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学者姓名：陈福集
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[研究目的]针对突发事件应急决策过程中，决策者表现出的参照依赖和损失规避心理倾向，决策属性间的多种关联与交互，以及决策专家间的领域知识互补与冗余等问题，提出基于2-可加模糊测度与TODIM的突发事件应急群决策模型。[研究方法]引入直觉模糊数表征决策属性的不确定信息；利用2-可加测度刻画决策属性之间的关联与交互，以及不同专家组合的决策权重；考虑应急决策过程中的有限理性行为特征，运用TODIM方法对备选方案各属性下的优劣势进行两两比较，以计算相对损益值。进而构建损益感知函数矩阵，求出备选方案相对于其他方案的综合感知函数值，以优选应急预案。[研究结论]通过新冠疫情应急管控现实案例，验证该模型的合理性和有效性，为突发事件应急决策提供参考。
Keyword ：
2-可加测度 2-可加测度 Choquet积分 Choquet积分 TODIM方法 TODIM方法 应急决策 应急决策 直觉模糊 直觉模糊 突发事件 突发事件
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| GB/T 7714 | 林玲 , 陈福集 , 谢加良 et al. 基于2-可加测度与TODIM的突发事件应急群决策 [J]. | 情报杂志 , 2023 , 42 (07) : 116-122,169 . |
| MLA | 林玲 et al. "基于2-可加测度与TODIM的突发事件应急群决策" . | 情报杂志 42 . 07 (2023) : 116-122,169 . |
| APA | 林玲 , 陈福集 , 谢加良 , 李凤 . 基于2-可加测度与TODIM的突发事件应急群决策 . | 情报杂志 , 2023 , 42 (07) , 116-122,169 . |
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针对网络推手参与社交媒体的舆情炒作和操纵行为,探讨如何实现对社交媒体平台的舆情传播进行有效管控.构建基于网络推手、社交媒体平台、政府监管部门和网民等四方利益主体的演化博弈模型,分析各博弈主体的稳定策略,并通过实验仿真探讨关键要素对博弈模型的影响.研究得到如下结论:1)权威媒体及时辟谣能够有效阻断社交媒体平台上虚假舆情信息传播:2)政府监管部门加强对网络推手和社交媒体平台的有效监管,加大违规惩罚力度,可对网络推手起到有力警醒,督促社交媒体规范舆情信息推送流程;3)主流媒体平台发布的舆情信息较为可靠,网民对小道消息应及时查证.据此,提出政府有效监管社交媒体平台舆情传播的相关建议,为净化网络信息空间,保障网民权益,维护社会安定团结,提供决策参考.
Keyword ：
演化博弈 演化博弈 社交媒体 社交媒体 网络信息监管 网络信息监管 网络推手 网络推手 舆情传播 舆情传播
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| GB/T 7714 | 林玲 , 陈福集 . 网络推手参与的社交媒体舆情传播四方演化博弈 [J]. | 系统科学与数学 , 2023 , 43 (02) : 379-398 . |
| MLA | 林玲 et al. "网络推手参与的社交媒体舆情传播四方演化博弈" . | 系统科学与数学 43 . 02 (2023) : 379-398 . |
| APA | 林玲 , 陈福集 . 网络推手参与的社交媒体舆情传播四方演化博弈 . | 系统科学与数学 , 2023 , 43 (02) , 379-398 . |
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网络舆情发展趋势预测对政府相关部门的舆情监测与管控有非常重要的参考意义.针对网络舆情的小样本特性,同时考虑适用模型的时效性和准确度,本文提出一种基于佳点集方法初始化、非线性参数控制以及对引领狼赋权的改进灰狼优化支持向量回归(IGWO-SVR)的网络舆情预测模型,以"新冠肺炎"、"中国梦"等百度指数作为舆情数据样本进行了实证研究.对比实验结果显示,改进后的灰狼优化算法有较强的全局搜索能力、较快的收敛速度以及较好的稳定性.IGWOSVR网络舆情预测模型有较为突出的准确性与稳定性,能够为政府舆情管控部门提供较好的决策参考.
