Query：
学者姓名：杨隆浩
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
Smart environment is an efficient and cost-effective way to afford intelligent supports for the elderly people. Human activity recognition is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based human activity recognition model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.
Keyword ：
Accuracy Accuracy activity recognition activity recognition Belief rule base Belief rule base Bioinformatics Bioinformatics combination explosion problem combination explosion problem Correlation Correlation Data models Data models Explosions Explosions Feature extraction Feature extraction Human activity recognition Human activity recognition Predictive models Predictive models Robustness Robustness sensor sensor time correlation time correlation Vectors Vectors
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Long-Hao , Ye, Fei-Fei , Nugent, Chris et al. Belief-Rule-Based System With Self-Organizing and Multi-Temporal Modeling for Sensor-Based Human Activity Recognition [J]. | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS , 2025 , 29 (2) : 1062-1073 . |
| MLA | Yang, Long-Hao et al. "Belief-Rule-Based System With Self-Organizing and Multi-Temporal Modeling for Sensor-Based Human Activity Recognition" . | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 29 . 2 (2025) : 1062-1073 . |
| APA | Yang, Long-Hao , Ye, Fei-Fei , Nugent, Chris , Liu, Jun , Wang, Ying-Ming . Belief-Rule-Based System With Self-Organizing and Multi-Temporal Modeling for Sensor-Based Human Activity Recognition . | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS , 2025 , 29 (2) , 1062-1073 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Reducing carbon emissions is an ongoing goal of the whole world and its achievement requires an outstanding approach to accurately predict future carbon emissions and explore the factors driving carbon emissions. Hence, this study proposes a driving factor decomposition-based data-driven rule-base (DFD-DDRB) approach for the aim of analyzing carbon emission reduction pathway from predictive perspective, where the approach includes three processes: 1) generating a rule-base from historical carbon emission data; 2) predicting multi-scenario carbon emissions using the rule-base; 3) providing predictive analytics for future carbon emission reduction. In empirical study, the China's provincial data from 2004 to 2021 are used to justify the applicability of the proposed approach. The experimental findings not only show that the approach can accurately predict multi-scenario carbon emissions until 2035 and reveal the factors driving carbon emissions, but also provide three implications for reducing China's carbon emissions: 1) resource endowment should be considered to establish carbon emission management policies of 30 Chinese provinces; 2) economic development effect can be regarded as the main factor driving China's future carbon emissions; 3) optimizing energy structure and consumption is much important for reducing China's provincial carbon emissions. Beside the work in China, the DFD-DDRB approach can be also used as the generic analytical framework served for some developed economies and other carbon-emitting countries. © 2025 Elsevier Ltd
Keyword ：
Carbon emissions Carbon emissions
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ye, Fei-Fei , You, Rongyan , Yang, Long-Hao et al. A novel data-driven rule-base approach with driving factor decomposition for multi-scenario prediction on carbon emission reduction [J]. | Computers and Industrial Engineering , 2025 , 206 . |
| MLA | Ye, Fei-Fei et al. "A novel data-driven rule-base approach with driving factor decomposition for multi-scenario prediction on carbon emission reduction" . | Computers and Industrial Engineering 206 (2025) . |
| APA | Ye, Fei-Fei , You, Rongyan , Yang, Long-Hao , Lu, Haitian , Xie, Hongzhong . A novel data-driven rule-base approach with driving factor decomposition for multi-scenario prediction on carbon emission reduction . | Computers and Industrial Engineering , 2025 , 206 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
At the 2020 United Nations Climate Summit, China officially announced the goal to achieve carbon peaking by 2030. Exploring whether it is possible to reach the peak of carbon emissions earlier necessitates an urgent and imperative need for precise long-term forecasting of China's carbon emissions dynamics. However, the current carbon peaking predictions mostly depend on mechanical or mathematical models, which failed to consider the interdependence between carbon emissions and the time series-based patterns existed in carbon emission data. Therefore, this study presents a novel carbon peaking prediction method based on the data-driven rule-base model, which is implemented by the adaption of the extended belief rule base (EBRB) model for time series forecasting (TSF), and thus the proposed method is referred to as TSF-EBRB model. The TSF-EBRB model not only captures and measures the temporal correlations within the data throughout the processes of modeling and inference, but also consists of a novel parameter optimization model based on the temporal correlations. The study collected carbon emission data from 30 provinces in China for empirical analysis. It computed and predicted the carbon peaking trajectories of each province under three different scenarios from 2022 to 2030, validating the effectiveness and superiority of the TSF-EBRB model better than other existing carbon peaking prediction methods. The results indicated that, with policy interventions, the majority of provinces are projected to reach carbon peaking before 2030.
