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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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Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

João Pedro SANTOS,Christian CREMONA,André D. ORCESI,Paulo SILVEIRA,Luis CALADO

《结构与土木工程前沿（英文）》 2015年第9卷第1期页码 1-16 doi: 10.1007/s11709-014-0277-3

摘要： A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.

关键词： structural health monitoring early-damage detection principal component analysis symbolic data symbolic dissimilarity measures cluster analysis numerical model damage simulations

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基于符号分析法的CMOS模拟单元电路自动优化技术

郑维山,邓青,刘朝霞,时龙兴

《中国工程科学》 2009年第11卷第4期页码 50-56

摘要：

在CMOS单元电路自动优化中提出了一个新的设计方法。此方法采用符号分析技术自动生成描述电路性能的精确解析方程，进而将性能符号方程作为遗传优化算法中的评价电路性能的准则。对于固定拓扑的电路，遗传优化算法通过寻优过程产生满足性能约束的电路设计参数集，从而实现设计者所希望的设计目标。实际电路的优化结果表明此方法能较好地满足性能要求，同时所需的设计时间较短，从而说明该方法是一个具有灵活性和可靠性的设计方法。

关键词：优化性能方程符号分析遗传算法

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Symbolic representation based on trend features for knowledge discovery in long time series

Hong YIN,Shu-qiang YANG,Xiao-qian ZHU,Shao-dong MA,Lu-min ZHANG

《信息与电子工程前沿（英文）》 2015年第16卷第9期页码 744-758 doi: 10.1631/FITEE.1400376

摘要： The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA). The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.

关键词： Long time series Segmentation Trend features Symbolic Knowledge discovery

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Modified condition/decision coverage (MC/DC) oriented compiler optimization for symbolic execution

Wei-jiang Hong, Yi-jun Liu, Zhen-bang Chen, Wei Dong, Ji Wang,zbchen@nudt.edu.cn,wdong@nudt.edu.cn,wj@nudt.edu.cn

《信息与电子工程前沿（英文）》 2020年第21卷第9期页码 1267-1412 doi: 10.1631/FITEE.1900213

摘要： is an effective way of systematically exploring the search space of a program, and is often used for automatic software testing and bug finding. The program to be analyzed is usually compiled into a binary or an intermediate representation, on which is carried out. During this process, s influence the effectiveness and efficiency of . However, to the best of our knowledge, there exists no work on recommendation for with respect to (w.r.t.) , which is an important testing coverage criterion widely used for mission-critical software. This study describes our use of a state-of-the-art tool to carry out extensive experiments to study the impact of s on w.r.t. MC/DC. The results indicate that instruction combining (IC) optimization is the important and dominant optimization for w.r.t MC/DC. We designed and implemented a support vector machine based method w.r.t. IC (denoted as auto). The experiments on two standard benchmarks (Coreutils and NECLA) showed that auto achieves the best MC/DC on 67.47% of Coreutils programs and 78.26% of NECLA programs.

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不确定条件下采用精确参数规划的非线性模型过程操作

Vassilis M. Charitopoulos,Lazaros G. Papageorgiou,Vivek Dua

《工程（英文）》 2017年第3卷第2期页码 202-213 doi: 10.1016/J.ENG.2017.02.008

摘要：

本文提出了新的两(多) 参数规划(mp-P) 启发算法以求解混合整数非线性规划(MINLP) 问题，并着重说明了算法在过程综合问题中的应用。对于因对数项导致的非线性，开发了针对确定性问题的参数算法(p-MINLP)。关键之处是通过将二进制变量和(或) 不确定参数作为符号参数重新生成和求解一阶Karush Kuhn Tucker(KKT) 系统的解析表达式。为此，采用了符号处理和求解技术。为了证明所提出的算法的适用性和有效性，对两个过程综合案例研究进行了验证，相应的结果经最新的数值MINLP 求解器验证是有效的。对于p-MINLP，给出了不确定参数的显函数表示的最优解。

关键词：参数规划不确定性过程综合混合整数非线性规划符号操作