• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.6
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• pp.258-269
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• 1999

It is important to evaluate fatigue damage of in-service material in respect to assure safety and remaining fatigue life in structure and mechanical components under cyclic load . Fatigue damage is represented by mathematical modelling with crack growth rate da/dN and cycle ration N/Nf and is detected by X-ray diffraction and ultrasonic wave method etc. But this is estimated generally by single parameter but influenced by many test conditions The characteristics of it indicates fatigue damage has complex fracture mechanism. Therefore, in this study we propose that back-propagation neural networks on the basis of ration of X-ray half-value breath B/Bo, fractal dimension Df and fracture mechanical parameters can construct artificial intelligent networks estimating crack growth rate da/dN and cycle ratio N/Nf without regard to stress amplitude Δ $\sigma$.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1716-1721
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• 2008

Currently, Wireless Networks have some nice characteristics such as multi-hop, multi-channel, multi-radio, etc but these kinds of resources are not fully used. The most difficulty to solve this issue is to solve mixed integer optimization. This paper proposes a method to solve a class of mixed integer optimization for wireless networks by using AMPL modeling language with CPLEX solver. The result of method is scheduling and congestion control in multi-channel multi-radio wireless networks.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2276-2282
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• 2023

We deploy artificial neural networks to unfold neutron spectra from measured energy-integrated quantities. These neutron spectra represent an important parameter allowing to compute the absorbed dose and the kerma to serve radiation protection in addition to nuclear safety. The built architectures are inspired from convolutional neural networks. The first architecture is made up of residual transposed convolution's blocks while the second is a modified version of the U-net architecture. A large and balanced dataset is simulated following "realistic" physical constraints to train the architectures in an efficient way. Results show a high accuracy prediction of neutron spectra ranging from thermal up to fast spectrum. The dataset processing, the attention paid to performances' metrics and the hyper-optimization are behind the architectures' robustness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.10
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• pp.3935-3941
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• 2010

With the advent of high-speed communications networks, many authentication and access control systems are being introduced to combat network security issues like system hacking. But the fact is that the security systems for protecting information used in these networks are themselves weak. In response to the mounting demands of existing users, there is a clear need for a new authentication system that provides both safety and reliability. This research presents an authentication method with excellent access authorization (explicit and implicit authentication) and safety performance, demonstrated through its use in online networks.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.723-730
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• 2019

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

• Journal of the Korea Safety Management & Science
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• v.2 no.4
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• pp.165-176
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• 2000

Broadband networks are designed to support a wide variety of services with different traffic characteristics and demands for Quality of Services. Bandwidth allocation methods can be classified into two major categories： static and dynamic. In static allocation, bandwidth is allocated only at call setup time and the allocated bandwidth is maintained during a session. In dynamic allocation, the allocated bandwidth is negotiated during a session. The purpose of this paper is to develop policies for deciding and for adjusting the amount of bandwidth requested for a best effort connection over such as ATM networks.. This method is to develop such policies that a good trade off between utilization and latency using cell delay variation to the forecast the incoming traffic in the next period. The performances of the different polices are compared by simulations.

• International Journal of Safety
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• v.1 no.1
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• pp.1-6
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• 2002

It is important to control the high accurate position and force to prevent unexpected accidents by a robot manipulator. Direct-drive robots are suitable to the position and force control with high accuracy, but it is difficult to design a controller because of the system's nonlinearity and link-interactions. This paper is concerned with the study of the stabilization force control of direct-drive robots. The proposed algorithm is consists of the feedback controllers and the neural networks. After the completion of learning, the outputs of feedback controllers are nearly equal to zero, and the neural networks play an important role in the control system. Therefore, the optimum adjustment of control parameters is unnecessary. In other words, the proposed algorithm does not need any knowledge of the controlled system in advance. The effectiveness of the proposed algorithm is demonstrated by the experiment on the force control of a parallelogram link-type robot.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1293-1298
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• 2005

In the environment where human coexists with robot, the problem of safety is very important. But it is difficult to separate the robot from the human in time-domain or space-domain unlike the case of factory automation, so a new concept is needed. One approach is to notice sensory and emotional feeling of human, and in this study "pain" is focused, which is a typical unpleasant feeling when the robot contacts us. In this paper, to design the controller based on the pain, an artificial superficial pain model caused by impact is proposed. This ASPM model consists of mechanical pain model, skin model and gate control by artificial neural networks (ANNs). The proposed ASPM is evaluated by experiments.

• Proceedings of the Safety Management and Science Conference
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• 2002.05a
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• pp.121-127
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• 2002

In this paper, we propose traffic management support and evaluate the performance through simulation. We suggest traffic management routing protocol that can guarantee reliance according to not only reduction of the Network traffic congestion but also distribution of the network load that prevents data transmission. For performance evaluation, we analyzed the average data reception rate and network load, considering the node mobility. We found that in the mobile Ad Hoc networks, the traffic management service increased the average data reception rate and reduced the network traffic congestion and network load in Mobile Ad Hoc Networks.

• Bulletin of the Korean Institute for Industrial Safety
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• v.2 no.1
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• pp.6-10
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• 2002

Requirements for Distributed Monitoring and Control Networks(DMCN) differ greatly from those of typical data networks. Specifically, my DMCN technology which employs a filedbus protocol is different from IP network protocol TCP/IP. In general, one needs to integrate fieldbus protocol and TCP/IP to realize DMCN over IP network or internet, which can be viewed as Virtual Device Network(VDN). Interoperability between devices and equipments is essential to enhance the quality and the performance of predictive maintenance(PM). This paper suggests a basic framework for VDN using DMCN over IP network and a method to guarantee interoperability between devices and equipments.