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人工智能 8

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卫星 2

拓扑关系 2

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业务架构集成；业务组件；组件识别；CRUD矩阵；启发式 1

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Intelligent high-speed cutting database system development

XIANG Kejun, LIU Zhanqiang, AI Xing

《机械工程前沿（英文）》 2008年第3卷第2期页码 180-188 doi: 10.1007/s11465-008-0038-x

摘要： In this paper, the components of a high-speed cutting system are analyzed firstly. The component variables of the high-speed cutting system are classified into four types: uncontrolled variables, process variables, control variables, and output variables. The relationships and interactions of these variables are discussed. Then, by analyzing and comparing intelligent reasoning methods frequently used, the hybrid reasoning is employed to build the high-speed cutting database system. Then, the data structures of high-speed cutting case base and databases are determined. Finally, the component parts and working process of the high-speed cutting database system on the basis of hybrid reasoning are presented.

关键词： control hybrid reasoning process component uncontrolled

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A novel task-oriented framework for dual-arm robotic assembly task

《机械工程前沿（英文）》 2021年第16卷第3期页码 528-545 doi: 10.1007/s11465-021-0638-2

摘要： In industrial manufacturing, the deployment of dual-arm robots in assembly tasks has become a trend. However, making the dual-arm robots more intelligent in such applications is still an open, challenging issue. This paper proposes a novel framework that combines task-oriented motion planning with visual perception to facilitate robot deployment from perception to execution and finish assembly problems by using dual-arm robots. In this framework, visual perception is first employed to track the effects of the robot behaviors and observe states of the workpieces, where the performance of tasks can be abstracted as a high-level state for intelligent reasoning. The assembly task and manipulation sequences can be obtained by analyzing and reasoning the state transition trajectory of the environment as well as the workpieces. Next, the corresponding assembly manipulation can be generated and parameterized according to the differences between adjacent states by combining with the prebuilt knowledge of the scenarios. Experiments are set up with a dual-arm robotic system (ABB YuMi and an RGB-D camera) to validate the proposed framework. Experimental results demonstrate the effectiveness of the proposed framework and the promising value of its practical application.

关键词： dual-arm assembly AI reasoning intelligent system task-oriented motion planning visual perception

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拓扑关系与性质及其在空间推理中的应用

李成名,刘晓丽

《中国工程科学》 2013年第15卷第5期页码 14-19

摘要：

首先根据笔者以前的研究成果，给出了两个空间实体之间完备、唯一的拓扑关系形式化表达，进而论述了它们之间存在的性质。从这些性质出发，得出了相互间空间关系复合的结果。空间推理从广义上而言，是指从已知信息推导未知信息的理论和方法，据其使用的理论基础，可以分为代数推理和逻辑推理，空间复合的结果可以直接用于代数推理，又可以作为前提条件用于逻辑推理。

关键词：拓扑关系空间关系复合空间推理代数推理逻辑推理

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挑战与希望：AI2.0时代从大数据到知识 Review

Yue-ting ZHUANG,Fei WU,Chun CHEN,Yun-he PAN

《信息与电子工程前沿（英文）》 2017年第18卷第1期页码 3-14 doi: 10.1631/FITEE.1601883

摘要： AI 2.0时代大数据人工智能具体表现为：从浅层计算到深度神经推理；从单纯依赖于数据驱动的模型到数据驱动与知识引导相结合学习；从领域任务驱动智能到更为通用条件下的强人工智能（从经验中学习）。下一代人工智能（AI 2.0）将改变计算本身，将大数据转变为知识以支持人类社会作出更好决策。

关键词：深度推理；知识库扩充；强人工智能；大数据；跨媒体

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Case-based reasoning for selection of the best practices in low-carbon city development

Zhenhua HUANG, Hongqin FAN, Liyin SHEN

《工程管理前沿（英文）》 2019年第6卷第3期页码 416-432 doi: 10.1007/s42524-019-0036-1

摘要： Cities emit extensive carbon emissions, which are considered a major contributor to the severe issue of climate change. Various low-carbon development programs have been initiated at the city level worldwide to address this problem. These practices are invaluable in promoting the development of low-carbon cities. Therefore, an effective approach should be developed to help decision makers select the best practices from previous experience on the basis of the impact features of carbon emission and city context features. This study introduces a case-based reasoning methodology for a specific city to select the best practices as references for low-carbon city development. The proposed methodology consists of three main components, namely, case representation, case retrieval, and case adaption and retention. For city representation, this study selects city context features and the impact features of carbon emission to characterize and represent a city. The proposed methodology is demonstrated by applying it to the selection of the best practices for low-carbon development of Chengdu City in Sichuan Province, China.

