• Korean Journal of Remote Sensing
• /
• v.30 no.6
• /
• pp.743-754
• /
• 2014

In this present paper, we, for the first time, calculated $SO_2$ inflow from China to Korea peninsula based on OMI $SO_2$ products and HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) backward trajectory calculations. The major factors used to estimate $SO_2$ flux are long range transported $SO_2$ concentration, transport speed of air mass, and thickness of transported air mass layer. The mean and maximum $SO_2$ fluxes are estimated to be 0.81 and $2.11g{\cdot}m^{-2}{\cdot}h^{-1}$, respectively based on OMI products while, those of $SO_2$ fluxes are 0.50 and $1.18g{\cdot}m^{-2}{\cdot}h^{-1}$ respectively using insitu data obtained at the surface. For most cases, larger $SO_2$ inflow values were found at the surface than those estimated for the air mass layer which extends from surface up to 1.5 km. However, increased transport speed of air mass leads to the enhanced $SO_2$ flux at the altitude up to 1.5 km at the receptor sites. Additionally, we calculate uncertainties of $SO_2$ flux using error propagation method.

• Journal of Korean Society of Steel Construction
• /
• v.19 no.6
• /
• pp.695-702
• /
• 2007

The Thermal Prestressing Method for continuous composite girder bridges is a new design and construction method developed to induce initial composite stresses in the concrete slab at negative bending regions. Due to the induced initial stresses, prevention of tensile cracks at the concrete slab, reduction of steel girder section, and reduction of reinforcing bars are possible. Thus, the construction efficiency can be improved and the construction can be made more economical. The method for determining the optimum heating region of the thermal prestressing method has not been established although such method is essential for improving the efficiency of the design process. The trial-and-error method used in previous studies is far from efficient, and a more rational method for computing optimal heating region is required. In this study, an efficient method for determining the optimum heating region in using the thermal prestressing method was developed based on the neural network algorithm, which is widely adopted to pattern recognition, optimization, diagnosis, and estimation problems in various fields. Back-propagation algorithm, commonly used as a learning algorithm in neural network problems, was used for the training of the neural network. Through case studies of two-span and three-span continuous composite girder bridges using the developed procedure, the optimal heating regions were obtained.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_1
• /
• pp.807-821
• /
• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.28 no.5
• /
• pp.436-442
• /
• 2008

Graphite material has been recognized as a very competitive candidate for reflector, moderator, and structural material for very high temperature reactor (VHTR). Since VHTR is operated up to $900-950^{\circ}C$, small amount of impurity may accelerate the oxidation and degradation of carbon graphite, which results in increased porosity and lowered fracture toughness. In this study, ultrasonic wave propagation properties were investigated for both as-received and degradated material, and the feasibility of ultrasonic testing (UT) was estimated based on the result of ultrasonic property measurements. The ultrasonic properties of carbon graphite were half, more than 5 times, and 1/3 for velocity, attenuation, and signal-to-noise (S/N) ratio respectively. Degradation reduces the ultrasonic velocity slightly by 100 m/s, however the attenuation is about 2 times of as-receive state. The results of probability of detection (POD) estimation based on S/N ratio for side-drilled-hole (SDHs) of which depths were less than 100 mm were merely affected by oxidation and degradation. This result suggests that UT would be reliable method for nondestructive testing of carbon graphite material of which thickness is not over 100 mm. In accordance with the result produced by commercial automated ultrasonic testing (AUT) system, human error of ultrasonic testing is barely expected for the material of which thickness is not over 80 mm.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.42 no.2 s.332
• /
• pp.61-74
• /
• 2005

