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44 results on '"Yue, Jiaqi"'

1. Less but Better: Enabling Generalized Zero-shot Learning Towards Unseen Domains by Intrinsic Learning from Redundant LLM Semantics

Author

Yue, Jiaqi, Zhao, Jiancheng, and Zhao, Chunhui

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Generalized zero-shot learning (GZSL) focuses on recognizing seen and unseen classes against domain shift problem (DSP) where data of unseen classes may be misclassified as seen classes. However, existing GZSL is still limited to seen domains. In the current work, we pioneer cross-domain GZSL (CDGZSL) which addresses GZSL towards unseen domains. Different from existing GZSL methods which alleviate DSP by generating features of unseen classes with semantics, CDGZSL needs to construct a common feature space across domains and acquire the corresponding intrinsic semantics shared among domains to transfer from seen to unseen domains. Considering the information asymmetry problem caused by redundant class semantics annotated with large language models (LLMs), we present Meta Domain Alignment Semantic Refinement (MDASR). Technically, MDASR consists of two parts: Inter-class Similarity Alignment (ISA), which eliminates the non-intrinsic semantics not shared across all domains under the guidance of inter-class feature relationships, and Unseen-class Meta Generation (UMG), which preserves intrinsic semantics to maintain connectivity between seen and unseen classes by simulating feature generation. MDASR effectively aligns the redundant semantic space with the common feature space, mitigating the information asymmetry in CDGZSL. The effectiveness of MDASR is demonstrated on the Office-Home and Mini-DomainNet, and we have shared the LLM-based semantics for these datasets as the benchmark., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2024

2. Learning to better see the unseen: Broad-Deep Mixed Anti-Forgetting Framework for Incremental Zero-Shot Fault Diagnosis

Author

Zhao, Jiancheng, Yue, Jiaqi, and Zhao, Chunhui

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

Zero-shot fault diagnosis (ZSFD) is capable of identifying unseen faults via predicting fault attributes labeled by human experts. We first recognize the demand of ZSFD to deal with continuous changes in industrial processes, i.e., the model's ability to adapt to new fault categories and attributes while avoiding forgetting the diagnosis ability learned previously. To overcome the issue that the existing ZSFD paradigm cannot learn from evolving streams of training data in industrial scenarios, the incremental ZSFD (IZSFD) paradigm is proposed for the first time, which incorporates category increment and attribute increment for both traditional ZSFD and generalized ZSFD paradigms. To achieve IZSFD, we present a broad-deep mixed anti-forgetting framework (BDMAFF) that aims to learn from new fault categories and attributes. To tackle the issue of forgetting, BDMAFF effectively accumulates previously acquired knowledge from two perspectives: features and attribute prototypes. The feature memory is established through a deep generative model that employs anti-forgetting training strategies, ensuring the generation quality of historical categories is supervised and maintained. The diagnosis model SEEs the UNSEEN faults with the help of generated samples from the generative model. The attribute prototype memory is established through a diagnosis model inspired by the broad learning system. Unlike traditional incremental learning algorithms, BDMAFF introduces a memory-driven iterative update strategy for the diagnosis model, which allows the model to learn new faults and attributes without requiring the storage of all historical training samples. The effectiveness of the proposed method is verified by a real hydraulic system and the Tennessee-Eastman benchmark process.