Keyword ：
佳点集 佳点集 支持向量机 支持向量机 新冠肺炎 新冠肺炎 灰狼优化 灰狼优化 网络舆情 网络舆情
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| GB/T 7714 | 林玲 , 陈福集 , 谢加良 et al. 基于改进灰狼优化支持向量回归的网络舆情预测 [J]. | 系统工程理论与实践 , 2022 , 42 (02) : 487-498 . |
| MLA | 林玲 et al. "基于改进灰狼优化支持向量回归的网络舆情预测" . | 系统工程理论与实践 42 . 02 (2022) : 487-498 . |
| APA | 林玲 , 陈福集 , 谢加良 , 李凤 . 基于改进灰狼优化支持向量回归的网络舆情预测 . | 系统工程理论与实践 , 2022 , 42 (02) , 487-498 . |
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为了解决具有非线性特征的未来24小时PM2.5浓度预测难题，将集合经验模态分解(ensemble empirical mode decomposition, EEMD)和相空间重构(phase space reconstruction, PSR)技术与布谷鸟算法优化的支持向量机(CS-SVR)模型结合起来，建立EEMD-PSR-CS-SVR组合预测模型。采用EEMD将PM2.5浓度时间序列分解为n个不同尺度的IMF子序列及余项，然后对各子序列进行相空间重构，再用重构后数据对支持向量机预测模型进行训练并得到未来24小时PM2.5浓度的预测结果，在其中采用布谷鸟算法对支持向量机参数进行优化。实验结...
Keyword ：
CS-SVR CS-SVR PM2.5 PM2.5 相空间重构 相空间重构 集合经验模态分解 集合经验模态分解 预测模型 预测模型
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| GB/T 7714 | 刘微 , 陈福集 . 基于EEMD-PSR-CS-SVR组合方法的PM2.5浓度预测模型研究 [J]. | 福州大学学报(哲学社会科学版) , 2022 , 36 (02) : 44-52 . |
| MLA | 刘微 et al. "基于EEMD-PSR-CS-SVR组合方法的PM2.5浓度预测模型研究" . | 福州大学学报(哲学社会科学版) 36 . 02 (2022) : 44-52 . |
| APA | 刘微 , 陈福集 . 基于EEMD-PSR-CS-SVR组合方法的PM2.5浓度预测模型研究 . | 福州大学学报(哲学社会科学版) , 2022 , 36 (02) , 44-52 . |
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The prediction of the development trend of internet public opinion is a very significant reference for monitoring and control of the network public opinion by the relevant government departments. On account of small sample characteristics of online public opinion and the needs for both accuracy and stability in the prediction model, in this paper, an improved grey wolf optimization algorithm (IGWO) based on the initialization of the good-point set method, nonlinear parameter control and the weighting of the leading wolf is proposed. Using IGWO to optimize the super parameters of SVM regression model, a network public opinion prediction model based on improved grey wolf optimized support vector machine regression (IGWO-SVR) is established. Empirical research is carried out with Baidu indexes such as COVID-19 as public opinion data samples. The experimental results of 12 test functions show that the improved grey wolf optimization algorithm has relatively strong global search ability, faster convergence speed and better stability. The IGWO-SVR model has relatively outstanding accuracy and stability in the prediction of the development trend of public opinion, which can provide better decision-making support for public opinion supervision department of government. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
Keyword ：
Decision making Decision making Forecasting Forecasting Geometry Geometry Optimization Optimization Regression analysis Regression analysis Social aspects Social aspects Support vector machines Support vector machines
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| GB/T 7714 | Lin, Ling , Chen, Fuji , Xie, Jialiang et al. Prediction of network public opinion based on improved grey wolf optimized support vector machine regression [J]. | System Engineering Theory and Practice , 2022 , 42 (2) : 487-498 . |
| MLA | Lin, Ling et al. "Prediction of network public opinion based on improved grey wolf optimized support vector machine regression" . | System Engineering Theory and Practice 42 . 2 (2022) : 487-498 . |
| APA | Lin, Ling , Chen, Fuji , Xie, Jialiang , Li, Feng . Prediction of network public opinion based on improved grey wolf optimized support vector machine regression . | System Engineering Theory and Practice , 2022 , 42 (2) , 487-498 . |
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[目的/意义]为完善传统的网络舆情预警模型,考虑网络舆情预警指标之间交互性和决策者的风险偏好,合理集结专家的评价意见,提出一种基于直觉模糊Choquet积分的网络舆情预警模型。[过程/方法]采用直觉模糊数刻画舆情预警指标信息的模糊性与不确定性,利用模糊Choquet积分算子对指标属性值进行综合;定义了考虑专家舆情风险偏好的直觉模糊得分函数,以计算舆情风险等级评价值;引入投影法求解专家权重,以合理地对专家们的评价信息进行集结。[结果/结论]通过算例验证了预警模型的合理性和有效性,并对风险偏好因子进行了灵敏度分析,结果显示该舆情预警模型能直观地展示网络舆情风险倾向,为舆情监管部门提供较好的决策支持...
