The Design Process Data Representation Based on Semantic Features Generalization

The majority of medical devices are monitoring devices. Therefore, data communication and analysis are playing a crucial rule in predicting the effectiveness and reliability of a device. Device related data, patient related data and device-patient related data stored in Data Bases (DBs) are great sources for enhancing either new designs or improving already existing ones. Analyzing such data can provide researchers and device development teams with a complete justification and patterns of interest about a device’s performance, life and reliability. Data can be formulated into stochastic models based their statistical characteristics to consider the variability in data and the uncertainty about processes and procedures during early stages of the design process. This strengthens the device’s ability to function under a broader range of operating conditions. The work herein aims at targeting unwanted variations in device performance during the device development process. It employs a novel technique for variation risk management of device performance based historical process data modeling and visualization. The introduced technique is a proactive systematic procedure comprises a tool set that is being placed in the larger framework of the risk management procedure and fully utilizing data from the DBs to predict and address the risk of variations at the early stages of the design process rather than at the end of each major stage.

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Co-Designing an Intervention to Prevent Overweight and Obesity among Young Children and Their Families in a Disadvantaged Municipality: Methodological Barriers and Potentials

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16245110 ◽

2019 ◽

Vol 16 (24) ◽

pp. 5110 ◽

Cited By ~ 1

Author(s):

Didde Hoeeg ◽

Ulla Christensen ◽

Dan Grabowski

Keyword(s):

Design Process ◽

Social Dynamics ◽

Real Life ◽

Organizational Structures ◽

Overweight And Obesity ◽

Process Data ◽

The Social ◽

Intervention Context ◽

New Perspective ◽

Family Based

Design-based research (DBR) is an innovative methodology for co-creation, but potentials, challenges, and differences between methodological ideals and the real-life intervention context are under-researched. This study analyzes the DBR process in which researchers, professionals, and families co-design a family-based intervention to prevent childhood overweight and obesity in a rural municipality. It involves interviews with six key stakeholders in the co-design process. Data were coded and analyzed using systematic text condensation, while the theory of the “social effectiveness of interventions” developed by Rod et al. (2014) was used as an analytical tool for unpacking the co-creation process and exploring methodological barriers and potentials. The DBR approach contributed with a feeling that everyone’s perspective was important, and the professionals got a new perspective on the families’ experiences with healthy living they did not previously consider. We also found that the iterative design process did not fully align with the organizational structures in the municipality or with the needs of stakeholders, leading to friction in the partnership. This study emphasizes the complexity of using an anti-hierarchical approach within a hierarchical context, and the importance of being aware of how the DBR approach shapes the partnership, as well as of how the social dynamics within the partnership shape the design process.

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Study on Product Design Process Management

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.156-157.660 ◽

2010 ◽

Vol 156-157 ◽

pp. 660-664

Author(s):

Shen Li ◽

Xiao Dong Shao ◽

Jian Tao Chang

Keyword(s):

Product Design ◽

Design Process ◽

Process Management ◽

Design Parameters ◽

Prototype System ◽

Management Problem ◽

Process Data ◽

Workflow Technology ◽

Product Design Process ◽

Design Process Management

A new workflow technology, which is developed for product design process management (PDPM), is studied in this paper. Firstly, a new product tree structure, which associates workflow with components and solves process-data management problem, is put forward. Secondly, an improved flow structure consisting of workflow and dataflow and being driven by design-parameters is developed. Lastly, Dataflow structure for parameter integration is designed. A PDPM prototype system is developed and applied in engineering.

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Activity model for homogenization of data sets in laser-based powder bed fusion

Rapid Prototyping Journal ◽

10.1108/rpj-11-2015-0160 ◽

2017 ◽

Vol 23 (1) ◽

pp. 137-148 ◽

Cited By ~ 6

Author(s):

Shaw C. Feng ◽

Paul Witherell ◽

Gaurav Ameta ◽

Duck Bong Kim

Keyword(s):

Design Process ◽

Process Planning ◽

Reference Model ◽

Information Modeling ◽

Powder Bed Fusion ◽

Process Data ◽

Activity Data ◽

Activity Model ◽

Content Type ◽

Powder Bed

Purpose Additive manufacturing (AM) processes are the integration of many different science and engineering-related disciplines, such as material metrology, design, process planning, in-situ and off-line measurements and controls. Major integration challenges arise because of the increasing complexity of AM systems and a lack of support among vendors for interoperability. The result is that data cannot be readily shared among the components of that system. In an attempt to better homogenization this data, this paper aims to provide a reference model for data sharing of the activities to be under-taken in the AM process, laser-based powder bed fusion (PBF). Design/methodology/approach The activity model identifies requirements for developing a process data model. The authors’ approach begins by formally decomposing the PBF processes using an activity-modeling methodology. The resulting activity model is a means to structure process-related PBF data and align that data with specific PBF sub-processes. Findings This model in this paper provides the means to understand the organization of process activities and sub-activities and the flows among them in AM PBF processes. Research limitations/implications The model is for modeling AM activities and data associated with these activity. Data modeling is not included in this work. Social implications After modeling the selected PBF process and its sub-processes as activities, the authors discuss requirements for developing the development of more advanced process data models. Such models will provide a common terminology and new process knowledge that improve data management from various stages in AM. Originality/value Fundamental challenges in sharing/reusing data among heterogeneous systems include the lack of common data structures, vocabulary management systems and data interoperability methods. In this paper, the authors investigate these challenges specifically as they relate to process information for PBF – how it is captured, represented, stored and accessed. To do this, they focus on using methodical, information-modeling techniques in the context of design, process planning, fabrication, inspection and quality control.

