• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.137-144
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• 2023

Cyberattack technology is evolving to an unpredictable degree, and it is a situation that can happen 'at any time' rather than 'someday'. Infrastructure that is becoming hyper-connected and global due to cloud computing and the Internet of Things is an environment where cyberattacks can be more damaging than ever, and cyberattacks are still ongoing. Even if damage occurs due to external influences such as cyberattacks or natural disasters, intelligent self-recovery must evolve from a cyber resilience perspective to minimize downtime of cyber assets (OS, WEB, WAS, DB). In this paper, we propose an intelligent self-recovery technology to ensure sustainable cyber resilience when cyber assets fail to function properly due to a cyberattack. The original and updated history of cyber assets is managed in real-time using timeslot design and snapshot backup technology. It is necessary to secure technology that can automatically detect damage situations in conjunction with a commercialized file integrity monitoring program and minimize downtime of cyber assets by analyzing the correlation of backup data to damaged files on an intelligent basis to self-recover to an optimal state. In the future, we plan to research a pilot system that applies the unique functions of self-recovery technology and an operating model that can learn and analyze self-recovery strategies appropriate for cyber assets in damaged states.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.5
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• pp.345-350
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• 2018

Nanomaterials have considerable potential to solve several key challenges in various electrochemical devices, such as fuel cells. However, the use of nanoparticles in high-temperature devices like solid-oxide fuel cells (SOFCs) is considered problematic because the nanostructured surface typically prepared by deposition techniques may easily coarsen and thus deactivate, especially when used in high-temperature redox conditions. Herein we report the synthesis of a self-regenerated Pd metal nanoparticle on the perovskite oxide anode surface for SOFCs that exhibit self-recovery from their degradation in redox cycle and $CH_4$ fuel running. Using Pd-doped perovskite, $La(Sr)Fe(Mn,Pd)O_3$, as an anode, fairly high maximum power densities of 0.5 and $0.2cm^{-2}$ were achieved at 1,073 K in $H_2$ and $CH_4$ respectively, despite using thick electrolyte support-type cell. Long-term stability was also examined in $CH_4$ and the redox cycle, when the anode is exposed to air. The cell with Pd-doped perovskite anode had high tolerance against re-oxidation and recovered the behavior of anodic performance from catalytic degradation. This recovery of power density can be explained by the surface segregation of Pd nanoparticles, which are self-recovered via re-oxidation and reduction. In addition, self-recovery of the anode by oxidation treatment was confirmed by X-ray diffraction (XRD) and scanning electron microscopy (SEM).

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.896-904
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• 2017

Several Generation IV nuclear reactor concepts have goals for optimizing investment recovery through phased introduction of multiple units on a common site with shared facilities and/or reconfigurable energy conversion systems. Additionally, small modular reactors are suitable for remote deployment to support highly localized microgrids in isolated, underdeveloped regions. The long-term economic viability of these advanced reactor plants depends on significant reductions in plant operations and maintenance costs. To accomplish these goals, intelligent control and diagnostic capabilities are needed to provide nearly autonomous operations with anticipatory maintenance. A nearly autonomous control system should enable automatic operation of a nuclear power plant while adapting to equipment faults and other upsets. It needs to have many intelligent capabilities, such as diagnosis, simulation, analysis, planning, reconfigurability, self-validation, and decision. These capabilities have been the subject of research for many years, but an autonomous control system for nuclear power generation remains as-yet an unrealized goal. This article describes a functional framework for intelligent, autonomous control that can facilitate the integration of control, diagnostic, and decision-making capabilities to satisfy the operational and performance goals of power plants based on multimodular advanced reactors.

• Journal of the Semiconductor & Display Technology
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• v.6 no.1 s.18
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• pp.53-57
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• 2007

In this paper, the intelligent estimation algorithm is developed for residual quantity estimate of lithium secondary cell and we suggest the control algorithm to get battery SOC through thermal modeling of electric cell. Lithium secondary cell gives cycle life, charge characteristic, discharge characteristic, temperature characteristic, self-discharge characteristic and the capacity recovery rate etc. Therefore, we make an accurate estimate of the capacity of battery according to thermal modeling to know the capacity of electric cell that is decreased by various special quality of lithium secondary cell. And we show effectiveness through comparison of value as result that use simulation and fuzzy logic.