scholarly journals Leveraging Team Expertise Location Awareness in Improving Team Improvisation: A Dynamic Knowledge Integration Perspective

2021 ◽  
Vol Volume 14 ◽  
pp. 2135-2146
Author(s):  
Suyang Ye ◽  
Min Chen
Keyword(s):  
Knowledge Integration ◽  
Location Awareness ◽  
Expertise Location

A study of practice based design research models from knowledge integration perspective

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Honghai LI ◽  
Jun CAI
Keyword(s):  
Knowledge Production ◽  
Theoretical Basis ◽  
Knowledge Integration ◽  
Design Research ◽  
Research Process ◽  
Two Dimensions ◽  
Design Knowledge ◽  
Research Projects ◽  
Proposed Model ◽  
Design Innovation

The transformation of China's design innovation industry has highlighted the importance of design research. The design research process in practice can be regarded as the process of knowledge production. The design 3.0 mode based on knowledge production MODE2 has been shown in the Chinese design innovation industry. On this cognition, this paper establishes a map with two dimensions of how knowledge integration occurs in practice based design research, which are the design knowledge transfer and contextual transformation of design knowledge. We use this map to carry out the analysis of design research cases. Through the analysis, we define four typical practice based design research models from the viewpoint of knowledge integration. This method and the proposed model can provide a theoretical basis and a path for better management design research projects.

Entscheidungsprozesse beim Übersetzen: Routine und Reflexion bei Novizen und Berufsübersetzern

Fachsprache ◽  
2017 ◽  
Vol 32 (3-4) ◽  
pp. 100-121
Author(s):  
Friederike Prassl
Keyword(s):  
Decision Making ◽  
Success Rate ◽  
Knowledge Integration ◽  
Popular Science ◽  
Failure Rates ◽  
Science Text ◽  
Success Rates ◽  
Preliminary Results ◽  
Decision Making Processes ◽  
Cognitive Involvement

This article focuses on the decision-making processes involved in research and knowledge integration in translation processes. First, the relevance of decision taking intranslation is discussed. Second, the psychology of decision making as seen by Jungermann et al. (2005) is introduced, who propose a categorization of decision-making processes intofour types: “routinized”, “stereotype”, “reflected” and “constructed”. This classification is then applied to the translations by five professional translators and five novices of five segments occurring in a popular-science text. The analysis reveals that the decision-making types are distributed differently among students and professional translators, which also has to be seen against the background of whether the decisions made were successful or not. The preliminary results of this study show that students resort to reflected decisions in most cases, but with a low success rate. Professionals achieve a higher success rate when making reflected decisions. As expected, they also make more routinized decisions than students. The professionals’ success rates improve with increasing cognitive involvement, while their failure rates are relatively high when making routinized decisions, an aspect worthwhile considering in translation didactics.

Collaboration and knowledge integration

2009 ◽  
Author(s):  
Libby Gerard ◽  
Erika Tate ◽  
Jennifer Chiu ◽  
Stephanie Corliss ◽  
Marcia Linn
Keyword(s):  
Knowledge Integration

Research on Smart Space Oriented Location Awareness Method

2009 ◽  
Vol 20 (3) ◽  
pp. 671-681
Author(s):  
Liang MING ◽  
Gang ZHAO ◽  
Gui-Hai XIE ◽  
Chun-Lei WANG
Keyword(s):  
Location Awareness ◽  
Smart Space

A Comparative Study of Computational Intelligence Algorithms for Sensor Localization

2019 ◽  
Vol 9 (2) ◽  
pp. 224-236
Author(s):  
Vaishali R. Kulkarni ◽  
Veena Desai ◽  
Raghavendra Kulkarni
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Computing Time ◽  
Sensor Nodes ◽  
Convergence Time ◽  
Localization Error ◽  
Location Awareness ◽  
Cultural Algorithm ◽  
Localization Accuracy ◽  
Multi Stage

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.

scholarly journals Domain knowledge integration into deep learning for typhoon intensity classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maiki Higa ◽  
Shinya Tanahara ◽  
Yoshitaka Adachi ◽  
Natsumi Ishiki ◽  
Shin Nakama ◽  
...  
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Cluster Formation ◽  
Satellite Image ◽  
Image Features ◽  
Feature Maps ◽  
Typhoon Intensity ◽  
Intensity Class ◽  
Intensity Classification

AbstractIn this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon’s eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts. A series of analyses revealed that the data-driven approach using only deep learning has limitations, and the integration of domain knowledge could bring new breakthroughs.

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