scholarly journals An analytical framework for smart manufacturing

2018 ◽  
Vol 249 ◽  
pp. 03010
Author(s):  
Amogh Kulkarni ◽  
Daniel Balasubramanian ◽  
Gabor Karsai ◽  
Peter Denno
Keyword(s):  
Manufacturing Systems ◽  
Ad Hoc ◽  
Analytical Framework ◽  
Smart Manufacturing ◽  
Production Data ◽  
Domain Specific Language ◽  
Domain Specific ◽  
Code Generators ◽  
Analysis Models ◽  
Descriptive Models

Smart manufacturing is an emerging paradigm for the next generation of manufacturing systems. One key to the success of smart manufacturing is the ability to use the production data for defining predictive and descriptive models and their analyses. However, the development and refinement of such models is a labor- and knowledgeintensive activity that involves acquiring data, selecting and refining an analytical method and validating results. This paper presents an analytical framework that facilitates these activities by allowing ad-hoc analyses to be rapidly specified and performed. The proposed framework uses a domain-specific language to allow manufacturing experts to specify analysis models in familiar terms and includes code generators that automatically generate the lower-level artifacts needed for performing the analysis. We also describe the use of our framework with an example problem.

scholarly journals Towards a Domain-Specific Language to Design Adaptive Software: the DMLAS Approach

2016 ◽  
Vol 20 (2) ◽  
pp. 335
Author(s):  
José Joaquín Bocanegra García ◽  
Jaime Andrés Pavlich Mariscal ◽  
Angela Cristina Carrillo Ramos
Keyword(s):  
Adaptive Systems ◽  
Ad Hoc ◽  
Domain Specific Language ◽  
Adaptive Software ◽  
Simple Task ◽  
Design Decisions ◽  
Specific Language ◽  
Domain Specific ◽  
Development Cycle ◽  
Functional Prototype

An adaptive software has the ability to modify its own behavior at runtime due to changes in the users and their context, in the system, in the requirements, in the environment in which the system is deployed, and thus, give to the users a better experience. However, the development of this kind of systems is not a simple task. There are two main issues. First, there is a lack of languages to specify, unambiguously, the elements related to the design phase. As a consequence, these systems are often developed in an ad-hoc manner, without the required formalism, difficulting the process of derivation of design models to the next phases of the development cycle. Second, design decisions and the adaptation model tend to be directly implemented into the source code and not thoroughly specified at the design level. Since the adaptation models become tangled with the code, system evolution becomes more difficult. To address the above issues, this paper proposes DMLAS, a Domain-Specific Language (DSL) to design adaptive systems. As proof of concept, this paper also provides a functional prototype based on the Sirius plugin for Eclipse

scholarly journals Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

2021 ◽  
Vol 3 (2) ◽  
pp. 299-317
Author(s):  
Patrick Schrempf ◽  
Hannah Watson ◽  
Eunsoo Park ◽  
Maciej Pajak ◽  
Hamish MacKinnon ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Imaging Data ◽  
Data Synthesis ◽  
Domain Specific ◽  
Radiology Reports ◽  
Training Examples ◽  
Analysis Models ◽  
Difficult Cases

Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

Domain-Specific Language Abstractions for Compression

2021 ◽  
Author(s):  
Jessica Ray ◽  
Ajav Brahmakshatriya ◽  
Richard Wang ◽  
Shoaib Kamil ◽  
Albert Reuther ◽  
...  
Keyword(s):  
Domain Specific Language ◽  
Specific Language ◽  
Domain Specific

scholarly journals Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Weixin Xu ◽  
Huihui Miao ◽  
Zhibin Zhao ◽  
Jinxin Liu ◽  
Chuang Sun ◽  
...  
Keyword(s):  
Tool Wear ◽  
Information Technologies ◽  
Manufacturing Systems ◽  
Smart Manufacturing ◽  
Wear Prediction ◽  
Multi Scale ◽  
Tool Wear Prediction ◽  
Machine Health Monitoring ◽  
Gated Recurrent Unit ◽  
Unit Network

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

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