Lessons Learned: An Effective Approach to Avoid Repeating the Same Old Mistakes

Ganopol, Abigail; Oglietti, Marcelo; Ambrosino, A.; Patt, Frederick S.; Scott, Allan; Hong, Li; Feldman, Gene C.

doi:10.2514/1.i010485

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…The NASA (2019) public LL system was particularly useful as it is well-structured and has proven its value over decades. A recent LL system development is described by Ganopol et al (2017). Their Continuous Improvement Method was aimed at improving the performance of the satellite operations team for a mission that lasted over four years.…”

Section: Kms Design and Implementationmentioning

confidence: 99%

Development of a knowledge management system for the NOMAD instrument onboard the ExoMars TGO spacecraft

Hetey

Neefs

Thomas

et al. 2019

AEAT

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Purpose This paper aims to describe the development of a knowledge management system (KMS) for the Nadir and Occultation for Mars Discovery (NOMAD) instrument on board the ESA/Roscosmos 2016 ExoMars Trace Gas Orbiter (TGO) spacecraft. The KMS collects knowledge acquired during the engineering process that involved over 30 project partners. In addition to the documentation and technical data (explicit knowledge), a dedicated effort was made to collect the gained experience (tacit knowledge) that is crucial for the operational phase of the TGO mission and also for future projects. The system is now in service and provides valuable information for the scientists and engineers working with NOMAD. Design/methodology/approach The NOMAD KMS was built around six areas: official documentation, technical specifications and test results, lessons learned, management data (proposals, deliverables, progress reports and minutes of meetings), picture files and movie files. Today, the KMS contains 110 GB of data spread over 11,000 documents and more than 13,000 media files. A computer-aided design (CAD) library contains a model of the full instrument as well as exported sub-parts in different formats. A context search engine for both documents and media files was implemented. Findings The conceived KMS design is basic, flexible and very robust. It can be adapted to future projects of a similar size. Practical implications The paper provides practical guidelines on how to retain the knowledge from a larger aerospace project. The KMS tool presented here works offline, requires no maintenance and conforms to data protection standards. Originality/value This paper shows how knowledge management requirements for space missions can be fulfilled. The paper demonstrates how to transform the large collection of project data into a useful tool and how to address usability aspects.

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Section: Kms Design and Implementationmentioning

confidence: 99%

Development of a knowledge management system for the NOMAD instrument onboard the ExoMars TGO spacecraft

Hetey

Neefs

Thomas

et al. 2019

AEAT

View full text Add to dashboard Cite

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“…For example, the word chancellor of the prime minister in ancient times is common, but to the present day. However, the pronunciation is the same; both the meaning and the shape of the word have undergone a very obvious change [12][13].…”

Section: Introductionmentioning

confidence: 99%

Semantic network model in Chinese vocabulary generation and evolution

2024

Applied Mathematics and Nonlinear Sciences

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This study delves into the semantics of Chinese vocabulary generation and evolution, providing insights into the profound connotations of the Chinese language and culture. We begin by vectorizing Chinese vocabulary data using the Word2Vec algorithm, which is subsequently input into a deep neural network model. Within this framework, the neurons are tasked with extracting semantic features pertinent to the developmental trajectory of Chinese vocabulary. Following this, the Intersection over Union (IoU) value for each semantic feature is computed, enabling the semantic annotation of the vocabulary. Additionally, the formulation of constraint rules significantly enhances the model’s accuracy in semantic recognition analysis. This approach not only advances our understanding of linguistic structures but also enriches our appreciation of the cultural dynamics embedded within the language. The accuracy and recall of the semantic network model in this study are much higher than those of other comparative models, with an average semantic annotation accuracy of 91.33% in the three Chinese vocabulary datasets, which supports further research and analysis in follow-up. The exploration of the practical application of the semantic network model shows that it achieves better results in the semantic identification and annotation of words in poems, and it is found that the lexical semantic class with the highest frequency in the Three Hundred Song Lyrics is {Func word}, which accounts for 0.056. The semantic network model designed in this paper provides an effective method in the field of Chinese vocabulary semantics analysis and lays the foundation for a deep understanding of the generation and evolution of the culture of the Chinese language. A foundation is laid for a deep understanding of the generation and evolution of Chinese language culture.

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