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1 |
Knowledge-Based Management of Virtual Training Scenarios
مدیریت دانش محور سناریوهای آموزش مجازی-2021 Virtual reality (VR) gains increasing attention as a method of implementing training systems in different domains, in particular,
when real training is potentially dangerous for the trainees or the environment, or requires expensive equipment. The essential
element of professional training is domain-specific knowledge, which can be represented using the semantic web approach. It
enables reasoning as well as complex queries against the representation of training scenarios, which can be valuable for teaching
purposes. However, the available methods and tools for creating VR training systems do not use semantic knowledge representation.
Currently, the creation, modification, and management of training scenarios require skills in programming and computer graphics.
Hence, they are unavailable to domain experts without expertise in IT. In this paper, we propose an ontology-based representation
and a method of modeling VR training scenarios. In our approach, trainees’ activities, potential mistakes as well as equipment
and its possible errors are represented using domain knowledge understandable to domain experts. We illustrate the approach by
modeling VR training scenarios for electrical operators of high-voltage installations.
Keywords: semantic web | knowledge representation | ontologies | training | virtual reality | 3D content |
مقاله انگلیسی |
2 |
Process-oriented Knowledge Representation of the Requirement Management Phase of TOGAF-ADM: an Empirical Evaluation
بازنمایی دانش فرآیند محور فاز مدیریت نیازمندی TOGAF-ADM: یک ارزیابی تجربی-2021 The Open Group Architecture Framework (TOGAF) is one of the most used by large-scale companies Enterprise Architecture
(EA) frameworks. It contains the Architecture Development Method (ADM) to represent knowledge used to analyze and build
the EA for an organization. It is a very detailed method and covers all phases of EA construction and maintenance. However, the
described guidelines are hard to follow as they are rich and numerous. Our goal is to provide a process-based knowledge
representation of the ADM method to better guide EA professionals using TOGAF and to facilitate the TOGAF teaching with
students. In our previous work, we have already proposed the process-oriented graphical representation of the requirements
management (RM) phase of TOGAF-ADM. In this work, we carried out a questionnaire to evaluate the process representation of
the TOGAF-ADM RM phase in comparison with its textual representation issue from the TOGAF documentation. The obtained
results confirmed that process models are helpful to better represent the knowledge included in TOGAF-ADM textual guidelines;
thus, practitioners have a more complete description of how to proceed while using TOGAF.
Keywords: Enterprise Architecture | TOGAF-ADM | Knowledge Representation | Process Models | Requirements Management | Questionnaire |
مقاله انگلیسی |
3 |
Stages of Knowledge Representation on the Example of the Typology of Interdisciplinarity: Philosophical Aspects
هیچ یک-2021 It is suggested, when creating knowledge management mechanisms to avoid any extremes in their presentation: various "centrisms", hypertrophy in the use of both mathematical and verbal-meaningful knowledge. It is shown that it is important to observe the principle of "ethics of engagement ", which can be implemented on the basis of a productive interdisciplinary synthesis. The stages of knowledge presentation are considered on the basis of the typology of interdisciplinarity. It is argued that the stage of semantisation (conceptualization) of knowledge representation, when creating control systems, should be preceded by the stage of ontologization. It enhances the distinctiveness of knowledge representation. The stage of ontologization is necessary for the construction of more detailed explanatory constructions, due to the greater formalization of the ontological representation, in comparison with the stage of semantisation. It is assumed that the taxonomy stage can become the basis for the ontologization of knowledge representation, for example, in knowledge engineering.
