• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.12
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• pp.637-644
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• 2004

This paper proposes an expert system with the knowledge learning capability which can enhance the safety and effectiveness of substation operation in the automated substation as well as existing substation by inferring multiple events such as main transformer fault, busbar fault and main transformer work schedule under multiple inference mode and multiple objective mode and by considering totally the switch status and the main transformer operating constraints. Especially inference mode includes the local minimum tree search method and pattern recognition method to enhance the performance of real-time bus reconfiguration strategy. The inference engine of the expert system consists of intuitive inferencing part and logical inferencing part. The intuitive inferencing part offers the control strategy corresponding to the event which is most similar to the real event by searching based on a minimum distance classification method of pattern recognition methods. On the other hand, logical inferencing part makes real-time control strategy using real-time mode(best-first search method) when the intuitive inferencing is failed. Also, it builds up a knowledge base or appends a new knowledge to the knowledge base using pattern learning function. The expert system has main transformer fault, main transformer maintenance work and bus fault processing function. It is implemented as computer language, Visual C++ which has a dynamic programming function for implementing of inference engine and a MFC function for implementing of MMI. Finally, it's accuracy and effectiveness is proved by several event simulation works for a typical substation.

• Journal of Software Engineering Society
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• v.24 no.1
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• pp.19-26
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• 2011

Since smart phones have various embedded sensors and high portability/usability, they have emerged as suitable targets to collect information and to provide intelligent services. That is, with a smart phone, we can collect information about user's circumstances and phone usage from sensors and infer his/her current state which is the significant basis for context-aware services. However, a service system should be founded on a context model to ensure reasonable context-awareness, because context information the system needs depends on its target services. Therefore, in this paper, we propose a hierarchical mobile context model for context inference of smart phone users in their daily life. We classify high-level context which can be draw from sensing data into three levels, Context-Behavior-Situation, and define inference methods for each level. With our mobile context model, we can user's meaningful context in his/her daily life besides simple actions or states.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.

• Journal of Information Management
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• v.38 no.2
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• pp.153-173
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• 2007

[ $OntoFrame^{(R)}$ ] system provides information dissemination service and inference service, based on Semantic Web framework to fully support R&D activities. Although it is one of state-of-the-art systems in the viewpoint of functionality, we are not able to declare whether it has satisfiable usability because of the omission of usability test in development process. Thus, this research tries to reveal the usability level of the $OntoFrame^{(R)}$, and further to find ways to achieve a user-center system. Both 'theory-based assessment' by a software ergonomics expert and 'user test' by four users are used for evaluating the usability of the $OntoFrame^{(R)}$. We look forward this research to being a basic reference for practical systems aiming at satisfiable usability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.87-88
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• 2008

As an information service system based on semantic web technology, $OntoFrame^{(R)}$ takes aim at a framework for providing analysis and fusion services of academic information. It currently consists of three parts: ontologies representing knowledge schema derived from academic information, $OntoURI^{(R)}$ which makes academic information into knowledge, and $OntoReasoner^{(R)}$ which performs inference and search on the knowledge. Unlike existing search engines which provides simple search services, our system provides, based on semantic web technology, several semantic and analytic services such as year-based topic trends in academic information, related topics, topic-based researchers and institutes, researcher network, statistics and regional distribution of academic information.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.75-88
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• 2021

Along with the advent of the Fourth Industrial Revolution, fostering young talents with convergent mind is getting important. Moreover pre-service teachers' ability to design proper convergent classes can be a meaningful issue for high quality future education. This study proposes the role of professors' exquisite feedback is so significant for developing their competence in STEAM education, It analyzed how various theories regarding feedback support them to enhance convergent knowledge with e-learning. They participated in the 5 step group and individual activities for creating STEAM lesson plan and received suitable feedback. Lastly a survey was performed. The researchers did modeling how integrative feedback was applied to the procedure step by step according to the 'Ladder of Inference' theory. This strategic model contributed to elevating the participants' convergent knowledge, competence, achievement and satisfaction.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.75-82
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• 2008

The times of service commencement and service completion had been used for inferring the queueing systems. However, the service commencement times are difficult to measure because of unobservable nature in queueing systems. In this paper, for inferring queueing systems, the service commencement times are replaced for arrival times which can be easily observed. Determining the service commencement time is very important in our methods. The methods for first come first served(FCFS), last come first served(LCFS) queueing discipline are already developed in our previous work. In this paper, we extend to random selection for service(RSS) queueing discipline. The performance measures we used are mean queueing time and mean service time, the variances of two. The simulation results verify our proposed methods to infer queueing systems under RSS discipline.

• International journal of advanced smart convergence
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• v.3 no.2
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• pp.18-21
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• 2014

A control application requirements currently used is very low, such as packet loss rate, minimum delay on sensor networks with quality of service (QoS) requirements some packet delivery guarantee. This paper is the sampling period at the end of the actuator and sensor data transfer related to the Miss ratio for each source sensor node, use the controller and the internal ANFIS. The proposed scheme has the advantages of simplicity, scalability, and General. Simulation results of the proposed scheme can provide QoS support in WSANs.

• Journal of IKEEE
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• v.11 no.4
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• pp.279-282
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• 2007

In this paper, we introduce the general way for producing context information to support context-aware applications. It can fetch raw data from the service environments, translate it to reasonable context information, and provide to multiple applications. It is designed originally for the ubiquitous computing middleware and based on the ontology processing model. Automated service applications can use this system as the form of libraries or of web services for deciding its semantic cause of action.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.158-161
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• 2004

In this paper, we propose a level of contexts such as low level and high level contexts, and its criteria of categorization are the existence of interaction and composite process done by the inference mechanism. When there is no matching high level service for the associated high level context, this context is described as meaningless. If the services cannot be provided with the entities by the system, this situational information is of little consequence. To provide services with the entities, we propose "community computing" architectural concept which provide the high level service to a group of agents in a community, and can be managed by the service scenario