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Causality fields in nonlinear causal effect analysis Correspondence
Aiguo WANG, Li LIU, Jiaoyun YANG, Lian LI,dcsliuli@cqu.edu.cn,jiaoyun@hfut.edu.cn,wangaiguo2546@163.com,llian@hfut.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 8, Pages 1277-1286 doi: 10.1631/FITEE.2200165
Keywords： 非线性因果效应；因果域；z-特异性因果效应；正向因果；负向因果；空因果
Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing
Yang Bingru,Tang Jing
Strategic Study of CAE 2000, Volume 2, Issue 5, Pages 44-50
New framework of knowledge representation of fuzzy language field and fuzzy language value structure is shown in this paper. Then the generalized cell automation that can synthetically process fuzzy indeterminacy and random indeterminacy and the generalized inductive logic causal model are brought forward. On this basis, the new logic indeterminate causal inductive automatic reasoning mechanism which is based on fuzzy state describing is brought forward. At the end of this paper its application in the development of intelligent controller is discussed.
Keywords： language field language value structure generalized cell automation generalized inductive logic causal model automatic reasoning intelligent controller
Causal Inference Review
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang
Engineering 2020, Volume 6, Issue 3, Pages 253-263 doi: 10.1016/j.eng.2019.08.016
Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop eXplainable Artificial Intelligence (XAI) algorithms is one of key steps towards to the artificial intelligence 2.0. With the aim of bringing knowledge of causal inference to scholars of machine learning
and artificial intelligence, we invited researchers working on causal inference to write this survey from different aspects of causal inference. This survey includes the following sections: "Estimating average treatment effect: A brief review and beyond" from Dr. Kun Kuang, "Attribution problems in counterfactual inference" from Prof. Lian Li, "The Yule-Simpson paradox and the surrogate paradox" from Prof. Zhi Geng, "Causal potential theory" from Prof. Lei Xu, "Discovering causal information from observational data" from Prof. Kun Zhang, "Formal argumentation in causal reasoning and explanation" from Profs. Beishui Liao and Huaxin Huang, "Causal inference with complex experiments" from Prof. Peng Ding, "Instrumental variables and negative controls for observational studies" from Prof. Wang Miao, and "Causal inference with interference" from Dr. Zhichao Jiang.
Keywords： Causal inference Instructive variables Negative control Causal reasoning and explanation Causal discovery Counter factual inference Treatment effect estimation
混合-增强智能：协作与认知 Review
南宁 郑,子熠 刘,鹏举 任,永强 马,仕韬 陈,思雨 余,建儒 薛,霸东 陈,飞跃 王
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 153-179 doi: 10.1631/FITEE.1700053
Yan-Ning Sun,Wei Qin,Jin-Hua Hu,Hong-Wei Xu,Poly Z.H. Sun
Engineering 2023, Volume 22, Issue 3, Pages 82-93 doi: 10.1016/j.eng.2022.06.019
The soft sensing of key performance indicators (KPIs) plays an essential role in the decision-making of complex industrial processes. Many researchers have developed data-driven soft sensors using cutting-edge machine learning (ML) or deep learning (DL) models. Moreover, feature selection is a crucial issue because a raw industrial dataset is usually high-dimensional, and not all features are conducive to the development of soft sensors. A perfect feature-selection method should not rely on hyperparameters and subsequent ML or DL models. Rather, it should be able to automatically select a subset of features for soft sensor modeling, in which each feature has a unique causal effect on industrial KPIs. Therefore, this study proposes a causal model-inspired automatic feature-selection method for the soft sensing of industrial KPIs. First, inspired by the post-nonlinear causal model, we integrate it with information theory to quantify the causal effect between each feature and the KPIs in the raw industrial dataset. After that, a novel feature-selection method is proposed to automatically select the feature with a non-zero causal effect to construct the subset of features. Finally, the constructed subset is used to develop soft sensors for the KPIs by means of an AdaBoost ensemble strategy. Experiments on two practical industrial applications confirm the effectiveness of the proposed method. In the future, this method can also be applied to other industrial processes to help develop more advanced data-driven soft sensors.