Keywords: ethics of engagement | knowledge representation | typology of interdisciplinarity semantisation (conceptualization) | ontologization and taxonomization stages |
مقاله انگلیسی |
4 |
Machine Learning Opportunities for Combining Knowledge Representation with Machine Learning
فرصتهای یادگیری ماشین برای ترکیب نمایندگی دانش با یادگیری ماشین-2021 Computational animal behavior analysis (CABA) is an emerging field which aims to apply AI techniques to support animal behavior analysis. The need for computational approaches which facilitate ‘objectivization’ and quantification of behavioral characteristics of animals is widely acknowledged within several animal-related scientific disciplines. State-of-the-art CABA approaches mainly apply machine learning (ML) techniques, combining it with approaches from computer vision and IoT. In this paper we high- light the potential applications of integrating knowledge representation approaches in the context of ML-based CABA systems, demonstrating the ideas using insights from an ongoing CABA project.© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 14th International Symposium “Intelligent Systems”. Keywords: Animal Behaviour | Computational Analysis | Machine Learning | Artificial Intelligence | Computer Vision | Spatio-temporal Data |
مقاله انگلیسی |
5 |
An equine disease diagnosis expert system based on improved reasoning of evidence credibility
سیستم خبره تشخیص بیماری های اسب بر اساس استدلال بهبود یافته از اعتبار شواهد-2019 In China, there is a troubling shortage of well-trained equine veterinarians, leaving the
needs of many equine farmers unmet. This is especially true with respect to the diagnosis
of equine diseases. To solve this shortcoming, an equine disease diagnosis expert system
was developed. For the aspect of knowledge representation, the structure of equine disease
diagnosis knowledge was analyzed using an ontology system. Next, the clinical signs were
described using an object-attribute-value (O-A-V) format, and the knowledge representation
was then expressed using production rules.With respect to the reasoning mechanism,
the weights of the clinical signs and promoted confidence factors (PCF) were combined to
express information and rules pertaining to clinical signs with an associated level of uncertainty.
The model was established based on improved reasoning of evidence credibility.
Finally, using the ASP.Net platform and the SQL Server 2008 database, the equine disease
diagnosis expert system based on the B/S structure has been developed, and is capable
of reliably diagnosing 40 of the most common equine diseases. A functional evaluation
of the system was conducted, and the diagnostic accuracy was observed to be 88%. This
study demonstrates a bright prospect for the popularization and application of the system
through continuous system maintenance and knowledge-based updates. Keywords: Equine disease | Diagnosis | Expert system | Object-based ontology | Evidence credibility |
مقاله انگلیسی |
6 |
Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes
رویکرد مبتنی بر هستی شناسی برای ارائه دانش شبیه سازی به دست آمده توسط فرآیندهای داده و متن کاوی-2019 Numerical simulation techniques such as Finite Element Analyses are essential in todays engineering design
practices. However, comprehensive knowledge is required for the setup of reliable simulations to verify strength
and further product properties. Due to limited capacities, design-accompanying simulations are performed too
rarely by experienced simulation engineers. Therefore, product models are not sufficiently verified or the simulations
lead to wrong design decisions, if they are applied by less experienced users. This results in belated
redesigns of already detailed product models and to highly cost- and time-intensive iterations in product development.
Thus, in order to support less experienced simulation users in setting up reliable Finite Element Analyses, a
novel ontology-based approach is presented. The knowledge management tools developed on the basis of this
approach allow an automated acquisition and target-oriented provision of necessary simulation knowledge. This
knowledge is acquired from existing simulation models and text-based documentations from previous product
developments by Text and Data Mining. By offering support to less experienced simulation users, the presented
approach may finally lead to a more efficient and extensive application of reliable FEA in product development Keywords: Knowledge-based engineering | Simulation, Finite Element Analysis | Ontology-based knowledge representation | Text Mining | Data Mining |
مقاله انگلیسی |
7 |
Semantic reasoning in service robots using expert systems
استدلال معنایی در روبات های کارگر با استفاده از سیستم های خبره-2019 This paper presents the semantic-reasoning module of VIRBOT, our proposed architecture for service
robots. We show that by combining symbolic AI with digital-signal processing techniques this module
achieves competitive performance. Our system translates a voice command into an unambiguous representation
that helps an inference engine, built around an expert system, to perform action and motion
planning. First, in the natural-language interpretation process, the system generates two outputs: (1)
conceptual dependence, expressing the linguistic meaning of the statement, and (2) verbal confirmation,
a paraphrase in natural language that is repeated to the user to confirm that the command has been
correctly understood. Then, a conceptual-dependency interpreter extracts semantic role structures from
the input sentence and looks for such structures in a set of known interpretation patterns. We evaluate
