zhijie, Huang, Shuyun, and Hou, Zeng-Guang

Subjects
Computer Science - Machine Learning, Computer Science - Robotics, Electrical Engineering and Systems Science - Signal Processing
Abstract

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system., Comment: This paper has been accepted by 2021 International Conference on Robotics and Automation (ICRA 2021)

Published
2021

40. A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments

Author

Chen, Sheng, Xie, Haiqun, Yang, Hongjun, Fan, Chenchen, Hou, Zengguang, Zhang, Chutian, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Yu, Zhiwen, editor, Hei, Xinhong, editor, Li, Duanling, editor, Song, Xianhua, editor, and Lu, Zeguang, editor

Published
2023
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