Keyword ：
Choquet积分 Choquet积分 投影法 投影法 直觉模糊 直觉模糊 网络舆情 网络舆情 预警模型 预警模型
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| GB/T 7714 | 林玲 , 陈福集 , 谢加良 et al. 考虑风险偏好的网络舆情预警模型——基于直觉模糊和Choquet积分 [J]. | 情报杂志 , 2021 , 40 (10) : 52-58 . |
| MLA | 林玲 et al. "考虑风险偏好的网络舆情预警模型——基于直觉模糊和Choquet积分" . | 情报杂志 40 . 10 (2021) : 52-58 . |
| APA | 林玲 , 陈福集 , 谢加良 , 李凤 . 考虑风险偏好的网络舆情预警模型——基于直觉模糊和Choquet积分 . | 情报杂志 , 2021 , 40 (10) , 52-58 . |
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No.2, Identifying the accurate boundaries of a species is critical to have a better perspective of any biological studies. Therefore, species delimitation is a subject of extensive part of studies in the framework of biology. Erodium species possess significant pharmacological and biological activities. The whole plant was used as astringent and haemostatic in uterine and other bleeding Therefore, due to the importance of these plant species, we performed a molecular data analysis for this species. For this study, we used 60 randomly collected plants from 5 species in five provinces. Amplification of genomic DNA using 10 primers produced 52 bands, of which 50 were polymorphic (98.48%). The obtained high average PIC and MI values revealed high capacity of ISSR primers to detect polymorphic loci among Erodium species. The genetic similarities of five collections were estimated from 0.77 to 0.91. According to Inter-Simple sequence repeats (ISSR) markers analysis, E. cicutarium and E. malacoides had the lowest similarity and the species of E. malacoides and E. oxyrrhynchum had the highest similarity. The aims of present study are: 1) can ISSR markers identify Erodium species, and 2) to investigate the species inter-relationship? The present study revealed that ISSR markers can identify the species.
Keyword ：
Erodium Erodium ISSR markers ISSR markers Species Identification Species Identification Structure Structure
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| GB/T 7714 | Mowang, Shu-Chen , Chen, Fu-Ji , Zeenat . GENETICKI DIVERZITET Erodium (Geranaiceae) VRSTA ZASNOVAN NA ISSR MARKERIMA [J]. | GENETIKA-BELGRADE , 2021 , 53 (2) : 837-849 . |
| MLA | Mowang, Shu-Chen et al. "GENETICKI DIVERZITET Erodium (Geranaiceae) VRSTA ZASNOVAN NA ISSR MARKERIMA" . | GENETIKA-BELGRADE 53 . 2 (2021) : 837-849 . |
| APA | Mowang, Shu-Chen , Chen, Fu-Ji , Zeenat . GENETICKI DIVERZITET Erodium (Geranaiceae) VRSTA ZASNOVAN NA ISSR MARKERIMA . | GENETIKA-BELGRADE , 2021 , 53 (2) , 837-849 . |
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[目的/意义]微博舆情监管是政府推进网络社会治理所面临的难题.对微博舆情进行研究有助于深入了解微博舆情传播规律,为政府监管微博舆情提供建议.[方法/过程]首先通过分析微博舆情的社交网络结构特点,对BA无标度网络进行改进.随后将模糊观点与Deffuant-Weisbuch模型融合,提出一种基于改进模糊相似度的舆情演化规则.最后通过仿真实验分析微博舆情演化特征.[结果/结论]研究发现,模糊观点的类型对舆情演化的周期与规模有影响.用户对于热门发现微博的关注度对舆情传播有影响.
