• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.343-346
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• 1996

A new method for on-line induction motor fault detection is presented in this paper. This system utilizes unsupervised-learning clustering algorithm, the Dignet, proposed by Thomopoulos etc., to learn the spectral characteristics of a good motor operating on-line. After a sufficient training period, the Dignet signals one-phase ground fault, or a potential failure condition when a new cluster is formed and persists for some time. Since a fault condition is found by comparison to a prior condition of the machine, on-line failure prediction is possible with this system without requiring information on the motor of load characteristics.

• Korean Journal of Child Studies
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• v.25 no.4
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• pp.177-190
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• 2004

Training for mothers and children first included information on water, ground, and air pollution, saving energy, and reducing trash. In the second phase, the family followed steps outlined in an environmental preservation guidebook. The experiment was of 16 weeks duration, conducted with 120 subjects, comprised of sixty 4-year-old children and their mothers. Measures included environmental preservation knowledge and the actual practice of environmental preservation on the part of the subjects. Mothers performed self-evaluations while teachers measured the children. SPSS was used for data collection and analysis. The results of this study indicate that the program increased perceptions on environmental preservation and the actual practice of the experimental group. The program application also resulted in meaningful co-relationships between perceptions and practice and between mothers and their children.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1324-1334
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• 2005

This study investigated the applicability of the Artificial Neural Network (ANN) technique for prediction of tunnel behavior. For training data collection, a series of finite element analyses were conducted for actual tunnel project site. Using the data, optimimzed ANNs were developed through a sensitivity study on internal parameters. The developed ANNs can make tunneling related predictions such as tunnel crown settlement, shotcrete lining stress, ground surface settlement, and groundwater inflow rate. The results indicated that the developed ANNs can be used as an effective and efficient tool for tunnelling related prediction in practical tunneling situations.

• 한국터널공학회:학술대회논문집
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• 2005.04a
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• pp.105-116
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• 2005

This study investigated the applicability of the Artificial Neural Network(ANN) technique for prediction of tunnel behavior. For training data collection, a series of finite element analyses were conducted for actual tunnel project site. Using the data, optimimzed ANNs were developed through a sensitivity study on internal parameters. The developed ANNs can make tunneling related predictions such as tunnel crown settlement, shotcrete lining stress, ground surface settlement, and groundwater inflow rate. The results indicated that the developed ANNs can be used as an effective and efficient tool for tunnelling related prediction in practical tunneling situations.

• Proceedings of the KIEE Conference
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• 1998.07c
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• pp.1114-1117
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• 1998

Recent applications of neural networks to power system fault diagnosis have provided positive results and have shown advantages in process speed over conventional approaches. This paper describes the application of neural network to fault detection and classification in distribution lines using the fundamental component, 2-5th harmonics index, even and odd harmonics index, and zero phase current. The Electromagnetic Transients Program (EMTP) is used to obtain fault patterns for the training and testing of neural networks. The proposed fault detection and classification method in distribution lines is obtained by analysing the difference among normal, HIF, ground fault, short circuit fault condition.

• Journal of Korean Society of Forest Science
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• v.20 no.1
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• pp.51-60
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• 1973

The trend and achievments of soil erosion control research in Japan were investigated through observation tours and reference work and following facts were found to be important aspects which should be considered in the soil erosion control research program in Korea. Experiments on forest and water relations, and ground water phenomena at the water source zone in Tokyo University. Studies on land-slides and erosion control dam in Kyoto University. Studies on mud-flow and snow avalanches in Hokkaido University. Studies on sanddune fixation and disaster damage prevention forests in Kyushu University. Studies on forest denudations in Nagoya University. Studies on Greening-works and soil erosion prevention chemicals in Tokyo Agriculture University. Training on planning of erosion control works and prevention of disaster damages in Forest Research Institute. Experiments on soil erosion phenomena and infiltration in Tohoku Branch, FRI. Experiments on erosion and surface stratum failure of steep slopes and their prevention methods in Railway Technical Research Institute.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.774-780
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• 2009

Simulation is becoming increasingly popular in construction for training, planning and for assessment of projects. There are, however, significant problems inherent in simulating construction which are not common to other simulations. This paper describes the development and use of computer-based game for teaching and learning of some aspects of construction project management. It is concerned with the development of a model used to simulate the construction of a rock- and clay-fill dam. It includes detailed physical modelling of the performance of individual pieces of equipment and their interaction with the ground, the geography of the project and the weather in which the equipment operates. The behaviour of all of the individual pieces of equipment when acting as fleets is also discussed. The paper also describes the modelling issues of non-technical aspects of earthmoving operations. These include environmental impact, safety, quality and risks. The problems of integrating these with the physics-based models of the equipment performance are discussed. The paper also draws on real experience of using the game in classes in three universities in different countries.

• ETRI Journal
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• v.45 no.5
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• pp.735-745
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• 2023

This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.21-31
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• 2024

Major global powers are investing heavily in artificial intelligence (AI) and hyper-connected networks, demonstrating their crucial role in future warfare. To advance and utilize AI in national defense, it is essential to have policy support at the governmental or national level. This includes establishing a research and development infrastructure, creating a common development environment, and fostering AI expertise through education and training programs. To achieve advancements in hyper-connected networks, it is essential to establish a foundation for a robust and resilient infrastructure by comprehensively building integrated satellite, aerial, and ground networks, along with developing 5G & edge computing and low-orbit satellite communication technologies. This multi-faceted approach will ensure the successful integration of AI and hyper-connected networks, strengthening national defense and positioning nations at the forefront of technological advancements in warfare.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.37-42
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• 2002

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this paper a liquefaction potential was estimated by using a back propagation neural network model applicated to cyclic triaxial test data, soil parameters and site investigation data. Training and testing of the network were based on a database of 43 cyclic triaxial test data from 00 sites. The neural networks are trained by modifying the weights of the neurons in response to the errors between the actual output values and the target output value. Training was done iteratively until the average sum squared errors over all the training patterns were minimized. This generally occurred after about 15,000 cycles of training. The accuracy from 72% to 98% was shown for the model equipped with two hidden layers and ten input variables. Important effective input variables have been identified as the NOC,$D_10$ and (N$_1$)$_60$. The study showed that the neural network model predicted a CSR(Cyclic shear stress Ratio) of silty-sand reasonably well. Analyzed results indicate that the neural-network model is more reliable than simplified method using N value of SPT.