Keyword ：
Carbon peaking prediction Carbon peaking prediction Data-driven rule-base Data-driven rule-base Extended belief rule base Extended belief rule base Time series forecasting Time series forecasting
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Long-Hao , Lei, Yu-Qiong , Ye, Fei-Fei et al. Forecasting carbon peaking in China using data-driven rule-base model: An in-depth analysis across regional and economic scenarios [J]. | JOURNAL OF CLEANER PRODUCTION , 2024 , 451 . |
| MLA | Yang, Long-Hao et al. "Forecasting carbon peaking in China using data-driven rule-base model: An in-depth analysis across regional and economic scenarios" . | JOURNAL OF CLEANER PRODUCTION 451 (2024) . |
| APA | Yang, Long-Hao , Lei, Yu-Qiong , Ye, Fei-Fei , Hu, Haibo , Lu, Haitian , Wang, Ying-Ming . Forecasting carbon peaking in China using data-driven rule-base model: An in-depth analysis across regional and economic scenarios . | JOURNAL OF CLEANER PRODUCTION , 2024 , 451 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The performance evaluation method based on data envelopment analysis (DEA) is one of the important tools to measure the competitiveness and productivity of enterprises. However, the input and output of enterprises may contain negative data and the essence of DEA is an iterative optimization model, resulting in a low applicability of the DEA-based performance evaluation method in the real word, especially for the dilemma of evaluating enterprise performance within a limited time for new enterprises. Therefore, this study firstly develops a DEA model that can handle negative data for enterprise performance evaluation, and then further establishes a new method base on the extended belief rule-base (EBRB) model for enterprise performance online evaluation. A case study about 35 Chinese state-owned enterprises are conducted to verify the effectiveness of the proposed enterprise performance online evaluation method. Experimental results showed that the proposed method has capable of evaluating enterprise performance with accurate efficiency values better than some existing performance evaluation methods, and its computation time is significantly less than the DEA-based performance evaluation method, which guarantee that the proposed enterprise performance online evaluation method can serve as a reference for the promotion of enterprise productivity and sustainable economic development.
Keyword ：
Data envelopment analysis Data envelopment analysis Online evaluation Online evaluation Performance Performance Rule-base Rule-base State-owned enterprises State-owned enterprises
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ye, Fei-Fei , Yang, Long-Hao , Lu, Haitian et al. Enterprise performance online evaluation based on extended belief rule-base model [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 247 . |
| MLA | Ye, Fei-Fei et al. "Enterprise performance online evaluation based on extended belief rule-base model" . | EXPERT SYSTEMS WITH APPLICATIONS 247 (2024) . |
| APA | Ye, Fei-Fei , Yang, Long-Hao , Lu, Haitian , Hu, Haibo , Wang, Ying-Ming . Enterprise performance online evaluation based on extended belief rule-base model . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 247 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Greenhouse gas emissions are widely recognized as the primary cause of global warming, leading to a growing attention on carbon emission management. However, the existing studies still failed to propose a feasible approach to directly forecast carbon emission trends and also did not take into account both environmental regulation and efficiency improvement. Hence, this study aims to propose a novel carbon emission trend forecast model based on data-driven rule-base with considering the intensity coefficient of environmental regulation and the management efficiency of carbon emissions. Carbon emission data of 30 Chinese provinces are collected to illustrate the effectiveness of the proposed model. Results indicated that: 1) the data-driven rule-base model is able to directly forecast carbon emission trends within range from -18.54 % to 19.18 %; 2) by integrating regulation intensity, the predicted results of the model have smaller carbon emission tends, e.g., decrease of average changing rate from 0.4100 to 0.2762; 3) by further integrating efficiency improvement, the predicted results align more with the expected objectives of policy makers, i.e., the average carbon emission efficiency approximates 0.8920 and the number of provinces being effective efficiency is increased to 8. These findings also highlighted the importance of carbon emission tend forecast with environmental regulation and efficiency improvement. The proposed carbon emission trend forecast model could serve as an alternative tool for achieving dual carbon goals in the context of China.