Keywords： Big data analytics Machine intelligence Quality prediction Soft sensors Intelligent manufacturing
Xiaoni Meng, Weijie Cao, Di Liu, Isinta Maranga Elijah, Weijia Xing, Haifeng Hou, Xizhu Xu, Manshu Song, Youxin Wang
Engineering 2023, Volume 26, Issue 7, Pages 74-88 doi: 10.1016/j.eng.2022.11.004
Bidirectional causalityAlthough the association between immunoglobulin G (IgG) N-glycosylation and metabolic traits has been previously identified, the causal association between them remains unclear. In this work, we used Mendelian randomization (MR) analysis to integrate genome-wide association studies (GWASs) and quantitative trait loci (QTLs) data in order to investigate the bidirectional causal association of IgG Nglycosylation with metabolic traits. In the forward MR analysis, 59 (including nine putatively causal glycan peaks (GPs) for body mass index (BMI) (GP1, GP6, etc.) and seven for fasting plasma glucose (FPG) (GP1, GP5, etc.)) and 15 (including five putatively causal GPs for BMI (GP2, GP11, etc.) and four for FPG (GP1, GP10, etc.)) genetically determined IgG N-glycans were identified as being associated with metabolic traits in one- and two-sample MR studies, respectively, by integrating IgG N-glycan-QTL variants with GWAS results for metabolic traits (all P < 0.05). Accordingly, in the reverse MR analysis of the integrated metabolic-QTL variants with the GWAS results for IgG N-glycosylation traits, 72 (including one putatively causal metabolic trait for GP1 (high-density lipoprotein cholesterol (HDL-C)) and five for GP2 (FPG, systolic blood pressure (SBP), etc.)) and four (including one putatively causal metabolic trait for GP3 (HDL-C) and one for GP9 (HDL-C)) genetically determined metabolic traits were found to be related to the risk of IgG N-glycosylation in one- and two-sample MR studies, respectively (all P < 0.05). Notably, genetically determined associations of GP11 → BMI (fixed-effects model-Beta with standard error (SE): 0.106 (0.034) and 0.010 (0.005)) and HDL-C → GP9 (fixed-effects model-Beta with SE: –0.071 (0.022) and –0.306 (0.151)) were identified in both the one- and two-sample MR settings, which were further confirmed by a meta-analysis combining the one- and two-sample MR results (fixed-effects model-Beta with 95% confidence interval (95% CI): 0.0109 (0.0012, 0.0207) and –0.0759 (–0.1186, –0.0332), respectively). In conclusion, the comprehensively bidirectional MR analyses provide suggestive evidence of bidirectional causality between IgG N-glycosylation and metabolic traits, possibly revealing a new richness in the biological mechanism between IgG N-glycosylation and metabolic traits.
Keywords： Mendelian randomization study Immunoglobulin G N-glycosylation Metabolic traits Quantitative trait loci Bidirectional causality
Dynamic relationship analysis among parties of the low-carbon building
Liu Hongyong,Zheng Junwei,Lin Cheng
Strategic Study of CAE 2012, Volume 14, Issue 12, Pages 94-99
According to the definition of academic circles about low carbon building, the definition of this paper are made from the special aspect and general aspect combined with the understanding of the energy saving building and low carbon building. It systematically analyzes the stakeholders based on the perspective of life cycle, including government, developer, design organization, construction organization, provider, consultation unit, technology development institution, evaluation testing institution, department of property management and public, puts forward to the diagram of the relationships among the stakeholders, and designs the causal dynamic relationship diagram about each stage, which are decision making stage, design stage, construction stage, operation stage and scrap processing stage, and analyzes the dynamic relationship among the parties.
Keywords： low-carbon building stakeholders causal relation diagram dynamic analysis
State of the Art of Compartment Fire Modeling
Zheng Xin,Yuan Hongyong
Strategic Study of CAE 2004, Volume 6, Issue 3, Pages 68-74
The aim of the present review is to provide the reader with a brief discussion on the mathematical modeling techniques, currently available for compartment fires. The relevant underlying physical assumptions are presented first and the conventional model performance is analyzed in the range of application. The final part of the review deals with current trends and perspective of mathematic fire models and highlights the need for extensive validation studies and interaction between theoretical and experimental investigations.
Keywords： compartment field model zone model network model FZN (field zone and network) model empirical model
Study on hydrodynamic and synthetic water quality model for river networks
Zhang Mingliang,Shen Yongming
Strategic Study of CAE 2008, Volume 10, Issue 10, Pages 78-83
The Preissmann implicit scheme is used to discrete the one-dimensional Saint-Venant equation and the river-junction-river method is applied to resolve the hydrodynamic mathematical model for river networks. Based on the characteristics of river-junction-river method and the theory of WASP, the synthetic water quality model is set up for river networks, which includes many contamination variables and considers the transform and transplant of the contamination variables. This model is applied to simulate four river networks, the results of elevations and flows agree with the data, the result of contamination variables agree with the measured data. These results show this model is credible and it is a practical tool for forecast and management of water quality in river networks.
Keywords： Preissmann implicit scheme river networks hydrodynamic model water quality model WASP model
Analysis on the principle-agent model in construction project supply chain
Wu Yuhua,Zhang Qingmin
Strategic Study of CAE 2008, Volume 10, Issue 5, Pages 75-78
General form of principle-agent model in integrated supply chain has been discussed from principle-agent Theories. Based on the above model, construction project supply chain one-period principle-agent model and multi-period construction project supply chain reputation model have been built with the characters of principle and agent of construction project integrated supply chain.