关键词： low-carbon city carbon emission best practices case-based reasoning

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A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis

Weichao Yue, Xiaofang Chen, Weihua Gui, Yongfang Xie, Hongliang Zhang

《化学科学与工程前沿（英文）》 2017年第11卷第3期页码 414-428 doi: 10.1007/s11705-017-1663-x

摘要： Root cause analysis (RCA) of abnormal aluminum electrolysis cell condition has long been a challenging industrial issue due to its inherent complexity in analyzing based on multi-source knowledge. In addition, accurate RCA of abnormal aluminum electrolysis cell condition is the precondition of improving current efficiency. RCA of abnormal condition is a complex work of multi-source knowledge fusion, which is difficult to ensure the RCA accuracy of abnormal cell condition because of dwindling and frequent flow of experienced technicians. In view of this, a method based on Fuzzy-Bayesian network to construct multi-source knowledge solidification reasoning model is proposed. The method can effectively fuse and solidify the knowledge, which is used to analyze the cause of abnormal condition by technicians providing a clear and intuitive framework to this complex task, and also achieve the result of root cause automatically. The proposed method was verified under 20 sets of abnormal cell conditions, and implements root cause analysis by finding the abnormal state of root node, which has a maximum posterior probability by Bayesian diagnosis reasoning. The accuracy of the test results is up to 95%, which shows that the knowledge reasoning feasibility for RCA of aluminum electrolysis cell.

关键词： abnormal aluminum electrolysis cell condition Fuzzy-Bayesian network multi-source knowledge solidification and reasoning root cause analysis

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Platform governance in the era of AI and the digital economy

《工程管理前沿（英文）》 2023年第10卷第1期页码 177-182 doi: 10.1007/s42524-022-0241-1

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Bridging the gap: Neuro-Symbolic Computing for advanced AI applications in construction

《工程管理前沿（英文）》页码 727-735 doi: 10.1007/s42524-023-0266-0

摘要： Deep Learning (DL) has revolutionized the field of Artificial Intelligence (AI) in various domains such as computer vision (CV) and natural language processing. However, DL models have limitations including the need for large labeled datasets, lack of interpretability and explainability, potential bias and fairness issues, and limitations in common sense reasoning and contextual understanding. On the other side, DL has shown significant potential in construction for safety and quality inspection tasks using CV models. However, current CV approaches may lack spatial context and measurement capabilities, and struggle with complex safety and quality requirements. The integration of Neuro-Symbolic Computing (NSC), an emerging field that combines DL and symbolic reasoning, has been proposed as a potential solution to address these limitations. NSC has the potential to enable more robust, interpretable, and accurate AI systems in construction by harnessing the strengths of DL and symbolic reasoning. The combination of symbolism and connectionism in NSC can lead to more efficient data usage, improved generalization ability, and enhanced interpretability. Further research and experimentation are needed to effectively integrate NSC with large models and advance CV technologies for precise reporting of safety and quality inspection results in construction.

关键词： advanced AI in construction safety and quality inspection Neuro-Symbolic Computing Deep Learning computer vision

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我国企业人工智能应用现状与挑战

徐文伟,肖立志,刘合

《中国工程科学》 2022年第24卷第6期页码 173-183 doi: 10.15302/J-SSCAE-2022.07.010

摘要：在国家宏观层面，有必要构建更友好的AI产业生态环境，促进AI全产业链协同发展；以更有力的具体举措支持AI产业的技术研发，特别是全栈AI、AI基础平台及工具体系、AI根技术等，提高我国AI核心技术的自主可控能力；鼓励企业积极实施数字化转型，采用AI技术进行智能化升级，形成AI产业技术研发、企业AI落地创新的强耦合及双向循环。">6]，旨在构建值得全球信赖的AI中心，加强欧盟范围内AI的使用、投资和创新活动。

在我国，AI技术持续创新和突破，AI资源持续优化整合，AI产业结构逐渐完善。相应地，我国应高度重视AI产业发展，积极发布AI企业落地配套政策，引导国产AI根技术、基础平台、关键行业应用等层面的协调发展，以更好推动AI技术的产业集聚、促进AI与实体经济的深度融合。