this approach in a series of skill-specific semantic-reasoning experiments. Finally, we demonstrate our
system in the general-purpose service robot test of the RoboCup-at-Home international competition,
where incomplete information is given to a robot and the robot must recognize and request the missing
information, and we compare our results with a series of baselines from the competition where our
proposal performed best. Keywords: Service robots | Semantic reasoning | Knowledge representation |
مقاله انگلیسی |
8 |
Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis
نمایش دانش با استفاده از شبکه های بیزی غیر پارامتری برای تجزیه و تحلیل ریسک تونل زنی-2019 Knowledge capture and reuse are critical in the risk management of tunneling works. Bayesian networks (BNs)
are promising for knowledge representation due to their ability to integrate domain knowledge, encode causal
relationships, and update models when evidence is available. However, the model development based on classic
BNs is challenging when expert opinions are solicited due to the discretization of variables and quantification of
large conditional probability tables. This study applies non-parametric BNs, which only require the elicitation of
the marginal distribution corresponding to each node and correlation coefficient associated with each edge, to
develop a knowledge-based expert system for tunneling risk analysis. In particular, we propose to use the pairwise
Pearsons linear correlations to parameterize the model because the assessment is intuitive and experts in
the engineering domain are more familiar and comfortable with this notion. However, when Spearmans rank
correlation is given, the method can also be used by modification of the marginals. The method is illustrated with
a tunnel case in the Wuhan metro project. The expert knowledge of risk assessment for common failures in shield
tunneling is integrated and visualized. The developed model is validated by real documented accidents. Potential
applications of the model are also explored, such as decision support for risk-based design. Keywords: Non-parametric Bayesian networks | Structured expert judgment | Expert system | Risk analysis | Tunneling |
مقاله انگلیسی |
9 |
An evolutionary framework for machine learning applied to medical data
یک چارچوب تکاملی برای یادگیری ماشین که برای داده های پزشکی کاربرد دارد-2019 Supervised learning problems can be faced by using a wide variety of approaches supported in
machine learning. In recent years there has been an increasing interest in using the evolutionary
computation paradigm as a search method for classifiers, helping the applied machine learning
technique. In this context, the knowledge representation in the form of logical rules has been one
of the most accepted machine learning approaches, because of its level of expressiveness. This paper
proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces
genetic programming to build a search method for classification-rules (IF/THEN). From this approach,
we deal with problems such as, maximum rule length and rule intersection. The experiments have
been carried out on our domain of interest, medical data. The achieved results define a methodology
to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the
results compared to other methods have shown that our proposal can be very useful in data analysis
and classification coming from the medical domain. Keywords: Machine learning | Logical rule induction | Data mining | Supervised learning | Evolutionary computation | Genetic programming | Ensemble classifier | Medical data |
مقاله انگلیسی |
10 |
Optimising virtual networks over time by using Windows Multiplicative DEA model
بهینه سازی شبکه های مجازی در طول زمان با استفاده از مدل تحلیل پوششی داده ها ویندوز ضربی-2019 Recently, the prediction of the most efficient configuration of a vast set of devices used for mounting an optimised cloud computing services and virtual networks environments have attracted growing atten- tion. This paper proposes a paradigm shift in modelling transmission control protocol (TCP) behaviour over time in virtual networks by using data envelopment analysis (DEA) models. Firstly, it proves that self-similarity with long-range dependency is presented differently in every network device. This study implements a novel fractal dimension concept on virtual networks for prediction, where this key in- dex informs if the transport layer forwards services with smooth or jagged behaviour over time. Another substantial contribution is proving that virtual network devices have a distinct fractal memory, TCP band- width performance, and fractal dimension over time, presenting themselves as important factor for fore- casting of spatiotemporal data. Thus, a continuous stepwise fractal performance evaluation framework methodology is developed as an expert system for virtual network assessment and performs a fractal analysis as a knowledge representation. In addition, due to the limitations of classical DEA models, the windows multiplicative data envelopment analysis (WMDEA) model is used to dynamically assess the fractal time series from virtual network hypervisors. For knowledge acquisition, 50 different virtual net- work hypervisors were appraised as decision-making units (DMU). Finally, this expert system also acts as a math hypervisor capable of determining the correct fractal pattern to follow when delivering TCP services in an optimised virtual network. Keywords: Cloud computing | Windows multiplicative data envelopment | analysis | Fractal expert system | Virtual Networks | Network Optimisation | Stepwise Performance Evaluation |
مقاله انگلیسی |