Knowledge Reuse Research Articles

Usefulness of digital tools when designing human robot collaboration layouts

The introduction of Human-Robot Collaboration assembly (HRC) adds complexity when designing layouts, as e.g. new safety and security issues need to be considered. In this paper, the usefulness of digital tools, such as simulation and emulation tools, to support the transdisciplinary activities in an integrated product and production development process for HRC layouts, is explored. The empirical findings was collected through interactive workshops between industrial experts and researchers, developing and analyzing challenges and needs while using digital tools. The conclusions indicate a need for an understanding of the cost drivers when utilizing digital tools, and how the reuse of digital models and knowledge can reduce development time and cost. This includes understanding of how new versions of digital tools may drive costs. Four important activities while designing a HRC layout, utilizing a digitalized production preparation process, were identified. It was also found that an efficient and reliable assessment method related to different regulations and standards is needed to support the selection and use of digital tools when designing a HRC layout.

Ontology-based assembly knowledge representation and process file generation

Aiming at the problems of low assembly knowledge shareability and reusability as well as long generation cycle of assembly process, this paper proposes an ontology-based assembly knowledge representation method, and generates assembly process file based on this method. The assembly ontology, modelling through protégé software, has three central classes: AssemblyObject, AssemblyElement, and AssemblyTool. The assembly ontology is described in OWL language and the assembly knowledge concepts including classes and individuals are linked through properties. In addition, the assembly ontology in OWL language is parsed through Python's RDFLib library, and it is called and displayed in LabVIEW. Finally, the assembly process file containing assembly sequence and assembly process parameters is generated. This method realizes the formal description of assembly process knowledge at the semantic level and improves the shareability and reusability of assembly knowledge. Besides, the corresponding assembly process knowledge can be quickly queried and obtained through this method, improving the efficiency of assembly process planning, and providing intelligent assembly basic knowledge.

АНАЛИЗ ПРОЦЕССА СОХРАНЕНИЯ ЗНАНИЙ НА ОСНОВЕ ЦЕПОЧКИ СОЗДАНИЯ ЦЕННОСТЕЙ: ЭТАПЫ СОЗДАНИЯ И ВНЕДРЕНИЯ ЦЕННОСТИ

The retention function is very important in a knowledge management system. Most organizations create knowledge that already exists but is lost or employees don't know how to get it. These processes are costly and inefficient, but at the same time, the systematic reuse of knowledge creates significant economic benefits. Therefore, the preservation of organizational knowledge is not only a necessity, but also is a cost savings, which is currently especially relevant. This article presents the structure of the processes of the stages creation and implementation of value within the value chain of the knowledge preservation process. The implementation of processes allows user to determine the types of knowledge, ways to save knowledge, conditions for its implementation and dissemination.

RSTC: A New Residual Swin Transformer for Offline Word-Level Writer Identification

Writer identification has steadily progressed in recent decades owing to its widespread application. Scenarios with extensive handwriting data such as page-level or sentence-level have achieved satisfactory accuracy, however, word-level offline writer identification is still challenging owing to the difficulty of learning good feature representations with scant handwriting data. This paper proposes a new Residual Swin Transformer Classifier (RSTC), comprehensively aggregating local and global handwriting styles and yielding robust feature representations with single-word images. Local information is modeled by the Transformer Block through interacting strokes. Global information is featurized by holistic encoding using the Identity Branch and Global Block. Moreover, the pre-training technique is exploited to transfer reusable knowledge learned from a task similar to writer identification, strengthening the model’s representation of handwriting features. The proposed method is tested on the IAM and CVL benchmark datasets and achieves state-of-the-art performance, which demonstrates RSTC’s superior modeling capability on word-level writer identification.

KNOWLEDGE MANAGEMENT SYSTEM OF THE INDUSTRIAL PRODUCTION, SCIENTIFIC AND EDUCATIONAL COMPLEX AS A DIDACTIC TOOL FOR THE DEVELOPING PROFESSIONAL COMPETENCE OF AN ENGINEER

