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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
Characters of topological relations and its applications in spatial reasoning
Li Chengming and Liu Xiaoli
Strategic Study of CAE 2013, Volume 15, Issue 5, Pages 14-19
In the paper,the formal representation of topological relationships are proposed according to our early research results, and the characters are also introduced. Based on these characters, the results for com-posite topological relations are introduced. At last, the applications in spatial reasoning are proposed.
Keywords： topological relationship composite of topological relations spatial reasoning algbra reasoning logical reasoning
Multi-focus image fusion based on fractional-order derivative and intuitionistic fuzzy sets Research Articles
Xue-feng Zhang, Hui Yan, Hao He,zhangxuefeng@mail.neu.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6, Pages 809-962 doi: 10.1631/FITEE.1900737
Keywords： 像融合；分数阶导数；直觉模糊集；多聚焦图像
Application of Uncertainty Reasoning Theory to Satellite Fault Detection and Diagnosis
Yang Tianshe,Li Huaizu,Cao Yuping
Strategic Study of CAE 2003, Volume 5, Issue 2, Pages 68-74
Generally, reasoning theory can be divided into certainty reasoning theory and uncertainty reasoning theory. Traditionally, certainty reasoning theory is used to detect and diagnose satellite faults. However, in practice, it is difficult to detect and diagnose some satellite faults automatically only by use of certainty reasoning theory. The reason is that detection and diagnosis of these faults requires reasonable reasoning and fault-tolerant capability, but certainty reasoning theory can not realize the capability. Fortunately, uncertainty reasoning theory can meet this requirement. Now, it is attracting attention of many researchers and practitioners in the space field all over the world that uncertainty reasoning theory is applied to detect and diagnose the satellite faults which can not be handled properly by certainty reasoning theory. Uncertainty reasoning theory includes several kinds of theories, such as inclusion degree theory, rough set theory, evidence reasoning theory, probabilistic reasoning theory, fuzzy reasoning theory, and so on. This paper introduces three new methods to detect and diagnose the satellite faults, in which inclusion degree theory, rough set theory and evidence reasoning theory of the uncertainty reasoning theory are used respectively.
Keywords： satellite fault detection diagnosis uncertainty reasoning theory
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
Progress in Neural NLP: Modeling, Learning, and Reasoning Review
Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum
Engineering 2020, Volume 6, Issue 3, Pages 275-290 doi: 10.1016/j.eng.2019.12.014
Natural language processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling computers to understand and process human languages. In the last five years, we have witnessed the rapid development of NLP in tasks such as machine translation, question-answering, and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data. In this paper, we will review the latest progress in the neural network-based NLP framework (neural NLP) from three perspectives: modeling, learning, and reasoning. In the modeling section, we will describe several fundamental neural network-based modeling paradigms, such as word embedding, sentence embedding, and sequence-to-sequence modeling, which are widely used in modern NLP engines. In the learning section, we will introduce widely used learning methods for NLP models, including supervised, semi-supervised, and unsupervised learning; multitask learning; transfer learning; and active learning. We view reasoning as a new and exciting direction for neural NLP, but it has yet to be well addressed. In the reasoning section, we will review reasoning mechanisms, including the knowledge, existing non-neural inference methods, and new neural inference methods. We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledge-driven neural NLP models to handle complex tasks. At the end of this paper, we will briefly outline our thoughts on the future directions of neural NLP.
Keywords： Natural language processing Deep learning Modeling learning and Reasoning
Mathematical Reasoning Challenges Artificial Intelligence
Sean O’Neill
Engineering 2019, Volume 5, Issue 5, Pages 817-818 doi: 10.1016/j.eng.2019.08.009
An ANFIS-based Approach for Predicting MiningInduced Surface Subsidence
Ding Dexin,Zhang Zhijun,Bi Zhongwei
Strategic Study of CAE 2007, Volume 9, Issue 1, Pages 33-39
Current approaches for predicting mining induced surface subsidence have a drawback in common that they predict the subsidence only on the basis of a physical or mechanical approach irrespective of the practical examples in engineering practice in mining induced surface subsidence.However,these experiences created in engineering practice are of great value and full use should be made of them to establish an approach for predicting mining induced surface subsidence.Therefore,this paper accumulated a lot of practical examples of mining induced surface subsidence,integrated these examples by using adaptive neuro-fuzzy inference system (ANFIS)and established an ANFIS-based approach for predicting mining induced surface subsidence.The approach was further tested by using practical examples of mining induced surface subsidence.The results show that the approach can converge quickly,fit the data in very good agreement and make generalization prediction with high accuracy.
Keywords： underground mining mining induced surface subsidence adaptive neuro唱fuzzy inferencesystem
A New Evolution-reasoning Method in Conceptual Design Based on Extension Theory
Hao Yanwei,Liu Haisheng,Zhang Guoxian
Strategic Study of CAE 2003, Volume 5, Issue 5, Pages 63-69
After existing study methods are analyzed, a new researching model for conceptual design based on extension and genetic algorithms was presented. The inner-model and outer-model of product project were set up with matter elements. In genetic algorithms the coding for individual, the means for cross-over and mutation were all founded based on the inner-model. The fitness function was set up combined with relationship function. The technique solves the innovative and incompatible problems in conceptual design. The feasibility of the new researching model is testified by three conceptual design examples for retarder.
