• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.43-49
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• 2023

Object tracking is being studied with various techniques such as Kalman filter and Luenberger tracker. Even in situations, such as the one in which the system model is not well specified, to which existing signal processing techniques are not successfully applicable, it is possible to design artificial neural networks to track objects. In this paper, we propose an artificial neural network, which we call 'maximum-likelihood weighted-average neural network', to continuously track unpredictably moving objects. This neural network does not directly estimate the locations of an object but obtains location estimates by making weighted average combining various results of maximum likelihood tracking with different data lengths. We compare the performance of the proposed system with those of Kalman filter and maximum likelihood object trackers and show that the proposed scheme exhibits excellent performance well adapting the change of object moving characteristics.

• Computers and Concrete
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• v.27 no.1
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• pp.21-28
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• 2021

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.7
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• pp.680-687
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• 2000

The objective of this study is to improve the control performance of Ondol system which causes overheating and underheating with 2-position on/off control. For this, a predictive control that determines the suitable on/off positions using Artificial Neural Network(ANN) model was proposed Dynamic analyses using computer simulation show that the neural network used in the predictive control is adapted to each room whose loads variation and thermal characteristics are different. To examine the applicability of this predictive control with ANN it was compared with 2-position on/off control through experiments.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.499-506
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• 2002

The purpose of this study is to develop landslide susceptibility analysis techniques using artificial neural networks and to apply the newly developed techniques for assessment of landslide susceptibility to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial Photographs and field survey data, and a spatial database of the topography, soil type and timber cover were constructed. The landslide-related factors such as topographic slope, topographic curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter were extracted from the spatial database. Using those factors, landslide susceptibility and weights of each factor were analyzed by two artificial neural network methods. In the first method, the landslide susceptibility index was calculated by the back propagation method, which is a type of artificial neural network method. Then, the susceptibility map was made with a GIS program. The results of the landslide susceptibility analysis were verified using landslide location data. The verification results show satisfactory agreement between the susceptibility index and existing landslide location data. In the second method, weights of each factor were determinated. The weights, relative importance of each factor, were calculated using importance-free characteristics method of artificial neural networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.417-420
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• 2014

Importance of security system to prevent recently increased financial security accident is increasing. Biometric system between the security systems is focused. Fingerprint recognition has many useful aspects such as security, reliability and portability. In this treatise, fingerprint recognition technique is realized by using artificial neural network. Artificial Neural Network(ANN) is a mathematics learning model that makes specific patterns that a program can recognize to show a nerve network's characteristic on a computer. Input fingerprint images have a preprocessing process such as equalization, binarization and thinning. We extract minutiae feature in the images and program can recognize a fingerprint through ANN.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.139-144
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• 2009

Neural network is a very exciting and generic framework to develop almost all ranges of machine learning technologies and its potential is far beyond its current capabilities. Among other characteristics, neural network acts as associative memory obtained from the values structurally stored in synaptic inherent structure. Due to innate complexity of neural networks system, in its practical implementation and maintenance, multifaceted problems are known to be unavoidable. In this paper, we present design and implementation details of GUI software which can be valuable tool to maintain and develop neural networks. It has capability of displaying every state of synaptic weights with network nodal relation in each learning step.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.182-186
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• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.489-497
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• 2000

After IMF situation, the money market environment is changing rapidly. Therefore, many companies including financial institutions and many individual investors are concerned about forecasting the money market, and they make an effort to insure the various profit and hedge methods using derivatives like option, futures and swap. In this research, we developed a prototype of forecasting system for KOSPI 200 option, especially call option, trading using artificial neural networks(ANN), To avoid the overfitting problem and the problem involved int the choice of ANN structure and parameters, we employed the ANN ensemble approach. We conducted two types of simulation. One is conducted with the hold signals taken into account, and the other is conducted without hold signals. Even though our models show low accuracy for the sample set extracted from the data collected in the early stage of IMF situation, they perform better in terms of profit and stability than the model that uses only the theoretical price.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.703-704
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• 2018

An artificial neural network design that traces the pupil for the disables suffering from Lou Gehrig disease is introduced. It grasps the position of the pupil required for the communication system. Tensorflow is used for generating and learning the neural network, and the pupil position is determined through the learned neural network. Convolution neural network(CNN) which consists of 2 stages of convolution layer and 2 layers of complete connection layer is implemented for the system.

• Journal of Navigation and Port Research
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• v.32 no.8
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• pp.589-596
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• 2008

In this paper, Artificial Neural Network(ANN) is applied to automatic berthing control for a ship. ANN is suitable for a maneuvering such as ship's berthing, because it can describe non-linearity of the system. Multi-layer perceptron which has more than one hidden layer between input layer and output layer is applied to ANN. Using a back-propagation algorithm with teaching data, we trained ANN to get a minimal error between output value and desired one. For the automatic berthing control of a containership, we introduced low speed maneuvering mathematical models. The berthing control with the structure of 8 input layer units in ANN is compared to 6 input layer units. From the simulation results, the berthing conditions are satisfied, even though the berthing paths are different.