Carlos Toro

Decisional DNA: A multi-technology shareable knowledge structure for decisional experience

Knowledge representation and engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different disciplines. These techniques offer features such as: learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this paper, we present a multi-domain knowledge representation structure called Decisional DNA that can be implemented and shared for the exploitation of embedded knowledge in multiple technologies. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making processes. Its applicability is shown in this paper when applied to different decisional technologies. The main advantages of using the Decisional DNA rely on: (i) versatility and dynamicity of the knowledge structure, (ii) storage of day-to-day explicit experience in a single structure, (iii) transportability and shareability of the knowledge, and (iv) predicting capabilities based on the collected experience. Thus, after analysis and results, we conclude that the Decisional DNA, as a unique multi-domain structure, can be applied and shared among multiple technologies while enhancing them with predicting capabilities and facilitating knowledge engineering processes inside decision making systems.

Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0

Abstract This paper reviews the theories, parallels and variances between Virtual Engineering Object (VEO) / Virtual Engineering Process (VEP) and Cyber Physical System (CPS). VEO and VEP is an experience based knowledge representation of engineering objects and processes respectively. Cyber–physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. The analysis of basic concepts and implementation method proves that VEO/VEP is a specialized form of CPS and it can play a vital role in the structure building of Industry 4.0. Integration of the two models may result in intelligent machines and advanced analytics.

REFLEXIVE ONTOLOGIES: ENHANCING ONTOLOGIES WITH SELF-CONTAINED QUERIES

In this article, we introduce the concept of reflexive ontologies. A reflexive ontology is a description of the concepts and relations in a domain with self-contained queries. This approach presents several advantages; (1) the speeding of the query process; (2) the addition of extra knowledge about the domain extending it with queries and answers; and (3), the self-containment of the knowledge structure. We present a framework that can be used to extend any existing ontology with the reflexivity approach. Additionally, as case study, we test the architecture with a previously presented knowledge structure called Set of Experience Knowledge Structure (SOEKS).

Virtual Engineering Object VEO: Toward Experience-Based Design and Manufacturing for Industry 4.0

In this article we propose the concept, its framework, and implementation methodology for Virtual Engineering Objects (VEO). A VEO is the knowledge representation of an engineering object that embodies its associated knowledge and experience. A VEO is capable of adding, storing, improving, and sharing knowledge through experience. Moreover, it is demonstrated that VEO is a specialization of a Cyber-Physical System (CPS). In this article, it is shown through test models how the concept of VEO can be implemented with the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The test model confirmed that the concept of VEO is able to capture and reuse the experience of engineering artifacts, which can be beneficial for efficient decision-making in industrial design and manufacturing.

USING SET OF EXPERIENCE KNOWLEDGE STRUCTURE TO EXTEND A RULE SET OF CLINICAL DECISION SUPPORT SYSTEM FOR ALZHEIMER'S DISEASE DIAGNOSIS

In this article we present an experience-based clinical decision support system (CDSS) for the diagnosis of Alzheimers disease, which enables the discovery of new knowledge in the system and the generation of new rules that drive reasoning. In order to evolve an initial set of production rules given by medical experts we make use of the Set of Experience Knowledge Structure (SOEKS). An illustrative case of our system is also presented.

Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer

The integration of Clinical Decision Support Systems (CDSS) in nowadays clinical environments has not been fully achieved yet. Although numerous approaches and technologies have been proposed since 1960, there are still open gaps that need to be bridged. In this work we present advances from the established state of the art, overcoming some of the most notorious reported difficulties in: (i) automating CDSS, (ii) clinical workflow integration, (iii) maintainability and extensibility of the system, (iv) timely advice, (v) evaluation of the costs and effects of clinical decision support, and (vi) the need of architectures that allow the sharing and reusing of CDSS modules and services. In order to do so, we introduce a new clinical task model oriented to clinical workflow integration, which follows a federated approach. Our work makes use of the reported benefits of semantics in order to fully take advantage of the knowledge present in every stage of clinical tasks and the experience acquired by physicians. In order to introduce a feasible extension of classical CDSS, we present a generic architecture that permits a semantic enhancement, namely Semantic CDSS (S-CDSS). A case study of the proposed architecture in the domain of breast cancer is also presented, pointing some highlights of our methodology.

Knowledge based industrial maintenance using portable devices and augmented reality

In this paper we present a framework and a system implementation for the exploitation of embedded knowledge in the domain of industrial maintenance in a mobile context, using Augmented Reality techniques. We base our approach in the SOUPA group of ontologies (Standard Ontology for Ubiquitous and Pervasive Applications). Our approach extends SOUPA with two new ontologies (i) the Set of Experience Knowledge Structure, used to model the users experience and (ii) the AR ontology which models an Augmented Reality environment that is used to enhance the maintenance experience through virtual elements. As test case, we implemented our approach in different portable devices with video input capabilities such as UMPCs, PDAs and Tablet PCs.

A Perspective on Knowledge Based and Intelligent Systems Implementation in Industrie 4.0

Abstract A worldwide trend in advanced manufacturing countries is defining Industrie 4.0, Industrial Internet and Factories of the Future as a new wave that can revolutionize the production and its associated services. Cyber-Physical Systems (CPS) are central to this vision and are entitled to be part of smart machines, storage systems and production facilities able to exchange information with autonomy and intelligence. Such systems should be able to decide and trigger actions, and control each other independently and for such reason it is required the use of Knowledge based and intelligent information approaches. In this paper we present our perspective on how to support Industrie 4.0 with Knowledge based and intelligent systems. We focus in the conceptual model, architecture and necessary elements we believe are required for a real world implementation. We base our conceptualization in the experiences gathered during the participation in different ongoing research projects where the presented architecture is being implemented.

Virtual Engineering Factory: Creating Experience Base for Industry 4.0

ABSTRACT In recent times, traditional manufacturing is upgrading and adopting Industry 4.0, which supports computerization of manufacturing by round-the-clock connection and communication of engineering objects. Consequently, Decisional DNA-based knowledge representation of manufacturing objects, processes, and system is achieved by virtual engineering objects (VEO), virtual engineering processes (VEP), and virtual engineering factories (VEF), respectively. In this study, assimilation of VEO-VEP-VEF concept in the Cyber-physical system-based Industry 4.0 is proposed. The planned concept is implemented on a case study. Also, Decisional DNA features such as similarity identification and phenotyping are explored for validation. It is concluded that this approach can support Industry 4.0 and can facilitate in real time critical, creative, and effective decision making.

A Knowledge-based Clinical Decision Support System for the diagnosis of Alzheimer Disease

Alzheimer Disease (AD) has become a major issue in developed countries due to medical advances that have extended the population longevity. Recent advances in early detection date the initial stages of AD several years before the first recognizable symptoms appear visible.