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

Frontiers of Engineering Management   Pages 727-735 doi: 10.1007/s42524-023-0266-0

Abstract: The integration of Neuro-Symbolic Computing (NSC), an emerging field that combines DL and symbolic reasoningrobust, interpretable, and accurate AI systems in construction by harnessing the strengths of DL and symbolic

Keywords： advanced AI in construction     safety and quality inspection     Neuro-Symbolic Computing     Deep Learning    

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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

Frontiers of Structural and Civil Engineering 2015, Volume 9, Issue 1,   Pages 1-16 doi: 10.1007/s11709-014-0277-3

Abstract: combination of advanced statistical and machine learning methods such as principal component analysis, symbolic

Keywords： structural health monitoring     early-damage detection     principal component analysis     symbolic data     symbolic    

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Automated optimization technique of CMOS analog cell circuit based on symbolic analysis

Zheng Weishan,Deng Qing,Liu Zhaoxia,Shi Longxing

Strategic Study of CAE 2009, Volume 11, Issue 4,   Pages 50-56

Abstract: The proposed method uses symbolic analysis technique to generate exact analytic performance equationsThe symbolic model is then passed to the genetic algorithm and is used as evaluating performance criterion

Keywords： optimization     performance equation     symbolic analysis     genetic algorithm    

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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

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 9,   Pages 744-758 doi: 10.1631/FITEE.1400376

Abstract: The symbolic representation of time series has attracted much research interest recently.In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA).Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns

Keywords： 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

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 9,   Pages 1267-1412 doi: 10.1631/FITEE.1900213

Abstract: 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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Nonlinear Model-Based Process Operation under Uncertainty Using Exact Parametric Programming

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

Engineering 2017, Volume 3, Issue 2,   Pages 202-213 doi: 10.1016/J.ENG.2017.02.008

Abstract: KKT) conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolicTo this effect, symbolic manipulation and solution techniques are employed.

Keywords： Parametric programming     Uncertainty     Process synthesis     Mixed-integer nonlinear programming     Symbolic manipulation    

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Title Author Date Type Operation

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

Journal Article

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

Journal Article

Automated optimization technique of CMOS analog cell circuit based on symbolic analysis

Zheng Weishan,Deng Qing,Liu Zhaoxia,Shi Longxing

Journal Article

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

Journal Article

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

Journal Article

Nonlinear Model-Based Process Operation under Uncertainty Using Exact Parametric Programming

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

Journal Article