Published
2024

3. Addressing Domain Shift via Knowledge Space Sharing for Generalized Zero-Shot Industrial Fault Diagnosis

Author

Zhao, Jiancheng, Yue, Jiaqi, Feng, Liangjun, Zhao, Chunhui, and Ding, Jinliang

Subjects
Computer Science - Artificial Intelligence
Abstract

Fault diagnosis is a critical aspect of industrial safety, and supervised industrial fault diagnosis has been extensively researched. However, obtaining fault samples of all categories for model training can be challenging due to cost and safety concerns. As a result, the generalized zero-shot industrial fault diagnosis has gained attention as it aims to diagnose both seen and unseen faults. Nevertheless, the lack of unseen fault data for training poses a challenging domain shift problem (DSP), where unseen faults are often identified as seen faults. In this article, we propose a knowledge space sharing (KSS) model to address the DSP in the generalized zero-shot industrial fault diagnosis task. The KSS model includes a generation mechanism (KSS-G) and a discrimination mechanism (KSS-D). KSS-G generates samples for rare faults by recombining transferable attribute features extracted from seen samples under the guidance of auxiliary knowledge. KSS-D is trained in a supervised way with the help of generated samples, which aims to address the DSP by modeling seen categories in the knowledge space. KSS-D avoids misclassifying rare faults as seen faults and identifies seen fault samples. We conduct generalized zero-shot diagnosis experiments on the benchmark Tennessee-Eastman process, and our results show that our approach outperforms state-of-the-art methods for the generalized zero-shot industrial fault diagnosis problem.

Published
2023

6. A Systematic Review of Educational Data Mining

Author

Xu, FangYao, Li, ZhiQiang, Yue, JiaQi, Qu, ShaoJie, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2021
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12. A Study on a Health Impact Assessment and Healthcare Cost Calculation of Beijing–Tianjin–Hebei Residents under PM 2.5 and O 3 Pollution.

Author

Hu, Yanyong, Chao, Kun, Zhu, Zhujun, Yue, Jiaqi, Qie, Xiaotong, and Wang, Meijia

Abstract

Excessive fine particulate matter (PM2.5) and ozone (O3) are invisible killers affecting our wellbeing and safety, which cause great harm to people's health, cause serious healthcare and economic losses, and affect the sustainable development of the social economy. The effective evaluation of the impact of pollutants on the human body, the associated costs, and the reduction of regional compound air pollution is an important research direction. Taking Beijing–Tianjin–Hebei (BTH) as the research area, this study constructs a comprehensive model for measuring the healthcare costs of PM2.5 and O3 using the Environmental Benefits Mapping and Analysis Program (BenMAP) as its basis. First, this study establishes a health impact assessment model and calculates the number of people affected by PM2.5 and O3 exposure using the health impact function in the BTH region. Then, the willingness to pay (WTP) and cost of illness (COI) methods are used to estimate the healthcare costs inflicted by the two pollutants upon residents from 2018 to 2021. The calculation results show that the total healthcare costs caused by PM2.5 and O3 pollution in BTH accounted for 1%, 0.7%, 0.5%, and 0.3% of the regional GDP in 2018, 2019, 2020, and 2021, respectively. Based on the research results, to further reduce these high healthcare costs, we propose policy suggestions for PM2.5 and O3 control in the BTH region. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Similarity Makes Difference: SSHTN for Generalized Zero-Shot Industrial Fault Diagnosis by Leveraging Auxiliary Set

Author

Yue, Jiaqi, Zhao, Jiancheng, and Zhao, Chunhui

Abstract

The generalized zero-shot diagnosis (GZSD) has attracted rising attention from researchers, but faces a major bottleneck: the domain shift problem (DSP), which causes most unseen faults to be misclassified as seen. In this study, we propose a novel method termed semisupervised hybrid triplet network (SSHTN) with high resistance toward DSP to achieve GZSD by first introducing auxiliary set. To leverage the unlabeled data of auxiliary set, which is different from known faults (both seen and unseen), SSHTN defines relative boundaries of seen and unseen faults in the similarity space and comprises two branches: the data–data branch (DDB) and the data–semantic branch (DSB). For DDB, it constructs homogeneous and heterogeneous data pairs to learn data–data similarity. With the support of auxiliary set, the semisupervised similarity learning mechanism is constructed. Specifically, DDB analyzes differences between data pairs mixed by seen and auxiliary fault data to narrow down the decision boundaries of seen faults. Meanwhile, similarities between data pairs formed by disturbing auxiliary set data are learned to improve the generalization of DDB. For DSB, it learns data–semantic similarity by matching fault data with corresponding descriptions and extends SSHTN discriminative ability toward unseen faults. The SSHTN obtained from the comprehensive learning of similarity by the two branches could determine the boundaries of seen and unseen faults more accurately, thereby alleviating the effect of DSP. Experiments are implemented on the Tennessee Eastman process and a real thermal power plant process, and results show the superior performance of SSHTN over state-of-the-art ZSD methods.

