• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.96-102
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• 2023

In this paper, we proposed Channel Transfer Function estimation based on Delay and Doppler Profile for underwater acoustic OFDM communication system. It improved the estimation accuracy of the channel transfer function by linear time interpolation the change of Scattered Pilot (SP) insertion frequency in the time direction and the time by Delay and Doppler profile that analyzes the multipath situation of the channel investigated the performance of interpolation by simulation and report it. Previous works is inserted SP every 4 OFDM. It was effective under the environment without multipath, but it has observed that the effect of CTF compensation has been lowered in multipath channel condition. In addition to be better when inserted SP every 2 OFDM. But the amount of sending data will be decrease. Therefore, we conducted research to improve 4 OFDM with new interpolator. A computer simulation was performed as a comparison of SP inserted every 4 OFDM, SP inserted every 2 OFDM, and 4 OFDM with new interpolator. the performance of the proposed system is overwhelmingly improved, and the performance is slightly improved even 64 QAM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.264-269
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• 2008

In this paper, two neural networks as a material model, which are based on the state-space method, have been proposed. One outputs the rates of inelastic strain and material internal variables whereas the outputs of the other are the next state of the inelastic strain and material internal variables. Both the neural networks were trained using input-output data generated from Chaboche's model and successfully converged. The former neural network could reproduce the original stress-strain curve. The neural network also demonstrated its ability of interpolation by generating untrained curve. It was also found that the neural network can extrapolate in close proximity to the training data.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.3
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• pp.101-113
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• 2015

The purpose of this study is increasing ventilation network usability for urban green space planning by enhancing its practicality and detail. A ventilation network feature extraction technique using roughness length($z_0$) was proposed. Continuously surfaced DZoMs generated from $z_0$(cadastral unit) using three interpolations(IDW, Spline, and Kriging) were compared to choose the most suitable interpolation method. Ventilation network features were extracted using the most suitable interpolation technique and studied with land cover and land surface temperature by spatial overlay comparison. Results show Kriging is most suitable for DZoM and feature extraction in comparison with IDW and Spline. Kriging based features are well fit to the land surface temperature(Landsat-7 ETM+) on summer and winter nights. Noteworthy is that the produced ventilation network appears to mitigate urban heat loads at night. The practical use of proposed ventilation network features are highly expected for urban green space planning, though strict validation and enhancement should follow. (1) $z_0$ enhancement, (2) additional ventilation network interpretation and editing, (3) linking disconnected ventilation network features, and (4) associated dataset enhancement with data integrity should technically preceded to enhance the applicability of a ventilation network for green space planning. The study domain will be expanded to the Seoul metropolitan area to apply the proposed ventilation network to green space planning practice.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.329-332
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• 1991

This paper presents an algorithm to reduce the time to solve Power Equations using a Neural Net. The Neural Net is trained with samples obtained through the conventional AC Load Flow. With these samples, the Neural Net is constructed and has the function of a linear interpolation network. Given arbitrary load level, this Neural Net generates voltage magnitudes and angles which are linear interpolation of real and reactive powers. Obtained voltage magnitudes and angles are substituted to Power Equations, Real and reactive powers are found. Thus, a new sample is generated. This new experience modifies weight matrix. Continuing to modify the weight matrix, the correct solution is achieved. comparing this method with AC Load flow, this method is faster. If we consider parallel processing, this method is far faster than conventional ones.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.511-515
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• 2009

For detecting of the faulty insulator, Correlation Propagation Neural Networks(CPNN) has been proposed. Faulty insulator is reduced the rate of insulation extremely, and taken the results dirty and injured. It is necessary to detect the faulty insulator and exchange the new one. And thus, we have designed the CPNN to be detected that insulators by the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. Information propagates among neighbor nodes laterally and inter-node interpolation is achieved. 1-D CPNN hardware has been implemented with general purpose. Experiments with static and dynamic signals have been done upon the CPNN hardware. Through the results of simulation experiments, we define the ability of real-time detecting the faulty insulators.

• Smart Structures and Systems
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• v.12 no.6
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• pp.679-694
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• 2013

In this paper the application of the Interpolation Damage Detection Method to the numerical model of a suspension bridge instrumented with a network of Micro-Electro-Mechanical System sensors is presented. The method, which, in its present formulation, belongs to Level II damage identification method, can identify the presence and the location of damage from responses recorded on the structure before and after a seismic damaging event. The application of the method does not require knowledge of the modal properties of the structure nor a numerical model of it. Emphasis is placed herein on the influence of recorded signals noise on the reliability of the results given by the Interpolation Damage Detection Method. The response of a suspension bridge to seismic excitation is computed from a numerical model and artificially corrupted with random noise characteristic of two families of Micro-Electro-Mechanical System accelerometers. The reliability of the results is checked for different damage scenarios.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11b
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• pp.7-12
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• 2004

New modular Lateral Information Propagation Networks(LIPN) has been designed. The LIPN has shown to be useful for interpolation of information[3]. The problem is the fact that only the small number of nodes can be implemented in a IC chip with the circuit VLSI technology. The proposed modular architecture is for enlarging the neural network through inter module connections. For such inter module connections, the host(computer or logic) mediates the exchange of information among modules. Also border nodes in each module have capacitors for temporarily retaining the information from outer modules. Simulation of interpolation with the designed LIPN has been done through various experiments.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2206-2208
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• 1998

The modular Lateral Information Propagation Networks(LIPN) has been designed. The LIPN has shown to be useful for interpolation of information[3]. The problem is the fact that only the small number of nodes can be implemented in a IC chip with the circuit VLSI technology. The proposed modular architecture is for enlarging the neural network through inter module connections. For such inter module connections, the host(computer or logic) mediates the exchange of information among modules. Also border nodes in each module have capacitors for temporarily retaining the information from outer modules. The LIPN with $4{\times}4$ modules has been designed and simulation of interpolation with the designed LIPN has been done.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.3
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• pp.149-156
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• 2011

Transmission delay and packet loss induced by a communication network can degrade the control performance and, even make the system unstable. This paper presents a method for compensating transmission delay and packet loss in a networked control system for unmanned underwater vehicle. The proposed method is based on Lagrange interpolation in order to satisfy the requirements of simplicity and model-independency. In this work, the lost/delayed data are estimated in real time by only using the past data without requiring any mathematical model of the controlled system. Consequently, the proposed method can be implemented independent of the controlled system, and also it can achieve fast and accurate compensation performance. The performance of the proposed technique is evaluated by numerical simulations with an unmanned underwater vehicle.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.