• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.263-269
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• 2017

'Goal Model' is core knowledge of 'Autonomic Control System' suggested to minimize human interference in system management. 'Autonomic Control System' performs 'Monitoring-Analysis-Plan-Execution', that is the four step of 'Autonomic Control', based on 'Goal Model'. Therefore, it is necessary to quantify achievement ratio of 'Goal Model' of target system. Thus, this paper present 'Probabilistic Evaluation of Goal Model' for methodology how to quantify achievement ratio of 'Goal Model'. It comprises 3-steps including 'Goal modeling and weighting', 'Goal model monitoring', 'Goal model evaluation and analysis'. Through these research, we provide core knowledge for 'Autonomic Control system' and it is possible to increase the reliability of system by evaluating 'Goal model' with applying weight. As case study, we apply 'Goal model' to a 'Smart IoT Kit' and we demonstrate the validity of the suggested research.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.127-133
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• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• The Journal of the Korea Contents Association
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• v.12 no.8
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• pp.13-19
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• 2012

This paper introduces a test-bed toolkit for evaluating and enhancing text analysis engines which extract technological knowledge from articles, patents, reports and so forth. The toolkit consists of two test-beds for technical entity recognition and relation extraction engines, which are capable of identifying technical entities and predicting semantic relation types between the entities. With using the introduced toolkits, users and developers can efficiently perform the execution monitoring and error analysis of the technical text analysis engines.

• ETRI Journal
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• v.29 no.3
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• pp.270-280
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• 2007

In Linux, real-time tasks are supported by separating real-time task priorities from non-real-time task priorities. However, this separation of priority ranges may not be effective when real-time tasks make the system calls that are taken care of by the kernel threads. Thus, Linux is considered a soft real-time system. Moreover, kernel threads are configured to have static priorities for throughputs. The static assignment of priorities to kernel threads causes trouble for real-time tasks when real-time tasks require kernel threads to be invoked to handle the system calls because kernel threads do not discriminate between real-time and non-real-time tasks. We present a dynamic kernel thread scheduling mechanism with weighted average priority inheritance protocol (PIP), a variation of the PIP. The scheduling algorithm assigns proper priorities to kernel threads at runtime by monitoring the activities of user-level real-time tasks. Experimental results show that the algorithms can greatly improve the unexpected execution latency of real-time tasks.

• Korean Journal of Computational Design and Engineering
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• v.16 no.6
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• pp.414-423
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• 2011

The collaboration hub has been developed since 2004 as an online collaboration space, which supports the various collaborative works amongst small and medium enterprises using information sharing, collaboration project management, and project history management. Because of the change of manufacturing environment and rapid development of information technologies, it should be evolved from the existing version called Collaboration Hub 1.0. Recently, a lot of manufacturing enterprises know the importance of business process management(BPM) and start to introduce BPM systems. Our research group has developed the new version of Collaboration Hub 1.0 called Collaboration Hub 2.0 which contains the BPM concept, the consistent product data management, and the specialized functions overcoming the various variation of manufacturing. This study scrutinizes the meaning and role of the Collaboration Hub 2.0 and introduces an application study of it to the value chain of automobile module development consisted of a leading company and subcontractors. The case study covers the definition, execution and monitoring of collaboration process, the specialized functions overcoming the manufacturing variation and the key performance index of collaboration business.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1756-1776
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• 2012

Wireless sensor networks (WSNs) are likely to be more prevalent as their cost-effectiveness improves. The spectrum of applications for WSNs spans multiple domains. In environmental sciences, in particular, they are on the way to become an essential technology for monitoring the natural environment and the dynamic behavior of transient physical phenomena over space. Existing sensor network query processors (SNQPs) have also demonstrated that in-network processing is an effective and efficient means of interaction with WSNs for performing queries over live data. Inspired by these findings, this paper investigates the question as to whether spatio-temporal and historical analysis can be carried over WSNs using distributed query-processing techniques. The emphasis of this work is on the spatial, temporal and historical aspects of sensed data, which are not adequately addressed in existing SNQPs. This paper surveys the novel approaches of storing the data and execution of spatio-temporal and historical queries. We introduce the challenges and opportunities of research in the field of in-network storage and in-network spatio-temporal query processing as well as illustrate the current status of research in this field. We also present new areas where the spatio-temporal and historical query processing can be of significant importance.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.165-170
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• 1999

Previous technology of remote sensing was focused on analyzing raster image and gaining information through image processing. But now it has extended to diverse fields like automatic map generation, material exploitation or monitoring environmental changes with effort to utilizing practical usage. And with rapid expansion of information exchange on Internet and high-speed network, the demand of public which want to utilize remotely sensed image has been increased. This makes growth of service on acquisition and processing remotely sensed image. GeoNet is a Java-based remotely sensed image processing system. It is based on Java object-oriented paradigm and features cross-platform, web-based execution and extensibility to client/server remotely sensed image processing model. Remotely sensed image processing software made by Java programming language can suggest alternatives to meet readily demand on remotely sensed image processing in proportion to increase of remotely sensed data. In this paper, we introduce GeoNet and explain its architecture.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.52-55
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• 2004

VMS (virtual manufacturing system) may be defined as a transparent interface/control mechanism to support human decision-making via simulation and monitoring of real operating situation through modeling of all activities in RMS (real manufacturing system). The three main layers in VMS are business process layer, manufacturing execution layer, and facility operation layer, and each layer is represented by a specific software system having its own input modeler module. The current version of these input modelers has been implemented based on its own 'local' framework, and as a result, there are no information sharing mechanism, nor a common user view among them. Proposed in this paper is a unified modeler framework covering the three VMS layers, in which the concept of PPR (product-process-resource) model is employed as a common semantics framework and a 2D graphic network model is used as a syntax framework. For this purpose, abstract class PPRObject and GraphicObject are defined and then a subclass is inherited from the abstract class for each application layer. This feature would make it easier to develop and maintain the individual software systems. For information sharing, XML is used as a common data format.

• Journal of Advanced Navigation Technology
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• v.14 no.1
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• pp.127-133
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• 2010

The process for establishment of Operational Continuity Management Plan is organized repeatedly of Business Risk Assessment, Crisis Analysis, Business Impact Analysis, Establishing Business Recovery Strategies, Detailed Planning, Plan Execution, Test and Maintenance(Including Monitoring). Therefore, in this paper in response to global environmental change and the construction and operation of social security systems to maximize operational continuity management, crisis management and crisis management systems, building integrated systems for building technology in general and operational continuity management within an organization to understand developed to provide a framework for implementing operational continuity management in terms of crisis management has proposed to build an integrated system.

• Journal of Korea Multimedia Society
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• v.20 no.11
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• pp.1811-1819
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• 2017

As the induction motor is the core production equipment of the industry, it is necessary to construct a fault prediction and diagnosis system through continuous monitoring. Many researches have been conducted on motor fault diagnosis algorithm based on signal processing techniques using Fourier transform, neural networks, and fuzzy inference techniques. In this paper, we propose a fault diagnosis method of induction motor using LPC and DNN. To evaluate the performance of the proposed method, the fault diagnosis was carried out using the vibration data of the induction motor in steady state and simulated various fault conditions. Experimental results show that the learning time of our proposed method and the conventional spectrum+DNN method is 139 seconds and 974 seconds each executed on the experimental PC, and our method reduces execution time by 1/8 compared with conventional method. And the success rate of the proposed method is 98.08%, which is similar to 99.54% of the conventional method.