The relevance of the research is justified by the need for systematic collaboration between universities and production in the context of digitalization, since other technologies are proposed that change the learning process. The paper presents an analysis of the literature on the problem of creating a knowledge management system in the industrial and scientific educational complex, which shows the need to create an information infrastructure. The application of the knowledge management system in educational practice allows you to establish multi-level connections (interpersonal, intergroup, interorganizational) in the intellectual field of the industrial production, scientific and educational complex, as well as comprehensively solve didactic and educational tasks. The paper also attempts to reveal the essence of the concept of "didactic tools". Didactic tools should be considered as a set of interrelated tools intended for participants in the learning process, the use of which in educational practice contributes to the effective solution of certain didactic tasks. A working definition of the developed didactic tools is proposed – the knowledge management system of the industrial, scientific and educational complex: a set of interrelated and systematic processes and tools intended for the subjects of interaction in the intellectual field of the industrial, scientific and educational complex. It provides collection, formalization, examination, updating, accumulation, preservation, enrichment, reuse and dissemination of industry knowledge. The means allocated as part of the didactic tools are always systematically interconnected, although each of them can solve its own separate task. There are two major subsystems in the knowledge management system: a subsystem for managing separated knowledge, which are separated from the personality of their bearer and are presented in some symbolic form (text, hypertext, ontology), and a subsystem for managing personal knowledge that people carry (students, teachers, researchers, experts from industry). The subsystem of personal knowledge management is analyzed, it involves the use of pedagogical technologies of a personality-oriented nature, and the need for the forming a culture of working with knowledge, the presence of professionalism among instructors when using the knowledge management system of an educational organization is substantiated. The development of a knowledge management system for an industrial scientific, industrial, scientific and educational complex requires the solution of many diverse tasks that require the involvement of experts from different subject areas in the team of developers, which create the knowledge management system.

Smart Knowledge Engineering for Cognitive Systems: A Brief Overview

Cognition in computer sciences refers to the ability of a system to learn at scale, reason with purpose, and naturally interact with humans and other smart systems, such as humans do. To enhance intelligence, as well as to introduce cognitive functions into machines, recent studies have brought humans into the loop, turning the system into a human–AI hybrid. To effectively integrate and manipulate hybrid knowledge, suitable technologies and guidelines are required to sustain the human–AI interface so that communication can occur. However, traditional Knowledge Management (KM) and Knowledge Engineering (KE) approaches encounter problems when dealing with cutting-edge technologies, imposing impediments for the use of traditional methods in cognitive systems (CS). This paper presents a brief overview of the Smart Knowledge Engineering for Cognitive Systems (SKECS), which is based on methods, technologies, and procedures that bring innovations to the fields of KE, KM, and CS. The goal is to bridge the gap in the hybrid cognitive interface by the combination of experience-based knowledge representation with the use of emerging technologies such as deep learning, context-aware indexing/retrieval, active learning with a human-in-the-loop, and stream reasoning. In this work Set of Experience Knowledge Structure (SOEKS) and Decision DNA (DDNA) is extended to the visual domain and utilized for knowledge capture, representation, reuse, and evolution. These technologies are examined throughout the layers of SKECS for applications in knowledge acquisition, formalization, storage/retrieval, learning, and reasoning, with the final goal of achieving knowledge augmentation (wisdom) in CS. Features of the SKECS and their practical implementation is discussed through a case study—the Cognitive Vision Platform for Hazard Control (CVP-HC)—suggesting that methods, techniques and procedures comprising the SKECS are suitable for advancing systems toward augmented cognition.

Disassembly task planning for end-of-life automotive traction batteries based on ontology and partial destructive rules

Disassembly is an essential step for the cascade utilization of end-of-life automobile power batteries. The diversity in types and structures of end-of-life automobile power batteries has led to much waste in time and cost in manual disassembly processes. With the incoming large-scale retirement of automobile power batteries, it is urgent to use artificial intelligence technology to enable the automation of battery disassembly planning. In order to establish a complete and open product information model to realize the automatic disassembly task planning of end-of-life automobile power battery, a disassembly task planning method of automobile power batteries is proposed based on ontology and partial destructive rules. This presented approach is in combination with case-based reasoning/rule-based reasoning, which is utilized as the mechanism for disassembly knowledge reuse and reasoning. Firstly, a disassembly information ontology of automobile power batteries is constructed to describe the components information and assembly relation. Then, a set of partial destructive rules are formulated to guide the dismount of parts with destructive connection. Thirdly, a disassembly sequence generation method is presented to infer feasible planning schemes from the rule base. Finally, the effectiveness of the method is tested with a case study of a power lithium-ion battery pack. The case study has indicated that this presented method can generate the disassembly task schemes quickly and effectively, when applied to the disassembly of large-scale heterogeneous automobile power batteries.

Towards Knowledge Sharing Oriented Adaptive Control

In this paper, we propose a knowledge sharing oriented approach to enable a robot to reuse other robots' knowledge by adapting itself to the inverse dynamics model of the knowledge-sharing robot. The purpose of this work is to remove the heavy fine-tuning procedure required before using a new robot for a task via reusing other robots' knowledge. We use the Neural Knowledge DNA (NK-DNA) to help robots gain empirical knowledge and introduce a Knowledge Adaption Module (KAM) utilizing the deep neural networks (DNN) for knowledge reuse. The initial experiment shows that the target robot can adapt to the inverse dynamic model of the source robot via our KAM and reuse the knowledge shared by the source robot.