Keyword ：
Carbon emission trend Carbon emission trend Data -driven rule -base Data -driven rule -base Efficiency improvement Efficiency improvement Environment regulation Environment regulation Forecast Forecast
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Long-Hao , Ye, Fei-Fei , Hu, Haibo et al. A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement [J]. | SUSTAINABLE PRODUCTION AND CONSUMPTION , 2024 , 45 : 316-332 . |
| MLA | Yang, Long-Hao et al. "A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement" . | SUSTAINABLE PRODUCTION AND CONSUMPTION 45 (2024) : 316-332 . |
| APA | Yang, Long-Hao , Ye, Fei-Fei , Hu, Haibo , Lu, Haitian , Wang, Ying-Ming , Chang, Wen -Jun . A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement . | SUSTAINABLE PRODUCTION AND CONSUMPTION , 2024 , 45 , 316-332 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Environmental governance cost prediction can avoid blind investment and waste of resources and achieve effective cost planning for sustainable development of resources and environment. For the sake of solving the problem that most previous studies failed to consider the causal relationship and data reliability of environmental governance inputs and outputs, a new environmental governance cost prediction method is proposed under the framework of the evidential reasoning (ER) rule with three improvements comparing to existing methods: (1) the causal relationship of environmental governance inputs and outputs is embedded into evidence representation for better extracting knowledge from data; (2) the efficiency about the minimum inputs to achieve the maximum outputs is used to evaluate the data reliability of environmental governance inputs and outputs; and (3) a new analytical ER rule is investigated to optimize the process of evidence combination. Hence, the new method includes the calculation of belief distributions, evidence reliabilities, and evidence weights, as well as the combination of evidences to predict environmental governance costs. In the case study, the data of 30 provinces in Mainland China from 2005 to 2020 are collected to verify the effectiveness of the new method. Results show a high level of accuracy of the new method over other existing methods.
Keyword ：
Cost prediction Cost prediction Environmental governance Environmental governance Evidential reasoning (ER) rule Evidential reasoning (ER) rule Reliability Reliability Weight Weight
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ye, Fei-Fei , Yang, Long-Hao , Uhomoibhi, James et al. Evidential reasoning rule for environmental governance cost prediction with considering causal relationship and data reliability [J]. | SOFT COMPUTING , 2023 , 27 (17) : 12309-12327 . |
| MLA | Ye, Fei-Fei et al. "Evidential reasoning rule for environmental governance cost prediction with considering causal relationship and data reliability" . | SOFT COMPUTING 27 . 17 (2023) : 12309-12327 . |