Published
2024
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19. Semantic Knowledge Integrated Graph Convolutional Network for Zero-Sample Tracing of Control Performance Degradation

Author

Chen, Xu, Wang, Jie, Yue, Jiaqi, and Zhao, Chunhui

Abstract

Tracing the cause of control performance degradation (CPD) as early as possible is important to reducing the loss and risks for industrial processes. The existing tracing algorithms for CPD generally require massive amounts of data for model training. However, in practical industries, many CPD types may not be supported by historical data, which are denoted as unseen CPD types here but require online identification. This common but challenging scenario is termed the zero-sample tracing of CPD. In this work, a semantic knowledge integrated graph convolutional network is proposed to solve this problem. While data corresponding to some CPD types are not available during the training phase, the CPD types themselves are known to the users. We describe all of the CPD types in both training and testing sets by a series of attributes, including degradation locations, characteristics, causal reasons, and so forth, which contain semantic knowledge. Unlike existing methods that directly predict class labels of CPD types, we predict the semantic attributes of the unseen CPD types to further infer the class labels. In this way, semantic attributes act as a bridge connecting degraded data and labels, which can transfer the knowledge of seen CPD types to unseen ones to realize the zero-sample tracing of CPD. Besides, the sparse graph convolutional network is designed to remove redundant information while concurrently analyzing the correlated and temporal relationships hidden in the loop data. The practical utility and feasibility of the proposed method are verified with the Tennessee–Eastman process.

Published
2023
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20. Insights into Genus <italic>Codonopsis</italic>: From past Achievements to Future Perspectives.

Author

Yue, Jiaqi, Xiao, Ying, and Chen, Wansheng

Abstract

Abstract Codonopsis plants, as a kind of medicinal and edible herb, have a long history of application and have been widely concerned by pharmacists and biologists. In this article, the species diversity, taxonomy and distribution, ethnic medicinal records, chemical composition, pharmacological activity, and quality evaluation methods of Codonopsis species were systematically reviewed. In addition, the research progress of Codonopsis plants using biotechnology in recent years was summarized. The phytochemistry and biological activities of Codonopsis are widely evaluated. To date, more than 350 compounds have been isolated from Codonopsis. Codonopsis pilosula polysaccharides are important functional components and biomarkers. Lobetyolin, atractylenolide III, tangshenoside I, and oligosaccharide can be considered as characteristic index components to evaluate the quality of Codonopsis plants. Although recent experimental evidence has confirmed the pharmacological value of this genus, its quality control, resource development and utilization, and active ingredient synthesis mechanisms are not well studied. In particular, molecular biology research is still in its infancy, but its application prospects are broad, and it is a hot spot for future research on Codonopsis. Therefore, it is urgent to conduct a detailed study on the single level of phytochemistry, pharmacology, and molecular biology of Codonopsis to establish a scientific evaluation system and modern medication guidelines. The multi-angle, multi-level, and multi-aspect integrated association analysis is also an inevitable trend for the future in-depth study of Codonopsis plants. This research status was summarized in order to provide a broader scientific research idea and theoretical reference for the in-depth study of Codonopsis. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Evaluation of Ecological Suitability and Quality Suitability of Panax notoginseng Under Multi-Regionalization Modeling Theory