Human exposome assessment platform.

The Human Exposome Assessment Platform (HEAP) is a research resource for the integrated and efficient management and analysis of human exposome data. The project will provide the complete workflow for obtaining exposome actionable knowledge from population-based cohorts. HEAP is a state-of-the-science service composed of computational resources from partner institutions, accessed through a software framework that provides the world’s fastest Hadoop platform for data warehousing and applied artificial intelligence (AI). The software, will provide a decision support system for researchers and policymakers. All the data managed and processed by HEAP, together with the analysis pipelines, will be available for future research. In addition, the platform enables adding new data and analysis pipelines. HEAP’s final product can be deployed in multiple instances to create a network of shareable and reusable knowledge on the impact of exposures on public health.

Patterns for Reuse in Production Systems Engineering

In Production Systems Engineering (PSE), domain experts aim at reusing partial system designs implemented as Industry 4.0 assets and software. However, the knowledge on assets is often scattered across engineering artifacts from multiple disciplines and domain experts, making it difficult to find reusable assets and map them to requirements. In this paper, we (i) identify challenges and requirements for the representation of reuse knowledge in PSE, based on the results of a domain analysis in automotive manufacturing; (ii) refine the Industry 4.0 Asset Network (I4AN) meta-model that integrates multi-disciplinary dependencies between the assets; (iii) introduce the I4AN reference model that exposes recurring patterns; and (iv) present basic and applied patterns for reuse in PSE that aim at improving reuse efficiency and lowering risks. We evaluate the I4AN reference model and patterns with reuse scenarios in a feasibility study in automotive manufacturing. The study results indicate that the I4AN reference model and patterns satisfy the elicited requirements and enable PSE domain experts to identify patterns for reuse and sufficiently complete sets of reusable assets in their contexts.

HAZOP Ontology Semantic Similarity Algorithm Based on ACO-GRNN

Hazard and operability (HAZOP) is an important safety analysis method, which is widely used in the safety evaluation of petrochemical industry. The HAZOP analysis report contains a large amount of expert knowledge and experience. In order to realize the effective expression and reuse of knowledge, the knowledge ontology is constructed to store the risk propagation path and realize the standardization of knowledge expression. On this basis, a comprehensive algorithm of ontology semantic similarity based on the ant clony optimization generalized neural network (ACO-GRNN) model is proposed to improve the accuracy of semantic comparison. This method combines the concept name, semantic distance, and improved attribute coincidence calculation method, and ACO-GRNN is used to train the weights of each part, avoiding the influence of manual weighting. The results show that the Pearson coefficient of this method reaches 0.9819, which is 45.83% higher than the traditional method. It could solve the problems of semantic comparison and matching, and lays a good foundation for subsequent knowledge retrieval and reuse.

Assembly process case matching based on a multilevel assembly ontology method

PurposeThe purpose of this study is to achieve accurate matching of new process cases to historical process cases and then complete the reuse of process knowledge and assembly experience.Design/methodology/approachBy integrating case-based reasoning (CBR) and ontology technology, a multilevel assembly ontology is proposed. Under the general framework, the knowledge of the assembly domain is described hierarchically and associatively. On this basis, an assembly process case matching method is developed.FindingsBy fully considering the influence of ontology individual, case structure, assembly scenario and introducing the correction factor, the similarity between non-correlated parts is significantly reduced. Compared with the Triple Matching-Distance Model, the degree of distinction and accuracy of parts matching are effectively improved. Finally, the usefulness of the proposed method is also proved by the matching of four practical assembly cases of precision components.Originality/valueThe process knowledge in historical assembly cases is expressed in a specific ontology framework, which makes up for the defects of the traditional CBR model. The proposed matching method takes into account all aspects of ontology construction and can be used well in cross-ontology similarity calculations.