Keyword ：
微博舆情 微博舆情 模糊理论 模糊理论 舆情演化 舆情演化
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| GB/T 7714 | 郭耿 , 陈福集 , 陈蕾雯 . 基于观点模糊相似度的微博舆情演化研究 [J]. | 情报科学 , 2020 , 38 (1) : 82-86 . |
| MLA | 郭耿 et al. "基于观点模糊相似度的微博舆情演化研究" . | 情报科学 38 . 1 (2020) : 82-86 . |
| APA | 郭耿 , 陈福集 , 陈蕾雯 . 基于观点模糊相似度的微博舆情演化研究 . | 情报科学 , 2020 , 38 (1) , 82-86 . |
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Daily PM2.5 level has significant influence on human health, which is attracting increasing attention. The prediction of PM2.5 grades has thus become an important factor closely related to social development. In the past decades, many prediction methodologies for PM2.5 have been developed, including regression analysis, neural network model, and support vector machine model. Despite these progresses, it still remains a great challenge to predict the PM2.5 grades more accurately and efficiently. In this work, we applied meteorological pattern analysis to assist the support vector machine (SVM) model for PM2.5 class prediction. Cosine similarity was first used to extract three most relevant ones from six common meteorological parameters (atmospheric pressure, relative humidity, air temperature, wind speed, wind direction, cumulative precipitation) to give the needed meteorological pattern for SVM model. Higher prediction accuracy was then obtained with the selected pattern composed by relative humidity, wind speed and wind direction. Moreover, genetic algorithm (GA) and particle swarm optimization (PSO) algorithm were investigated for optimizing the parameters in the process of SVM classification, with PSO-SVM presenting the highest accuracy and efficiency (forecasting time significantly reduced by 25%). We further introduced the criteria of precision, recall and F1-score to evaluate the prediction results of PSO-SVM in each PM2.5 grade. Meanwhile, comparative studies confirmed that PSO-SVM displayed better performance than Adaboost and ANN models for the applied meteorological pattern analysis assisted PM2.5 grades prediction. These obtained results indicate the validity of meteorological pattern analysis for efficient air quality forecasting.
Keyword ：
Air pollution Air pollution Meteorological pattern analysis Meteorological pattern analysis Particle swarm optimization algorithm Particle swarm optimization algorithm PM2.5 grades PM2.5 grades SVM SVM
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| GB/T 7714 | Liu, Wei , Guo, Geng , Chen, Fuji et al. Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm [J]. | ATMOSPHERIC POLLUTION RESEARCH , 2019 , 10 (5) : 1482-1491 . |
| MLA | Liu, Wei et al. "Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm" . | ATMOSPHERIC POLLUTION RESEARCH 10 . 5 (2019) : 1482-1491 . |
| APA | Liu, Wei , Guo, Geng , Chen, Fuji , Chen, Yihui . Meteorological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm . | ATMOSPHERIC POLLUTION RESEARCH , 2019 , 10 (5) , 1482-1491 . |
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Collaborative filtering (CF) is a widely used technique in recommender systems. With rapid development in deep learning, neural network-based CF models have gained great attention in the recent years, especially autoencoder-based CF model. Although autoencoder-based CF model is faster compared with some existing neural network-based models (eg, Deep Restricted Boltzmann Machine-based CF), it is still impractical to handle extremely large-scale data. In this paper, we practically verify that most non-zero entries of the input matrix are concentrated in a few rows. Considering this sparse characteristic, we propose a new method for training autoencoder-based CF. We run experiments on two popular datasets MovieLens 1 M and MovieLens 10 M. Experimental results show that our algorithm leads to orders of magnitude speed-up for training (stacked) autoencoder-based CF model while achieving comparable performance compared with existing state-of-the-art models.
Keyword ：
autoencoder autoencoder collaborative filtering collaborative filtering deep learning deep learning recommender system recommender system
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| GB/T 7714 | Wang, Yi-Lei , Tang, Wen-Zhe , Yang, Xian-Jun et al. An efficient method for autoencoder-based collaborative filtering [J]. | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , 2019 , 31 (23) . |
| MLA | Wang, Yi-Lei et al. "An efficient method for autoencoder-based collaborative filtering" . | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 31 . 23 (2019) . |
| APA | Wang, Yi-Lei , Tang, Wen-Zhe , Yang, Xian-Jun , Wu, Ying-Jie , Chen, Fu-Ji . An efficient method for autoencoder-based collaborative filtering . | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , 2019 , 31 (23) . |
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