• Journal of Communications and Networks
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• v.14 no.6
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• pp.597-605
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• 2012

With the smart grid coming near, wide-area supervisory control and data acquisition (SCADA) becomes more and more important. However, traditional SCADA systems are not suitable for the openness and distribution requirements of smart grid. Distributed SCADA services should be openly composable and secure. Event-driven methodology makes service collaborations more real-time and flexible because of the space, time and control decoupling of event producer and consumer, which gives us an appropriate foundation. Our SCADA services are constructed and integrated based on distributed events in this paper. Unfortunately, an event-driven SCADA service does not know who consumes its events, and consumers do not know who produces the events either. In this environment, a SCADA service cannot directly control access because of anonymous and multicast interactions. In this paper, a distributed security framework is proposed to protect not only service operations but also data contents in smart grid environments. Finally, a security implementation scheme is given for SCADA services.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.4
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• pp.223-230
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• 2016

This paper presents data-driven thermal protection algorithm for preventing overheating of automotive sunroof motor. When a sunroof motor operates abnormally, its coil is overheated and it is failed. Besides, drivers and passengers are damaged. Hence, the sunroof motor observes its temperature and will be stoped when its temperature reach a predefined level. In order to implement low-cost thermal protection function, we drew a knowledge-based temperature increasing and decreasing curve from the result of experimental test. And then, we implemented data-driven thermal protection algorithm which prevents motor's On/Off operation according to motor operating voltage and motor speed. Finally, we implemented experimental test bed and evaluated its feasibility.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.44-51
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• 2016

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

• Journal of the Korea Computer Graphics Society
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• v.23 no.2
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• pp.23-27
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• 2017

We propose an inter-leg collision avoidance method that compensates the disadvantage of the data-driven biped control method. The data-driven biped control technique proposed by Lee et. al [1] sometimes generates the movement that the two legs intersect with each other while walking, which can not be realized in walking of a real person or a biped robot. The proposed method changes the angle of the swing hip so that the swing foot can move inward only after passing the stance foot. This process introduces an additional angle adjustment algorithm to avoid collisions with the stance leg to the original feedback rule of the stance hip. It generates a stable walking simulation without any inter-leg collisions, by adding minimal changes and additional calculations to the existing controller behavior.

• ETRI Journal
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• v.38 no.2
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• pp.337-346
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• 2016

We propose a data-driven kinematic control method for a robotic spatial augmented reality (RSAR) system. We assume a scenario where a robotic device and a projector-camera unit (PCU) are assembled in an ad hoc manner with loose kinematic specifications, which hinders the application of a conventional kinematic control method based on the exact link and joint specifications. In the proposed method, the kinematic relation between a PCU and joints is represented as a set of B-spline surfaces based on sample data rather than analytic or differential equations. The sampling process, which automatically records the values of joint angles and the corresponding external parameters of a PCU, is performed as an off-line process when an RSAR system is installed. In an on-line process, an external parameter of a PCU at a certain joint configuration, which is directly readable from motors, can be computed by evaluating the pre-built B-spline surfaces. We provide details of the proposed method and validate the model through a comparison with an analytic RSAR model with synthetic noises to simulate assembly errors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.836-842
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• 2016

When an accident occurs in the nuclear power plant, the faulted information might mislead to the high possibility of aggravating the accident. At the Fukushima accident, the operators misunderstood that there was no core exposure despite in the processing of core damage, because the instrument information of the reactor water level was provided to the operators optimistically other than the actual situation. Thus, this misunderstanding actually caused to much confusions on the rapid countermeasure on the accident, and then resulted in multiplying the accident propagation. It is necessary to be equipped with the function that informs operators the status of instrument integrity in real time. If plant operators verify that the instruments are working properly during accident conditions, they are able to make a decision more safely. In this study, we have performed various tests for the fault detection sensitivity of an data-driven empirical model to review the usability of the model in the accident conditions. The test was performed by using simulation data from the compact nuclear simulator that is numerically simulated to PWR type nuclear power plant. As a result of the test, the proposed model has shown good performance for detecting the specified instrument faults during normal plant conditions. Although the instrument fault detection sensitivity during plant accident conditions is lower than that during normal condition, the data-drive empirical model can be detected an instrument fault during early stage of plant accidents.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.7 s.262
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• pp.747-755
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• 2007

To improve the reality of motion simulator, the method of data-driven motion generation has been introduced to simply record and replay the motion of real vehicles. We can achieve high quality of reality from real samples, but it has no interactions between users and simulations. However, in character animation, user controllable motions are generated by the database made up of motion capture signals and appropriate control algorithms. In this study, as a tool for the interactive data-driven driving simulator, we proposed a new motion generation method. We sample the motion data from a real vehicle, transform the data into the appropriate data structure(motion block), and store a series of them into a database. While simulation, our system searches and synthesizes optimal motion blocks from database and generates motion stream reflecting current simulation conditions and parameterized user demands. We demonstrate the value of the proposed method through experiments with the integrated motion platform system.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1418-1432
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• 1996

In addition t improved availability, replication of data can enhance performance of distributed real-time transaction processing system by allowing transactions initiated at multiple node to be processed concurrently. To satisfy both the consistency and real-time constraints, it is necessary to integrate concurrency control and atomic commitment protocols with time-driven scheduling methods. blocking caused by existing concurrency control protocols is incompatible with time-driven scheduling because they cannot schedule transactions to meet given deadlines. To maintain consistency of replicated data and to provide a high degree of schedulability and predictability , the proposed time-driven scheduling methods integrate optimistic concurrency control protocols that minimize the duration of blocking and produce the serialization by reflecting the priority transactions. The atomicity of transactions is maintained to ensure successful commitment in distributed environment. Specific time-driven scheduling techniqueare discussed, together with an analysis of the performance of this scheduling.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.340-367
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• 2018

Storage services integration can be done for achieving high availability, improving data access performance and scalability while preventing vendor lock-in. However, multiple services environment management and interoperability have become a critical issue as a result of service architectures and communication interfaces heterogeneity. Storage federation model provides the integration of multiple heterogeneous and self-sufficient storage systems with a single control point and automated decision making about data distribution. In order to integrate diverse heterogeneous storage services into a single storage pool, we are proposing a storage service federation framework named BoxBroker. Moreover, an automated decision model based on a policy-driven data distribution algorithm and a service evaluation method is proposed enabling BoxBroker to make optimal decisions. Finally, a demonstration of our proposal capabilities is presented and discussed.

• Design & Manufacturing
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• v.16 no.1
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• pp.15-20
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• 2022

This paper is about elimination the situation in which gas sensor data becomes inaccurate due to temperature control when a semiconductor gas sensor is driven. Recently, interest in semiconductor gas sensors is high because semiconductor sensors can be driven with small and low power. Although semiconductor-type gas sensors have various advantages, there is a problem that they must operate at high temperatures. First temperature control was configured to adjust the temperature value of the heater mounted on the gas sensor. At that time, in controlling the heater temperature, gas sensor data are fluctuated despite supplying same gas concentration according to the temperature controlled. To resolve this problem, gas and temperature are extracted as a data. And then, a relation function is constructed between gas and temperature data. At this time, it is included low pass filter to get the stable data. In this paper, we can find optimal gain and parameters between gas and temperature data by using genetic algorithm.