Digital Multimedia Broadcasting(DMB) is a reliable multi-service system for reception by mobile and portable receivers. DMB system allows interference-free reception under the conditions of multipath propagation and transmission errors using COFDM modulation scheme, simultaneously, needs powerful channel error's correction ability. Viterbi Decoder for DMB receiver uses punctured convolutional code and needs lots of computations for real-time operation. So, it is desired to design a high speed and low-power hardware scheme for Viterbi decoder. This paper proposes a combined add-compare-select(ACS) and path metric normalization(PMN) unit for computation power. The proposed PMN architecture reduces the problem of the critical path by applying fixed value for selection algorithm due to the comparison tree which has a weak point from structure with the high-speed operation. The proposed ACS uses the decomposition and the pre-computation technique for reducing the complicated degree of the adder, the comparator and multiplexer. According to a simulation result, reduction of area $3.78\%$, power consumption $12.22\%$, maximum gate delay $23.80\%$ occurred from punctured viterbi decoder for DMB system.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.487-496
• /
• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.4
• /
• pp.351-361
• /
• 2014

Compared to a terrestrial communication, the high BER(Bit Error Ratio) and low channel bandwidth are the major factor of throughput degradation due to characteristics of underwater channel. Therefore, a MAC protocol must be designed to solve this problem in UWASNs(Underwater Acoustic Sensor Networks). MAC protocols for UWASNs can be classified into two major types according to the contention scheme(Contention-free scheme and Contention-based scheme). In large scale of sensor networks, a Contention-based scheme is commonly used due to time-synchronize problem of Contention-free scheme. In the contention-based scheme, Each node contends with neighbor nodes to access network channel by using Back-off algorithm. But a Slot-Time of Back-off algorithm has long delay times which are cause of decrease network throughput. In this paper, we propose a new scheme to solve this problem. The proposed scheme uses variable Slot-Time instead of fixed Slot-Time. Each node measures propagation delay from neighbors which are used by Slot-time. Therefore, Slot-Times of each node are optimized by considering node deployment. Consequently, the wasted-time for Back-off is reduced and network throughput is improved. A new mac protocol performance in throughput and delay is assessed through NS3 and compared with existing MAC protocol(MACA-U). Finally, it was proved that the MAC protocol using the proposed scheme has better performance than existing MAC protocol as a result of comparison.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.48 no.1
• /
• pp.16-24
• /
• 2011

The sound speed of seawater can be calculated by the empirical formula as a function of temperature, salinity and pressure. It is little affected by salinity because the average salinity is 34 psu and varies within a few psu seasonally and spatially in the ocean. Recently, low-salinity water of 24 psu flows into the western sea area of Jeju Island due to the flood of the Yangtze River in China during summer, affecting sound speed profile. In this paper, it was analyzed how environmental changes affected to the underwater communication - the sound speed of low-salinity water was calculated, and the communication channel was estimated by the simulated acoustic rays while the transmitting and receiving depth and the range were varied with and without the low-salinity layer. And The BER (Bit error rate) was calculated by BPSK(Binary phase shift key) modulation and the effects of the low-salinity water on the BER was investigated. The sound speed profile was changed to have positive slope by the low-salinity layer at the sub-surface up to 20 m of depth, forming acoustic wave propagation channel at the sub-surface resulting in the decrease of most of the BER Consequently, this paper suggests that it is important to consider changes of the ocean environment for correctly analyzing the underwater communication and the detection capability.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.39 no.3
• /
• pp.18-31
• /
• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of Korea Water Resources Association
• /
• v.51 no.11
• /
• pp.1021-1029
• /
• 2018

A primary objective of this study is to develop a drought forecasting technique based on groundwater which can be exploit for water supply under drought stress. For this purpose, we explored the lagged relationships between regionalized SGI (standardized groundwater level index) and SPI (standardized precipitation index) in view of the drought propagation. A regional prediction model was constructed using a NARX (nonlinear autoregressive exogenous) artificial neural network model which can effectively capture nonlinear relationships with the lagged independent variable. During the training phase, model performance in terms of correlation coefficient was found to be satisfactory with the correlation coefficient over 0.7. Moreover, the model performance was described by root mean squared error (RMSE). It can be concluded that the proposed approach is able to provide a reliable SGI forecasts along with rainfall forecasts provided by the Korea Meteorological Administration.