A novel construction scheduling framework for a mixed construction process of precast components and cast-in-place parts in prefabricated buildings

The mixing of precast components and cast-in-place parts is a major feature of prefabricated buildings , but the existing pattern-based scheduling methods lack consideration of it. The main objective of this paper is to describe the mixing construction process of precast building faithfully, and schedule the process in a single BIM environment. This study presents a novel scheduling framework for prefabricated buildings. By using IFC, flexible work break-down structure (WBS) and constraints based simulation (CBS) method, this framework integrates scheduling information in a single BIM model, and organizes construction process in a flexible WBS structure to consistent with the mixing construction of precast and cast-in-place, and finally generate valid schedule to meet various constraints by using CBS. A case study is conducted to verify the framework, and a comparison with the traditional method is carried out. The result shows that this scheduling framework can faithfully scheduling the mixed construction process at different scale in a single BIM environment, and the comparison with traditional way shows it reduces the scheduling burden, improve the resources usage efficiency. This study contributes to solving the knowledge gap of the inapplicability of the process pattern-based method in describing the mixed construction process, and the heterogeneous and scattered information sources in scheduling. This methodology can conveniently organize large amount of mesoscale tasks into mixed scale task network, and facilitate the reuse of scheduling-related knowledge, improve the resource usage efficiency, help managers understand the construction process at different scales, improve the BIM-based information collaboration in scheduling. In the future, a faithful construction process description at the activity level can digitalize the actual construction process, pave the way to apply the activity data obtained by ICT. ● The existing scheduling method cannot truly reflect the actual construction process of the fabricated components and cast-in-place parts hybrid building. ● This paper presents a novel scheduling framework that schedule the mixed construction process at different scales in a single BIM environment. ● Organized in flexible WBS structure, the generated schedule could be detailed to the activity level while conform to the large-scale tasks at the same time. ● The results show that this methodology could reduce the scheduling burden, improve the resources usage efficiency; and facilitate the reuse of scheduling-related knowledge.

A decision model for programming language ecosystem selection: Seven industry case studies

Software development is a continuous decision-making process that mainly relies on the software engineer’s experience and intuition. One of the essential decisions in the early stages of the process is selecting the best fitting programming language ecosystem based on the project requirements. A significant number of criteria, such as developer availability and consistent documentation, in addition to the number of available options in the market, lead to a challenging decision-making process. As the selection of programming language ecosystems depends on the application to be developed and its environment, a decision model is required to analyze the selection problem using systematic identification and evaluation of potential alternatives for a development project. Recently, we introduced a framework to build decision models for technology selection problems in software production. Furthermore, we designed and implemented a decision support system that uses such decision models to support software engineers with their decision-making problems. This study presents a decision model based on the framework for the programming language ecosystem selection problem. The decision model has been evaluated through seven real-world case studies at seven software development companies. The case study participants declared that the approach provides significantly more insight into the programming language ecosystem selection process and decreases the decision-making process’s time and cost. With the decision model, software engineers can more rapidly evaluate and select programming language ecosystems. Having the knowledge in the decision model readily available supports software engineers in making more efficient and effective decisions that meet their requirements and priorities. Furthermore, such reusable knowledge can be employed by other researchers to develop new concepts and solutions for future challenges.

Freehand-Sketched Part Recognition Using VGG-CapsNet

<p indent=0mm>To solve the problem that the existing CAD system is difficult to match the corresponding parts accurately through the freehand sketch in the conceptual design, a recognition model (VGG-CapsNet) for freehand sketch of part is proposed, which combining the pre-trained network (VGG) and capsule network (CapsNet). Five designers are recruited to sketch parts, and build 23 kinds of freehand sketch of parts including standard parts and non-standard parts. The between-group experiment and within-group experiment are designed, and then the recognition models of VGG-CapsNet are constructed respectively. The recognition results of the VGG-CapsNet models are compared with the rVGG-13 models and the rCNN-13 models. The experimental results show that the mean accuracy of VGG-CapsNet model is higher than the other two models, which provides technical support for the retrieval and reuse of part design knowledge.

English

The quest for a better way of enhancing firm performance has led to the discovery of knowledge as a unique and firm-specific resource for achieving competitive advantage. Knowledge management has gained the attention of practitioners and scholars in recent times. Knowledge management involves obtaining the right set of information and making them available to the right people, at the right time. When an adequate knowledge management system is in place, employees can create, share and re-use knowledge. Existing literature shows that knowledge management is still evolving and factors like intellectual capital, innovation and knowledge application play a significant mediating role in its effect on firm performance. Also, knowledge management processes ought to be followed by effective knowledge management implementation strategies to avoid knowledge proliferation and structuration. Modern businesses are gradually evolving from document-based knowledge management systems towards people-based knowledge management systems as a result of higher capacity for improvement and alignment of knowledge management strategies. Modern businesses also try to foster the effectiveness of their knowledge management process through gamification. Several challenges and limitations to knowledge management like organizational culture, lack of knowledge sharing incentive, cultural differences, lack of proper information structures and change management issues were also identified. &nbsp; Key words: Knowledge management, tacit knowledge, implied knowledge, knowledge management theory, knowledge management system, knowledge sharing.