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Integration of product design, process planning, scheduling, and FMS control using XML data representation

Robotics and Computer-Integrated Manufacturing ◽

10.1016/j.rcim.2010.07.014 ◽

2010 ◽

Vol 26 (6) ◽

pp. 583-595 ◽

Cited By ~ 24

Author(s):

Dušan N Šormaz ◽

Jaikumar Arumugam ◽

Ramachandra S Harihara ◽

Chintankumar Patel ◽

Narender Neerukonda

Keyword(s):

Product Design ◽

Design Process ◽

Process Planning ◽

Data Representation ◽

Xml Data ◽

Product Design Process

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A Knowledge Base Data Representation for Collaborative Mechanical Design

3rd International Conference on Design Theory and Methodology ◽

10.1115/detc1991-0032 ◽

1991 ◽

Author(s):

Richard L. Nagy ◽

David G. Ullman ◽

Thomas G. Dietterich

Keyword(s):

Knowledge Base ◽

Design Process ◽

Collaborative Design ◽

Mechanical Design ◽

Data Representation ◽

Design Parameters ◽

Base System ◽

Design Decision ◽

Design Information ◽

Final Design

Abstract Collaborative design projects place additional burdens on current design documentation practices. The literature on group design has repeatedly documented the existence of problems in design decision making due to the unavailability of design information. This paper describes a data representation developed for collaborative mechanical design information. The data representation is used to record the history of the design as a sequence of design decisions. The resulting knowledge base records the final specifications, the alternatives which were considered during the design process, and the designers’ rationale for choosing the final design parameters. It is currently used in a computerized knowledge base system under development by the Design Process Research Group (DPRG), at the authors’ institution (OSU).

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Data-Driven Cybersecurity Knowledge Graph Construction for Industrial Control System Security

Wireless Communications and Mobile Computing ◽

10.1155/2020/8883696 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Guowei Shen ◽

Wanling Wang ◽

Qilin Mu ◽

Yanhong Pu ◽

Ya Qin ◽

...

Keyword(s):

Control System ◽

Security Analysis ◽

Relation Extraction ◽

Data Driven ◽

Knowledge Graph ◽

Semantic Features ◽

Industrial Control System ◽

Process Data ◽

Industrial Control ◽

Distant Supervision

Industrial control systems (ICS) involve many key industries, which once attacked will cause heavy losses. However, traditional passive defense methods of cybersecurity have difficulty effectively dealing with increasingly complex threats; a knowledge graph is a new idea to analyze and process data in cybersecurity analysis. We propose a novel overall framework of data-driven industrial control network security defense, which integrated fragmented multisource threat data with an industrial network layout by a cybersecurity knowledge graph. In order to better correlate data to construct a knowledge graph, we propose a distant supervised relation extraction model ResPCNN-ATT; it is based on a deep residual convolutional neural network and attention mechanism, reduces the influence of noisy data in distant supervision, and better extracts deep semantic features in sentences by using deep residuals. We empirically demonstrate the performance of the proposed method in the field of general cybersecurity by using dataset CSER; the model proposed in this paper achieves higher accuracy than other models. And then, the dataset ICSER was used to construct a cybersecurity knowledge graph (CSKG) on the basis of analyzing specific industrial control scenarios, visualizing the knowledge graph for further security analysis to the industrial control system.

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Sarcasm Detection Algorithms

International Journal of Semantic Computing ◽

10.1142/s1793351x18300017 ◽

2018 ◽

Vol 12 (03) ◽

pp. 457-478 ◽

Cited By ~ 1

Author(s):

Uraz Yavanoglu ◽

Taha Yasin Ibisoglu ◽

Setra Genyang Wıcana

Keyword(s):

Machine Learning ◽

Opinion Mining ◽

Learning Approach ◽

Semantic Features ◽

Process Data ◽

Detection Algorithms ◽

Lexical Feature ◽

Machine Learning Approach ◽

The Future ◽

Semantic Properties

In this paper, we want to review one of the challenging problems for the opinion mining task, which is sarcasm detection. To be able to do that, many researchers tried to explore such properties in sarcasm like theories of sarcasm, syntactical properties, psycholinguistic of sarcasm, lexical feature, semantic properties, etc. Studies conducted within last 15 years have not only made progress in semantic features but have also shown increasing amounts of methods of analysis using a machine-learning approach to process data. Therefore, this paper will try to explain the most currently used methods to detect sarcasm. Lastly, we will present a result of our finding, which might help other researchers to gain a better result in the future.

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