• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.95-100
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• 1998

A notion of 'fuzzy' convergence of filters on a set is introduced. We show that the collection of fuzzy L-limit spaces forms a cartesian closed topological category and obtain an interesting relationship between the notions of 'fuzzy' convergence structure and convergence approach spaces.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.685-686
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• 2008

• Journal of KSNVE
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• v.8 no.6
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• pp.1121-1129
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• 1998

This research is concerned with the multiple PPF and the modified LQG controller design for active vibration control of intelligent structures. The intelligent structure is defined as the structure equipped with smart actuators and sensors. Various control techniques aimed for the piezoceramic sensors and actuators have been proposed for the active vibration control of smart structures and some of them prove their effectiveness experimentally. In this paper, the multiple PPF controller and the modified LQG controller are developed and applied to the smart grid structure. The multiple PPF control and the modified LQG control can be classified as the classical and the modern control techniques. respectively. The experimental results show that both control techniques are effective in suppressing vibrations. Two control techniques are compared with respect to the design process. the ease of implementation and the effectiveness

• Journal of information and communication convergence engineering
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• v.18 no.3
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• pp.183-187
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• 2020

The general Wi-Fi network connection structure is that a number of IoT (Internet of Things) sensor nodes are directly connected to one AP (Access Point) node. In this structure, the range of the network that can be established within the specified specifications such as the range of signal strength (RSSI) to which the AP node can connect and the maximum connection capacity is limited. To overcome these limitations, multiple middleware bridge technologies for dynamic scalability and load balancing were studied. However, these network expansion technologies have difficulties in terms of the rules and conditions of AP nodes installed during the initial network deployment phase In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road danger context aware and notification system through an intelligent risk situation recognition algorithm.

• Advances in concrete construction
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• v.14 no.1
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• pp.57-70
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• 2022

The spiral case concrete (SCC) used in the underground powerhouse of large hydropower stations is complex, difficult to pour, and has high requirements for temperature control and crack prevention. In this study, based on the closed-loop control theory of "multi-source sensing, real analysis, and intelligent control", a new intelligent cooling control system (ICCS) suitable for the SCC is developed and is further applied to the Wudongde large-scale underground powerhouse. By employing the site monitoring data, numerical simulation, and field investigation, the temperature control quality of the SCC is evaluated. The results show that the target temperature control curve can be accurately tracked, and the temperature control indicators such as the maximum temperature can meet the design requirements by adopting the ICCS. Moreover, the numerical results and site investigation indicate that a safety factor of the spiral case structure was sure, and no cracking was found in the concrete blocks, by which the effectiveness of the system for improving the quality of temperature control of the SCC is verified. Finally, an intelligent cooling control procedure suitable for the SCC is proposed, which can provide a reference for improving the design and construction level for similar projects.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.459-462
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• 2000

In this paper, we propose the initial optimized structure of the Radial Basis Function Network which is more simple in the part of the structure and converges more faster than Neural Network with the analysis method using Time-Frequency Localization. When we construct the hidden node with the Radial Basis Function whose localization is similar with an approximation target function in the plane of the Time and Frequency, we make a good decision of the initial structure having an ability of approximation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.219-224
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• 1998

We introduce the notion of ${\gamma}$-fuzzy filter and ${\gamma}$-limit structure to L-fuzzy point. We show that the category ${\gamma}$Lim of ${\gamma}$-limit spaces is a cartesian closed topological construct containing the category LFTop of stratified L-fuzzy topological spaces as a bireflective subcategory.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.182-186
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• 2002

We investigate the quotient structure of a product BCK-algebra and fundamental homomorphism theorem and show their some properties.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.49-52
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• 1998

Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1171-1174
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• 1993

A promising approach to get the benefits of neural networks and fuzzy logic is to combine them into an integrated system to merge the computational power of neural networks and the representation and reasoning properties of fuzzy logic. In this context, this paper presents a fuzzy neural network which is able to code fuzzy knowledge in the form of it-then rules in its structure. The network also provides an efficient structure not only to code knowledge, but also to support fuzzy reasoning and information processing. A learning scheme is also derived for a class of membership functions.