| APA | Ye, Fei-Fei , Yang, Long-Hao , Uhomoibhi, James , Liu, Jun , Wang, Ying-Ming , Lu, Haitian . Evidential reasoning rule for environmental governance cost prediction with considering causal relationship and data reliability . | SOFT COMPUTING , 2023 , 27 (17) , 12309-12327 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
1.本外观设计产品的名称：带切换功能模块图形用户界面的显示屏幕面板。2.本外观设计产品的用途：本外观设计产品用于运行程序、用户与机器的交互以及显示界面内容。3.本外观设计产品的设计要点：在于产品屏幕中的图形用户界面内容，其余部分为惯常设计。4.最能表明设计要点的图片或照片：主视图。5.不涉及设计要点，省略后视图; 不涉及设计要点，省略左视图; 不涉及设计要点，省略右视图; 不涉及设计要点，省略俯视图; 不涉及设计要点，省略仰视图。6.图形用户界面的用途：本外观设计的图形用户界面主要用于 切换功能模块、用户与机器的交互以及显示每个步骤输出的结果。7.图形用户界面的变化状态说明：账号登录后进入主视图的交互初始界面，点击主视图的图形用户界面中的“Drop file here or click to upload”，呈现界面变化状态图1的图形用户界面；当用户在界面变化状态图1中选择需要处理的Excel数据文件后，点击运行按钮后，呈现界面变化状态图2的图形用户界面，生成最优alpha图；当用户点击界面变化状态图2的图形用户界面中“Next step”按钮时，呈现变化状态图3的图形用户界面，生成alpha取值；当点击界面变化状态图3的图形用户界面中“Next step”按钮时，呈现变化状态图4的图形用户界面，生成Lasso回归分析评分；当用户点击界面变化状态图4的图形用户界面中“Next step”按钮时，呈现变化状态图5的图形用户界面，生成各数据指标相关系数。当用户点击主视图的图形用户界面中的“置信规律库分析”并点击“click to upload TrainSet”按钮时，呈现变化状态图6的图形用户界面，用以选择框选择训练集数据文件；当用户点击主视图的图形用户界面中的“置信规律库分析” 并点击“click to upload TestSet”按钮时，呈现变化状态图7的图形用户界面，用以选择框选择测试集数据文件；完成后拖动滑块或填写评价等级个数，呈现变化状态图8的图形用户界面；当用户点击界面变化状态图8的图形用户界面中“运行”按钮时，呈现变化状态图9的图形用户界面，生成第 条规则中候选等级 的置信度；当用户点击界面变化状态图9的图形用户界面中“Next step”按钮时，呈现变化状态图10的图形用户界面，生成第 条扩展置信规则中第 个前提属性的个体匹配度；当用户点击界面变化状态图10的图形用户界面中“Next step”按钮时，呈现变化状态图11的图形用户界面，生成激活权重；当用户点击界面变化状态图11的图形用户界面中“Next step”按钮时，呈现变化状态图12的图形用户界面，生成预测结果。当用户点击右上角“admin”按钮时，呈现变化状态图13的图形用户界面，进入用户个人信息界面或退出登录。
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 杨隆浩 , 高奕人 , 李裕普 et al. 带切换功能模块图形用户界面的显示屏幕面板 : CN202330028012.5[P]. | 2023-01-19 00:00:00 . |
| MLA | 杨隆浩 et al. "带切换功能模块图形用户界面的显示屏幕面板" : CN202330028012.5. | 2023-01-19 00:00:00 . |
| APA | 杨隆浩 , 高奕人 , 李裕普 , 王士萌 , 徐可楹 , 潘丽梅 et al. 带切换功能模块图形用户界面的显示屏幕面板 : CN202330028012.5. | 2023-01-19 00:00:00 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The development of new energy vehicles is a key factor in the adjustment of China's energy structure and the decrease in carbon emissions. It is a frontier field for China to achieve high-quality development and construct a modern socialist nation fully. However, because lithium-ion battery used in new energy vehicles have a limited lifespan, it is likely to have a very significant security risk when the lithium-ion battery is not replaced in a timely manner. Predicting the lithium-ion battery's remaining useful life (RUL) is crucial for this reason. In order to forecast the RUL while taking health indicators (HI) into account, the extended belief rule base (EBRB) model is introduced in this paper. The EBRB model's capacity to handle complicated modeling issues helps to increase the RUL prediction's accuracy and interpretability. This study is of great significance for promoting the development of new energy vehicles, adjusting China's energy structure, and reducing carbon emissions. © 2023 IEEE.
