• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.71-79
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• 2011

Traditionally, a dynamic network model is considered as a tool for solving real-time traffic problems. One of useful and practical ways of using such models is to use it to produce and disseminate forecast travel time information so that the travelers can switch their routes from congested to less-congested or uncongested, which can enhance the performance of the network. This approach seems to be promising when the traffic congestion is severe, especially when sudden incidents happen. A consideration that should be given in implementing this method is that travel time information may affect the future traffic condition itself, creating undesirable side effects such as the over-reaction problem. Furthermore incorrect forecast travel time can make the information unreliable. In this paper, a network-wide travel time prediction model under incidents is developed. The model assumes that all drivers have access to detailed traffic information through personalized in-vehicle devices such as car navigation systems. Drivers are assumed to make their own travel choice based on the travel time information provided. A route-based stochastic variational inequality is formulated, which is used as a basic model for the travel time prediction. A diversion function is introduced to account for the motorists' willingness to divert. An inverse function of the diversion curve is derived to develop a variational inequality formulation for the travel time prediction model. Computational results illustrate the characteristics of the proposed model.

• Journal of Korean Society of Transportation
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• v.19 no.5
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• pp.119-134
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• 2001

This paper proposed a dynamic transit vehicle simulation model and a dynamic transit passengers simulation model, which can simultaneously simulate the transit vehicles and passengers traveling on a transit network, and also developed an algorithm of dynamic departure time choice model based on individual passenger. The proposed model assumes that each passenger's behavior is heterogeneous based on stochastic process by relaxing the assumption of homogeneity among passengers and travelers have imperfect information and bounded rationality to more actually represent and to simulate each passenger's behavior. The proposed model integrated a inference and preference reforming procedure into the learning and decision making process in order to describe and to analyze the departure time choices of transit passengers. To analyze and evaluate the model an example transit line heading for work place was used. Numerical results indicated that in the model based on heterogeneous passengers the travelers' preference influenced more seriously on the departure time choice behavior, while in the model based on homogeneous passengers it does not. The results based on homogeneous passengers seemed to be unrealistic in the view of rational behavior. These results imply that the aggregated travel demand models such as the traditional network assignment models based on user equilibrium, assuming perfect information on the network, homogeneity and rationality, might be different from the real dynamic travel demand patterns occurred on actual network.

• The KIPS Transactions:PartC
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• v.8C no.1
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• pp.97-104
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• 2001

In this paper, we present a new hierarchical model for performance analysis of channel allocation and packet service protocol in wireless n network. The proposed hierarchical model consists of two parts : upper and lower layer models. The upper layer model is the structure state model representing the state of the channel allocation and call service. The lower layer model, which captures the performance of the system within a given structure state, is the wireless packet retransmission protocol model. These models are developed using SRN which is an modeling tool. SRN, an extension of stochastic Petri net, provides compact modeling facilities for system analysis. To get the performance index, appropriate reward rates are assigned to its SRN. Fixed point iteration is used to determine the model parameters that are not available directly as input. That is, the call service time of the upper model can be obtained by packet delay in the lower model, and the packet generation rates of the lower model come from call generation rates of the upper model.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• Journal of Korean Society of Transportation
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• v.21 no.6
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• pp.77-88
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• 2003

This paper proposes a solution algorithm for solving a multi-class analytical DTA model. In the DTA model, three traveler classes are classified according to different assumptions of traveler's route choice behavior: including fixed route, Stochastic Dynamic User Optimum(SDUO), and Dynamic User Optimum(DUO). To solve this DTA model, variables of link flow and exit flow are represented solely by inflow. The resulting Linear Program(LP) subproblem in the inner iteration is solved as a typical time-dependent shortest route problem over a physical network. Accordingly, the required time-space network expansion in solving DTA models is no longer needed.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.899-914
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• 2003

It is generally accepted that self-similar (or fractal) processes may provide better models for teletraffic in modern telecommunication networks than Poisson Processes. If this is not taken into account, it can lead to inaccurate conclusions about performance of telecommunication networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. Three generators of pseudo-random self-similar sequences, based on the FFT〔20〕, RMD〔12〕 and SRA methods〔5, 10〕, are compared and analysed in this paper. Properties of these generators were experimentally studied in the sense of their statistical accuracy and times required to produce sequences of a given (long) length. While all three generators show similar levels of accuracy of the output data (in the sense of relative accuracy of the Horst parameter), the RMD- and SRA-based generators appear to be much faster than the generator based on FFT. Our results also show that a robust method for comparative studies of self-similarity in pseudo-random sequences is needed.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.125-125
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• 2020

European Space Agency's Sentinel-1 has an improved spatial and temporal resolution, as compared to previous satellite data such as Envisat Advanced SAR (ASAR) or Advanced Scatterometer (ASCAT). Thus, the assumption used for low-resolution retrieval algorithms used by ENVISAT ASAR or ASCAT is not applicable to Sentinel-1, because a higher degree of land surface heterogeneity should be considered for retrieval. The assumption of homogeneity over land surface is not valid any more. In this study, considering that soil roughness is one of the key parameters sensitive to soil moisture retrievals, various approaches are discussed. First, soil roughness is spatially inverted from Sentinel-1 backscattering over Yanco sites in Australia. Based upon this, Artificial Neural Networks data (feedforward multiplayer perception, MLP, Levenberg-Marquadt algorithm) are compared with Fractal approach (brownian fractal, Hurst exponent of 0.5). When using ANNs, training data are achieved from theoretical forward scattering models, Integral Equation Model (IEM). and Sentinel-1 measurements. The network is trained by 20 neurons and one hidden layer, and one input layer. On the other hand, fractal surface roughness is generated by fitting 1D power spectrum model with roughness spectra. Fractal roughness profile is produced by a stochastic process describing probability between two points, and Hurst exponent, as well as rms heights (a standard deviation of surface height). Main interest of this study is to estimate a spatial variability of roughness without the need of local measurements. This non-local approach is significant, because we operationally have to be independent from local stations, due to its few spatial coverage at the global level. More fundamentally, SAR roughness is much different from local measurements, Remote sensing data are influenced by incidence angle, large scale topography, or a mixing regime of sensors, although probe deployed in the field indicate point data. Finally, demerit and merit of these approaches will be discussed.

• Journal of Wetlands Research
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• v.21 no.1
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• pp.16-26
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• 2019

Wetlands that provide diverse ecosystem services, such as habitat provision and hydrological control of flora and fauna, constitute ecosystems through interaction between wetlands existing in a wetlandscape. Therefore, to evaluate the wetland functions such as resilience, it is necessary to analyze the ecological connectivity that is formed between wetlands which also show hydrologically dynamic behaviors. In this study, by defining wetlands as ecological nodes, we generated ecological networks through the connection of wetlands according to the dispersal model of wetland species. The characteristics of these networks were then analyzed using various network metrics. In the case of the dispersal based on a threshold distance, while a high local clustering is observed compared to the exponential dispersal kernel and heavy-tailed dispersal model, it showed a low efficiency in the movement between wetlands. On the other hand, in the case of the stochastic dispersion model, a low local clustering with high efficiency in the movement was observed. Our results confirmed that the ecological network characteristics are completely different depending on which dispersal model is chosen, and one should be careful on selecting the appropriate model for identifying network properties which highly affect the interpretation of network structure and function.