Author

Yue, JiaQi, Li, ZhiMin, Zuo, ZhiTian, and Wang, YuanZhong

Subjects
Plant Science
Abstract

Panax notoginseng is an important medicinal plant in China, but there are some limitations in the ecological suitability study, such as incomplete investigation of species distribution, single regionalization modeling, and lack of collaborative evaluation of ecological suitability, and quality suitability. In this study, the maximum entropy model was used to analyze the ecological suitability of P. notoginseng under current and future climates. The multi-source chemical information of samples was collected to evaluate the uniformity between quality and ecology. The results showed that the current suitable habitat was mainly in southwest China. In the future climate scenarios, the high suitable habitat will be severely degraded. Modeling based on different regionalization could predict larger suitable habitat areas. The samples in the high suitable habitat had both quality suitability and ecological suitability, and the accumulation of chemical components had different responses to different environmental factors. Two-dimensional correlation spectroscopy combined with deep learning could achieve rapid identification of samples from different suitable habitats. In conclusion, global warming is not conducive to the distribution and spread of P. notoginseng. The high suitable habitat was conducive to the cultivation of high-quality medicinal materials. Actual regionalization modeling had more guiding significance for the selection of suitable habitats in a small area. The multi-regionalization modeling theory proposed in this study could provide a new perspective for the ecological suitability study of similar medicinal plants. The results provided a reference for the introduction and cultivation, and lay the foundation for the scientific and standardized production of high-quality P. notoginseng.

Published
2022

38. Application of Identification and Evaluation Techniques for Ethnobotanical Medicinal Plant of Genus Panax: A Review.

Author

Yue, Jiaqi, Zuo, Zhitian, Huang, Hengyu, and Wang, Yuanzhong

Subjects
*PANAX, *MEDICINAL plants, *CHINESE medicine, *ELECTRONIC noses, *GENETIC barcoding, *NANOTECHNOLOGY
Abstract

Genus Panax, as worldwide medicinal plants, has a medical history for thousands of years. Most of the entire genus are traditional ethnobotanical medicine in China, Myanmar, Thailand, Vietnam and Laos, which have given rise to international attention and use. This paper reviewed more than 210 articles and related books on the research of Panax medicinal plants and their Chinese patent medicines published in the last 30 years. The purpose was to review and summarize the species classification, geographical distribution, and ethnic minorities medicinal records of the genus Panax, and further to review the analytical tools and data analysis methods for the authentication and quality assessment of Panax medicinal materials and Chinese patent medicines. Five main technologies applied in the identification and evaluation of Panax have been introduced and summarized. Chromatography was the most widely used one. Further research and development of molecular identification technology had the potential to become a mainstream identification technology. In addition, some novel, controversial, and worthy methods including electronic noses, electronic eyes, and DNA barcoding were also introduced. At the same time, more than 80% of the researches were carried out by a combination of chemometric pattern-recognition technologies and multi-analysis technologies. All the technologies and methods applied can provide strong support and guarantee for the identification and evaluation of genus Panax, and also conduce to excellent reference value for the development and in-depth research of new technologies in Panax. [ABSTRACT FROM AUTHOR]

Published
2021
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44. Efficacy and safety of thoracoscopic resection for early-stage non-small cell lung cancer.

Author

Cao P, Yue J, Hu S, Kong K, Han P, Yang Z, Deng Y, Li F, and Zhao B

Abstract

Objective: To explore the efficacy and safety of thoracoscopic resection for early-stage non-small cell lung cancer (NSCLC)., Methods: A total of 110 patients with early-stage NSCLC admitted to Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology between August 2017 and December 2019 were enrolled and retrospectively analysed. Among them, 60 patients receiving thoracoscopic resection for lung cancer (LC) were assigned to the research group (Res group) and another 50 patients treated with routine open radical resection for LC were included in the control group (Con group). The following items of the two groups were evaluated and compared: treatment efficacy, operation indexes, VAS score, lung function, lung capacity, complications, 2-year tumour-free survival rate, 2-year survival rate, and quality of life (QoL)., Results: The Res group showed significantly better efficacy, and lower incidence of complications and VAS score than the Con group. In addition, patients in the Res group experienced less intraoperative blood loss, earlier anal exhaust, shorter hospital stay and indwelling time of drainage tube as well as less drainage volume. Furthermore, better recovery in pulmonary function and lung capacity, and significantly higher 2-year tumour-free survival rate, 2-year survival rate as well as postoperative QoL were noted in the Res group compared with the Con group., Conclusion: Thoracoscopic resection for LC is effective in the treatment of patients with early-stage NSCLC. It can substantially shorten the hospital stay and indwelling time of drainage tube and reduce drainage volume and blood loss, with high safety., Competing Interests: None., (AJTR Copyright © 2022.)

Published
2022

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