Keyword ：
Carbon Carbon Electric vehicles Electric vehicles Forecasting Forecasting Ions Ions Lithium-ion batteries Lithium-ion batteries
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Long-Hao , Qian, Bei-Ya , Huang, Chen-Xi et al. Predicting Remaining Useful Life of Lithium-Ion Battery Using Extended Belief Rule Base Model [C] . 2023 : 585-591 . |
| MLA | Yang, Long-Hao et al. "Predicting Remaining Useful Life of Lithium-Ion Battery Using Extended Belief Rule Base Model" . (2023) : 585-591 . |
| APA | Yang, Long-Hao , Qian, Bei-Ya , Huang, Chen-Xi , Ye, Fei-Fei , Hu, Haibo , Wu, Hai-Dong . Predicting Remaining Useful Life of Lithium-Ion Battery Using Extended Belief Rule Base Model . (2023) : 585-591 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With the aging of the population gradually serious in recent years, the research of multi-resident activity recognition in smart home has been paid much attention. For this reason, an advanced rule-based expert system, called cumulative belief rule-based expert system, is introduced to develop a novel multi-resident activity recognition model, which not only makes full use of the multiple labels of residents' activities, but also can overcome the problem of excessive data collected from smart home. In the case study, the experimental study shows that the proposed model is more efficient and accurate than the traditional machine learning models and the commonly used activity recognition model for achieving multi-resident activity recognition. © 2023 IEEE.
Keyword ：
Automation Automation Expert systems Expert systems Pattern recognition Pattern recognition
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Long-Hao , Lu, Yi-Xuan , Huang, Peng-Peng et al. Cumulative Belief Rule-Based Expert System for Multi-Resident Activity Recognition in Smart Home [C] . 2023 : 610-614 . |
| MLA | Yang, Long-Hao et al. "Cumulative Belief Rule-Based Expert System for Multi-Resident Activity Recognition in Smart Home" . (2023) : 610-614 . |
| APA | Yang, Long-Hao , Lu, Yi-Xuan , Huang, Peng-Peng , Ye, Fei-Fei , Wu, Hai-Dong , Liu, Jun . Cumulative Belief Rule-Based Expert System for Multi-Resident Activity Recognition in Smart Home . (2023) : 610-614 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
As the population ages and health-care costs increase, smart environments can be an effective and economical way to provide care and support for the aged population. Human activity recognition (HAR), a key element of the smart environment research domain, has garnered a lot of attention lately. The present work is to provide a data-driven solution based on the extended belief rule base (EBRB) model for sensor-based HAR in the context of big data. More specifically, in order to increase the efficiency of the EBRB model, this research first offers a new rule generation method based on probability estimation, which forms the link between the extended belief rules and human activities. The number of extended belief rules used to extract knowledge from a sensor-based HAR dataset is exactly equal to the types of human activities, and each rule can be thought of as a collection of class conditional probability distributions. As a result, it is possible to create an EBRB-BD model, an EBRB model for HAR using big data that has a compact but representative rule base. The effectiveness of the EBRB-BD model is further supported by case studies. Experimental findings demonstrate that the modelling time of the EBRB-BD model is one in ten-thousand of the original EBRB model, and the EBRB-BD model also achieves the best area under the curve value (AUC) of 94.95%, surpassing the original EBRB model and some other benchmark classifiers.
Keyword ：
Big data Big data Extended belief rule base Extended belief rule base Human activity recognition Human activity recognition Smart environment Smart environment
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ren, Tian-Yu , Yang, Long-Hao , Nugent, Chris et al. Extended Belief Rule Base Model with Novel Rule Generation for Sensor-Based Human Activity Recognition Under Big Data [J]. | PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING & AMBIENT INTELLIGENCE (UCAMI 2022) , 2023 , 594 : 735-746 . |
| MLA | Ren, Tian-Yu et al. "Extended Belief Rule Base Model with Novel Rule Generation for Sensor-Based Human Activity Recognition Under Big Data" . | PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING & AMBIENT INTELLIGENCE (UCAMI 2022) 594 (2023) : 735-746 . |
| APA | Ren, Tian-Yu , Yang, Long-Hao , Nugent, Chris , Ye, Fei-Fei , Irvine, Naomi , Liu, Jun . Extended Belief Rule Base Model with Novel Rule Generation for Sensor-Based Human Activity Recognition Under Big Data . | PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING & AMBIENT INTELLIGENCE (UCAMI 2022) , 2023 , 594 , 735-746 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