Keywords： principle- agent model incentive compatibility reputation model random walk
Research on 4-adj model for determination of spatial relations in spattio model
Li Chengming
Strategic Study of CAE 2013, Volume 15, Issue 5, Pages 37-41
The concept of spattio model is proposed firstly in this paper. Furthermore, it points out that the graphic system based on spattio model will degenerate into a simple geodatabase if it doesn't have the ability to dynamically generate spatial relations. At last, the paper proposes the new method called four-adjacent mathematical model (4-adj model) based on Voronoi diagram to dynamically infer the spatial relations in spattio model, and gives the only complete inference rules, together with the advantage and disadvantage.
Keywords： spattio model spattio object Voronoi diagram 4-adj model
Research of trust valuation based on cloud model
Lu Feng,Wu Huizhong
Strategic Study of CAE 2008, Volume 10, Issue 10, Pages 84-90
At present, the existing descriptions of trust by trust valuation models lack comprehensiveness. To solve this problem, this paper discusses the co-existence and integration of fuzziness and randomness of trust relation, analyzes the ways cloud models describe uncertain concepts and the algorithms cloud models transform between qualitative concepts and their quantitative expressions, and presents trust cloud, a trust evaluation model based on cloud theory. This model provides the algorithms of trust information transfer and combination described by digital characteristics. While describing accurate trust expectation, this model portrays the uncertainty through entropy and hyper entropy. Compared with traditional trust valuation models, trust values obtained in this model contains more semantic information, indicating that valuation results from the model are more suitable as evidence for decision-making on trust.
Keywords： trust valuation trust model cloud model cloud generator
A saliency and Gaussian net model for retinal vessel segmentation Research Articles
Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 8, Pages 1075-1086 doi: 10.1631/FITEE.1700404
Keywords： Retinal vessel segmentation Saliency model Gaussian net (GNET) Feature learning
Multi-band Synchronization Model for Speech Recognition Under Noisy Condition
Sun Wei,Wu Zhenyang
Strategic Study of CAE 2006, Volume 8, Issue 3, Pages 31-34
Based on perception characteristic of human ear, this paper proposes synchronization multi-band maximum likelihood linear regression algorithm for robust speech recognition under noisy condition. The algorithm utilizes maximum likelihood as estimation criteria to compensate the effects of noisy condition with multi-band synchronization model and noise corruption assumption. The tests show that the proposed algorithm improves the performance of recognition system effectively.
Keywords： hidden Markov model maximum likelihood multi-band synchronization model speech recognition
Supervised topic models with weighted words: multi-label document classification None
Yue-peng ZOU, Ji-hong OUYANG, Xi-ming LI
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4, Pages 513-523 doi: 10.1631/FITEE.1601668
Keywords： Supervised topic model Multi-label classification Class frequency Labeled latent Dirichlet allocation (L-LDA) Dependency-LDA
Title Author Date Type Operation
Causality fields in nonlinear causal effect analysis
Aiguo WANG, Li LIU, Jiaoyun YANG, Lian LI,dcsliuli@cqu.edu.cn,jiaoyun@hfut.edu.cn,wangaiguo2546@163.com,llian@hfut.edu.cn
Journal Article
Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing
Yang Bingru,Tang Jing
Journal Article
Causal Inference
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang
Journal Article
A Causal Model-Inspired Automatic Feature-Selection Method for Developing Data-Driven Soft Sensors in Complex Industrial Processes
Yan-Ning Sun,Wei Qin,Jin-Hua Hu,Hong-Wei Xu,Poly Z.H. Sun
Journal Article
Bidirectional Causality Between Immunoglobulin G N-Glycosylation and Metabolic Traits: A Mendelian Randomization Study
Xiaoni Meng, Weijie Cao, Di Liu, Isinta Maranga Elijah, Weijia Xing, Haifeng Hou, Xizhu Xu, Manshu Song, Youxin Wang
Journal Article
Dynamic relationship analysis among parties of the low-carbon building
Liu Hongyong,Zheng Junwei,Lin Cheng
Journal Article
Study on hydrodynamic and synthetic water quality model for river networks
Zhang Mingliang,Shen Yongming
Journal Article
Analysis on the principle-agent model in construction project supply chain
Wu Yuhua,Zhang Qingmin
Journal Article
Research on 4-adj model for determination of spatial relations in spattio model
Li Chengming
Journal Article
A saliency and Gaussian net model for retinal vessel segmentation
Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU
Journal Article
Multi-band Synchronization Model for Speech Recognition Under Noisy Condition
Sun Wei,Wu Zhenyang
Journal Article
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