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学者姓名：杨隆浩
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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
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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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。
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| 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 . |
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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
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| 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 . |
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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
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| 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 . |
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Belief rule-base (BRB) expert system is one of recognized and fast-growing approaches in the areas of complex problems modeling. However, the conventional BRB has to suffer from the combinatorial explosion problem since the number of rules in BRB expands exponentially with the number of attributes in complex problems, although many alternative techniques have been looked at with the purpose of downsizing BRB. Motivated by this challenge, in this paper, multilayer tree structure (MTS) is introduced for the first time to define hierarchical BRB, also known as MTS-BRB. MTS-BRB is able to overcome the combinatorial explosion problem of the con-ventional BRB. Thereafter, the additional modeling, inferencing, and learning procedures are proposed to create a self-organized MTS-BRB expert system. To demonstrate the development process and benefits of the MTS-BRB expert system, case studies including benchmark classification datasets and research and development (R&D) project risk assessment have been done. The comparative results showed that, in terms of modelling effectiveness and/or prediction accuracy, MTS-BRB expert system surpasses various existing, as well as traditional fuzzy system-related and machine learning-related methodologies.
Keyword ：
Belief rule base Belief rule base Combinatorial explosion problem Combinatorial explosion problem Complex problems Complex problems Expert system Expert system Multilayer tree structure Multilayer tree structure
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| GB/T 7714 | Yang, Long-Hao , Ye, Fei-Fei , Liu, Jun et al. Belief rule-base expert system with multilayer tree structure for complex problems modeling [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 217 . |
| MLA | Yang, Long-Hao et al. "Belief rule-base expert system with multilayer tree structure for complex problems modeling" . | EXPERT SYSTEMS WITH APPLICATIONS 217 (2023) . |
| APA | Yang, Long-Hao , Ye, Fei-Fei , Liu, Jun , Wang, Ying-Ming . Belief rule-base expert system with multilayer tree structure for complex problems modeling . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 217 . |
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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
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| 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 . |
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规则约减和规则激活是扩展置信规则库(EBRB)推理模型优化研究中的两个重要方向.然而,现有研究成果大多存在方法参数确定主观性强和计算复杂度高等不足.为此,通过引入聚类集成和激活因子提出改进的EBRB推理模型,称为CEAF-EBRB模型.该模型先基于聚类集成对历史数据进行多次的数据聚类分析,再以簇为单位将所有历史数据生成扩展置信规则;同时,通过激活因子修正个体匹配度计算公式以及离线的方式计算激活因子取值,以确保高效地激活一致性的规则.最后,在非线性函数拟合、模式识别、医疗诊断等常见问题中验证了所提CEAF-EBRB模型的可行性和有效性,从而为决策者提供更准确的决策支持.
Keyword ：
扩展置信规则库 扩展置信规则库 激活因子 激活因子 聚类集成 聚类集成 规则激活 规则激活 规则约减 规则约减
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| GB/T 7714 | 杨隆浩 , 任天宇 , 胡海波 et al. 基于聚类集成和激活因子的扩展置信规则库推理模型 [J]. | 控制与决策 , 2023 , 38 (3) : 815-824 . |
| MLA | 杨隆浩 et al. "基于聚类集成和激活因子的扩展置信规则库推理模型" . | 控制与决策 38 . 3 (2023) : 815-824 . |
| APA | 杨隆浩 , 任天宇 , 胡海波 , 叶菲菲 , 王应明 . 基于聚类集成和激活因子的扩展置信规则库推理模型 . | 控制与决策 , 2023 , 38 (3) , 815-824 . |
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为综合比较多个多指标对象在某时刻的发展状况和在不同时刻的整体发展态势,构建了基于相对熵距离的动态改进理想解法.该方法在传统理想解法基础上用改进熵值法确定不同时刻的指标权重,通过相对熵计算与理想解的距离避免了欧式距离的一些弊端,并增加考虑正负距离的相对重要性.利用基于波动性和时间度的时间权向量二次加权以推广到动态数据应用场景,最后通过实例验证该方法的可行性.
Keyword ：
动态评价方法 动态评价方法 时间权重 时间权重 理想解法 理想解法 相对熵 相对熵
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| GB/T 7714 | 李美娟 , 刘佳鸿 , 杨隆浩 et al. 基于相对熵距离的动态改进理想解法及其应用研究 [J]. | 系统科学与数学 , 2023 , 43 (1) : 174-185 . |
| MLA | 李美娟 et al. "基于相对熵距离的动态改进理想解法及其应用研究" . | 系统科学与数学 43 . 1 (2023) : 174-185 . |
| APA | 李美娟 , 刘佳鸿 , 杨隆浩 , 胡慧芳 . 基于相对熵距离的动态改进理想解法及其应用研究 . | 系统科学与数学 , 2023 , 43 (1) , 174-185 . |
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