Model and Knowledge Representation for the Reuse of Design Process Knowledge Supporting Design Automation in Mass Customization

Mass customization aims to meet individual requirements and, therefore, is one way to attract and retain customers—a key challenge in the design industry. The increase in design automation has offered new opportunities to design customized products at high speed in a way that is cost equivalent to mass production. Design automation is built upon the reuse of product and process knowledge. Ontologies have proven to be a feasible, highly aggregated knowledge representation in engineering design. While product and process knowledge from other lifecycle phases are represented in multiple approaches, the design process of the product as well as the adaption process of product variants is missing, causing breakpoints or additional iterations in design automation. Therefore, suitable knowledge representation tailored to design automation is still missing. Accordingly, this contribution proposes a novel knowledge representation approach to enable design automation for mass customization. Methodically, this novel approach uses semantic enrichment of CAD environments to automatically deduce information about a design task, design rationale, and design process represented by a formal ontology. The integration of the design process significantly differentiates the approach from previous ones. The feasibility of the approach is demonstrated by a bike crank customization process.

Pose-free assembly retrieval based on spatial-contact skeleton

In complicated product industry such as aircraft manufacturing, an assembly model contains abundant engineering information for use in design, manufacture, and maintenance. Assembly retrieval can be used to find relevant models for knowledge reuse. However, an assembly with rotatable joints may have many poses, which brings difficulty to assembly retrieval, since there is no pose principle for assembly design. Therefore, focused on rotatable joints in assembly, a skeleton-based descriptor for pose-free assembly retrieval is proposed. The centroid points of part surfaces and contact faces in an assembly are extracted to construct a spatial-contact skeleton. The skeleton-based distance is proposed to measure the distance between two surface points, which is invariant to the rotatable joints. The distribution of skeleton distances between two parts is used to describe the pair. Considering a part paired with all other parts in the assembly, the set of part pairs is used to represent a part, and the modified Hausdorff distance is used to measure the dissimilarity between parts for assembly retrieval. Experiments are conducted to compare the accuracy of the proposed descriptor to holistic and structureless descriptors. The proposed method is shown to retrieve assemblies with similar parts and structures regardless of their rotatable joints.

Knowledge reuse for ontology modelling in Maintenance and Industrial Asset Management

Maintenance and Industrial Asset Management (AM) are fundamental business processes in guaranteeing the availability of physical assets at minimum risk and cost, while balancing the interests of several stakeholders. To reach operational excellence, intra- and inter-enterprise interoperability of systems is needed to support information management and integration between several involved parties. To this end, ontology engineering is relevant since it supports interoperability at technical and semantic levels. However, ontology modelling methodologies are varied, and several best practices exist, amongst which knowledge reuse. Nevertheless, reusing extant knowledge is not completely exploited so far, causing a heterogeneous ensemble of ontologies that are not orchestrated. The present work aims at promoting the adoption of knowledge reuse for ontology modelling in maintenance and AM. Therefore, an extensive review of existing ontologies for the two targeted business processes is performed with a twofold objective: firstly, to realise a cross-industrial ontological compendium, and secondly to understand the state of art of ontology modelling in maintenance and AM. To support the adoption of knowledge reuse, this practice is framed in AMODO (Asset Management Ontology Development methOdology). Finally, a laboratory-sized showcase is provided to prove the usefulness of relying on knowledge reuse during the ontology development. The results show that the developed ontology is realised faster and is inherently aligned with established ontologies, towards enterprise systems interoperability. Consequently, maintenance and AM business processes may rely on information management and integration to pursue operational excellence.

Transfer Learning for Distributed Intelligence in Aerial Edge Networks

Unmanned aerial vehicle (UAV)-enabled edge computing is a promising paradigm to provide task offloading service for computation-intensive applications. Catering for highly dynamic edge networks, learning agents are placed on the UAVs to perceive environmental changes and adjust edge service strategies. These multiple agents build distributed intelligence in edge service scheduling. However, the agents, which independently learn service strategies from scratch, always cost high computing resources, cause extra delays, and bring a critical challenge to UAVs' service efficiency. To address this challenge, we propose a transfer learning empowered aerial edge network (AEN), which uses multi-agent machine learning to draw optimal service strategies, while leveraging transfer learning to share and reuse knowledge between the UAVs for saving resource costs and reducing training latency. Furthermore, we develop efficient transfer learning schemes where the edge resources are optimally scheduled for knowledge sharing and task offloading. Numerical results indicate that the proposed schemes maximize edge service utility while significantly improving machine learning efficiency.