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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: The present paper aims at detecting this type of damage by using static SHM data and by assuming thatTo achieve this objective a data driven strategy is proposed, consisting of the combination of advancedstatistical and machine learning methods such as principal component analysis, symbolic data analysis

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

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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 symbolicThe combination of symbolism and connectionism in NSC can lead to more efficient data usage, improved

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

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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.The high dimensionality typical of the data is challenging, especially as the time series becomes longerThe wide distribution of sensors collecting more and more data exacerbates the problem., 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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A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

Frontiers of Engineering Management 2020, Volume 7, Issue 1,   Pages 27-46 doi: 10.1007/s42524-020-0092-6

Abstract: The era of big data brings unprecedented opportunities and challenges to management research.Exploring the applicable evaluation methods in the big data environment has become an important subjectpaper is to provide an overview and discussion of systematic evaluation and improvement in the big dataWe first review the evaluation methods based on the main analytic techniques of big data such as dataFocused on the characteristics of big data (association feature, data loss, data noise, and visualization

Keywords： big data     evaluation methods     systematic improvement     big data analytic techniques     data mining    

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Data quality assessment for studies investigating microplastics and nanoplastics in food products: Arecurrent data reliable?

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1694-0

Abstract:

● Data quality assessment criteria for MP/NPs in food products were

Keywords： Microplastic     Nanoplastic     Food products     Data quality     Human health risk    

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Blockchain application in healthcare service mode based on Health Data Bank

Jianxia GONG, Lindu ZHAO

Frontiers of Engineering Management 2020, Volume 7, Issue 4,   Pages 605-614 doi: 10.1007/s42524-020-0138-9

Abstract: To guarantee data privacy and data security as well as to harness the value of health data, the conceptof Health Data Bank (HDB) is proposed.In this study, HDB is defined as an integrated health data service institution, which bears no “ownership” of health data and operates health data under the principal–agent model.; (2) data rights; (3) health data supervision; (4) and willingness to share health data.

Keywords： Health Data Bank     blockchain     data assets     smart contract     incentive mechanism    

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Challenges to Engineering Management in the Big Data Era

Yong Shi

Frontiers of Engineering Management 2015, Volume 2, Issue 3,   Pages 293-303 doi: 10.15302/J-FEM-2015042

Abstract: as the Big Data applications.First, it outlines the definitions of big data, data science and intelligent knowledge and the historyof big data.Second, the paper reviews the academic activities about big data in China.and non-structured data into “structured format” and explores the relationship of data heterogeneity

Keywords： big data     data science     intelligent knowledge     engineering management     real-life applications    

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Intelligent data analytics is here to change engineering management

Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG

Frontiers of Engineering Management 2017, Volume 4, Issue 1,   Pages 41-48 doi: 10.15302/J-FEM-2017003

Abstract: Data enabling technology plays an important role in modern scientific discovery and technologic advancementFactual data enables managers to measure, to understand their businesses, and to directly translate thatareas: 1) by making relevant historical data available to the manager at the time when it’s needed;2) by filtering out actionable intelligence from the ocean of data; and 3) by integrating useful dataacquisition and data analytics.

Keywords： engineering management     project management     big data     data analytics     planning     execution    

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Special issue: Innovative applications of big data and artificial intelligence

Frontiers of Engineering Management 2022, Volume 9, Issue 4,   Pages 517-519 doi: 10.1007/s42524-022-0234-0

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Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1059-1068 doi: 10.1631/FITEE.1400398

Abstract: One recent area of interest in computer science is data stream management and processing.By ‘data stream’, we refer to continuous and rapidly generated packages of data.Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data.An issue for the data stream is classification of input data.

Keywords： Data stream     Classificaion     Ensemble classifiers     Concept drift    

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Methodological considerations for redesigning sustainable cropping systems: the value of data-mininglarge and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN, Martin LECHENET

Frontiers of Agricultural Science and Engineering 2020, Volume 7, Issue 1,   Pages 21-27 doi: 10.15302/J-FASE-2019292

Abstract: Large data sets collected from real farms allow for the development of innovative methods to produceData mining methods allow for the diversity of systems to be considered holistically and can take intouse and their effect on farm performance, we advocate further investment in the development of large data

Keywords： data mining     holistic     Integrated Pest Management     economics     DEPHY network.    

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Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

Frontiers in Energy doi: 10.1007/s11708-023-0912-6

Abstract: With the promotion of “dual carbon” strategy, data center (DC) access to high-penetration renewable energyIn this paper, a multi-timescale optimal scheduling model is established for interconnected data centers

Keywords： model predictive control     interconnected data center     multi-timescale     optimized scheduling     distributed    

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Big data and machine learning: A roadmap towards smart plants

Frontiers of Engineering Management   Pages 623-639 doi: 10.1007/s42524-022-0218-0

Abstract: components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced dataessential element to this transformation is the exploitation of large amounts of historical process dataand large volumes of data generated in real-time by smart sensors widely used in industry.Exploitation of the information contained in these data requires the use of advanced machine learning

Keywords： big data     machine learning     artificial intelligence     smart sensor     cyber–physical system     Industry 4.0    

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Title Author Date Type Operation

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

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

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

A review of systematic evaluation and improvement in the big data environment

Feng YANG, Manman WANG

Journal Article

Data quality assessment for studies investigating microplastics and nanoplastics in food products: Arecurrent data reliable?

Journal Article

Blockchain application in healthcare service mode based on Health Data Bank

Jianxia GONG, Lindu ZHAO

Journal Article

Challenges to Engineering Management in the Big Data Era

Yong Shi

Journal Article

Intelligent data analytics is here to change engineering management

Jonathan Jingsheng SHI, Saixing ZENG, Xiaohua MENG

Journal Article

Special issue: Innovative applications of big data and artificial intelligence

Journal Article

Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

Journal Article

Methodological considerations for redesigning sustainable cropping systems: the value of data-mininglarge and detailed farm data sets at the cropping system level

Nicolas MUNIER-JOLAIN, Martin LECHENET

Journal Article

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

Journal Article

Big data and machine learning: A roadmap towards smart plants

Journal Article