Manufacturability Analysis and Design Feedback System Developed Using Semantic Framework

Author(s):  
Arvind Rangarajan ◽  
Pradeep Radhakrishnan ◽  
Abha Moitra ◽  
Andrew Crapo ◽  
Dean Robinson

Early manufacturability feedback is critical for reducing product cost and lead-time. This paper describes a new architecture and platform for authoring and applying manufacturability rules for design. The key step is to define a domain-specific ontology by creating a higher-level semantic language that describes design and manufacturing concepts relevant to specific manufacturing processes. This language has two primary uses; express design in the context of manufacturing and relate manufacturing constraints on design as declarative rules. OWL and Jena (a reasoning engine) are used in the background to reason about specific designs and provide manufacturability feedback in a client-server model. The use of Semantic Web technology makes it easier to augment manufacturability feedback with a query system for the designer that utilizes the same rule knowledge base to answer what-if scenarios. This is implemented using SPARQL and using the CAD design context and so enhances the user experience. This novel approach makes it easier for the domain experts to write or verify rules and the designers to validate concepts before changing the CAD model. This helps in maintaining the independence between the CAD platform and core enterprise knowledge. A pilot study in the sheet metal domain is implemented to demonstrate the steps necessary for complete early manufacturability analysis software and highlights the benefits of this approach.

2021 ◽  
Vol 11 (12) ◽  
pp. 5476
Author(s):  
Ana Pajić Simović ◽  
Slađan Babarogić ◽  
Ognjen Pantelić ◽  
Stefan Krstović

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.


2000 ◽  
Author(s):  
Wei Wu ◽  
Suhada Jayasuriya

Abstract In this paper, we consider the sufficient and/or necessary conditions under which responses of unstable plants with zero initial conditions would be bounded under step inputs. Several possible unstable pole patterns are examined, and corresponding criteria are derived. It is shown that an unstable plant can be stabilized to have bounded responses using an alternate step input sequence. Step inputs simulate the saturated inputs in a feedback system with bounded control, where the closed-loop stability of an unstable plant is really difficult to study. Results from this open-loop study may lend some insight into the analysis and design of such feedback systems under input saturation nonlinearities.


2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.


Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8010
Author(s):  
Ismail Butun ◽  
Yusuf Tuncel ◽  
Kasim Oztoprak

This paper investigates and proposes a solution for Protocol Independent Switch Architecture (PISA) to process application layer data, enabling the inspection of application content. PISA is a novel approach in networking where the switch does not run any embedded binary code but rather an interpreted code written in a domain-specific language. The main motivation behind this approach is that telecommunication operators do not want to be locked in by a vendor for any type of networking equipment, develop their own networking code in a hardware environment that is not governed by a single equipment manufacturer. This approach also eases the modeling of equipment in a simulation environment as all of the components of a hardware switch run the same compatible code in a software modeled switch. The novel techniques in this paper exploit the main functions of a programmable switch and combine the streaming data processor to create the desired effect from a telecommunication operator perspective to lower the costs and govern the network in a comprehensive manner. The results indicate that the proposed solution using PISA switches enables application visibility in an outstanding performance. This ability helps the operators to remove a fundamental gap between flexibility and scalability by making the best use of limited compute resources in application identification and the response to them. The experimental study indicates that, without any optimization, the proposed solution increases the performance of application identification systems 5.5 to 47.0 times. This study promises that DPI, NGFW (Next-Generation Firewall), and such application layer systems which have quite high costs per unit traffic volume and could not scale to a Tbps level, can be combined with PISA to overcome the cost and scalability issues.


Author(s):  
Weijian Ni ◽  
Tong Liu ◽  
Qingtian Zeng ◽  
Nengfu Xie

Domain terminologies are a basic resource for various natural language processing tasks. To automatically discover terminologies for a domain of interest, most traditional approaches mostly rely on a domain-specific corpus given in advance; thus, the performance of traditional approaches can only be guaranteed when collecting a high-quality domain-specific corpus, which requires extensive human involvement and domain expertise. In this article, we propose a novel approach that is capable of automatically mining domain terminologies using search engine's query log—a type of domain-independent corpus of higher availability, coverage, and timeliness than a manually collected domain-specific corpus. In particular, we represent query log as a heterogeneous network and formulate the task of mining domain terminology as transductive learning on the heterogeneous network. In the proposed approach, the manifold structure of domain-specificity inherent in query log is captured by using a novel network embedding algorithm and further exploited to reduce the need for the manual annotation efforts for domain terminology classification. We select Agriculture and Healthcare as the target domains and experiment using a real query log from a commercial search engine. Experimental results show that the proposed approach outperforms several state-of-the-art approaches.


Author(s):  
Janina Fengel

Business process modeling has become an accepted means for designing and describing business operations. However, due to dissimilar utilization of modeling languages and, even more importantly, the natural language for labeling model elements, models can differ. As a result, comparisons are a non-trivial task that is presently to be performed manually. Thereby, one of the major challenges is the alignment of the business semantics contained, which is an indispensable pre-requisite for structural comparisons. For easing this workload, the authors present a novel approach for aligning business process models semantically in an automated manner. Semantic matching is enabled through a combination of ontology matching and information linguistics processing techniques. This provides for a heuristic to support domain experts in identifying similarities or discrepancies.


Author(s):  
Valentina Dragos

Supporting anomaly analysis in the maritime field is a challenging problem because of the dynamic nature of the task: the definition of abnormal or suspicious behaviour is subject to change and depends on user interests. This paper provides a novel approach to support anomaly analysis in the maritime domain through the exploration of large collections of interpretation reports. Based on observables or more sophisticated patterns, the approach provides information retrieval strategies going from basic facts retrieval that guide short-term corrective actions to more complex networks of related concepts that help domain experts to understand or to explain abnormal vessel behaviours. Semantic integration is used to link various information sources, by using a commonly adopted standard. The paper seeks to explore different aspects of using information retrieval to support the analysis and interpretation of abnormal vessel behaviours for maritime surveillance.


2009 ◽  
pp. 2708-2734
Author(s):  
Christine Julien ◽  
Sanem Kabadayi

Emerging pervasive computing scenarios involve client applications that dynamically collect information directly from the local environment. The sophisticated distribution and dynamics involved in these applications place an increased burden on developers that create applications for these environments. The heightened desire for rapid deployment of a wide variety of pervasive computing applications demands a new approach to application development in which domain experts with minimal programming expertise are empowered to rapidly construct and deploy domain-specific applications. This chapter introduces the DAIS (Declarative Applications in Immersive Sensor networks) middleware that abstracts a heterogeneous and dynamic pervasive computing environment into intuitive and accessible programming constructs. At the programming interface level, this requires exposing some aspects of the physical world to the developer, and DAIS accomplishes this through a suite of novel programming abstractions that enable on-demand access to dynamic local data sources. A fundamental component of the model is a hierarchical view of pervasive computing middleware that allows devices with differing capabilities to support differing amounts of functionality. This chapter reports on our design of the DAIS middleware and highlights the abstractions, the programming interface, and the reification of the middleware on a heterogeneous combination of client devices and resource-constrained sensors.


2010 ◽  
Vol 5 (5) ◽  
pp. 72-80 ◽  
Author(s):  
Debajyoti Mukhopadhyay ◽  
Sukanta Sinha

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