Keywords： conceptual design matter elements transform genetic algorithms evolution reasoning extension appraisal
Visual commonsense reasoning with directional visual connections Research Articles
Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000722
Keywords： 视觉常识推理；有向连接网络；视觉神经元连接；情景化连接；有向连接
Study of Dynamic Fuzzy Inference Mechanism of Fault Diagnosis Expert System for Production Line
Tan Li,Liu Jin,Mei Liting
Strategic Study of CAE 2005, Volume 7, Issue 6, Pages 57-60
Developing fault diagnosis expert system for production line, the principle and method of structuring fuzzy inference engine are presented in this paper. Moreover, the idea of dynamic fuzzy relation with real time is introduced. And, it is illustrated that this idea is realized by defining a dynamic membership function changing with non-fault-time.
Keywords： fault diagnosis expert system fuzzy inference
Study and implementation of claim decision support system for large water transfer project
Wang Wei
Strategic Study of CAE 2011, Volume 13, Issue 12, Pages 108-112
Considering the shortage of the experience and expert in construction claim of large water transfer project, case-based reasoning (CBR) and rule-based reasoning (RBR) in artificial intelligence are used in claim management. The knowledge-based claim decision support system is designed and implemented, in which the previous cases of hydraulic engineering claims are stored structurally. The system is used for the construction management of a trans-basin water transfer project.
Keywords： water transfer project claim management case-based reasoning (CBR) rule-based reasoning (RBR) decision support system
Adaptive network fuzzy inference system based navigation controller for mobile robot Research Article
Panati SUBBASH, Kil To CHONG
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2, Pages 141-151 doi: 10.1631/FITEE.1700206
Autonomous navigation of a mobile robot in an unknown environment with highly cluttered obstacles is a fundamental issue in mobile robotics research. We propose an adaptive network fuzzy inference system (ANFIS) based navigation controller for a differential drive mobile robot in an unknown environment with cluttered obstacles. Ultrasonic sensors are used to capture the environmental information around the mobile robot. A training data set required to train the ANFIS controller has been obtained by designing a fuzzy logic based navigation controller. Additive white Gaussian noise has been added to the sensor readings and fed to the trained ANFIS controller during mobile robot navigation, to account for the effect of environmental noise on sensor readings. The robustness of the proposed navigation controller has been evaluated by navigating the mobile robot in three different environments. The performance of the proposed controller has been verified by comparing the travelled path length/efficiency and bending energy obtained by the proposed method with reference mobile robot navigation controllers, such as neural network, fuzzy logic, and ANFIS. Simulation results presented in this paper show that the proposed controller has better performance compared with reference controllers and can successfully navigate in different environments without any collision with obstacles.
Keywords： Adaptive network fuzzy inference system Additive white Gaussian noise Autonomous navigation Mobile robot
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
Fuzzy Control on Vehicle Motion Based on Subjective-objectiveJudgment of Driving Tenseness
Chen Xuemei and Gao Li
Strategic Study of CAE 2007, Volume 9, Issue 1, Pages 53-57
It is very important whether a driver can give correct decision and precise operation in emergency． So，it is necessary to judge the emergency degree of environment and provide control algorithm of vehicle motion for ensuring the lives and properties’safety．The emergency degree is firstly given based on relative distance， velocity and drivers’characteristics．Then the control algorithm of vehicle motion based on fuzzy logic is established and is simulated with Simulink．The results show that the higher the emergency degree，the bigger the maximum deceleration is used to control the vehicle．The results also show that the drivers’characteristics have obvious effect on the braking operation．The fuzzy logic is valid to control vehicle’s deceleration．
Title Author Date Type Operation
Characters of topological relations and its applications in spatial reasoning
Li Chengming and Liu Xiaoli
Journal Article
Multi-focus image fusion based on fractional-order derivative and intuitionistic fuzzy sets
Xue-feng Zhang, Hui Yan, Hao He,zhangxuefeng@mail.neu.edu.cn
Journal Article
Application of Uncertainty Reasoning Theory to Satellite Fault Detection and Diagnosis
Yang Tianshe,Li Huaizu,Cao Yuping
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
Progress in Neural NLP: Modeling, Learning, and Reasoning
Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum
Journal Article
An ANFIS-based Approach for Predicting MiningInduced Surface Subsidence
Ding Dexin,Zhang Zhijun,Bi Zhongwei
Journal Article
A New Evolution-reasoning Method in Conceptual Design Based on Extension Theory
Hao Yanwei,Liu Haisheng,Zhang Guoxian
Journal Article
Visual commonsense reasoning with directional visual connections
Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn
Journal Article
Study of Dynamic Fuzzy Inference Mechanism of Fault Diagnosis Expert System for Production Line
Tan Li,Liu Jin,Mei Liting
Journal Article
Study and implementation of claim decision support system for large water transfer project
Wang Wei
Journal Article
Adaptive network fuzzy inference system based navigation controller for mobile robot
Panati SUBBASH, Kil To CHONG
Journal Article
Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing
Yang Bingru,Tang Jing
Journal Article
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