建议支持AI产业链发展，扶持全栈AI国产化，完善AI基础平台及工具体系，培育国产AI根技术，提高AI核心技术的自主可控能力。

关键词：人工智能企业场景智能解决方案落地应用全栈AI AI根技术

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基于可拓学的设计方案进化推理方法

赵燕伟,,刘海生,张国贤

《中国工程科学》 2003年第5卷第5期页码 63-69

摘要：

在分析现有概念设计求解策略的基础上，提出了基于可拓学理论与遗传算法相结合的概念设计求解模型。利用遗传算法模拟物元变换过程，建立了产品方案物元描述的内部模型和外部模型，探讨了内、外模型的转换关系并通过关联函数将内、外部模型联系起来。根据内部模型给出了遗传算法的编码形式以及与之相适应的交叉、变异策略，并通过可拓评价方法建立了遗传算法的适应值函数，基本解决了产品概念设计中的知识组合爆炸和矛盾冲突问题。最后通过求解减速器方案验证了该方法的可行性。

关键词：概念设计物元变换遗传算法进化推理可拓评价

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不确定性推理理论在卫星故障检测和诊断中的应用

杨天社,李怀祖,曹雨平

《中国工程科学》 2003年第5卷第2期页码 68-74

摘要：

推理理论一般分为确定性推理理论和不确定性推理理论。传统的卫星故障检测和诊断应用的是确定性推理。然而，在卫星故障检测和诊断的实践中，仅使用确定性推理是很难对某些故障进行检测和诊断的，因为这时需要合情推理和容错能力。不确定性推理理论可以满足此要求。目前，航天领域的许多专家和实际工作者正致力于应用不确定性推理理论检测和诊断那些用确定性推理无法检测和诊断的故障。不确定性推理理论包括诸如包含度理论、粗糙集理论、证据推理理论、概率推理理论、模糊推理理论等。笔者研究的卫星故障检测和诊断的三种新方法，分别应用了包含度理论、粗糙集理论和证据推理理论。

关键词：卫星故障检测诊断不确定性推理

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to “High-Speed Railway Train Timetable Conflict Prediction Based on Fuzzy Temporal Knowledge Reasoning

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《工程（英文）》 2017年第3卷第1期页码 150-150 doi: 10.1016/J.ENG.2017.01.002

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神经自然语言处理最新进展——模型、训练和推理 Review

周明, 段楠, 刘树杰, 沈向洋

《工程（英文）》 2020年第6卷第3期页码 275-290 doi: 10.1016/j.eng.2019.12.014

摘要：

自然语言处理（natural language processing, NLP）是人工智能研究的一个重要领域，旨在构建能够理解和生成自然语言、实现人机自然交互的技术方案。近5年，基于神经网络的自然语言处理方法取得突飞猛进的发展。基于海量无标注数据和大量标注数据进行建模，使得机器翻译、自动问答和阅读理解等很多任务的水准都得到了极大的提高。本文将从3个角度回顾神经自然语言处理的最新进展，包括模型、训练和推理。在模型部分，我们将介绍典型的神经网络建模方法，包括词嵌入建模、句子嵌入建模和序列到序列建模等。在训练部分，我们将介绍常用的学习方法，包括监督学习、半监督学习、无监督学习、多任务学习、迁移学习和主动学习等。在推理部分，我们将介绍典型的推理框架，包括非神经网络方法和神经网络方法。之所以强调推理方面的研究，是因为推理是构建基于知识的可解释自然语言处理模型的关键技术。本文的最后将概括介绍我们对自然语言处理未来发展方向的一些思考。

关键词：自然语言处理深度学习建模、学习和推理

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Application of AI techniques in monitoring and operation of power systems

David Wenzhong GAO, Qiang WANG, Fang ZHANG, Xiaojing YANG, Zhigang HUANG, Shiqian MA, Qiao LI, Xiaoyan GONG, Fei-Yue WANG

《能源前沿（英文）》 2019年第13卷第1期页码 71-85 doi: 10.1007/s11708-018-0589-4

摘要： In recent years, the artificial intelligence (AI) technology is becoming more and more popular in many areas due to its amazing performance. However, the application of AI techniques in power systems is still in its infancy. Therefore, in this paper, the application potentials of AI technologies in power systems will be discussed by mainly focusing on the power system operation and monitoring. For the power system operation, the problems, the demands, and the possible applications of AI techniques in control, optimization, and decision making problems are discussed. Subsequently, the fault detection and stability analysis problems in power system monitoring are studied. At the end of the paper, a case study to use the neural network (NN) for power flow analysis is provided as a simple example to demonstrate the viability of AI techniques in solving power system problems.

关键词： power system operation and monitoring artificial intelligence (AI) deep learning power flow analysis